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1
Hasan, Waqas. "Virtual Machine Migration in Cloud Computing." Oriental journal of computer science and technology 14, no. 010203 (February 28, 2022): 46–51. http://dx.doi.org/10.13005/ojcst14.010203.06.
Повний текст джерелаАнотація:
Cloud computing provides multiple services to users through the internet and these services include cloud storage, applications, servers, security and large network access. Virtual Machine allows the user to emulate multiple operating systems on a single computer; with the help of virtual machine migration users can transfer operating system instances from one computer to multiple computer machines. In this paper we will be discussing VM migration in cloud and also I will explain the whole procedure of VM migration. The two methods through which we can perform VM migration are Live VM migration and NON-live VM migration.VM migration also helps in managing the loads of the multiple machines and with VM we can save power consumption. People have written about cloud computing and virtual machines in previous studies, but in this research, we'll speak about virtual machine migration in cloud computing, as well as the techniques that are used in the VM migration process. I have used table to show the differences between VM migration techniques.
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Hasan, Waqas. "A Survey on Virtual Machine Migration in Cloud Computing." International Journal of Scientific & Engineering Research 13, no. 03 (March 25, 2022): 648–53. http://dx.doi.org/10.14299/ijser.2022.03.03.
Повний текст джерелаАнотація:
Cloud computing provides multiple services to users through the internet and these services include cloud storage, applications, servers, security and large network access. Virtual Machine allows the user to emulate multiple operating systems on a single computer; with the help of virtual machine migration users can transfer operating system instances from one computer to multiple computer machines. In this paper we will be discussing VM migration in cloud and also I will explain the whole procedure of VM migration. The two methods through which we can perform VM migration are Live VM migration and NON-live VM migration.VM migration also helps in managing the loads of the multiple machines and with VM we can save power consumption. People have written about cloud computing and virtual machines in previous studies, but in this research, we'll speak about virtual machine migration in cloud computing, as well as the techniques that are used in the VM migration process. I have used table to show the differences between VM migration techniques.
3
Ge, Jun Wei, Hai Ming Zheng, and Yi Qiu Fang. "A Hybird Virtual Machine Placement Aglrithm for Virtualized Desktop Infrastructure." Advanced Materials Research 760-762 (September 2013): 1906–10. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1906.
Повний текст джерелаАнотація:
As we all kown, The virtual machine placement is one kind of bin-packing problem. By optimizing placement of virtual machine. We can improve VM performance, enhance resource utilization, reduce energy comsumption. After analysis the existing virtual machine placement aglrithm. We propose a hybird virtual machine placement aglrithm (HTA) which based on network latency threshold for the requirement of low network latence and low VM migraiton ratio in Virtualized Desktop Infrastructure. It elect qualified node set based on network latency threshold and palce the virtual machines with load-balance policy, taking into account the preformance of the network and vitual machines. According to analysis and comparison. The simulation result show that the algorithm can effectively lessen the network latency and reduce the VM migration ratio.
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Chen, Ji-Ming, Shi Chen, Xiang Wang, Lin Lin, and Li Wang. "A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks." Security and Communication Networks 2021 (December 21, 2021): 1–17. http://dx.doi.org/10.1155/2021/2729949.
Повний текст джерелаАнотація:
With the rapid development of Internet of Things technology, a large amount of user information needs to be uploaded to the cloud server for computing and storage. Side-channel attacks steal the private information of other virtual machines by coresident virtual machines to bring huge security threats to edge computing. Virtual machine migration technology is currently the main way to defend against side-channel attacks. VM migration can effectively prevent attackers from realizing coresident virtual machines, thereby ensuring data security and privacy protection of edge computing based on the Internet of Things. This paper considers the relevance between application services and proposes a VM migration strategy based on service correlation. This strategy defines service relevance factors to quantify the degree of service relevance, build VM migration groups through service relevance factors, and effectively reduce communication overhead between servers during migration, design and implement the VM memory migration based on the post-copy method, effectively reduce the occurrence of page fault interruption, and improve the efficiency of VM migration.
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Liu, Yanbing, Bo Gong, Congcong Xing, and Yi Jian. "A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/973069.
Повний текст джерелаАнотація:
Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM) migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM), the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.
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Liu, Zhenpeng, Jiahuan Lu, Nan Su, Bin Zhang, and Xiaofei Li. "Location-Constrained Virtual Machine Placement (LCVP) Algorithm." Scientific Programming 2020 (November 5, 2020): 1–8. http://dx.doi.org/10.1155/2020/8846087.
Повний текст джерелаАнотація:
Virtual machine (VM) placement is the current day research topic in cloud computing area. In order to solve the problem of imposing location constraints on VMs to meet their requirements in the process of VM placement, the location-constrained VM placement (LCVP) algorithm is proposed in this paper. In LCVP, each VM can only be placed onto one of the specified candidate physical machines (PMs) with enough computing resources and there must be sufficient bandwidth between the selected PMs to meet the communication requirement of the corresponding VMs. Simulation results show that LCVP is feasible and outperforms other benchmark algorithms in terms of computation time and blocking probability.
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T. Y. J., Naga Malleswari, Senthil Kumar T., and JothiKumar C. "Resumption of virtual machines after adaptive deduplication of virtual machine images in live migration." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 654. http://dx.doi.org/10.11591/ijece.v11i1.pp654-663.
Повний текст джерелаАнотація:
In cloud computing, load balancing, energy utilization are the critical problems solved by virtual machine (VM) migration. Live migration is the live movement of VMs from an overloaded/underloaded physical machine to a suitable one. During this process, transferring large disk image files take more time, hence more migration and down time. In the proposed adaptive deduplication, based on the image file size, the file undergoes both fixed, variable length deduplication processes. The significance of this paper is resumption of VMs with reunited deduplicated disk image files. The performance measured by calculating the percentage reduction of VM image size after deduplication, the time taken to migrate the deduplicated file and the time taken for each VM to resume after the migration. The results show that 83%, 89.76% reduction overall image size and migration time respectively. For a deduplication ratio of 92%, it takes an overall time of 3.52 minutes, 7% reduction in resumption time, compared with the time taken for the total QCOW2 files with original size. For VMDK files the resumption time reduced by a maximum 17% (7.63 mins) compared with that of for original files.
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Sushmitha, G. M. Karthik, and M. Sayeekumar. "Power and Performance Based Genetic Ant Colony Algorithm for Virtual Machine Placement." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 32–36. http://dx.doi.org/10.1166/jctn.2020.8625.
Повний текст джерелаАнотація:
Cloud Computing is the provisioning of computing services over the Internet. A Virtual Machine (VM) creation request has to be processed in any one data center of the physical machines. Virtual Machine Placement refers to choosing appropriate host for the VM. One of the major concerns in datacenter management is reducing the power consumption and performance filth of virtual machines. For solving the problem, GACO algorithm is proposed which uses PpW, IPR and LDR as heuristic information for ACO algorithm and for selection in Genetic algorithm. It also uses a non-linear power consumption model for quantifying power. The performance evaluation shows the efficiency of the algorithm.
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Srinivasa Rao, L., and I. Raviprakash Reddy. "A novel energy efficient virtual machine configuration and migration technique." International Journal of Engineering & Technology 7, no. 4 (September 17, 2018): 2391. http://dx.doi.org/10.14419/ijet.v7i4.13236.
Повний текст джерелаАнотація:
The recent growth in the data centre usage and the higher cost of managing virtual machines clearly demands focused research in reducing the cost of managing and migrating virtual machines. The cost of virtual machine management majorly includes the energy cost, thus the best available virtual machine management and migration techniques must have the lowest energy consumption. The management of virtual machine is solely dependent on the number of applications running on that virtual machine, where there is a very little scope for researchers to improve the energy. The second parameter is migration in order to balance the load, where a number of researches are been carried out to reduce the energy consumption. This work addresses the issue of energy consumption during virtual machine migration and proposes a novel virtual machine migration technique with improvement of energy consumption. The novel algorithm is been proposed in two enhancements as VM selection and VM migration, which demonstrates over 47% reduction in energy consumption.
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Muhammad, Shoaib, Muhammad Nabeel Mustafa Syed, and Shabhi Ul Hasan Naqvi Syed. "Techniques of migration in live virtual machine and its challenges." i-manager's Journal on Computer Science 9, no. 4 (2022): 31. http://dx.doi.org/10.26634/jcom.9.4.18540.
Повний текст джерелаАнотація:
Cloud computing is the on-demand availability of computer system resources. Most technology industries are moving to the cloud. Cloud structures can be costly for users. Virtualization is used in cloud computing that helps the cloud at a low cost. Migrating virtual machines (VMs) helps to manage computation. Migration of virtual machines is a core feature of virtualization. The technique of migrating a running virtual machine from one physical host to another with minimal downtime is called "live virtual machine migration." This paper discusses the migration technique, i.e., migration before and after copying, and also issues related to live migration. This paper presents a better approach to the VM migration method and future challenges by differentiating from the previous live VM migration method.
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Dhule, Chetan, and Urmila Shrawankar. "Performance Analysis for Pareto-Optimal Green Consolidation Based on Virtual Machines Live Migration." International Journal of Grid and High Performance Computing 9, no. 4 (October 2017): 36–56. http://dx.doi.org/10.4018/ijghpc.2017100103.
Повний текст джерелаАнотація:
Huge energy requirement of cloud data centers is prime concern. Dynamic Virtual Machine (VM) consolidation based on VM live migration to switched-off or put some of the under-loaded host Physical Machines (PMs) into a low power consumption mode can significantly save energy in data centers and achieve green cloud computing. Performance overheads imposed on source and destination hosts during and after VM live migration is the main focus of research. Existing VM consolidation approaches are inefficient regarding VM live migration time, application downtime, VM pre and post-migration overheads which results in Quality of Service (QoS) degradation. So, near-optimal solution which optimizes these overheads is main challenge. This paper discusses the causes of VM live migration performance overheads and comparison of different overhead optimization techniques on the basis of parameters like accuracy and migration cost. Pareto-Optimal solution is proposed to eliminate the VM performance overheads.
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Kumar Bhatia, Arvind, and Gursharan Singh. "A Review on Different Types of Live VM Migration Methods with Proposed Pre-Copy Approach." International Journal of Engineering & Technology 7, no. 4.12 (October 4, 2018): 6. http://dx.doi.org/10.14419/ijet.v7i4.12.20983.
Повний текст джерелаАнотація:
Cloud computing is being considered as the future architecture of IT world. Virtualization creates logical resources from physical resources which are allocated with flexibility to applications. Server virtualization is a technique for the division of the physical machine into many Virtual Machines; every Virtual Machine has the capacity of applications execution similar to physical machine. The capability of Virtual Machine migration i.e. dynamic movement of Virtual Machines between physical machines is achieved by virtualization. Migration techniques differ w.r.t order of state transfer. Pre-copy migration method transfers all pages of memory from source to destination while Virtual Machine is executing on source. Post-copy migration is transfer of memory. content after the transfer of process state. Specially, post-copy migration, first copied the process states to the destination machine. Total time of migration, Total pages transferred and Downtime are important parameters considered during live Virtual Machine migration. Many improved live pre copy Virtual Machine migration techniques tries to decrease all the three above mentioned parameters. Proposed approach also tries to minimize all the three performance parameters.
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Sansanwal, Suman, and Nitin Jain. "A comprehensive survey on load balancing techniques for virtual machines." System research and information technologies, no. 4 (December 26, 2023): 135–47. http://dx.doi.org/10.20535/srit.2308-8893.2023.4.10.
Повний текст джерелаАнотація:
Cloud computing is an emerging technique with remarkable features such as scalability, high flexibility, and reliability. Since this field is growing exponentially, more users are attracted to fast and better service. Virtual Machine (VM) allocation plays a crucial role in cloud computing optimization; hence, resource distribution is not impacted by machine failure and is migrated with no downtime. Therefore, effective management of virtual machines is necessary for increasing profit, energy-saving, etc. However, it could utilize the virtual machine resources more efficiently because of the increased load, so load balancing is more concentrated. The predominant purpose of load balancing is to balance the available load equally among the nodes to avoid overloading or underloading problems. The present study conducted an extensive survey on virtual machine placement to describe the application of prediction algorithms and to provide more efficient, reliable, high response, and low overhead VM placement. Furthermore, the survey attempted to overview the challenges in load balancing in VM placement and various ideas of state-of-the-art techniques to resolve the issues.
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Kumar, Kamal, and Jyoti Thaman. "Opportunistic Two Virtual Machines Placements in Distributed Cloud Environment." International Journal of Grid and High Performance Computing 12, no. 4 (October 2020): 13–34. http://dx.doi.org/10.4018/ijghpc.2020100102.
Повний текст джерелаАнотація:
Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution for the Two VM Placement problem. The experimental validation was done against a well-known placement algorithm, the power aware best fit decreasing (PABFD). PABFD and TVMP were applied on a given context and results were obtained for three important parameters, which include the number of VM migrations, reallocation means, and energy efficiency. Improvements on these parameters may prove beneficial.
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Barthwal, Varun, M. M. S. Rauthan, and Rohan Varma. "A survey on application of machine learning to manage the virtual machines in cloud computing." International Review of Applied Sciences and Engineering 11, no. 3 (November 12, 2020): 197–208. http://dx.doi.org/10.1556/1848.2020.00065.
Повний текст джерелаАнотація:
AbstractVirtual machine (VM) management is a fundamental challenge in the cloud datacenter, as it requires not only scheduling and placement, but also optimization of the method to maintain the energy cost and service quality. This paper reviews the different areas of literature that deal with the resource utilization prediction, VM migration, VM placement and the selection of physical machines (PMs) for hosting the VMs. The main features of VM management policies were also examined using a comparative analysis of the current policies. Many research works include Machine Learning (ML) for detecting the PM overloading, the selection of VMs from over-utilized PM and VM placement as the main activities. This article aims to identify and classify research done in the area of scheduling and placement of VMs using the ML with resource utilization history. Energy efficiency, VM migration counts and Service quality were the key performance parameters that were used to assess the performance of the cloud datacenter.
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Dow, Eli M. "Decomposed multi-objective bin-packing for virtual machine consolidation." PeerJ Computer Science 2 (February 24, 2016): e47. http://dx.doi.org/10.7717/peerj-cs.47.
Повний текст джерелаАнотація:
In this paper, we describe a novel solution to the problem of virtual machine (VM) consolidation, otherwise known as VM-Packing, as applicable to Infrastructure-as-a-Service cloud data centers. Our solution relies on the observation that virtual machines are not infinitely variable in resource consumption. Generally, cloud compute providers offer them in fixed resource allocations. Effectively this makes all VMs of that allocation type (or instance type) generally interchangeable for the purposes of consolidation from a cloud compute provider viewpoint. The main contribution of this work is to demonstrate the advantages to our approach of deconstructing the VM consolidation problem into a two-step process of multidimensional bin packing. The first step is to determine the optimal, but abstract, solution composed of finite groups of equivalent VMs that should reside on each host. The second step selects concrete VMs from the managed compute pool to satisfy the optimal abstract solution while enforcing anti-colocation and preferential colocation of the virtual machines through VM contracts. We demonstrate our high-performance, deterministic packing solution generation, with over 7,500 VMs packed in under 2 min. We demonstrating comparable runtimes to other VM management solutions published in the literature allowing for favorable extrapolations of the prior work in the field in order to deal with larger VM management problem sizes our solution scales to.
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Majhi, Santosh Kumar, and Sunil Kumar Dhal. "Formal Analysis of Virtual Machine Migration and Identification of Faults." International Journal of Knowledge-Based Organizations 8, no. 1 (January 2018): 16–28. http://dx.doi.org/10.4018/ijkbo.2018010102.
Повний текст джерелаАнотація:
Infrastructure as a service (IaaS) cloud supports flexible and agile execution of applications by creating virtualized execution environment namely, virtual machines (VMs) with on-demand infrastructural resources. In such environment, VM migration is used as a tool to facilitate system maintenance, load balancing and fault tolerance. The use of VM migration is to establish the portfolio of using dynamic and scalable infrastructure services offered by the service providers. In this paper, we study the VM migration process and investigate the potential faults which can occur during migration. Also, the state changes of a VM throughout its lifetime has been systematically analyzed and modeled as concurrent state machines. The potential faults are presented considering the live migration process of VM and accordingly VM state changes. In addition, a methodology for identifying the migration faults has been presented.
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Hidayat, Taufik, Kalamullah Ramli, Nadia Thereza, Amarudin Daulay, Rushendra Rushendra, and Rahutomo Mahardiko. "Machine Learning to Estimate Workload and Balance Resources with Live Migration and VM Placement." Informatics 11, no. 3 (July 19, 2024): 50. http://dx.doi.org/10.3390/informatics11030050.
Повний текст джерелаАнотація:
Currently, utilizing virtualization technology in data centers often imposes an increasing burden on the host machine (HM), leading to a decline in VM performance. To address this issue, live virtual migration (LVM) is employed to alleviate the load on the VM. This study introduces a hybrid machine learning model designed to estimate the direct migration of pre-copied migration virtual machines within the data center. The proposed model integrates Markov Decision Process (MDP), genetic algorithm (GA), and random forest (RF) algorithms to forecast the prioritized movement of virtual machines and identify the optimal host machine target. The hybrid models achieve a 99% accuracy rate with quicker training times compared to the previous studies that utilized K-nearest neighbor, decision tree classification, support vector machines, logistic regression, and neural networks. The authors recommend further exploration of a deep learning approach (DL) to address other data center performance issues. This paper outlines promising strategies for enhancing virtual machine migration in data centers. The hybrid models demonstrate high accuracy and faster training times than previous research, indicating the potential for optimizing virtual machine placement and minimizing downtime. The authors emphasize the significance of considering data center performance and propose further investigation. Moreover, it would be beneficial to delve into the practical implementation and dissemination of the proposed model in real-world data centers.
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Zhao, Guo Yu, Hao Fu Tang, Li Min Xiao, and Xiu Qiao Li. "Efficient Inline Deduplication on VM Images in Desktop Virtualization Environment." Applied Mechanics and Materials 307 (February 2013): 488–93. http://dx.doi.org/10.4028/www.scientific.net/amm.307.488.
Повний текст джерелаАнотація:
Enterprise service is transforming from traditional physical computing nodes to virtual machines which provides better isolation and more effective use of computing ability of the hardware. However, the widely deployment of virtual machines also increase the pressure of storage significantly with the fact that each must has at list one multi-gigabytes image file to store. To address the high pressure of storage from virtual machine images, we developed a user level inline deduplication file system with Content Addressable Storage (CAS). We use the open-source framework FUSE to encapsulate the deduplication process so as to achieve portability and flexibility. Compared to an ordinary file system without deduplication, we show that our file system can save at least 30% of space of single VM image and even more of multiple VM images while achieving considerable run time performance.
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Paneru, Dinesh Raj, Madhu B. R., and Santosh Naik. "A SURVEY FOR ENERGY EFFICIENCY IN CLOUD DATA CENTERS." International Journal of Research -GRANTHAALAYAH 5, no. 4RACSIT (April 30, 2017): 63–68. http://dx.doi.org/10.29121/granthaalayah.v5.i4racsit.2017.3353.
Повний текст джерелаАнотація:
Services such as Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Software as a Service (SaaS) are provided by Cloud Computing. Subscription based computing resources and storage is offered in cloud. Cloud Computing is boosted by Virtualization technology. To move running applications or VMs starting with one physical machine then onto the next, while the customer is associated is named as Live VM migration. VM migration is empowered by means of Virtualization innovation to adjust stack in the server farms.
Movement is done fundamentally to deal with the assets progressively. Server Consolidation’s main goal is to expel the issue of server sprawl. It tries to pack VMs from daintily stacked host on to fewer machines to satisfy assets needs. On other hand Load balancing helps in distributing workloads across multiple computing resources. Also in the presence of low loaded machines it avoids machines from getting overloaded and maintains efficiency. To balance the load across the systems in various cases, live migration technique is used with the application of various algorithms. The movement of virtual machines from completely stacked physical machines to low stacked physical machines is the instrument to adjust the entire framework stack. When we are worried about the energy consumption in Cloud Computing, VM consolidation & Server Consolidation comes into scenario in Virtual Machine movement method which itself implies that there is low energy consumption.
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Yuan, Ling, Zhenjiang Wang, Ping Sun, and Yinzhen Wei. "An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing." Entropy 25, no. 2 (February 14, 2023): 351. http://dx.doi.org/10.3390/e25020351.
Повний текст джерелаАнотація:
With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve the energy efficiency and service quality of cloud computing in the blockchain. The current VMC algorithm is not effective enough because it does not regard the load of the virtual machine (VM) as an analyzed time series. Therefore, we proposed a VMC algorithm based on load forecast to improve efficiency. First, we proposed a migration VM selection strategy based on load increment prediction called LIP. Combined with the current load and load increment, this strategy can effectively improve the accuracy of selecting VM from the overloaded physical machines (PMs). Then, we proposed a VM migration point selection strategy based on the load sequence prediction called SIR. We merged VMs with complementary load series into the same PM, effectively improving the stability of the PM load, thereby reducing the service level agreement violation (SLAV) and the number of VM migrations due to the resource competition of the PM. Finally, we proposed a better virtual machine consolidation (VMC) algorithm based on the load prediction of LIP and SIR. The experimental results show that our VMC algorithm can effectively improve energy efficiency.
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Teyeb, Hana, Nejib Ben Hadj-Alouane, Samir Tata, and Ali Balma. "Optimal Dynamic Placement of Virtual Machines in Geographically Distributed Cloud Data Centers." International Journal of Cooperative Information Systems 26, no. 03 (August 14, 2017): 1750001. http://dx.doi.org/10.1142/s0218843017500010.
Повний текст джерелаАнотація:
In geo-distributed cloud systems, a key challenge faced by cloud providers is to optimally tune and configure the underlying cloud infrastructure. An important problem in this context, deals with finding an optimal virtual machine (VM) placement, minimizing costs, while at the same time, ensuring good system performance. Moreover, due to the fluctuations of demand and traffic patterns, it is crucial to dynamically adjust the VM placement scheme over time. It should be noted that most of the existing studies, however, dealt with this problem either by ignoring its dynamic aspect or by proposing solutions that are not suitable for a geographically distributed cloud infrastructure. In this paper, exact as well as heuristic solutions based on Integer Linear programming (ILP) formulations are proposed. Our work focuses also on the problem of scheduling the VM migration by finding the best migration sequence of intercommunicating VMs that minimizes the resulting traffic on the backbone network. The proposed algorithms execute within a reasonable time frame to readjust VM placement scheme according to the perceived demand. Our aim is to use VM migration as a tool for dynamically adjusting the VM placement scheme while minimizing the network traffic generated by VM communication and migration. Finally, we demonstrate the effectiveness of our proposed algorithms by performing extensive experiments and simulation.
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Cai, Yu. "A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing." International Journal of Green Computing 8, no. 2 (July 2017): 20–36. http://dx.doi.org/10.4018/ijgc.2017070102.
Повний текст джерелаАнотація:
Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption and is ready to be extended with more considerations on user demands. The authors conducted extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of the proposed scheme.
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Kothari, Sukhesh, Chandan Parsad, Shubham Mahajan, and Laith Abualigah. "Energy optimization using virtual machine migration for power aware." Applied and Computational Engineering 17, no. 1 (October 23, 2023): 169–75. http://dx.doi.org/10.54254/2755-2721/17/20230933.
Повний текст джерелаАнотація:
Using cloud computing, the cloud service can be delivered to the user via an internet connection. A major concept in cloud computing is virtualization, which allows for the dynamic sharing of physical resources. The deployment of virtual machines (VMs) in the cloud is a difficult problem since they are placed on top of real machines in the data center. A good VM placement policy should increase resource utilization and also provide energy optimization, as saving energy has become crucial due to the high demand for the cloud and its data centers consuming high power. In this work, an approach is made to optimize the energy consumption during VM migration called, Power-Aware Energy Optimized VM Migration (PAEOVMM). The approach uses the maxPower of the host to allocate VM to the host. Our proposed approach is analyzed using CloudSim. As per the simulation results, PAEOVVM performs better in energy consumption than existing baseline CloudSim algorithm. PAEOVVM performs improvement in energy consumption on an average of 27-40%.
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Fatima, Aisha, Nadeem Javaid, Tanzeela Sultana, Waqar Hussain, Muhammad Bilal, Shaista Shabbir, Yousra Asim, Mariam Akbar, and Manzoor Ilahi. "Virtual Machine Placement via Bin Packing in Cloud Data Centers." Electronics 7, no. 12 (December 4, 2018): 389. http://dx.doi.org/10.3390/electronics7120389.
Повний текст джерелаАнотація:
With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.
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Gaykar, Reshma S., Velu Khanaa, and Shashank D. Joshi. "Mapping of Virtual Machines Using Machine Learning Algorithms for Detection of Faulty Nodes." International Journal of Safety and Security Engineering 12, no. 6 (December 31, 2022): 681–90. http://dx.doi.org/10.18280/ijsse.120603.
Повний текст джерелаАнотація:
A distributed system is characterized by a large number of nodes that are linked to a network and are mostly used for transaction processing. Large set of users are likely to communicate information over the network to the nodes, consistency and dependability remain a critical problem in the distributed environments. Independent failure of the component is one of the major problems in the distributed systems as it slowly impacts the performance of the other nodes in the system. The quality of service - QoS of a distributed network may be improved by a quick way of detecting problematic nodes. Sometime heavy nodes required high computation for transaction processing while idle nodes take low computation. In this paper, we proposed identification of straggler nodes in distributed environment with the help of hybrid machine learning algorithm. The work basically carried out to set up of large number of virtual machines and collect current log audits of each VM. According to the available parameters of audit files to each machine, algorithms decide that specific node is overheated or ideal condition. In expensive experimental analysis we demonstrate a accuracy of proposed hybrid machine learning algorithm. The proposed algorithm produces higher precision up to 4.5% than state-of-art methods. Key highlights of the VM mapping strategy were also investigated through a scrutiny of ongoing contracts. Main focus remains on machine learning (ML) to distinguish PM (Physical Machine) congestion, determining VMs from crowded PMs, and VM conditions as major exercises. This paper aims to review and characterize research on the planning and status of VMs that use ML using asset usage history. Energy productivity, VM migration, and quality of service were the main exhibition boundaries used to investigate cloud data center presentations.
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Gunasekhar, T., V. Vinaykumar, J. Laharipriya, and P. Raghubabu. "An Effective Method for Scheduling Virtual Machines in Cloud." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 481. http://dx.doi.org/10.14419/ijet.v7i2.32.16273.
Повний текст джерелаАнотація:
With the rise of cloud figuring, processing assets (i.e., systems, servers, stockpiling, applications, and so forth.) are provisioned as metered on-request benefits over systems, and can be quickly dispensed and discharged with negligible management exertion. In the cloud registering worldview, the virtual machine (VM) is a standout amongst the most usually utilized asset units in which business administrations are epitomized. VM scheduling advancement, i.e., finding ideal position plans for VMs and reconfigurations as indicated by the evolving conditions, winds up testing issues for cloud framework suppliers and their clients.
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J. A. Nair, Susmita, and T. R. Gopalakrishnan Nair. "Performance degradation assessment and VM placement policy in cloud." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 4961. http://dx.doi.org/10.11591/ijece.v9i6.pp4961-4969.
Повний текст джерелаАнотація:
In virtualized servers, with live migration technique pages are copied from one physical machine to another while the virtual machine (VM) is running. The dynamic migration of virtual machines encumbers the data center which in turn reduces the performance of applications running on that particular physical machine. A considerable number of studies have been carried out in the area of performance evaluation during live VM migration. However, all the aspects related to the migration process have not been examined for the performance assessment. In this paper, we propose a novel approach to evaluate the performance during migration process in different types of coupled machine environment. It is presented here that the state of art VM migration technology requires further improvement in realizing effective migration by monitoring comprehensive performance value. We introduced the parameter, θ, to compare performance value which can be used for controlling and halting unsuccessful migration and save significant amount of time in migration operation. Our model is capable of analyzing real time scenario of cloud performance assessment targeting VM migration strategies. It also offers the possibility of further expanding to universal models for analyzing the performance variations that occurs as a result of VM migration.
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Sharma, Oshin, and Hemraj Saini. "Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment." International Journal of Business Data Communications and Networking 13, no. 1 (January 2017): 45–57. http://dx.doi.org/10.4018/ijbdcn.2017010104.
Повний текст джерелаАнотація:
In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.
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Kani, A. M. Serma, and D. Paulraj. "Dynamic Consolidation of Virtual Machine: A Survey of Challenges for Resource Optimization in Cloud Computing." Recent Advances in Computer Science and Communications 13, no. 3 (August 12, 2020): 491–501. http://dx.doi.org/10.2174/2213275912666190716124749.
Повний текст джерелаАнотація:
Background:: Virtualization is an efficient technology that accelerates available data center to support efficient workload for the application. It completely based on guest operating system which keeps track of infrastructure that keeps track of real time usage of hardware and utilization of software. Objective:: To address the issues with Virtualization this paper analyzed various virtualization terminology for treating best effective way to reduce IT expenses while boosting efficiency and deployment for all levels of businesses. Methods: This paper discusses about the scenarios where various challenges met by Dynamic VM consolidation. Dynamic conclusion of virtual machines has the ability to increase the consumption of physical setup and focus on reducing power utilization with VM movement for stipulated period. Gathering the needs of all VM working in the application, adjusting the Virtual machine and suitably fit the virtual resource on a physical machine. Profiling and scheduling the virtual CPU to another Physical resource. This can be increased by making live migration with regards to planned schedule of virtual machine allotment. Results:: The recent trends followed in comprehending dynamic VM consolidation is applicable either in heuristic-based techniques which has further approaches based on static as well as adaptive utilization threshold. SLA with unit of time with variant HOST adoption (SLATAH) which is dependent on CPU utilization threshold with 100% for active host. Conclusion:: The cloud provider decision upon choosing the virtual machine for their application also varies with their decision support system that considers data storage and other parameters. It is being compared for the continuous workload distribution as well as eventually compared with changing demands of computation and in various optimization VM placement strategies.
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Khani, Hadi, and Hamed Khanmirza. "Randomized routing of virtual machines in IaaS data centers." PeerJ Computer Science 5 (September 2, 2019): e211. http://dx.doi.org/10.7717/peerj-cs.211.
Повний текст джерелаАнотація:
Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.
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Liu, Jun, Shuyu Chen, Zhen Zhou, and Tianshu Wu. "An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3570305.
Повний текст джерелаАнотація:
Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM) for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.
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Zhao, Jiaqi, Yousri Mhedheb, Jie Tao, Foued Jrad, Qinghuai Liu, and Achim Streit. "Using a vision cognitive algorithm to schedule virtual machines." International Journal of Applied Mathematics and Computer Science 24, no. 3 (September 1, 2014): 535–50. http://dx.doi.org/10.2478/amcs-2014-0039.
Повний текст джерелаАнотація:
Abstract Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption
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Toutov, Andrew, Natalia Toutova, Anatoly Vorozhtsov, and Ilya Andreev. "Optimizing the Migration of Virtual Machines in Cloud Data Centers." International Journal of Embedded and Real-Time Communication Systems 13, no. 1 (January 2022): 1–19. http://dx.doi.org/10.4018/ijertcs.289200.
Повний текст джерелаАнотація:
Dynamic resource allocation of cloud data centers is implemented with the use of virtual machine migration. Selected virtual machines (VM) should be migrated on appropriate destination servers. This is a critical step and should be performed according to several criteria. It is proposed to use the criteria of minimum resource wastage and service level agreement violation. The optimization problem of the VM placement according to two criteria is formulated, which is equivalent to the well-known main assignment problem in terms of the structure, necessary conditions, and the nature of variables. It is suggested to use the Hungarian method or to reduce the problem to a closed transport problem. This allows the exact solution to be obtained in real time. Simulation has shown that the proposed approach outperforms widely used bin-packing heuristics in both criteria.
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Dewangan, Bhupesh Kumar, Anurag Jain, and Tanupriya Choudhury. "AP: Hybrid Task Scheduling Algorithm for Cloud." Revue d'Intelligence Artificielle 34, no. 4 (September 30, 2020): 479–85. http://dx.doi.org/10.18280/ria.340413.
Повний текст джерелаАнотація:
Resource optimization is cost effective process in cloud. The efficiency of load balancing completely depends on how the infrastructure is utilizing. As per the current study, the resource optimization techniques are very costly and taking more convergence time to execute the task and load distribution among different virtual machines (VM). The objective of this paper is to develop a hybrid optimization algorithm to find the best virtual machine based on their fitness values and schedule different task to the fittest VM so that each task should get complete on time, and system can utilize the VM as well. The proposed algorithm is hybrid version of genetic (GA), ant-colony (Aco), and particle-swarm (Pso) algorithms, which is implemented and tested in amazon web service and compared with existing algorithms based on VM utilization, completion time, and cost. The proposed hybrid system genetic-aco-pso based algorithm (GAP) perform utmost while comparing with the existing systems.
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Han, Tianxu, Jian Mao, Sipeng Xie, Qiyuan Gao, Qin Wang, Pinge Zhang, and Yijia Fang. "VM-Studio: A Universal Crosschain Smart Contract Verification and Execution Scheme." Security and Communication Networks 2023 (April 18, 2023): 1–14. http://dx.doi.org/10.1155/2023/2413532.
Повний текст джерелаАнотація:
Blockchain interoperability promotes value delivery, application expansion, and ecological compatibility across heterogeneous blockchain systems. However, the contract framework and virtual machine construction in these systems are significantly different, and crosschaining becomes a challenging issue for system universality and compatibility. Starting from this problem, in this study, we propose VM-Studio, a crosschain smart contract verification and execution scheme to migrate the virtual machines (VMs) from the origin blockchain to the target blockchain. In our scheme, the migrated VMs are loaded as independent components enclosed in containers. We also design a unified system schedule to enable VM-Studio to allocate transactions into different containers. Loaded with origin blockchain VMs, these containers can accordingly solve crosschain transaction execution and smart contract verification. We implement VM-Studio and evaluate the transaction execution performance in the origin environment with multiple blockchains and the container environment. Experiment results demonstrate that VM-Studio achieves broad universality without compromising the execution performance of original blockchain transactions.
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Kamoun-Abid, Ferdaous, Hounaida Frikha, Amel Meddeb-Makhoulf, and Faouzi Zarai. "Automating cloud virtual machines allocation via machine learning." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (July 1, 2024): 191. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp191-202.
Повний текст джерелаАнотація:
In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.
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Anitha, H. M., and P. Jayarekha. "An Software Defined Network Based Secured Model for Malicious Virtual Machine Detection in Cloud Environment." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 526–30. http://dx.doi.org/10.1166/jctn.2020.8481.
Повний текст джерелаАнотація:
Cloud computing is an emerging technology that offers the services to all the users as per their demand. Services are leveraged according to the Service level agreement (SLA). Service level agreement is monitored so that services are offered to the users without any problem and deprival. Software Defined Network (SDN) is used in order to monitor the trust score of the deployed Virtual Machines (VM) and Quality of Service (QoS) parameters offered. Software Defined Network controller is used to compute the trust score of the Virtual Machines and find whether Virtual Machine is malicious or trusted. Genetic algorithm is used to find the trusted Virtual Machine and release the resources allocated to the malicious Virtual Machine. This monitored information is intimated to cloud provider for further action. Security is enhanced by avoiding attacks from the malicious Virtual Machine in the cloud environment. The main objective of the paper is to enhance the security in the system using Software Defined Network based secured model.
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Sharma, Oshin, and Hemraj Saini. "Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm." International Journal of Information Security and Privacy 13, no. 1 (January 2019): 1–16. http://dx.doi.org/10.4018/ijisp.2019010101.
Повний текст джерелаАнотація:
To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.
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Satveer and Mahendra Singh Aswal. "VM Consolidation Plan for Improving the Energy Efficiency of Cloud." Cybernetics and Information Technologies 21, no. 3 (September 1, 2021): 145–59. http://dx.doi.org/10.2478/cait-2021-0035.
Повний текст джерелаАнотація:
Abstract Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.
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Sridharan R. and Domnic S. "Placement for Intercommunicating Virtual Machines in Autoscaling Cloud Infrastructure." Journal of Organizational and End User Computing 33, no. 2 (July 2021): 17–35. http://dx.doi.org/10.4018/joeuc.20210301.oa2.
Повний текст джерелаАнотація:
Due to pay-as-you-go style adopted by cloud datacenters (DC), modern day applications having intercommunicating tasks depend on DC for their computing power. Due to unpredictability of rate at which data arrives for immediate processing, application performance depends on autoscaling service of DC. Normal VM placement schemes place these tasks arbitrarily onto different physical machines (PM) leading to unwanted network traffic resulting in poor application performance and increases the DC operating cost. This paper formulates autoscaling and intercommunication aware task placements (AIATP) as an optimization problem, with additional constraints and proposes solution, which uses the placement knowledge of prior tasks of individual applications. When compared with well-known algorithms, CloudsimPlus-based simulation demonstrates that AIATP reduces the resource fragmentation (30%) and increases the resource utilization (18%) leading to minimal number of active PMs. AIATP places 90% tasks of an application together and thus reduces the number of VM migration (39%) while balancing the PMs.
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Mekonnen, Dawit, Alemayehu Megersa, Rakesh Kumar Sharma, and Durga Prasad Sharma. "Designing a Component-Based Throttled Load Balancing Algorithm for Cloud Data Centers." Mathematical Problems in Engineering 2022 (October 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/4640443.
Повний текст джерелаАнотація:
Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, “available” and “free.” To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm’s performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm’s performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed “component-based throttled load balancing algorithm” is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization.
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Lin, Hao, Jiaxing Qiu, Hongyi Wang, Zhenhua Li, Liangyi Gong, Di Gao, Yunhao Liu, et al. "Take the Blue Pill: Pursuing Mobile App Testing Fidelity, Efficiency, and Accessibility with Virtual Device Farms." GetMobile: Mobile Computing and Communications 28, no. 1 (May 9, 2024): 5–9. http://dx.doi.org/10.1145/3665112.3665114.
Повний текст джерелаАнотація:
For more than a decade, researchers have been extensively exploring mobile app testing on virtual devices [1-9], which are software-emulated mobile devices running on commodity servers, in a similar vein as virtual machines (VM) in the cloud. Building on server virtualization, virtual devices naturally inherit the benefits of VM, such as scalability, elasticity, and cost efficiency. Moreover, virtualization enables useful features not offered by physical devices, such as service instrumentation [3], whole-system snapshot [9], and memory introspection [1,2], atop which a series of advanced testing and debugging techniques are developed.
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Cheong, Il Ahn, Seul Gi Lee, and Kyung Ho Son. "A Study on the Virtualization Security Management in the Cloud Computing Environment." Applied Mechanics and Materials 336-338 (July 2013): 2035–39. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2035.
Повний текст джерелаАнотація:
Service providers provide virtualized IT resources by using Internet technologies in their services. Users use the provided IT resources as much as they want and pay the corresponding fee. In such cloud computing environments, issues of malicious codes, hacking and leak of confidential information are the biggest concerns of the people involved. In this paper, we study the virtualization security management, which is important for the proper management of collection of virtualization resources information and security events, monitoring and analysis of cloud security situations, and cloud security policies. Specifically, we propose the structuring of the virtualization security management system and the management method to reduce the complexity of administering the virtual machines installed in the cloud data centers caused by the inherent properties of virtual machines characterized by dynamic changes in the form of image files. Therefore, we studied the tracing method for history of infected virtual machines following an analysis of the VM lifecycle status and VM running states.
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Li, Zhihua, Meini Pan, and Lei Yu. "Multi-resource collaborative optimization for adaptive virtual machine placement." PeerJ Computer Science 8 (January 6, 2022): e852. http://dx.doi.org/10.7717/peerj-cs.852.
Повний текст джерелаАнотація:
The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.
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M., Mohankumar, Balamurugan K., Singaravel G., and Menaka S.R. "A Dynamic Workflow Scheduling Method based on MCDM Optimization that Manages Priority Tasks for Fault Tolerance." International Academic Journal of Science and Engineering 11, no. 1 (January 6, 2024): 09–14. http://dx.doi.org/10.9756/iajse/v11i1/iajse1102.
Повний текст джерелаАнотація:
Because it offers efficient on-demand service delivery over the internet, cloud computing has become more and more popular. An architectural model for cloud computing energy management is provided by the suggested Ant Lion algorithm. Virtual Machines (VMs) in cloud systems are assigned to hosts based not so much on their overall and long-term use, but rather on their immediate resource consumption, including RAM availability. The placement and scheduling processes are frequently computationally demanding and have the potential to affect the performance of deployed virtual machines. In this research work, we offer a strategy that considers the historical resource use of virtual machines (VMs) over time while scheduling them in the cloud. Our goal is to use the Ant lion approach to schedule virtual machines (VMs) in a way that maximizes performance by evaluating the utilization levels of prior VMs. The goal is to reduce the degradation of performance brought about by Cloud administration tasks like as virtual machine deployment, which might impact systems that have already been installed. Furthermore, congested virtual machines (VMs) sometimes take up resources from nearby VMs, increasing the VMs' actual CPU use. Our results show that by learning and adjusting to system behavior over time, our strategy outperforms conventional instant-based physical machine selection. We offer the idea of scheduling virtual machines (VMs) using resource monitoring data from past VM resource use. By using the Ant lion classifier, four fewer physical machines are needed.
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Selvin Paul Pete, J., G. Mahadevan, and S. Selvakumar. "QOS Aware Self Adaptable Virtual Machines Management System for Cloud Computing." International Journal of Engineering & Technology 7, no. 4.19 (November 27, 2018): 177–81. http://dx.doi.org/10.14419/ijet.v7i4.19.22043.
Повний текст джерелаАнотація:
Cloud Computing as of now is the most important distributed environment because of low level user management and system integration. But most important challenge cloud computing faces is effective resource provisioning, Solving the issue will result in effective consumption of service offered, better user satisfaction and resources for more people during peak hours, reduce operational burden to cloud service providers and less pay to clients. Current works are aimed at determining the usage, VM (Virtual Machine) establishment and setting up. The above process requires considerable time to construct and kill VMs which may be used to cater more user. So here we have provided, a Quality of Service Aware Virtual Machine management mechanism for creating new VM’s that makes use of the system resources efficiently. The existing VM for related type of requests are identified to minimize VM creation time. In our system, QOS is guaranteed by making all tasks adhere to the SLA necessities. Services are divided using need of the hour and the critical job is given higher significance. The experimental results show that a large number of users are serviced in relation to others algorithm which will fulfil clients needs during the peak traffic. Â
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Nkenyereye, Lionel, Lewis Nkenyereye, Bayu Adhi Tama, Alavalapati Reddy, and JaeSeung Song. "Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration." Sensors 20, no. 4 (February 17, 2020): 1092. http://dx.doi.org/10.3390/s20041092.
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Cloud computing supports many unprecedented cloud-based vehicular applications. To improve connectivity and bandwidth through programmable networking architectures, Software- Defined (SD) Vehicular Network (SDVN) is introduced. SDVN architecture enables vehicles to be equipped with SDN OpenFlow switch on which the routing rules are updated from a SDN OpenFlow controller. From SDVN, new vehicular architectures are introduced, for instance SD Vehicular Cloud (SDVC). In SDVC, vehicles are SDN devices that host virtualization technology for enabling deployment of cloud-based vehicular applications. In addition, the migration of Virtual Machines (VM) over SDVC challenges the performance of cloud-based vehicular applications due the highly mobility of vehicles. However, the current literature that discusses VM migration in SDVC is very limited. In this paper, we first analyze the evolution of computation and networking technologies of SDVC with a focus on its architecture within the cloud-based vehicular environment. Then, we discuss the potential cloud-based vehicular applications assisted by the SDVC along with its ability to manage several VM migration scenarios. Lastly, we provide a detailed comparison of existing frameworks in SDVC that integrate the VM migration approach and different emulators or simulators network used to evaluate VM frameworks’ use cases.
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Buchbinder, Niv, Yaron Fairstein, Konstantina Mellou, Ishai Menache, and Joseph (Seffi) Naor. "Online Virtual Machine Allocation with Lifetime and Load Predictions." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (June 22, 2022): 9–10. http://dx.doi.org/10.1145/3543516.3456278.
Повний текст джерелаАнотація:
The cloud computing industry has grown rapidly over the last decade, and with this growth there is a significant increase in demand for compute resources. Demand is manifested in the form of Virtual Machine (VM) requests, which need to be assigned to physical machines in a way that minimizes resource fragmentation and efficiently utilizes the available machines. This problem can be modeled as a dynamic version of the bin packing problem with the objective of minimizing the total usage time of the bins (physical machines). Motivated by advances in Machine Learning that provide good estimates of workload characteristics, this paper studies the effect of having extra information about future (total) demand. We show that the competitive factor can be dramatically improved with this additional information; in some cases, we achieve constant competitiveness, or even a competitive factor that approaches 1. Along the way, we design new offline algorithms with improved approximation ratios for the dynamic bin-packing problem.
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Kourai, Kenichi, Takeshi Azumi, and Shigeru Chiba. "Efficient and Fine-Grained VMM-Level Packet Filtering for Self-Protection." International Journal of Adaptive, Resilient and Autonomic Systems 5, no. 2 (April 2014): 83–100. http://dx.doi.org/10.4018/ijaras.2014040105.
Повний текст джерелаАнотація:
In Infrastructure-as-a-Service (IaaS) clouds, stepping-stone attacks via hosted virtual machines (VMs) are critical for the credibility. This type of attack uses compromised VMs as stepping stones for attacking the outside hosts. For self-protection, IaaS clouds should perform active responses against stepping-stone attacks. However, it is difficult to stop only outgoing attacks at edge firewalls, which can only use packet headers. In this paper, we propose a new self-protection mechanism against stepping-stone attacks, which is called xFilter. xFilter is a packet filter running in the virtual machine monitor (VMM) underlying VMs and achieves pinpoint active responses by using VM introspection. VM introspection enables xFilter to directly obtain information on packet senders inside VMs. On attack detection, xFilter automatically generates filtering rules based on packet senders. To make packet filtering with VM introspection efficient, we introduced several optimization techniques. Our experiments showed that the performance degradation due to xFilter was usually less than 16%.