Energy and locality aware load balancing in cloud computing

Soaring power consumption and limited communication bandwidth are the most critical issues involved in cloud computing. Reducing energy consumption will not only cut down the operational costs of data centers, but also reduce the amount of greenhouse gases emissions. In order to improve energy efficiency of servers, a new multi-objective bi-level programming model based on MapReduce is proposed in this paper. In the model, first, the relationship between performance and energy consumption of servers is taken into account. Second, data locality can be adjusted dynamically according to current network state. Third, data placement policies and task-scheduling strategies are considered simultaneously as a whole. In order to solve the model efficiently, specific-designed encoding and decoding methods are introduced. With these, a new effective multi-objective genetic algorithm on the basis of Multiobjective Evolutionary Algorithm Based on Decomposition MOEA/D is presented. As tasks involved in cloud computing are usually tens of thousands, a local search operator is designed in order to accelerate convergent speed of the proposed algorithm. Finally, numerical experiments are made and the results indicate the reasonableness of the model and the effectiveness of the proposed algorithm.

[1]  Antonio M. Vidal,et al.  An efficient GPU implementation of fixed-complexity sphere decoders for MIMO wireless systems , 2012, Integr. Comput. Aided Eng..

[2]  Hojjat Adeli,et al.  Life‐cycle cost optimization of steel structures , 2002 .

[3]  Yuping Wang,et al.  U-measure: a quality measure for multiobjective programming , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[5]  Hojjat Adeli,et al.  Distributed Computer-Aided Engineering: For Analysis, Design, and Visualization , 1998 .

[6]  Hojjat Adeli,et al.  DISCRETE COST OPTIMIZATION OF COMPOSITE FLOORS USING A FLOATING-POINT GENETIC ALGORITHM , 2001 .

[7]  Yuping Wang,et al.  Energy-Efficient Multi-Job Scheduling Model for Cloud Computing and Its Genetic Algorithm , 2012 .

[8]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[9]  Heinrich von Stackelberg,et al.  Stackelberg (Heinrich von) - The Theory of the Market Economy, translated from the German and with an introduction by Alan T. PEACOCK. , 1953 .

[10]  Hojjat Adeli,et al.  Neuro‐genetic algorithm for non‐linear active control of structures , 2008 .

[11]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[12]  Sami F. Masri,et al.  Finite Element Model Updating Using Evolutionary Strategy for Damage Detection , 2011, Comput. Aided Civ. Infrastructure Eng..

[13]  Hojjat Adeli,et al.  Bilevel Parallel Genetic Algorithms for Optimization of Large Steel Structures , 2001 .

[14]  Hong Liu,et al.  Energy proportional datacenter networks , 2010, ISCA.

[15]  Yu Jiong,et al.  Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing , 2012, 2012 Seventh ChinaGrid Annual Conference.

[16]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[17]  Giuseppe Quaranta,et al.  Modified Genetic Algorithm for the Dynamic Identification of Structural Systems Using Incomplete Measurements , 2011, Comput. Aided Civ. Infrastructure Eng..

[18]  H. Adeli,et al.  Integrated Genetic Algorithm for Optimization of Space Structures , 1993 .

[19]  Dusan M. Stipanovic,et al.  Teleoperation of multi-agent systems with nonuniform control input delays , 2012, Integr. Comput. Aided Eng..

[20]  Hojjat Adeli,et al.  Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms, and Parallel Computing , 2006 .

[21]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[22]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

[23]  Franco Bontempi,et al.  Genetic Algorithms for the Dependability Assurance in the Design of a Long‐Span Suspension Bridge , 2012, Comput. Aided Civ. Infrastructure Eng..

[24]  Hojjat Adeli,et al.  Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems , 1994 .

[25]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[26]  Yuping Wang,et al.  An evolutionary algorithm for solving nonlinear bilevel programming based on a new constraint-handling scheme , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[27]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[28]  H. Adeli,et al.  Augmented Lagrangian genetic algorithm for structural optimization , 1994 .

[29]  Enrico Zio,et al.  Genetic algorithm-based wrapper approach for grouping condition monitoring signals of nuclear power plant components , 2011, Integr. Comput. Aided Eng..

[30]  Kamal C. Sarma.  Fuzzy discrete multicriteria cost optimization of steel structures using genetic algorithm , 2000 .

[31]  J. Bard.  Some properties of the bilevel programming problem , 1991 .

[32]  Shih-Hsu Wang,et al.  Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction , 2012, Comput. Aided Civ. Infrastructure Eng..

[33]  Kamal C. Sarma,et al.  FUZZY GENETIC ALGORITHM FOR OPTIMIZATION OF STEEL STRUCTURES , 2000 .

[34]  Yuping Wang,et al.  Energy-efficient Task Scheduling Model based on MapReduce for Cloud Computing using Genetic Algorithm , 2012, J. Comput..

[35]  Hojjat Adeli,et al.  Distributed Genetic Algorithm for Structural Optimization , 1995 .

[36]  Zhenyu He,et al.  Writer identification using fractal dimension of wavelet subbands in gabor domain , 2010, Integr. Comput. Aided Eng..

[37]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[38]  H. Adeli,et al.  Concurrent genetic algorithms for optimization of large structures , 1994 .

[39]  Jaewoo Kim,et al.  Network management framework and lifetime evaluation method for wireless sensor networks , 2012, Integr. Comput. Aided Eng..

[40]  Robert G. Jeroslow,et al.  The polynomial hierarchy and a simple model for competitive analysis , 1985, Math. Program..

[41]  Hojjat Adeli,et al.  Concurrent Structural Optimization on Massively Parallel Supercomputer , 1995 .

[42]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[43]  Jeffrey Horn,et al.  The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems , 2001, EMO.

[44]  James A. Reggia,et al.  Causally-guided evolutionary optimization and its application to antenna array design , 2012, Integr. Comput. Aided Eng..

[45]  George K. Knopf,et al.  Consistent Point Clouds of Narrow Spaces Using Multiscan Domain Mapping , 2012, Comput. Aided Civ. Infrastructure Eng..

[46]  Germain Forestier,et al.  Discovering Significant Evolution Patterns from Satellite Image Time Series , 2011, Int. J. Neural Syst..

[47]  Luca Quadrifoglio,et al.  Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions , 2012, Comput. Aided Civ. Infrastructure Eng..