Reliable Energy-Aware Scheduling Algorithm With Multi-Level Budget for Real-Time Embedded System

Energy consumption of embedded applications has rapidly increased with the advancement of technology and computing. There is a little improvement in energy consumption as compared to computing and storage capacity. Although computing performance has been continuously increasing, power/energy consumption is more critical in the design of real-time embedded systems. Real-time embedded applications need a power management technique to judicially balance the energy consumption and computing performance. It should be done in such a way that the system performance improves along with an increase in the lifespan of the system. The proposed methodology presented in this paper deals with the minimization of energy for time-critical embedded applications. Simulation studies, along with theoretical analysis, have been carried out to show the effectiveness of the proposed three-phase reliable energy-aware scheduling method. It is observed that the proposed approach provides better tolerance (approximately four times) and consumes less energy (35% to 45%) for a wide range of applications.

Energy and SLA-driven MapReduce Job Scheduling Framework for Cloud-based Cyber-Physical Systems

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.