B-MAPS: a self-adaptive resource scheduling framework for heterogeneous cloud systems

Authors:
Joal Wood;Brian Romoser;Ivan Zecena;Ziliang Zong
Affiliations:
Texas State University - San Marcos;Texas State University - San Marcos;Texas State University - San Marcos;Texas State University - San Marcos
Venue:
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
Year:
2013

Citing 3
Cited 0

A Power-Aware Run-Time System for High-Performance Computing

SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Minimizing execution time in MPI programs on an energy-constrained, power-scalable cluster

Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Profile-based optimization of power performance by using dynamic voltage scaling on a PC cluster

IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Future cloud systems will become increasingly complicated and highly heterogeneous. It is paramount to develop new techniques that can achieve high performance and low energy consumption in future cloud systems. However, this is not a trivial task because the dynamic nature of system status and user workloads requires that the system must be able to trade off performance and energy efficiency at real time. In this paper, we propose B-MAPS, a self-adaptive resource scheduling framework, which has the potential to improve the performance and energy-efficiency of multi-core or many-core based heterogeneous cloud systems.