ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Managing multi-configuration hardware via dynamic working set analysis
ISCA '02 Proceedings of the 29th annual international symposium on Computer architecture
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On evaluating request-distribution schemes for saving energy in server clusters
ISPASS '03 Proceedings of the 2003 IEEE International Symposium on Performance Analysis of Systems and Software
Grid Resource Allocation and Task Scheduling for Resource Intensive Applications
ICPPW '06 Proceedings of the 2006 International Conference Workshops on Parallel Processing
A Pure Nash Equilibrium-Based Game Theoretical Method for Data Replication across Multiple Servers
IEEE Transactions on Knowledge and Data Engineering
Workflow scheduling in computational grids: Opportunistic vs. planned
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Energy-efficient deadline scheduling for heterogeneous systems
Journal of Parallel and Distributed Computing
DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
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Computation grids and computational clouds are becoming increasingly popular in the organizations which require massive computational capabilities. Building such infrastructures makes a lucrative business case, thanks to availability of cheap hardware components and affordable software. Maintaining computational grids or cloud, however, require careful planning as in such dedicated environments, round-the-clock availability of workstations is very crucial. Ensuring uninterrupted availability, not only demands mechanism for failover redundancy but also results in constant power drainage. The tradeoff between the cost and the performance is the constant dilemma that the operations of the data centers face today. In this paper, we propose various heuristics for power-aware scheduling algorithms for scheduling jobs with dependent tasks onto the computational grid. We formulate the problem as a multi-objective function which results in various cost-performance tradeoffs each lying within the solution boundary.