Journal Article10.1007/S10586-017-1272-Y
An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment
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TL;DR: Simulation studies show that the proposed resource-aware load balancing clonal algorithm for task scheduling can effectively reduce energy consumption in green cloud computing, and its exploration and exploitation abilities can be enhanced and well balanced.
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Abstract: With the expanding of its scale and the energy cost factors being ignored in green cloud computing, the problem of high energy cost and low efficiency is exposed. Based on the concepts and principles of load balancing, a novel energy-efficient load balancing global optimization algorithm, called resource-aware load balancing clonal algorithm for task scheduling, is proposed to deal with the problem of energy consumption in green cloud computing. Firstly, the problem is formulated as a combinatorial optimization problem that aims to optimize both energy consumption and load balancing. Then, the resource-aware scheduling algorithm is proposed based on load balancing strategy and clonal selection principle. Finally, simulation studies show that the proposed algorithm can effectively reduce energy consumption in green cloud computing, and its exploration and exploitation abilities can be enhanced and well balanced.
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Maria Alejandra Rodriguez,Rajkumar Buyya +1 more
- 02 Apr 2014
TL;DR: An algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints is presented.
Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications
TL;DR: This article makes the first attempt to present a survey of mobile cloud computing from the perspective of its intended usages, and introduces three common mobile cloud architectures and classify comprehensive existing work into two fundamental categories: computation offloading and capability extending.
402
Task Scheduling with Dynamic Voltage and Frequency Scaling for Energy Minimization in the Mobile Cloud Computing Environment
TL;DR: This work investigates the problem of scheduling tasks (which belong to the same or possibly different applications) in the MCC environment and presents a novel algorithm, which starts from a minimal-delay scheduling solution and performs energy reduction by migrating tasks among the local cores and the cloud and by applying the dynamic voltage and frequency scaling technique.
268
Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment
TL;DR: Key resource allocation challenges are highlighted, and some potential solutions to reduce cloud data center energy consumption are presented, and special focus is given to power management techniques that exploit the virtualization technology to save energy.
Energy-Efficiency Optimization for MIMO-OFDM Mobile Multimedia Communication Systems With QoS Constraints
TL;DR: In this article, an energy-efficiency optimized power allocation (EEOPA) algorithm is proposed to improve the energy efficiency of MIMO-OFDM mobile multimedia communication systems, where all subchannels are classified by their channel characteristics.
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