Power and performance management for parallel computations in clouds and data centers
TLDR
This work addresses scheduling independent and precedence constrained parallel tasks on multiple homogeneous processors in a data center with dynamically variable voltage and speed as combinatorial optimization problems by adopting a two-level energy/time/power allocation scheme.About:
This article is published in Journal of Computer and System Sciences.The article was published on 2016-03-01 and is currently open access. It has received 56 citations till now. The article focuses on the topics: Schedule & Energy consumption.read more
Citations
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Journal ArticleDOI
Energy-Efficient Fault-Tolerant Scheduling of Reliable Parallel Applications on Heterogeneous Distributed Embedded Systems
TL;DR: This study aims to implement energy-efficient fault-tolerant scheduling for a reliable parallel application on heterogeneous distributed embedded systems, where the parallel application is described by a directed acyclic graph (DAG).
Journal ArticleDOI
A multi-objective co-evolutionary algorithm for energy-efficient scheduling on a green data center
TL;DR: An enhanced multi- objective co-evolutionary algorithm, called OL-PICEA-g, is proposed for solving the multi-objective energy-efficient task scheduling problem on a green data center partially powered by the renewable energy, where the computing nodes of the data center are DVFS-enabled.
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Minimizing Energy Consumption of Real-Time Parallel Applications Using Downward and Upward Approaches on Heterogeneous Systems
TL;DR: The concepts of deadline-slack and task level are introduced to transfer the deadline of the parallel application to each task, and the proposed algorithm can generate the minimum energy consumption compared with the state-of-the-art algorithms under different real-time and scale conditions.
Journal ArticleDOI
Energy aware resource allocation of cloud data center: review and open issues
Nasrin Akhter,Mohamed Othman +1 more
TL;DR: This paper presents a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud and comes up with further research issues and challenges for future cloud environments.
Journal ArticleDOI
Energy-Aware Processor Merging Algorithms for Deadline Constrained Parallel Applications in Heterogeneous Cloud Computing
TL;DR: Experimental results on real and randomly generated parallel applications validate that the proposed EPM and QEPM algorithms can reduce more energy than existing methods at different scales, parallelism, and heterogeneity degrees.
References
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Journal ArticleDOI
Low-power CMOS digital design
TL;DR: In this paper, techniques for low power operation are presented which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations to reduce power consumption in CMOS digital circuits while maintaining computational throughput.
Journal Article
Low-Power CMOS Digital Design
TL;DR: An architecturally based scaling strategy is presented which indicates that the optimum voltage is much lower than that determined by other scaling considerations, and is achieved by trading increased silicon area for reduced power consumption.
Proceedings ArticleDOI
A scheduling model for reduced CPU energy
TL;DR: This paper proposes a simple model of job scheduling aimed at capturing some key aspects of energy minimization, and gives an off-line algorithm that computes, for any set of jobs, a minimum-energy schedule.
Proceedings ArticleDOI
Scheduling for reduced CPU energy
TL;DR: A new metric for cpu energy performance, millions-of-instructions-per-joule (MIPJ), and several methods for varying the clock speed dynamically under control of the operating system, and examine the performance of these methods against workstation traces.
Journal Article
A survey of design techniques for system-level dynamic power management : Special section on low-power electronics and design
TL;DR: Dynamic power management (DPM) is a design methodology for dynamically reconfiguring systems to provide the requested services and performance levels with a minimum number of active components or a minimum load on such components as mentioned in this paper.
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