Dynamic Voltage Frequency Scaling Simulator for Real Workflows Energy-Aware Management in Green Cloud Computing

Abstract

Nowadays, the growing computational capabilities of Cloud systems rely on the reduction of the consumed power of their data centers to make them sustainable and economically profitable. The efficient management of computing resources is at the heart of any energy-aware data center and of special relevance is the adaptation of its performance to workload. Intensive computing applications in diverse areas of science generate complex workload called workflows, whose successful management in terms of energy saving is still at its beginning. WorkflowSim is currently one of the most advanced simulators for research on workflows processing, offering advanced features such as task clustering and failure policies. In this work, an expected power-aware extension of WorkflowSim is presented. This new tool integrates a power model based on a computing-plus-communication design to allow the optimization of new management strategies in energy saving considering computing, reconfiguration and networks costs as well as quality of service, and it incorporates the preeminent strategy for on host energy saving: Dynamic Voltage Frequency Scaling (DVFS). The simulator is designed to be consistent in different real scenarios and to include a wide repertory of DVFS governors. Results showing the validity of the simulator in terms of resources utilization, frequency and voltage scaling, power, energy and time saving are presented. Also, results achieved by the intra-host DVFS strategy with different governors are compared to those of the data center using a recent and successful DVFS-based inter-host scheduling strategy as overlapped mechanism to the DVFS intra-host technique.

Why High-Performance Modelling and Simulation for Big Data Applications Matters

“…The goal is to reduce the gap between the capacity provided by distributed computing environments and application requirements, especially during low workload periods. Various efforts are undertaken to develop energy efficient task scheduling and balancing of loads [8][9][10][11] and frequency scaling techniques [12][13][14].…”

Infrastructure and Energy Conservation in Big Data Computing: A Survey

“…It is believed that considerable energy savings may be achieved in this scenario if only operations of computing and network devices are coordinated and adjusted to workload and traffic patterns observed. In some scenarios, the energy consumed by clusters and network infrastructures may be minimized by switching off or idling servers, routers or line cards [81], [82]. The ability to control the activity of computing nodes is provided by various tools and platforms described in Sections 3 and 4, for instance by Slurm.…”

Extended Balanced Scheduler with Clustering and Rep- lication for Data Intensive Scientific Workflow Applications in Cloud Computing

“…In this paper, author proposed a new tool that collaborates power model on the basis of communication design and computing that permit the atomization of advanced management strategies in power saving in the consideration of network cost, computing, reconfiguration, Quality of Service and also associate Dynamic Voltage Frequency Scaling (DVFS) [16].…”