Processor Allocation for Tasks that is Robust Against Errors in Computation Time Estimates
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02
Resource Allocation for Periodic Applications in a Shipboard Environment
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02
Journal of Parallel and Distributed Computing
Static heuristics for robust resource allocation of continuously executing applications
Journal of Parallel and Distributed Computing
The robustness of resource allocations in parallel and distributed computing systems
ARCS'06 Proceedings of the 19th international conference on Architecture of Computing Systems
Evolving robust networks for systems-of-systems
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
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This research investigates two distinct issues related to a resource allocation: its robustness and the failure rate of the heuristic used to determine the allocation. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are a number of quality of service (QoS) constraints that must be satisfied. A heuristic failure occurs if the heuristic cannot find an allocation that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, i.e., the load presented by the set of sensors, is likely to change unpredictably, possibly invalidating a resource allocation that was based on the initial workload estimate. The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate. This study proposes a heuristic that performs well with respect to the failure rates and robustness to unpredictable workload increases. This heuristic is, therefore, very desirable for systems where low failure rates can be a critical requirement and where unpredictable circumstances can lead to unknown increases in the system workload.