Relevant bibliographies by topics / Energy-aware Scheduling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Energy-aware Scheduling'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Energy-aware Scheduling"

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Van Den Dooren, David, Thomas Sys, Túlio A. M. Toffolo, Tony Wauters, and Greet Vanden Berghe. "Multi-machine energy-aware scheduling." EURO Journal on Computational Optimization 5, no. 1-2 (July 14, 2016): 285–307. http://dx.doi.org/10.1007/s13675-016-0072-0.

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

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Long, Limin Shao, Jing Wu, Chengnian. "Reliability-Aware Energy Scheduling for Microgrids." IFAC Proceedings Volumes 47, no. 3 (2014): 6374–79. http://dx.doi.org/10.3182/20140824-6-za-1003.01554.

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Akgun, Osman T., Douglas G. Down, and Rhonda Righter. "Energy-Aware Scheduling on Heterogeneous Processors." IEEE Transactions on Automatic Control 59, no. 3 (March 2014): 599–613. http://dx.doi.org/10.1109/tac.2013.2286756.

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Agrawal, Pragati, and Shrisha Rao. "Energy-Aware Scheduling of Distributed Systems." IEEE Transactions on Automation Science and Engineering 11, no. 4 (October 2014): 1163–75. http://dx.doi.org/10.1109/tase.2014.2308955.

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Zhiming Wang, Kai Shuang, Long Yang, and Fangchun Yang. "Energy-aware Combinatorial Scheduling in Cloud Datacenter." International Journal of Digital Content Technology and its Applications 6, no. 3 (February 29, 2012): 9–18. http://dx.doi.org/10.4156/jdcta.vol6.issue3.2.

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Bambagini, Mario, Mauro Marinoni, Hakan Aydin, and Giorgio Buttazzo. "Energy-Aware Scheduling for Real-Time Systems." ACM Transactions on Embedded Computing Systems 15, no. 1 (February 20, 2016): 1–34. http://dx.doi.org/10.1145/2808231.

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Vinod Kumar, K., and Ranvijay. "Enhanced energy aware scheduling in multicore processors." Journal of Intelligent & Fuzzy Systems 35, no. 2 (August 26, 2018): 1375–85. http://dx.doi.org/10.3233/jifs-169680.

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Chanderwal, Nitin, Vivek Kumar Sehgal, and Aastha Modgil. "Energy-efficient fairness-aware memory access scheduling." International Journal of Services Technology and Management 26, no. 6 (2020): 520. http://dx.doi.org/10.1504/ijstm.2020.10029154.

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Modgil, Aastha, Vivek Kumar Sehgal, and Nitin Chanderwal. "Energy-efficient fairness-aware memory access scheduling." International Journal of Services Technology and Management 26, no. 6 (2020): 520. http://dx.doi.org/10.1504/ijstm.2020.110367.

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Dissertations / Theses on the topic "Energy-aware Scheduling"

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Renaud-Goud, Paul. "Energy-aware scheduling : complexity and algorithms." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2012. http://tel.archives-ouvertes.fr/tel-00744247.

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In this thesis we have tackled a few scheduling problems under energy constraint, since the energy issue is becoming crucial, for both economical and environmental reasons. In the first chapter, we exhibit tight bounds on the energy metric of a classical algorithm that minimizes the makespan of independent tasks. In the second chapter, we schedule several independent but concurrent pipelined applications and address problems combining multiple criteria, which are period, latency and energy. We perform an exhaustive complexity study and describe the performance of new heuristics. In the third chapter, we study the replica placement problem in a tree network. We try to minimize the energy consumption in a dynamic frame. After a complexity study, we confirm the quality of our heuristics through a complete set of simulations. In the fourth chapter, we come back to streaming applications, but in the form of series-parallel graphs, and try to map them onto a chip multiprocessor. The design of a polynomial algorithm on a simple problem allows us to derive heuristics on the most general problem, whose NP-completeness has been proven. In the fifth chapter, we study energy bounds of different routing policies in chip multiprocessors, compared to the classical XY routing, and develop new routing heuristics. In the last chapter, we compare the performance of different algorithms of the literature that tackle the problem of mapping DAG applications to minimize the energy consumption.
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Alabi, Derrick. "Energy-Aware Task Scheduling in Contiki." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-348877.

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Applications for the Internet of Things often run on devices that have very limited energy capacity. Energy harvesting can offset this inherent weakness of these devices by extracting energy from the environment. Energy harvesting increases the total energy available to a device, but efficient energy consumption is still important to maximize the availability of the device. Energy-aware task scheduling is a way to efficiently consume energy in an energy constrained device with energy harvesting capabilities to extend the device's availability. In this thesis, prediction of future incoming harvest energy is combined with hard real-time and reward-based weakly-hard real-time task scheduling schemes to achieve efficient energy usage. Linear regression and artificial neural networks are evaluated individually on their ability to predict future energy harvests. The artificial neural network used contains a single hidden layer and is evaluated with ReLU, Leaky ReLU ,and sine as activation functions. The performance of linear regression and the artificial neural network with varying activation functions and number of hidden nodes are tested and compared. Linear regression is shown to be a sufficient means of predicting future energy harvests. A hard real-time and a reward-based weakly-hard real-time task scheduling scheme are also presented and compared. The experimental results show that the hard real-time scheme can extend the life of the device compared to a non-energy-aware scheduler, but the weakly-hard real-time scheme will allow the device to function indefinitely.
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Bhatti, K. "Energy-aware Scheduling for Multiprocessor Real-time Systems." Phd thesis, Université de Nice Sophia-Antipolis, 2011. http://tel.archives-ouvertes.fr/tel-00599980.

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Les applications temps réel modernes deviennent plus exigeantes en termes de ressources et de débit amenant la conception d'architectures multiprocesseurs. Ces systèmes, des équipements embarqués au calculateur haute performance, sont, pour des raisons d'autonomie et de fiabilité, confrontés des problèmes cruciaux de consommation d'énergie. Pour ces raisons, cette thèse propose de nouvelles techniques d'optimisation de la consommation d'énergie dans l'ordonnancement de systèmes multiprocesseur. La premiére contribution est un algorithme d'ordonnancement hiérarchique á deux niveaux qui autorise la migration restreinte des tâches. Cet algorithme vise á réduire la sous-optimalité de l'algorithme global EDF. La deuxiéme contribution de cette thèse est une technique de gestion dynamique de la consommation nommée Assertive Dynamic Power Management (AsDPM). Cette technique, qui régit le contrôle d'admission des tâches, vise á exploiter de manière optimale les modes repos des processeurs dans le but de réduire le nombre de processeurs actifs. La troisiéme contribution propose une nouvelle technique, nommée Deterministic Stretch-to-Fit (DSF), permettant d'exploiter le DVFS des processeurs. Les gains énergétiques observés s'approchent des solutions déjà existantes tout en offrant une complexité plus réduite. Ces techniques ont une efficacité variable selon les applications, amenant á définir une approche plus générique de gestion de la consommation appelée Hybrid Power Management (HyPowMan). Cette approche sélectionne, en cours d'exécution, la technique qui répond le mieux aux exigences énergie/performance.
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Alsughayyir, Aeshah Yahya. "Energy-aware scheduling in decentralised multi-cloud systems." Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42407.

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Cloud computing is an emerging Internet-based computing paradigm that aims to provide many on-demand services, requested nowadays by almost all online users. Although it greatly utilises virtualised environments for applications to be executed efficiently in low-cost hosting, it has turned energy wasting and overconsumption issues into major concerns. Many studies have projected that the energy consumption of cloud data-centres would grow significantly to reach 35% of the total energy consumed worldwide, threatening to further boost the world's energy crisis. Moreover, cloud infrastructure is built on a great amount of server equipment, including high performance computing (HPC), and the servers are naturally prone to failures. In this thesis, we study practically as well as theoretically the feasibility of optimising energy consumption in multi-cloud systems. We explore a deadline-based scheduling problem of executing HPC-applications by a heterogeneous set of clouds that are geographically distributed worldwide. We assume that these clouds participate in a federated approach. The practical part of the thesis has focused on combining two energy dimensions while scheduling HPC-applications (i.e., energy consumed for execution and data transmission). It has considered simultaneously minimising application rejections and deadline violations, to support resource reliability, with energy optimisation. In the theoretical part, we have presented the first online algorithms for the non-pre-emptive scheduling of jobs with agreeable deadlines on heterogeneous parallel processors. Through our developed simulation and experimental analysis using real parallel workloads from large-scale systems, the results evidenced that it is possible to reduce a considerable amount of energy while carefully scheduling cloud applications over a multi-cloud system. We have shown that our practical approaches provide promising energy savings with acceptable level of resource reliability. We believe that our scheduling approaches have particular importance in relation with the main aim of green cloud computing for the necessity of increasing energy efficiency.
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Bhatti, Muhammad Khurram. "Energy-aware scheduling for multiprocessor real-time systems." Nice, 2011. http://www.theses.fr/2011NICE4010.

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Les applications temps réel modernes deviennent plus exigeantes en termes de ressources et de débit amenant la conception d’architectures multiprocesseurs. Ces systèmes, des équipements embarqués au calculateur haute performance, sont, pour des raisons d’autonomie et de fiabilité, confrontés à des problèmes cruciaux de consommation d’énergie. Pour ces raisons, cette thèse propose de nouvelles techniques d’optimisation de la consommation d’énergie dans l’ordonnancement de systèmes multiprocesseur. La première contribution est un algorithme d’ordonnancement hiérarchique à deux niveaux qui autorise la migration restreinte des tâches. Cet algorithme vise à réduire la sous-optimalité de l’algorithme global EDF. La deuxième contribution de cette thèse est une technique de gestion dynamique de la consommation nommée Assertive Dynamic Power Management (AsDPM). Cette technique, qui régit le contrôle d’admission des tâches, vise à exploiter de manière optimale les modes repos des processeurs dans le but de réduire le nombre de processeurs actifs. La troisième contribution propose une nouvelle technique, nommée Deterministic Strech-to-Fit (DSF), permettant d’exploiter le DVFS des processeurs. Les gains énergétiques observés s’approchent des solutions déjà existantes tout en offrant une complexité plus réduite. Ces techniques ont une efficacité variable selon les applications, amenant à définir une approche plus générique de gestion de la consommation appelée Hybrid Power Management (HyPowMan). Cette approche sélectionne, en cours d’exécution, la technique qui répond le mieux aux exigences énergie / performance
Modern real-time applications have become more sophisticated and complex in their behaviour over the time. Contemporaneously, multiprocessor architectures have emerged. Multiprocessor systems, due to their autonomy and reliability, face critical problem of energy consumption. To address this issue in real-time systems, many software-based approaches have emerged. This thesis proposes new techniques for energy-efficient scheduling of multiprocessor systems. Our first contribution is a hierarchical scheduling algorithm that allows restricted migration of tasks. This algorithm aims at reducing the sub-optimality of global EDF algorithm. The second contribution of this thesis is a dynamic power management technique called Assertive Dynamic Power Management (AsDPM). This technique is an admission control technique for real-time tasks, which decides when exactly a ready task shall execute, thereby reducing the number of active processors. The third contribution of this dissertation is a DVFS technique, referred as Deterministic Strech-to-Fit (DSF) technique, which falls in the category of inter-task DVFS techniques. Both DPM and DVFS techniques are efficient for specific operating conditions. However, they often outperform each other when these conditions change. Our fourth and final contribution is a generic power / energy management scheme, called Hybrid Power Management (HyPowMan) scheme. This scheme, instead of designing new power / energy management techniques for specific operating conditions, takes a set of well-known existing policies. At runtime, the best-performing policy for given workload is adapted by HyPowMan scheme through machine-learning approach
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Bokar, Ali. "Energy- Aware Task Scheduling Over Mobile Ad Hoc Networks." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610313/index.pdf.

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Mobile ad hoc networks (MANETs) can be formed dynamically without the support of any existing infrastructure or any centralized administration. They consist of heterogeneous mobile nodes which are powered by batteries, move arbitrarily and are connected by wireless links. Battery energy limitation is one of the main challenges in the MANETs. Several hardware and software based techniques have been proposed in this field. Most of the previous studies have considered only the energy minimization of individual nodes and disregarded the overall network lifetime. Topology management is another important problem in MANETs, in this sense
several new computing paradigms have been developed by the researchers, and the topology management has not been studied clearly in most of these models. In this study, we propose two new techniques that deal with the topology management in order to facilitate the nodes&rsquo
cooperation towards energy saving. The developed computing model considers heterogeneous mobile nodes. A node that faces shortage in its resources (energy and processing capability) sends its work to one of the nearby devices which is able to execute the work. In addition, we propose two algorithm for dynamic and two for static task scheduling, to prolong the network life time. Comprehensive experiments showed that the proposed schemes achieve a significant improvement in the network lifetime while simultaneously reducing the energy consumption and time delay for each task.
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Nélis, Vincent. "Energy-aware real-time scheduling in embedded multiprocessor systems." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210058.

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Nowadays, computer systems are everywhere. From simple portable devices such as watches and MP3 players to large stationary installations that control nuclear power plants, computer systems are now present in all aspects of our modern and every-day life. In about only 70 years, they have completely perturbed our way of life and they reached a so high degree of sophistication that they will be soon capable of driving our cars and cleaning our houses without any human intervention. As computer systems gain in responsibilities, it becomes essential that they provide both safety and reliability. Indeed, a failure in systems such as the anti-lock braking system (ABS) in cars could threaten human lives and generate catastrophic and irreversible consequences. Hence, for many years, researchers have addressed these emerging problems of system safety and reliability which come along with this fulgurant evolution.

This thesis provides a general overview of embedded real-time computer systems, i.e. a particular kind of computer system whose number grows daily. We provide the reader with some preliminary knowledge and a good understanding of the concepts that underlie this emerging technology. We focus especially on the theoretical problems related to the real-time issue and briefly summarizes the main solutions, together with their advantages and drawbacks. This brings the reader through all the conceptual layers constituting a computer system, from the software level---the logical part---that specifies both the system behavior and requirements to the hardware level---the physical part---that actually performs the expected treatments and reacts to the environment. In the meanwhile, we introduce the theoretical models that allow researchers for theoretical analyses which ensure that all the system requirements are fulfilled. Finally, we address the energy consumption problem in embedded systems. We describe the various factors of power dissipation in modern technologies and we introduce different solutions to reduce this consumption./Cette thèse se focalise sur un type de systèmes informatiques bien précis appelés “systèmes embarqués temps réel”. Un système est dit “embarqué” lorsqu’il est développé afin de servir un but bien précis. Un téléphone portable est un parfait exemple de système embarqué étant donné que toutes ses fonctionnalités sont rigoureusement définies avant même sa conception. Au contraire, un ordinateur personnel n’est généralement pas considéré comme un système embarqué, les concepteurs ne sachant pas à l’avance à quelles fins il sera utilisé. Une grande partie de ces systèmes embarqués ont des contraintes temporelles très fortes, ce qui les distingue encore plus des ordinateurs grand public. A titre d’exemple, lorsqu’un conducteur de voiture freine brusquement, l’ordinateur de bord déclenche l’application ABS et il est primordial que cette application soit traitée endéans une courte échéance. Autrement dit, cette fonctionnalité ABS doit être traitée prioritairement par rapport aux autres fonctionnalités du véhicule. Ce type de système embarqué est alors dit “temps réel”, dû à ces notions de temps et de priorités entre les applications. La problèmatique posée par les systèmes temps réel est la suivante. Comment déterminer, à tout moment, un ordre d’exécution des différentes fonctionnalités de telle sorte qu’elles soient toutes exécutées entièrement endéans leur échéance ?De plus, avec l’apparition récente des systèmes multiprocesseurs, cette problématique s’est fortement complexifiée, vu que le système doit à présent déterminer quelle fonctionnalité s’exécute à quel moment sur quel processeur afin que toutes les contraintes temporelles soient respectées. Pour finir, ces systèmes embarqués temp réel multiprocesseurs se sont rapidement retrouvés confrontés à un problème de consommation d’énergie. Leur demande en terme de performance (et donc en terme d’énergie) à évolué beaucoup plus rapidement que la capacité des batteries qui les alimentent. Ce problème est actuellement rencontré par de nombreux systèmes, tels que les téléphones portables par exemple. L’objectif de cette thèse est de parcourir les différents composants de tels système embarqués et de proposer des solutions afin de réduire leur consommation d’énergie.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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Liu, H. "Combinatorial challenges and algorithms in new energy aware scheduling problems." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3008036/.

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In this thesis, we study the theoretical approach on energy-efficient scheduling problems arising in demand response management in the modern electrical smart grid. Consumers send in power requests with flexible feasible timeslots during which their requests can be served. The grid controller, upon receiving power requests, schedules each request within the specified interval. The electricity cost is measured by a convex function of the load in each timeslot. The objective is to schedule all requests with the minimum total electricity cost. We study the smart grid scheduling problem in different models. For the offline model, we prove the problem is NP-hard for the general case. We propose a polynomial time algorithm for special input where jobs have unit power request and unit time duration. By adapting the polynomial time algorithm for unit-size jobs, we propose an approximation algorithm for more general input. On the other hand, we also present an exact algorithm to find the optimal schedule for the problem with general input. For the online model, we propose an online algorithm for jobs with jobs with arbitrary power request, arbitrary time duration, and arbitrary contiguous feasible intervals. We also show a lower bound of the competitive ratio for the smart grid scheduling problem with unit height and arbitrary width. For special cases, we design different online algorithms with better competitive ratios. Finally, we look at other optimization problems and show how to solve them by adapting our techniques. We prove that our online algorithm can solve the machine minimization problem with an asymptotically optimal competitive ratio. We also show that our exact algorithm can be adapted to solve other demand response management problems.
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Han, Qiushi. "Energy-aware Fault-tolerant Scheduling for Hard Real-time Systems." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2222.

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Over the past several decades, we have experienced tremendous growth of real-time systems in both scale and complexity. This progress is made possible largely due to advancements in semiconductor technology that have enabled the continuous scaling and massive integration of transistors on a single chip. In the meantime, however, the relentless transistor scaling and integration have dramatically increased the power consumption and degraded the system reliability substantially. Traditional real-time scheduling techniques with the sole emphasis on guaranteeing timing constraints have become insufficient. In this research, we studied the problem of how to develop advanced scheduling methods on hard real-time systems that are subject to multiple design constraints, in particular, timing, energy consumption, and reliability constraints. To this end, we first investigated the energy minimization problem with fault-tolerance requirements for dynamic-priority based hard real-time tasks on a single-core processor. Three scheduling algorithms have been developed to judiciously make tradeoffs between fault tolerance and energy reduction since both design objectives usually conflict with each other. We then shifted our research focus from single-core platforms to multi-core platforms as the latter are becoming mainstream. Specifically, we launched our research in fault-tolerant multi-core scheduling for fixed-priority tasks as fixed-priority scheduling is one of the most commonly used schemes in the industry today. For such systems, we developed several checkpointing-based partitioning strategies with the joint consideration of fault tolerance and energy minimization. At last, we exploited the implicit relations between real-time tasks in order to judiciously make partitioning decisions with the aim of improving system schedulability. According to the simulation results, our design strategies have been shown to be very promising for emerging systems and applications where timeliness, fault-tolerance, and energy reduction need to be simultaneously addressed.
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Khaleel, Mustafa Ibrahim. "ENERGY-AWARE JOB SCHEDULING AND CONSOLIDATION APPROACHES FOR WORKFLOWS IN CLOUD." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1165.

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Cloud computing offers several types of on-demand and scalable access to software, computing resources, and storage services through web browsers based on pay-as-you- go model. In order to meet the growing demand of active users and reduce the skyrocketing cost of electricity for powering the data centers, cloud service providers are highly motivated to implement performance guaranteed and cost-effective job schedulers. Many researchers have been focusing on scheduling jobs with high performance, and their primary concern has been execution time considerations. As a result of this thinking, little attention was paid to energy consumption and energy costs. However, nowadays energy cost has gained more and more attention from the service providers. This new reality has posed many new challenges for providers who are both concerned about meeting the execution time constraints and reducing energy costs. In recent years, there has been a growing body of research which focused on improving resource utilization by adopting new strategies and ideas that can be used to improve energy efficiency while maintaining high system throughput. One of these strategies used is known as task consolidation. This is one of the most effective techniques for increasing system-wide resource utilization. The research clearly shows that by switching o idle servers to sleep mode a vast amount of energy can be saved. In this research, a job scheduling approach called multi-procedure energy-aware heuristic scientific workflow scheduling method referred to as Time and Energy Aware Scheduling (TEAS) is proposed to tackle an energy optimization problem. This method is based on a rigorous cost and energy model that could be used to maximize resource utilization performance. The objectives focused on maximizing resource utilization and minimizing power consumption without compromising Quality of Service (QoS) such as workflow response time specified in the Service Level Agreements (SLA). The scientific applications are formulated as Directed Acycle Graph (DAG)-structured workflow to be processed as a group using virtualization techniques over cloud resources. Furthermore, the underlying cloud hardware/Virtual Machine (VM) resource availability is time-dependent because of the dual operation modes of on-demand and reservation. The resource provision and allocation algorithm can be separated into three steps with different objectives. The first step (Datacenter Selection) selects the most efficient data center to execute module applications. The second step (Time and Energy Aware Scheduling Forward Mapping) primarily focuses on estimating the execution time of scheduling a batch of workflows over VMs on underlying cloud servers and the objective is to achieve the minimum End-to-End Delay (EED). The last, and the most important step is related to the energy saving and resource utilization (Time and Energy Aware Scheduling Backward Mapping) which is concerned with minimizing energy consumption. This task is accomplished by restricting CPU usage between double thresholds and keeping the total utilization of the CPU by all the VMs allocated to a single server between these two thresholds. In addition, cloud module could migrate to other servers to either reduce the number of active servers or achieve better performance. In this case, the communication cost would be factored into the energy cost model. The performance of our algorithm is compared to algorithms such as the Pegasus Workflow Management system, Minimum Power Consumption Minimum Power Consumption (MPC-MPC) algorithm, and Greedy algorithm. The simulation results show that the Time and Energy Aware Scheduling heuristic can significantly decrease the power consumption of cloud servers with high resource utilization for the underlying clouds.
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Books on the topic "Energy-aware Scheduling"

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Li, Dawei. Energy-aware Scheduling on Multiprocessor Platforms. New York, NY: Springer New York, 2013.

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Li, Dawei, and Jie Wu. Energy-aware Scheduling on Multiprocessor Platforms. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5224-9.

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Wu, Jie, and Dawei Li. Energy-aware Scheduling on Multiprocessor Platforms. Springer, 2012.

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Book chapters on the topic "Energy-aware Scheduling"

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Parikh, A., M. Kandemir, N. Vijaykrishnan, and M. J. Irwin. "Energy-Aware Instruction Scheduling." In High Performance Computing — HiPC 2000, 335–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44467-x_30.

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Li, Dawei, and Jie Wu. "Scheduling on Homogeneous DVFS Multiprocessor Platforms." In Energy-aware Scheduling on Multiprocessor Platforms, 13–40. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5224-9_3.

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Li, Dawei, and Jie Wu. "Scheduling on Heterogeneous DVFS Multiprocessor Platforms." In Energy-aware Scheduling on Multiprocessor Platforms, 41–52. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5224-9_4.

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Li, Dawei, and Jie Wu. "Introduction." In Energy-aware Scheduling on Multiprocessor Platforms, 1–2. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5224-9_1.

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Li, Dawei, and Jie Wu. "System Model." In Energy-aware Scheduling on Multiprocessor Platforms, 3–11. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5224-9_2.

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Li, Dawei, and Jie Wu. "Related Work." In Energy-aware Scheduling on Multiprocessor Platforms, 53–54. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5224-9_5.

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Li, Dawei, and Jie Wu. "Conclusion and Future Directions." In Energy-aware Scheduling on Multiprocessor Platforms, 55–56. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5224-9_6.

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Cohen, Johanne, Daniel Cordeiro, and Pedro Luis F. Raphael. "Energy-Aware Multi-Organization Scheduling Problem." In Lecture Notes in Computer Science, 186–97. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09873-9_16.

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Berral, Josep Ll, Iñigo Goiri, Ramon Nou, Ferran Julià, Josep O. Fitó, Jordi Guitart, Ricard Gavaldá, and Jordi Torres. "Toward Energy-Aware Scheduling Using Machine Learning." In Energy-Efficient Distributed Computing Systems, 215–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118342015.ch8.

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Kessaci, Yacine, Mohand Mezmaz, Nouredine Melab, El-Ghazali Talbi, and Daniel Tuyttens. "Parallel Evolutionary Algorithms for Energy Aware Scheduling." In Intelligent Decision Systems in Large-Scale Distributed Environments, 75–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21271-0_4.

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Conference papers on the topic "Energy-aware Scheduling"

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Guerout, Tom, and Mahdi Ben Alaya. "Autonomic Energy-Aware Tasks Scheduling." In 2013 IEEE 22nd International Workshop On Enabling Technologies: Infrastructure For Collaborative Enterprises (WETICE). IEEE, 2013. http://dx.doi.org/10.1109/wetice.2013.29.

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Kamara Esteban, Oihane, and Yoseba K. Penya. "Energy-aware foundry production scheduling." In IECON 2012 - 38th Annual Conference of IEEE Industrial Electronics. IEEE, 2012. http://dx.doi.org/10.1109/iecon.2012.6389447.

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Mashayekhy, Lena, Mahyar Movahed Nejad, Daniel Grosu, Dajun Lu, and Weisong Shi. "Energy-Aware Scheduling of MapReduce Jobs." In 2014 IEEE International Congress on Big Data (BigData Congress). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.congress.2014.15.

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Goiri, Inigo, Ferran Julia, Ramon Nou, Josep Ll Berral, Jordi Guitart, and Jordi Torres. "Energy-Aware Scheduling in Virtualized Datacenters." In 2010 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2010. http://dx.doi.org/10.1109/cluster.2010.15.

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Knauth, Thomas, and Christof Fetzer. "Energy-aware scheduling for infrastructure clouds." In 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2012. http://dx.doi.org/10.1109/cloudcom.2012.6427569.

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Brochard, Luigi, Raj Panda, Don DeSota, Francois Thomas, and Robert H. Bell. "Power and energy-aware processor scheduling." In Proceeding of the second joint WOSP/SIPEW international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1958746.1958780.

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Elisseev, Vadim, John Baker, Neil Morgan, Luigi Brochard, and Terry Hewitt. "Energy Aware Scheduling Study on BlueWonder." In 2016 4th International Workshop on Energy-Efficient Supercomputing (E2SC). IEEE, 2016. http://dx.doi.org/10.1109/e2sc.2016.014.

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Aupy, Guillaume. "Energy-aware Scheduling: Models and Complexity Results." In 2012 26th IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2012. http://dx.doi.org/10.1109/ipdpsw.2012.307.

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Miao, Lei, Yong Qi, Di Hou, and Yuehua Dai. "Energy-Aware Scheduling Tasks on Chip Multiprocessor." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.356.

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Chou, Jerry, Jinoh Kim, and Doron Rotem. "Energy-Aware Scheduling in Disk Storage Systems." In 2011 31st International Conference on Distributed Computing Systems (ICDCS). IEEE, 2011. http://dx.doi.org/10.1109/icdcs.2011.40.

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Reports on the topic "Energy-aware Scheduling"

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Swaminathan, Vishnu, and Krishnendu Chakrabarty. Real-Time Task Scheduling for Energy-Aware Embedded Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439593.

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