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Artículos de revistas sobre el tema "Energy-aware Scheduling"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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1

Van Den Dooren, David, Thomas Sys, Túlio A. M. Toffolo, Tony Wauters y Greet Vanden Berghe. "Multi-machine energy-aware scheduling". EURO Journal on Computational Optimization 5, n.º 1-2 (14 de julio de 2016): 285–307. http://dx.doi.org/10.1007/s13675-016-0072-0.

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2

Moulik, Sanjay, Arnab Sarkar y Hemangee K. Kapoor. "Energy aware frame based fair scheduling". Sustainable Computing: Informatics and Systems 18 (junio de 2018): 66–77. http://dx.doi.org/10.1016/j.suscom.2018.03.003.

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3

Long, Limin Shao, Jing Wu, Chengnian. "Reliability-Aware Energy Scheduling for Microgrids". IFAC Proceedings Volumes 47, n.º 3 (2014): 6374–79. http://dx.doi.org/10.3182/20140824-6-za-1003.01554.

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4

Akgun, Osman T., Douglas G. Down y Rhonda Righter. "Energy-Aware Scheduling on Heterogeneous Processors". IEEE Transactions on Automatic Control 59, n.º 3 (marzo de 2014): 599–613. http://dx.doi.org/10.1109/tac.2013.2286756.

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5

Agrawal, Pragati y Shrisha Rao. "Energy-Aware Scheduling of Distributed Systems". IEEE Transactions on Automation Science and Engineering 11, n.º 4 (octubre de 2014): 1163–75. http://dx.doi.org/10.1109/tase.2014.2308955.

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6

Zhiming Wang, Kai Shuang, Long Yang y Fangchun Yang. "Energy-aware Combinatorial Scheduling in Cloud Datacenter". International Journal of Digital Content Technology and its Applications 6, n.º 3 (29 de febrero de 2012): 9–18. http://dx.doi.org/10.4156/jdcta.vol6.issue3.2.

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7

Bambagini, Mario, Mauro Marinoni, Hakan Aydin y Giorgio Buttazzo. "Energy-Aware Scheduling for Real-Time Systems". ACM Transactions on Embedded Computing Systems 15, n.º 1 (20 de febrero de 2016): 1–34. http://dx.doi.org/10.1145/2808231.

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8

Vinod Kumar, K. y Ranvijay. "Enhanced energy aware scheduling in multicore processors". Journal of Intelligent & Fuzzy Systems 35, n.º 2 (26 de agosto de 2018): 1375–85. http://dx.doi.org/10.3233/jifs-169680.

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9

Chanderwal, Nitin, Vivek Kumar Sehgal y Aastha Modgil. "Energy-efficient fairness-aware memory access scheduling". International Journal of Services Technology and Management 26, n.º 6 (2020): 520. http://dx.doi.org/10.1504/ijstm.2020.10029154.

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10

Modgil, Aastha, Vivek Kumar Sehgal y Nitin Chanderwal. "Energy-efficient fairness-aware memory access scheduling". International Journal of Services Technology and Management 26, n.º 6 (2020): 520. http://dx.doi.org/10.1504/ijstm.2020.110367.

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11

Baskiyar, Sanjeev y Rabab Abdel-Kader. "Energy aware DAG scheduling on heterogeneous systems". Cluster Computing 13, n.º 4 (29 de enero de 2010): 373–83. http://dx.doi.org/10.1007/s10586-009-0119-6.

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12

Plitsos, Stathis, Panagiotis P. Repoussis, Ioannis Mourtos y Christos D. Tarantilis. "Energy-aware decision support for production scheduling". Decision Support Systems 93 (enero de 2017): 88–97. http://dx.doi.org/10.1016/j.dss.2016.09.017.

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13

Karimi, Sajad, Soongeol Kwon y Fuda Ning. "Energy-aware production scheduling for additive manufacturing". Journal of Cleaner Production 278 (enero de 2021): 123183. http://dx.doi.org/10.1016/j.jclepro.2020.123183.

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14

Zhang, Qing, Xiaoyong Lin, Yongsheng Hao y Jie Cao. "Energy-Aware Scheduling in Edge Computing Based on Energy Internet". IEEE Access 8 (2020): 229052–65. http://dx.doi.org/10.1109/access.2020.3044932.

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15

Severini, Marco, Stefano Squartini y Francesco Piazza. "Energy-aware lazy scheduling algorithm for energy-harvesting sensor nodes". Neural Computing and Applications 23, n.º 7-8 (27 de julio de 2012): 1899–908. http://dx.doi.org/10.1007/s00521-012-1088-x.

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16

Qiu, Yeliang, Congfeng Jiang, Yumei Wang, Dongyang Ou, Youhuizi Li y Jian Wan. "Energy Aware Virtual Machine Scheduling in Data Centers". Energies 12, n.º 4 (17 de febrero de 2019): 646. http://dx.doi.org/10.3390/en12040646.

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Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

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17

Dewangan, Bhupesh Kumar, Amit Agarwal, Venkatadri M. y Ashutosh Pasricha. "Energy-Aware Autonomic Resource Scheduling Framework for Cloud". International Journal of Mathematical, Engineering and Management Sciences 4, n.º 1 (1 de febrero de 2019): 41–55. http://dx.doi.org/10.33889/ijmems.2019.4.1-004.

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Cloud computing is a platform where services are provided through the internet either free of cost or rent basis. Many cloud service providers (CSP) offer cloud services on the rental basis. Due to increasing demand for cloud services, the existing infrastructure needs to be scale. However, the scaling comes at the cost of heavy energy consumption due to the inclusion of a number of data centers, and servers. The extraneous power consumption affects the operating costs, which in turn, affects its users. In addition, CO2 emissions affect the environment as well. Moreover, inadequate allocation of resources like servers, data centers, and virtual machines increases operational costs. This may ultimately lead to customer distraction from the cloud service. In all, an optimal usage of the resources is required. This paper proposes to calculate different multi-objective functions to find the optimal solution for resource utilization and their allocation through an improved Antlion (ALO) algorithm. The proposed method simulated in cloudsim environments, and compute energy consumption for different workloads quantity and it increases the performance of different multi-objectives functions to maximize the resource utilization. It compared with existing frameworks and experiment results shows that the proposed framework performs utmost.

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18

Singh, Jagpreet, Sandeep Betha, Bhargav Mangipudi y Nitin Auluck. "Contention Aware Energy Efficient Scheduling on Heterogeneous Multiprocessors". IEEE Transactions on Parallel and Distributed Systems 26, n.º 5 (1 de mayo de 2015): 1251–64. http://dx.doi.org/10.1109/tpds.2014.2322354.

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19

Chunlin, Li, Li FangYun y Li Layuan. "Energy-aware grid resource scheduling: model and algorithm". International Journal of Computer Applications in Technology 37, n.º 1 (2010): 39. http://dx.doi.org/10.1504/ijcat.2010.031523.

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20

Karthikeyan, G. K., Prassanna Jayachandran y Neelanarayanan Venkataraman. "Energy aware network scheduling for a data centre". International Journal of Big Data Intelligence 2, n.º 1 (2015): 37. http://dx.doi.org/10.1504/ijbdi.2015.067573.

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21

Zhang, Yi-Wen. "Energy-Aware Mixed-criticality Sporadic Task Scheduling Algorithm". IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 40, n.º 1 (enero de 2021): 78–86. http://dx.doi.org/10.1109/tcad.2020.2992999.

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22

Brochard, Luigi, Raj Panda, Don DeSota, Francois Thomas y Robert H. Bell. "Power and energy-aware processor scheduling (abstracts only)". ACM SIGMETRICS Performance Evaluation Review 39, n.º 3 (21 de diciembre de 2011): 16. http://dx.doi.org/10.1145/2160803.2160828.

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23

Peng Yang, Chung Wong, P. Marchal, F. Catthoor, D. Desmet, D. Verkest y R. Lauwereins. "Energy-aware runtime scheduling for embedded-multiprocessor SOCs". IEEE Design & Test of Computers 18, n.º 5 (2001): 46–58. http://dx.doi.org/10.1109/54.953271.

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24

Kliazovich, Dzmitry, Pascal Bouvry y Samee Ullah Khan. "DENS: data center energy-efficient network-aware scheduling". Cluster Computing 16, n.º 1 (22 de septiembre de 2011): 65–75. http://dx.doi.org/10.1007/s10586-011-0177-4.

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25

Mei, Jing, Kenli Li y Keqin Li. "Energy-aware task scheduling in heterogeneous computing environments". Cluster Computing 17, n.º 2 (6 de septiembre de 2013): 537–50. http://dx.doi.org/10.1007/s10586-013-0297-0.

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26

Tang, Chaogang, Mingyang Hao, Xianglin Wei y Wei Chen. "Energy-aware task scheduling in mobile cloud computing". Distributed and Parallel Databases 36, n.º 3 (18 de junio de 2018): 529–53. http://dx.doi.org/10.1007/s10619-018-7231-7.

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27

Wang, Lizhe, Samee U. Khan, Dan Chen, Joanna Kołodziej, Rajiv Ranjan, Cheng-zhong Xu y Albert Zomaya. "Energy-aware parallel task scheduling in a cluster". Future Generation Computer Systems 29, n.º 7 (septiembre de 2013): 1661–70. http://dx.doi.org/10.1016/j.future.2013.02.010.

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28

Ranga Swamy, Sirisati y Sridhar Mandapati. "A fuzzy energy and security aware scheduling in cloud". International Journal of Engineering & Technology 7, n.º 1.2 (28 de diciembre de 2017): 117. http://dx.doi.org/10.14419/ijet.v7i1.2.9021.

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The cloud computing is the one that deals with the trading of the resources efficiently in accordance to the user’s need. A Job scheduling is the choice of an ideal resource for any job to be executed with regard to waiting time, cost or turnaround time. A cloud job scheduling will be an NP-hard problem that contains n jobs and m machines and every job is processed with each of these m machines to minimize the make span. The security here is one of the top most concerns in the cloud. In order to calculate the value of fitness the fuzzy inference system makes use of the membership function for determining the degree up to which the input parameters that belong to every fuzzy set is relevant. Here the fuzzy is used for the purpose of scheduling energy as well as security in the cloud computing.

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29

Wang, Yu Jing, Lin Wu y Chao Kun Yan. "Research on Intelligent Algorithms for Energy-Aware Scheduling in Computational Grids". Advanced Materials Research 926-930 (mayo de 2014): 3187–90. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3187.

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As the features in Computational Grids such as heterogeneous and dynamic, grid task scheduling is an NP-complete problem. For existing energy consumption in CGs ,and based on the study of scheduling algorithms and energy, this thesis selects four kinds of intelligent algorithms GA, DE, HC and SA to analysis and implementation, and relatively researchs their makespan and energy consumption for energy-aware scheduling.

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30

Chen, Quan, Zhipeng Cai, Lianglun Cheng, Hong Gao y Jianzhong Li. "Energy-collision-aware Minimum Latency Aggregation Scheduling for Energy-harvesting Sensor Networks". ACM Transactions on Sensor Networks 17, n.º 4 (16 de julio de 2021): 1–34. http://dx.doi.org/10.1145/3461013.

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The emerging energy-harvesting technology enables charging sensor batteries with renewable energy sources, which has been effectively integrated into Wireless Sensor Networks (EH-WSNs). Due to the limited energy-harvesting capacities of tiny sensors, the captured energy remains scarce and differs greatly among nodes, which makes the data aggregation scheduling problem more challenging than that in energy-abundant WSNs. In this article, we investigate the Minimum Latency Aggregation Scheduling (MLAS) problem in EH-WSNs. First, we identify a new kind of collision in EH-WSNs, named as energy-collision, and design several special structures to avoid it during data aggregation. To reduce the latency, we try to choose the parent adaptively according to nodes’ transmission tasks and energy-harvesting ability, under the consideration of collisions avoidance. By considering transmitting time, residual energy, and energy-collision, three scheduling algorithms are proposed under protocol interference model. Under physical interference model, several approximate algorithms are also designed by taking account of the interference from the nodes several hops away. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

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31

Tang, Xiaoyong y Weizhen Tan. "Energy-Efficient Reliability-Aware Scheduling Algorithm on Heterogeneous Systems". Scientific Programming 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/9823213.

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The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy consumption inevitably contains failures and reduces system reliability. However, there has been considerably less work of simultaneous management of system performance, reliability, and energy consumption on heterogeneous systems. In this paper, we first build the precedence-constrained parallel applications and energy consumption model. Then, we deduce the relation between reliability and processor frequencies and get their parameters approximation value by least squares curve fitting method. Thirdly, we establish a task execution reliability model and formulate this reliability and energy aware scheduling problem as a linear programming. Lastly, we propose a heuristic Reliability-Energy Aware Scheduling (REAS) algorithm to solve this problem, which can get good tradeoff among system performance, reliability, and energy consumption with lower complexity. Our extensive simulation performance evaluation study clearly demonstrates the tradeoff performance of our proposed heuristic algorithm.

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32

Seol, Ye-In y Young-Kuk Kim. "Applying Dynamic Priority Scheduling Scheme to Static Systems of Pinwheel Task Model in Power-Aware Scheduling". Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/587321.

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Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling (DVS). In this paper, we deal with pinwheel task model which is known as static and predictable task model and could be applied to various embedded or ubiquitous systems. In pinwheel task model, each task’s priority is static and its execution sequence could be predetermined. There have been many static approaches to power-aware scheduling in pinwheel task model. But, in this paper, we will show that the dynamic priority scheduling results in power-aware scheduling could be applied to pinwheel task model. This method is more effective than adopting the previous static priority scheduling methods in saving energy consumption and, for the system being still static, it is more tractable and applicable to small sized embedded or ubiquitous computing. Also, we introduce a novel power-aware scheduling algorithm which exploits all slacks under preemptive earliest-deadline first scheduling which is optimal in uniprocessor system. The dynamic priority method presented in this paper could be applied directly to static systems of pinwheel task model. The simulation results show that the proposed algorithm with the algorithmic complexity ofO(n) reduces the energy consumption by 10–80% over the existing algorithms.

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33

Fernández-Cerero, Damián, Agnieszka Jakóbik, Daniel Grzonka, Joanna Kołodziej y Alejandro Fernández-Montes. "Security supportive energy-aware scheduling and energy policies for cloud environments". Journal of Parallel and Distributed Computing 119 (septiembre de 2018): 191–202. http://dx.doi.org/10.1016/j.jpdc.2018.04.015.

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34

Yassa, Sonia, Rachid Chelouah, Hubert Kadima y Bertrand Granado. "Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments". Scientific World Journal 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/350934.

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We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

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35

LI, DAWEI y JIE WU. "Energy-Aware Scheduling for Acyclic Synchronous Data Flows on Multiprocessors". Journal of Interconnection Networks 14, n.º 03 (septiembre de 2013): 1350011. http://dx.doi.org/10.1142/s0219265913500114.

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Synchronous Data Flow (SDF) is a useful computational model in image processing, computer vision, and DSP. Previously, throughput and buffer requirement analyses have been studied for SDFs. In this paper, we address energy-aware scheduling for acyclic SDFs on multiprocessors. The multiprocessor considered here has the capability of Dynamic Voltage and Frequency Scaling (DVFS), which allows processors to operate at different power/energy levels to reduce the energy consumption. An acyclic SDF graph can first be transformed to an equivalent homogeneous SDF graph and then to a Directed Acyclic Graph (DAG) for one iteration of it. We propose pipeline scheduling to address two closely related problems. The first problem is minimizing energy consumption per iteration of the acyclic SDF given a throughput constraint; the second problem is maximizing throughput given an energy consumption constraint per iteration of the acyclic SDF. Since the space of valid total orders of the transformed DAG is exponential, and finding the optimal order is no easy task, we first derive a valid total order, which can be achieved via various strategies, based on the DAG. Given the derived order, we design two dynamic programming algorithms, which produce optimal pipeline scheduling (including pipeline stage partitioning and the frequency setting for each stage), for the two problems, respectively. We also compare the performances of using various strategies to derive a valid total order. Analyses, experiments, and simulations demonstrate the strength of our proposed pipeline scheduling and dynamic programming algorithms.

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36

Hongyou, Li, Wang Jiangyong, Peng Jian, Wang Junfeng y Liu Tang. "Energy-aware scheduling scheme using workload-aware consolidation technique in cloud data centres". China Communications 10, n.º 12 (diciembre de 2013): 114–24. http://dx.doi.org/10.1109/cc.2013.6723884.

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37

WEI, Kun, Wuxiong ZHANG, Yang YANG, Guannan SONG y Zhengming ZHANG. "Battery-Aware Task Scheduling for Energy Efficient Mobile Devices". IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E97.A, n.º 9 (2014): 1971–74. http://dx.doi.org/10.1587/transfun.e97.a.1971.

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38

KIM, Yong-Hee, Myoung-Jo JUNG y Cheol-Hoon LEE. "Energy-Aware Real-Time Task Scheduling Exploiting Temporal Locality". IEICE Transactions on Information and Systems E93-D, n.º 5 (2010): 1147–53. http://dx.doi.org/10.1587/transinf.e93.d.1147.

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39

Han, Sangchul. "Energy-aware EDZL Real-Time Scheduling on Multicore Platforms". Journal of KIISE 43, n.º 3 (15 de marzo de 2016): 296–303. http://dx.doi.org/10.5626/jok.2016.43.3.296.

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40

Singh, Sukhpal y Inderveer Chana. "EARTH: Energy-aware autonomic resource scheduling in cloud computing". Journal of Intelligent & Fuzzy Systems 30, n.º 3 (1 de marzo de 2016): 1581–600. http://dx.doi.org/10.3233/ifs-151866.

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41

ZHANG, Binlian y Hongzhi XU. "On-line energy-aware scheduling algorithm in multiprocessor system". Journal of Computer Applications 33, n.º 10 (12 de noviembre de 2013): 2787–91. http://dx.doi.org/10.3724/sp.j.1087.2013.02787.

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42

Kumar, Prakash, Krishna Gopal y Jai P. Gupta. "QoS Based Scheduling Algorithms in Energy Aware Cloud Environment". Recent Patents on Computer Science 9, n.º 3 (16 de enero de 2017): 222–30. http://dx.doi.org/10.2174/2213275909999160428121152.

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43

Zhang, Yiwen y Haibo Li. "Energy aware mixed tasks scheduling in real-time systems". Sustainable Computing: Informatics and Systems 23 (septiembre de 2019): 38–48. http://dx.doi.org/10.1016/j.suscom.2019.06.004.

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44

Hasanloo, Mahmoud, Mehdi Kargahi y Shahrokh Jalilian. "Dynamic harvesting- and energy-aware real-time task scheduling". Sustainable Computing: Informatics and Systems 28 (diciembre de 2020): 100413. http://dx.doi.org/10.1016/j.suscom.2020.100413.

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45

Ismail, Leila y Huned Materwala. "EATSVM: Energy-Aware Task Scheduling on Cloud Virtual Machines". Procedia Computer Science 135 (2018): 248–58. http://dx.doi.org/10.1016/j.procs.2018.08.172.

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46

Gong, Xu, Toon De Pessemier, Wout Joseph y Luc Martens. "An Energy-Cost-Aware Scheduling Methodology for Sustainable Manufacturing". Procedia CIRP 29 (2015): 185–90. http://dx.doi.org/10.1016/j.procir.2015.01.041.

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47

Ramesh, Pasupuleti y Uppu Ramachandraiah. "Energy Aware Proportionate Slack Management Scheduling for Multiprocessor Systems". Procedia Computer Science 133 (2018): 855–63. http://dx.doi.org/10.1016/j.procs.2018.07.109.

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48

Rehani, Nidhi y Ritu Garg. "Energy Efficient Reliability Aware Workflow Scheduling in Cloud Computing". International Journal of Sensors, Wireless Communications and Control 7, n.º 3 (19 de junio de 2018): 198–210. http://dx.doi.org/10.2174/2210327908666180123162717.

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49

Ismail, Leila y Abbas Fardoun. "EATS: Energy-Aware Tasks Scheduling in Cloud Computing Systems". Procedia Computer Science 83 (2016): 870–77. http://dx.doi.org/10.1016/j.procs.2016.04.178.

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50

Swaminathan, Vishnu y Krishnendu Chakrabarty. "Real-time task scheduling for energy-aware embedded systems". Journal of the Franklin Institute 338, n.º 6 (septiembre de 2001): 729–50. http://dx.doi.org/10.1016/s0016-0032(01)00021-7.

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