An integer programming approach for static mapping onto heterogeneous real-time systems
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Run-Time Adaptation with Resource Co-Allocation for Grid Environments
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
The Influence of Communication on the Performance of Co-allocation
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Bounds on the multi-clients incremental computing for homogeneous decreasing computation sequences
Information Processing Letters
Rescheduling co-allocation requests based on flexible advance reservations and processor remapping
GRID '08 Proceedings of the 2008 9th IEEE/ACM International Conference on Grid Computing
An improved algorithm for Alhusaini's algorithm in heterogeneous distributed systems
ICA3PP'07 Proceedings of the 7th international conference on Algorithms and architectures for parallel processing
An open computing resource management framework for real-time computing
HiPC'08 Proceedings of the 15th international conference on High performance computing
Parallel resource co-allocation for the computational grid
Computer Languages, Systems and Structures
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It is often the case in Heterogeneous Computing (HC) systems that an application requires multiple resources of different types to be allocated simultaneously. In general, this problem is the resource co-allocation problem. In this paper, we develop a general framework for mapping a collection of applications with resource co-allocation requirements. In our framework, application tasks have two types of constraints to be satisfied: precedence constraints and resource sharing constraints. We use a graph theoretic framework to capture these constraints. A Directed Acyclic Graph is used to represent precedence constraints of tasks within an application and a Compatibility Graph is used to represent resource sharing constraints among tasks of applications. Both these graphs are used to find maximal independent sets of tasks that can be executed concurrently.The objective of the mapping is to minimize the overall schedule length for a given set of applications. We develop heuristic algorithms to solve the mapping problem with resource co-allocation constraints. We also provide a two-phase algorithm that can be used for run-time adaptation. We conducted extensive simulation experiments to evaluate the performance of our heuristic algorithms. Simulation results for our algorithms show a performance improvement of 10% to 30% over a baseline algorithm of list scheduling which considers only the precedence constraints and allocates tasks from the resulting order. This paper demonstrates the importance of considering the co-allocation requirements when mapping applications in heterogeneous computing environments including grid environments.