Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.

中文翻译：

---

基于云的网络物理系统的能源和 SLA 驱动的 MapReduce 作业调度框架

云数据中心（DC）的能耗最小化近年来引起了研究界的广泛关注；特别是由于新兴的网络物理系统越来越依赖它们。提高数据中心能源效率的有效方法是使用高效的作业调度策略。然而，在选择有效的作业调度策略时最具挑战性的问题是确保调度任务的服务水平协议（SLA）绑定。因此，本文提出了一种基于 MapReduce 的能源感知和 SLA 驱动的作业调度框架。所提出框架的主要目的是探索任务到槽/容器映射问题，作为在截止日期受限场景中的能量感知调度的一个特例。因此，这个问题可以看作是一个由不同约束组成的复杂多目标问题。为了有效地解决这个问题，它被分成三个主要的子问题（SP），即截止日期分离、映射和减少阶段能量感知调度。这些 SP 是使用整数线性规划单独制定的。为了有效地解决这些 SP，使用了基于贪婪策略的启发式算法以及用于串行和串行并行系统的经典匈牙利算法。此外，所提出的方案还探索了将 Map/Reduce 阶段拆分为多个阶段以实现更高能量减少的潜力。这是通过利用经典贪婪方法和优先级队列的概念来实现的。所提出的方案已使用从 OpenCloud 获取的实时数据跟踪进行了验证。而且，使用不同的评估指标，即阶段数、总能耗、总制造时间和违反的 SLA，将所提出方案的性能与现有方案进行比较。所获得的结果证明了所提出的方案在不同工作负载场景下与其他方案相比的有效性。