Deadline and energy constrained dynamic resource allocation in a heterogeneous computing environment

Authors:
B. Dalton Young;Jonathan Apodaca;Luis Diego Briceño;Jay Smith;Sudeep Pasricha;Anthony A. Maciejewski;Howard Jay Siegel;Bhavesh Khemka;Shirish Bahirat;Adrian Ramirez;Yong Zou
Affiliations:
Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA;Department of Computer Science, Colorado State University, Fort Collins, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA and DigitalGlobe, Longmont, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA and Department of Computer Science, Colorado State University, Fort Collins, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA and Department of Computer Science, Colorado State University, Fort Collins, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA;Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, USA
Venue:
The Journal of Supercomputing
Year:
2013

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Abstract

Energy-efficient resource allocation within clusters and data centers is important because of the growing cost of energy. We study the problem of energy-constrained dynamic allocation of tasks to a heterogeneous cluster computing environment. Our goal is to complete as many tasks by their individual deadlines and within the system energy constraint as possible given that task execution times are uncertain and the system is oversubscribed at times. We use Dynamic Voltage and Frequency Scaling (DVFS) to balance the energy consumption and execution time of each task. We design and evaluate (via simulation) a set of heuristics and filtering mechanisms for making allocations in our system. We show that the appropriate choice of filtering mechanisms improves performance more than the choice of heuristic (among the heuristics we tested).