Journal of Parallel and Distributed Computing
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Journal of Parallel and Distributed Computing
Proceedings of the 3rd International Conference on Genetic Algorithms
WCET Analysis of Probabilistic Hard Real-Time Systems
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Measuring the Robustness of a Resource Allocation
IEEE Transactions on Parallel and Distributed Systems
Static Determination of Probabilistic Execution Times
ECRTS '04 Proceedings of the 16th Euromicro Conference on Real-Time Systems
Journal of Parallel and Distributed Computing
Dynamic resource allocation heuristics that manage tradeoff between makespan and robustness
The Journal of Supercomputing
Genetic algorithms, path relinking, and the flowshop sequencing problem
Evolutionary Computation
Static heuristics for robust resource allocation of continuously executing applications
Journal of Parallel and Distributed Computing
Stochastic robustness metric and its use for static resource allocations
Journal of Parallel and Distributed Computing
Evaluation and Optimization of the Robustness of DAG Schedules in Heterogeneous Environments
IEEE Transactions on Parallel and Distributed Systems
Rescheduling for reliable job completion with the support of clouds
Future Generation Computer Systems
A stochastic scheduling algorithm for precedence constrained tasks on Grid
Future Generation Computer Systems
Resource allocation robustness in multi-core embedded systems with inaccurate information
Journal of Systems Architecture: the EUROMICRO Journal
Robust Scheduling of Task Graphs under Execution Time Uncertainty
IEEE Transactions on Computers
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This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in an environment where uncertainty in system parameters is common. In such an environment, the amount of processing required to complete a task may fluctuate substantially. Determining a resource allocation that accounts for this uncertainty-in a way that can provide a probability that a given level of QoS is achieved-is an important area of research. We have designed novel techniques for maximizing the probability that a given level of QoS is achieved. These techniques feature a unique application of both path relinking and local search within a Genetic Algorithm. In addition, we define a new methodology for finding resource allocations that are guaranteed to have a non-zero probability of addressing the timing constraints of the system. We demonstrate the use of this methodology within two unique steady-state genetic algorithms designed to maximize the robustness of resource allocations. The performance results for our techniques are presented for a simulated environment that models a heterogeneous cluster-based radar data processing center.