Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
Improving dynamic voltage scaling algorithms with PACE
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Workload Evolution on the Cornell Theory Center IBM SP2
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
High-density computing: a 240-processor Beowulf in one cubic meter
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Task Execution Time Modeling for Heterogeneous Computing Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Dynamic Voltage Scaling Techniques for Distributed Microsensor Networks
WVLSI '00 Proceedings of the IEEE Computer Society Annual Workshop on VLSI (WVLSI'00)
Adaptive scheduling server for power-aware real-time tasks
ACM Transactions on Embedded Computing Systems (TECS)
CMOS: Circuit Design, Layout, and Simulation (IEEE Press Series on Microelectronic Systems)
CMOS: Circuit Design, Layout, and Simulation (IEEE Press Series on Microelectronic Systems)
Gordon: using flash memory to build fast, power-efficient clusters for data-intensive applications
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
IEEE Transactions on Parallel and Distributed Systems
Scheduling Algorithms
Minimizing Energy Consumption for Precedence-Constrained Applications Using Dynamic Voltage Scaling
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Keynote: Energy-Aware Scheduling and Resource Allocation for Large-Scale Distributed Systems
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
FAWN: a fast array of wimpy nodes
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Memory-Aware Green Scheduling on Multi-core Processors
ICPPW '10 Proceedings of the 2010 39th International Conference on Parallel Processing Workshops
A new evolutionary algorithm using shadow price guided operators
Applied Soft Computing
DENS: Data Center Energy-Efficient Network-Aware Scheduling
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Future Generation Computer Systems
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
Journal of Parallel and Distributed Computing
A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
A taxonomy and survey on autonomic management of applications in grid computing environments
Concurrency and Computation: Practice & Experience
Multi-organization scheduling approximation algorithms
Concurrency and Computation: Practice & Experience
Genetic Algorithms for Energy-Aware Scheduling in Computational Grids
3PGCIC '11 Proceedings of the 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems
Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems
Review of performance metrics for green data centers: a taxonomy study
The Journal of Supercomputing
An overview of energy efficiency techniques in cluster computing systems
Cluster Computing
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An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds.In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler (Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios.