Relevant bibliographies by topics / VM instance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'VM instance'

Author: Grafiati
Published: 1 July 2021
Last updated: 2 February 2022

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Journal articles on the topic "VM instance"

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Fan, Chih-Tien, Yue-Shan Chang, and Shyan-Ming Yuan. "VM instance selection for deadline constraint job on agent-based interconnected cloud." Future Generation Computer Systems 87 (October 2018): 470–87. http://dx.doi.org/10.1016/j.future.2018.04.017.

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Wan, Jianxiong, Limin Liu, Jie Lv, and Zhiwei Xu. "Coarse-Grain QoS-Aware Dynamic Instance Provisioning for Interactive Workload in the Cloud." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/215016.

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Cloud computing paradigm renders the Internet service providers (ISPs) with a new approach to deliver their service with less cost. ISPs can rent virtual machines from the Infrastructure-as-a-Service (IaaS) provided by the cloud rather than purchasing them. In addition, commercial cloud providers (CPs) offer diverse VM instance rental services in various time granularities, which provide another opportunity for ISPs to reduce cost. We investigate a Coarse-grain QoS-aware Dynamic Instance Provisioning (CDIP) problem for interactive workload in the cloud from the perspective of ISPs. We formulate the CDIP problem as an optimization problem where the objective is to minimize the VM instance rental cost and the constraint is the percentile delay bound. Since the Internet traffic shows a strong self-similar property, it is hard to get an analytical form of the percentile delay constraint. To address this issue, we purpose a lookup table structure together with a learning algorithm to estimate the performance of the instance provisioning policy. This approach is further extended with two function approximations to enhance the scalability of the learning algorithm. We also present an efficient dynamic instance provisioning algorithm, which takes full advantage of the rental service diversity, to determine the instance rental policy. Extensive simulations are conducted to validate the effectiveness of the proposed algorithms.
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B.Rathod, Suresh, and V. Krishna Reddy. "Decision Making Framework for Decentralized Virtual Machine Placement in Cloud Computing." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 705. http://dx.doi.org/10.14419/ijet.v7i2.7.10926.

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In distributed cloud environment hosts are configured with Local Resource Monitors (LRM). This LRM monitors the underlying hosts’ resource usage, runs independently and balances the underling host’s load by migrating Virtual Machine (VM) instance. For the dynamic environment, each hosts has varying resource requirement, hosts load cannot remain constant. LRM at each host takes decision for VM migration considering static threshold on its own and other hosts current CPU utilization. This result in chances of getting selected same host for VM placement by multiple hosts to reduce resource usage of underlying hosts. The decision making at each server causes the problem of same host identification by multiple hosts during VM placement and consumes extra CPU power and network bandwidth consumption towards each server. This paper addresses the above said issue by proposing decentralized decision making framework for cloud considering hybrid Peer to Peer (P2P) network topology. Proposed solution results avoiding above said issues and balances the load across servers in DC.
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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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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.

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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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Shin, Youngjoo. "A VM-Based Detection Framework against Remote Code Execution Attacks for Closed Source Network Devices." Applied Sciences 9, no. 7 (March 28, 2019): 1294. http://dx.doi.org/10.3390/app9071294.

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Remote code execution attacks against network devices become major challenges in securing networking environments. In this paper, we propose a detection framework against remote code execution attacks for closed source network devices using virtualization technologies. Without disturbing a target device in any way, our solution deploys an emulated device as a virtual machine (VM) instance running the same firmware image as the target in a way that ingress packets are mirrored to the emulated device. By doing so, remote code execution attacks mounted by maliciously crafted packets will be captured in memory of the VM. This way, our solution enables successful detection of any kind of intrusions that leaves memory footprints.
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Haragi L, Darshan, Mahith S, and Prof Sahana B. "Infrastructure Optimization in Kubernetes Cluster." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 17, 2021): 546–55. http://dx.doi.org/10.51201/jusst/21/05292.

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Kubernetes is a compact, extensible, open-source stage for overseeing containerized responsibilities and administrations, that works with both decisive setup and robotization. Kubernetes is like VMs, however having loosened up isolation properties to share the Operating System (OS) among the applications. The container conversely with VM, has its own document framework, a portion of Central Processing Unit(CPU), memory, process space, and much more. Kubernetes cluster is a bunch of node machines for running containerized applications. Each cluster contains a control plane and at least one node. Infrastructure Optimization is the process of analyzing and arranging the portion of cloud resources that power applications and workloads to augment the presentation and limit squander due to over-provisioning. In the paper, a “Movie Review System” web application is designed using GoLang for backend components and HTML, CSS, and JS for frontend components. Using AWS, an EC2 instance is created and the web application is deployed onto EC2 and hosted in the instance server. The web application is also deployed on Kubernetes locally using the MiniKube tool. A performance analysis is performed for both the deployments on considering common performance metrics for both AWS EC2 / Virtual Machine (VM) and Kubernetes.
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Kernodle, Michael W., Robert N. McKethan, and Erik Rabinowitz. "Observational Learning of Fly Casting Using Traditional and Virtual Modeling with and without Authority Figure." Perceptual and Motor Skills 107, no. 2 (October 2008): 535–46. http://dx.doi.org/10.2466/pms.107.2.535-546.

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Traditional and virtual modeling were compared during learning of a multiple degree-of-freedom skill (fly casting) to assess the effect of the presence or absence of an authority figure on observational learning via virtual modeling. Participants were randomly assigned to one of four groups: Virtual Modeling with an authority figure present (VM-A) ( n = 16), Virtual Modeling without an authority figure (VM-NA) ( n = 16), Traditional Instruction ( n = 17), and Control ( n = 19). Results showed significant between-group differences on Form and Skill Acquisition scores. Except for one instance, all three learning procedures resulted in significant learning of fly casting. Virtual modeling with or without an authority figure present was as effective as traditional instruction; however, learning without an authority figure was less effective with regard to Accuracy scores.
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Kenga, Derdus, Vincent Omwenga, and Patrick Ogao. "Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds." Journal of Information Technology and Computer Science 6, no. 2 (September 3, 2021): 170–82. http://dx.doi.org/10.25126/jitecs.202162196.

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The main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing approaches focus on VM allocation and migration, which only leads to physical machine (PM) level optimization. Other approaches use horizontal auto-scaling, which is not a visible solution in the case of IaaS public cloud. In this paper, we propose an approach of customizing user VM’s size to match the resources requirements of their application workloads based on an analysis of real backend traces collected from a VM in a production data centre. In this approach, a VM is given fixed size resources that match applications workload demands and any demand that exceeds the fixed resource allocation is predicted and handled through vertical VM auto-scaling. In this approach, energy consumption by PMs is reduced through efficient resource utilization. Experimental results obtained from a simulation on CloudSim Plus using GWA-T-13 Materna real backend traces shows that data center energy consumption can be reduced via efficient resource utilization
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Seok, Soonhwa, Boaventura DaCosta, Mikayla McHenry-Powell, Linda Heitzman-Powell, and Katrina Ostmeyer. "A Systematic Review of Evidence-Based Video Modeling for Students with Emotional and Behavioral Disorders." Education Sciences 8, no. 4 (October 16, 2018): 170. http://dx.doi.org/10.3390/educsci8040170.

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This systematic review examined eight studies showing that video modeling (VM) can have a positive and significant effect for students with emotional and behavioral disorders (EBD). Building upon meta-analyses that sought evidence of video-based interventions decreasing problem behaviors of students with EBD in K-12 education, the review examined the standards of the Council for Exceptional Children (CEC) for evidence-based practice as well as additional quality indicators, neglected quality indicators, strategies combined with VM, the impact of the independent variables on the dependent variables, and common recommendations offered for future research. Findings revealed that the eight studies met the CEC standards for evidence-based practices as well as other quality indicators. For instance, all studies reported content and setting, participants, intervention agents, description of practice, as well as interobserver agreement and experimental control. According to the findings, fidelity index and effect size were the two most neglected quality indicators. Furthermore, instructions, reinforcement system, and feedback or discussion were the most common strategies used. Finally, generalizability—across settings, populations, treatment agents, target behaviors in the real world, and subject matter—was the most common recommendation for future research. While further investigation is warranted, these findings suggest that VM is an effective evidence-based practice for students with EBD when the CEC standards are met.
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Dissertations / Theses on the topic "VM instance"

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Cherukuri, Prudhvi Nath Naidu, and Sree Kavya Ganja. "Comparison of GCP and AWS using usability heuristic and cognitive walkthrough while creating and launching Virtual Machine instances in VirtualPrivate Cloud." Thesis, Blekinge Tekniska Högskola, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21896.

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ABSTRACT Cloud computing has become increasingly important over the years, as the need for computational resources, data storage, and networking capabilities in the field of information technology has been increased. There are several large corporations that offer these services to small companies or to end-users such as GCP, AWS, Microsoft Azure, IBM Cloud, and many more. The main aim of this thesis is to perform the comparison of GCP and AWS consoles in terms of the user interface while performing tasks related to compute engine. The cognitive walkthrough has been performed on tasks such as the creation of VPC, creation of VM instances, and launching them and then from the results, both the interfaces are compared using usability heuristics. Background: As the usage of cloud computing has increased over the years, the companies that are offering these services have grown eventually. Though there are many cloud services available in the market the user might always choose the services that are more flexible and efficient to use. In this manner, the choice of our research is made to compare the cloud services in terms of user interaction user experience. As we dig deep into the topic of user interaction and experience there are evaluation techniques and principles such as cognitive walkthrough and usability heuristics are suitable for our research. Here the comparison is made among GCP and AWS user interfaces while performing some tasks related to compute engine. Objectives: The main objectives of this thesis are to create VPC, VM instances,s and launch VM instances in two different cloud services such as GCP and AWS. To find out the best user interface among these two cloud services from the perspective of the user. Method: The process of finding best user interface among GCP and AWS cloud services is based on the cognitive walkthrough and comparing with usability heuristics. The cognitive walkthrough is performed on chosen tasks in both the services and then compared using usability heuristics to get the results of our research. Results: The results that are obtained from cognitive walkthrough and comparison with usability heuristics shown in graphical formats such as bar graphs, pie charts, and the comparison results are shown in the form of tabular form. The results cannot be universal, as they are just observational results from cognitive walkthrough and usability heuristic evaluation. Conclusion: After performing the above-mentioned methods it is observed that the user interface of GCP is more flexible and efficient in terms of user interaction and experience. Though the user experience may vary based on the experience level of users in cloud services, as per our research the novice user and moderate users have chosen GCP as a better interactive system over AWS. Keywords: Cloud computing, VM instance, Cognitive walkthrough, Usability heuristics, User-interface.
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Nyquist, Johan, and Alexander Manfredsson. "Jämförelse av Hypervisor & Zoner : Belastningstester vid drift av webbservrar." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-28576.

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Virtualisering av datorer rent generellt innebär att man delar upp hela eller delar av enmaskinkonfiguration i flera exekveringsmiljöer. Det är inte bara datorn i sig som kanvirtualiseras utan även delar av det, såsom minnen, lagring och nätverk. Virtualiseringanvänds ofta för att kunna nyttja systemets resurser mer effektivt. En hypervisorfungerar som ett lager mellan operativsystemet och den underliggande hårdvaran. Meden hypervisor har virtuella maskiner sitt egna operativsystems kärna. En annan tekniksom bortser från detta mellanlager kallas zoner. Zoner är en naturlig del avoperativsystemet och alla instanser delar på samma kärna, vilket inte ger någon extraoverhead. Problemet är att hypervisorn är en resurskrävande teknik. Genom att användazoner kan detta problem undkommas genom att ta bort hypervisorlagret och istället köramed instanser som kommunicerar direkt med operativsystemets kärna. Detta ärteoretiskt grundande och ingen tidigare forskning har utförts, därmed påkallades dennautredning. För att belysa problemet använde vi oss av Apache som webbserver.Verktyget Httperf användes för att kunna utföra belastningstester mot webbservern.Genom att göra detta kunde vi identifiera att den virtualiserade servern presterade sämreän en fysisk server (referensmaskin). Även att den nyare tekniken zoner bidrar till lägreoverhead, vilket gör att systemet presterar bättre än med den traditionella hypervisorn.För att styrka vår teori utfördes två tester. Det första testet bestod utav en virtualiseradserver, andra testet bestod av tre virtuella servrar. Anledningen var att se hur de olikateknikerna presterade vid olika scenarion. Det visade sig i båda fallen att zonerpresterade bättre och att det inte tappade lika mycket i prestanda i förhållande tillreferensmaskinerna.
Virtualization of computers in general means that the whole or parts of a machineconfiguration is split in multiple execution enviornments. It is not just the computeritself that can be virtualized, but also the resources such as memory, storage andnetworking. Virtualization is often used to utilize system resources more efficient. Ahypervisor acts as a layer between the operating system and the underlying hardware.With a hypervisor a virtual machine has its own operating system kernel. Anothertechnique that doesn't use this middle layer is called zones. Zones are a natural part ofthe operating system and all instances share the same core, this does not provide anyadditional overhead. The problem with hypervisors is that it is a rescource-demandingtechnique. The advantage with zones is that you should be able to avoid the problem byremoving the hypervisor layer and instead run instances that communicate directly tothe operating system kernel. This is just a theoretical foundation. No previous researchhas been done, which result in this investigation. To illustrate the problem we usedApache as a web server. Httperf will be used as a tool to benchmark the web server. Bydoing this we were able to identify that the virtualized server did not perform quite aswell as a physical server. Also that the new technique (zones) did contribute with loweroverhead, making the system perform better than the traditional hypervisor. In order toprove our theory two tests were performed. The first test consisted of one virtual serverand the other test consisted of three virtual servers. The reason behind this was to seehow the different techniques performed in different scenarios. In both cases we foundthat zones performed better and did not drop as much performance in relation to ourreference machines.
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Book chapters on the topic "VM instance"

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Srinivasan, Madhan Kumar, P. Revathy, and Keerthi Balasundaram. "Cloud VM/Instance Monitor Phase-II (CIM-PII) Subsystem of eCloudIDS." In Innovations in Computer Science and Engineering, 121–30. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3818-1_14.

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Jung, Daeyong, SungHo Chin, Kwang Sik Chung, and HeonChang Yu. "VM Migration for Fault Tolerance in Spot Instance Based Cloud Computing." In Grid and Pervasive Computing, 142–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38027-3_15.

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De, Pradipta, Manish Gupta, Manoj Soni, and Aditya Thatte. "Caching VM Instances for Fast VM Provisioning: A Comparative Evaluation." In Euro-Par 2012 Parallel Processing, 325–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32820-6_33.

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Phani Praveen, S., and K. Thirupathi Rao. "An Optimized Rendering Solution for Ranking Heterogeneous VM Instances." In Advances in Intelligent Systems and Computing, 159–67. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7566-7_17.

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Brown, Alan W., David J. Carney, Edwin J. Morris, Dennis B. Smith, and Paul F. Zarrella. "Integration of CASE Tools with CM Systems: Lessons Learned." In Principles of CASE Tool Integration. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195094787.003.0016.

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The problem of CASE tool integration has several concerns that overlap with those of configuration management (CM), so much so that a discussion of one topic is often difficult to separate from a discussion of the other. To illustrate, we note that when choosing a solution to a problem in configuration management, we often must make choices that involve understanding process requirements, examining the services available (and their semantics), and analyzing implementation constraints. We also note that these activities are done simultaneously with making design trade-off decisions related to the integration of these process, service, and mechanism concepts. These factors are applicable to CM, but are not particular to CM: most of these same issues underlie any set of decisions one makes about combining a set of CASE tools into an integrated environment. However, CM and its relationship to CASE tool integration in general poses a unique set of problems. This is most apparent when we realize that CM is sometimes considered as a service (or set of services) provided by the environment or its framework, sometimes as a service provided by a separate stand-alone tool, and sometimes as an integral aspect of each individual CASE tool. These competing views lead to overlaps in functionality and responsibility between individual tools and the environment’s (or framework’s) CM capabilities. For instance: • A fundamental issue for CM is data redundancy. This results when different tools store the same data in separate repositories. Correspondingly, different data models may make data sharing (a fundamental issue for tool integration) difficult. • Version management (VM) and CM services provided by individual tools are frequently linked with private tool data model and data management services. These VM and CM services are not always delegable — sometimes these services are an intricate part of tool function (e.g., for multi-user support and build). • The VM and CM services provided by individual tools may imply or enforce vendor-specific VM/CM policies, as opposed to the CM policies of the environment.
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Sharma, Oshin, and Hemraj Saini. "Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 1456–70. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch068.

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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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Patra, Sudhansu Shekhar, and R. K. Barik. "Dynamic Dedicated Server Allocation for Service Oriented Multi-Agent Data Intensive Architecture in Biomedical and Geospatial Cloud." In Cloud Technology, 2262–73. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6539-2.ch107.

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Cloud computing has recently received considerable attention, as a promising approach for delivering Information and Communication Technologies (ICT) services as a utility. In the process of providing these services it is necessary to improve the utilization of data centre resources which are operating in most dynamic workload environments. Datacenters are integral parts of cloud computing. In the datacenter generally hundreds and thousands of virtual servers run at any instance of time, hosting many tasks and at the same time the cloud system keeps receiving the batches of task requests. It provides services and computing through the networks. Service Oriented Architecture (SOA) and agent frameworks renders tools for developing distributed and multi agent systems which can be used for the administration of cloud computing environments which supports the above characteristics. This paper presents a SOQM (Service Oriented QoS Assured and Multi Agent Cloud Computing) architecture which supports QoS assured cloud service provision and request. Biomedical and geospatial data on cloud can be analyzed through SOQM and has allowed the efficient management of the allocation of resources to the different system agents. It has proposed a finite heterogeneous multiple vm model which are dynamically allocated depending on the request from biomedical and geospatial stakeholders.
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Furbish, David Jon. "Fluids and Porous Media as Continua." In Fluid Physics in Geology. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780195077018.003.0006.

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Let us anticipate that we wish to treat fluids as continuous substances at a microscopic scale. To see the motivation for this, it is instructive to consider the possibility of describing fluid behavior at a molecular scale by making use of Lagrangian mechanics to track the behavior of each molecule, just as we would describe the ballistics of a moving, rigid body. Consider describing the state of a simple diatomic molecule at some instant; to do this, we must decide what minimum set of coordinates completely specifies the position and configuration of the molecule. For example, we must specify its position within an inertial reference frame, which requires the three Cartesian coordinates x, y, and z. We also must specify its velocity with respect to this coordinate system, which requires the three corresponding components of velocity um, vm, and wm. The molecule may be spinning; to describe this, we must assign to the molecule three local coordinate axes to specify three angular coordinates that give its orientation within the inertial reference frame. Because the axis of rotation may not coincide with one of the local axes, we also must specify two angular coordinates that give the orientation of the axis of rotation within the local coordinate system. Finally, we must specify the angular velocity about this axis of rotation. Thus, in addition to specifying the mass of a molecule, we need twelve variables or generalized coordinates to specify its state. Moreover, we must know initial values of these twelve coordinates, just as we need to know the initial position and velocity of a ballistic body to track its course. To track the behavior of N molecules, we therefore must know N masses plus 12N initial position, velocity, and orientation coordinates. It becomes clear that to adopt this approach to describe the behavior of a fluid constitutes a formidable task! One has no choice but to abandon a molecular scale treatment and adopt a view involving the microscopic scale, where behaviors of individual molecules are ignored, and instead, the collective behavior of a suitably defined ensemble of molecules is treated in a statistical (average) sense in terms of bulk properties such as fluid density, temperature, and viscosity.
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Conference papers on the topic "VM instance"

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Bierbrauer, Azden, Konstantin Nasartschuk, Adam Richard, Kenneth B. Kent, and Peter Shipton. "User controlled object sharing between Java VM instances." In 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE, 2017. http://dx.doi.org/10.1109/pacrim.2017.8121934.

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Mishra, Ashish Kumar, Abhishek Kesarwani, and Dharmendra K. Yadav. "Short Term Price Prediction for Preemptible VM Instances in Cloud Computing." In 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). IEEE, 2019. http://dx.doi.org/10.1109/i2ct45611.2019.9033677.

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Razavi, Kaveh, Gerrit Van Der Kolk, and Thilo Kielmann. "Prebaked µVMs: Scalable, Instant VM Startup for IaaS Clouds." In 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2015. http://dx.doi.org/10.1109/icdcs.2015.33.

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Lin, Yuhui, Jack Briggs, and Adam Barker. "FIFE: an Infrastructure-as-Code Based Framework for Evaluating VM Instances from Multiple Clouds." In 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC). IEEE, 2020. http://dx.doi.org/10.1109/ucc48980.2020.00028.

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Abundo, Marco, Valerio Di Valerio, Valeria Cardellini, and Francesco Lo Presti. "QoS-aware bidding strategies for VM spot instances: A reinforcement learning approach applied to periodic long running jobs." In 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 2015. http://dx.doi.org/10.1109/inm.2015.7140276.

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