Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Heuristic Strategy <s> Thermal constraints have limited the performance improvement of modern computing systems in recent years. As a system could fail if the peak temperature exceeds its thermal constraint, overheating should be avoided while designing a system. Moreover, higher temperature also leads to higher leakage power consumption. This paper explores dynamic thermal management to minimize the energy consumption for a specified computing demand under the thermal constraint. We develop energy-efficient speed scheduling schemes for frame-based real-time tasks under thermal constraints. Experimental results reveal the effectiveness of the proposed scheme in terms of energy consumption in comparison with the reactive schemes in the literature. <s> BIB001 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Heuristic Strategy <s> Temperature-constraint computing environments are emerging those years, especially in embedding computing. A game theoretic temperature-aware scheduling algorithm for multi-core systems is proposed in this paper, namely, GTFTTS (Generalized Tit-For-Tat Temperature-aware Scheduling). GTFTTS is designed to work in a resource-rich environment where resources always compete for tasks. A generalized Tit-for-Tat based method, where whether a core will corporate or not is decided by a hardness factor, is considered in this paper. The algorithm is implemented in our TASS simulator. Simulations results show that the proposed game can reduce the temperature difference between different group of cores which effectively avoids the local hotspot of a processor. <s> BIB002 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Heuristic Strategy <s> Large-scale distributed computing systems (LDSs), such as grids and clouds are primarily designed to provide massive computing capacity. These systems dissipate often excessive energy to both power and cool them. Concerns over greening these systems have prompted a call for scheduling policies with energy awareness (e.g., energy proportionality). The dynamic and heterogeneous nature of resources and tasks in LDSs is a major hurdle to be overcome for energy efficiency when designing scheduling policies. In this paper, we address the problem of scheduling tasks with different priorities (deadlines) for energy efficiency exploiting resource heterogeneity. Specifically, our investigation for energy efficiency focuses on two issues: (1) balancing the workload in the way utilization is maximized and (2) power management by controlling execution of tasks on processor for ensuring the energy is optimally consumed. We form a hierarchical scheduler that exploits the multi-core architecture for effective scheduling. Our scheduling approach exploits the diversity of task priority for proper load balancing across heterogeneous processors while observing energy consumption in the system. Simulation experiments prove the efficacy of our approach, and the comparison results indicate our scheduling policy helps improve energy efficiency of the system. <s> BIB003 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Heuristic Strategy <s> Power efficiency is one of the main challenges in large-scale distributed systems such as datacenters, Grids, and Clouds. One can study the scheduling of applications in such large-scale distributed systems by representing applications as a set of precedence-constrained tasks and modeling them by a Directed Acyclic Graph. In this paper we address the problem of scheduling a set of tasks with precedence constraints on a heterogeneous set of Computing Resources (CRs) with the dual objective of minimizing the overall makespan and reducing the aggregate power consumption of CRs. Most of the related works in this area use Dynamic Voltage and Frequency Scaling (DVFS) approach to achieve these objectives. However, DVFS requires special hardware support that may not be available on all processors in large-scale distributed systems. In contrast, we propose a novel two-phase solution called PASTA that does not require any special hardware support. In its first phase, it uses a novel algorithm to select a subset of available CRs for running an application that can balance between lower overall power consumption of CRs and shorter makespan of application task schedules. In its second phase, it uses a low-complexity power-aware algorithm that creates a schedule for running application tasks on the selected CRs. We show that the overall time complexity of PASTA is $$O(p.v^{2})$$ where $$p$$ is the number of CRs and $$v$$ is the number of tasks. By using simulative experiments on real-world task graphs, we show that the makespan of schedules produced by PASTA are approximately 20 % longer than the ones produced by the well-known HEFT algorithm. However, the schedules produced by PASTA consume nearly 60 % less energy than those produced by HEFT. Empirical experiments on a physical test-bed confirm the power efficiency of PASTA in comparison with HEFT too. <s> BIB004 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Heuristic Strategy <s> The sensitivity analysis of a Cellular Genetic Algorithm (CGA) with local search is used to design a new and faster heuristic for the problem of mapping independent tasks to a distributed system (such as a computer cluster or grid) in order to minimize makespan (the time when the last task finishes). The proposed heuristic improves the previously known Min-Min heuristic. Moreover, the heuristic finds mappings of similar quality to the original CGA but in a significantly reduced runtime (1,000 faster). The proposed heuristic is evaluated across twelve different classes of scheduling instances. In addition, a proof of the energy-efficiency of the algorithm is provided. This convergence study suggests how additional energy reduction can be achieved by inserting low power computing nodes to the distributed computer system. Simulation results show that this approach reduces both energy consumption and makespan. <s> BIB005
Heuristic is one of the most popular scheduling approaches to be chosen for focusing on green Cloud. The strategy performs effectively for scheduling jobs into processors in uncertain, dynamic and large-scale environments BIB004 BIB003 [ BIB005 BIB002 BIB001 . The heuristic strategy is initiated by designing a small companion problem and is extended to complex issues to solve big problems. An effective guess can be made through manipulating a lot of rules and knowledge in this strategy. With the prediction technique, researchers are able to organize the overall structure of problem solving while identifying which parts to focus on. This strategy is really helpful if it involves the dynamic environment of the Cloud.