Bibliografías temáticas / Heterogenous scheduling

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Literatura académica sobre el tema "Heterogenous scheduling"

Autor: Grafiati

Publicado: 11 de marzo de 2023

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Artículos de revistas sobre el tema "Heterogenous scheduling"

Patil, Shailesh. "Measurement-Based Opportunistic Scheduling for Heterogenous Wireless Systems." IEEE Transactions on Communications 57, no. 9 (2009): 2745–53. http://dx.doi.org/10.1109/tcomm.2009.09.0800902.

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Al-Saqabi, Khaled, Syed Sarwar, and Kassem Saleh. "Distributed gang scheduling in networks of heterogenous workstations." Computer Communications 20, no. 5 (1997): 338–48. http://dx.doi.org/10.1016/s0140-3664(97)00020-0.

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Sun, Donglai, Yang Liu, Jianhua Li, and Yue Wu. "Collaborative Opportunistic Scheduling in Heterogeneous Networks: A Distributed Approach." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/414689.

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We consider a collaborative opportunistic scheduling problem in a decentralized network with heterogeneous users. While most related researches focus on solutions for optimizing decentralized systems’ total performance, we proceed in another direction. Two problems are specifically investigated. (1) With heterogenous users having personal demands, is it possible to have it met by designing distributed opportunistic policies? (2) With a decentralized mechanism, how can we prevent selfish behaviors and enforce collaboration? In our research, we first introduce a multiuser network model along with a scheduling problem constrained by individual throughput requirement at each user’s side. An iterative algorithm is then proposed to characterize a solution for the scheduling problem, based on which collaborative opportunistic scheduling scheme is enabled. Properties of the algorithm, including convergence, will be discussed. Furthermore in order to keep the users staying with the collaboration state, an additional punishment strategy is designed. Therefore selfish deviation can be detected and disciplined so that collaboration is enforced. We demonstrate our main findings with both analysis and simulations.

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Ponsy. "Balanced Scheduling of Independent File-Sharing Tasks in Heterogenous Environment." Journal of Computer Science 7, no. 12 (2011): 1793–97. http://dx.doi.org/10.3844/jcssp.2011.1793.1797.

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Lai, Shouwen, Binoy Ravindran, and Hyeonjoong Cho. "Heterogenous Quorum-Based Wake-Up Scheduling in Wireless Sensor Networks." IEEE Transactions on Computers 59, no. 11 (2010): 1562–75. http://dx.doi.org/10.1109/tc.2010.20.

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Chraibi, Amine, Said Ben Alla, and Abdellah Ezzati. "An efficient cloudlet scheduling via bin packing in cloud computing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 3226. http://dx.doi.org/10.11591/ijece.v12i3.pp3226-3237.

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<p>In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS).</p>

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Incrocci, Luca, Paolo Marzialetti, Giorgio Incrocci, et al. "Substrate water status and evapotranspiration irrigation scheduling in heterogenous container nursery crops." Agricultural Water Management 131 (January 2014): 30–40. http://dx.doi.org/10.1016/j.agwat.2013.09.004.

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Hamdi, S., E. Bouazizi, and S. Faiz. "QOS MANAGEMENT IN REAL-TIME SPATIAL BIG DATA USING FEEDBACK CONTROL SCHEDULING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 243–48. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-243-2015.

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Geographic Information System (GIS) is a computer system designed to capture, store, manipulate, analyze, manage, and present all types of spatial data. Spatial data, whether captured through remote sensors or large scale simulations has always been big and heterogenous. The issue of real-time and heterogeneity have been extremely important for taking effective decision. Thus, heterogeneous real-time spatial data management has become a very active research domain. Existing research has principally focused on querying of real-time spatial data and their updates. But the unpredictability of access to data maintain the behavior of the real-time GIS unstable. In this paper, we propose the use of the real-time Spatial Big Data and we define a new architecture called FCSA-RTSBD (Feedback Control Scheduling Architecture for Real-Time Spatial Big Data). The main objectives of this architecture are the following: take in account the heterogeneity of data, guarantee the data freshness, enhance the deadline miss ratio even in the presence of conflicts and unpredictable workloads and finally satisfy the requirements of users by the improving of the quality of service (QoS).

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Kaur, Sumanpreet, and Mr Navtej Singh Ghumman. "ALLOCATION OF HETEROGENOUS CLOUDLETS ON PRIORITY BASIS IN CLOUD ENVIRONMENT." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 3 (2017): 6240–46. http://dx.doi.org/10.24297/ijct.v16i3.6177.

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Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. In the natural environment, the cloudlets will be processed in the FIFO (First in First Out approach). We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, various types of leases have been assigned to the cloudlets like cancellable, suspendable and non-preemtable. The leases have been assigned on the basis of cost assigned to them and the requirement specified by the user. The datacenter broker will receive the list of all the virtual machines and will categorize them into two classes i.e. Class A and Class B. Class A will have high end virtual machines and will process the non-preemptable cloudlets. Class B will contain the low end virtual machines and will process the suspendable and cancellable cloudlets. The machines in each class will be further sorted in descending order according to their MIPS. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. Further, this research also provides the anticipated results with the implementation of the proposed algorithm. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. The main contributions of the research work are to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

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Lin, Na, Huimin Yang, Ya Li, and Xuping Wang. "Scheduling multi-pattern precooling service resources for post-harvest fruits and vegetables using the adaptive large neighborhood search." Journal of Physics: Conference Series 2425, no. 1 (2023): 012006. http://dx.doi.org/10.1088/1742-6596/2425/1/012006.

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Abstract This study focuses on the multi-pattern precooling service resources scheduling (MPPSRS) issue recently derived from the practice of small-scale farming. In this issue, a heterogeneous fleet of vehicles belonging to different service pattens is considered to fulfil precooling requests of farmers in the service time windows. The studied issue is regarded as a variant of heterogeneous fleet vehicle routing problem with time windows (HFVRPTW) in which heterogeneous service efficiencies among vehicles are considered. To the best of our knowledge, this is the first time that such a characteristic of vehicles’ heterogenous service efficiencies is considered in vehicle routing problems. This new characteristic increases the potential combination ways of nodes in the temporal aspect, which expands the search space of the problem and leads to a more challenging optimization issue. This study formulates the MPPSRS issue using a mixed-integer programming model in which details from the real world are considered. Motivated by the challenge of computational time, an adaptive large neighborhood search (ALNS) metaheuristic is proposed to solve the model. Results obtained from a case study of the apple industry from Luochuan County, China show advantages of using multi-pattern precooling service resources in reducing total operating cost.

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