Abstract not found

Often, parallel and distributed computing systems must operate in an environment replete with uncertainty. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of quality of service (QoS) is achieved is an important research problem. This paper defines a stochastic methodology for quantifiably determining a resource allocation’s ability to satisfy QoS constraints in the midst of uncertainty in system parameters. Uncertainty in system parameters and its impact on system performance are modeled stochastically. This stochastic model is then used to derive a quantitative expression for the robustness of a resource allocation. The paper investigates the utility of the proposed stochastic robustness metric by applying the metric to resource allocations in a simulated distributed system. The simulation results are then compared with deterministically defined metrics from the literature. 1.

Abstract — This research investigates the problem of robust resource allocation for a large class of systems operating on periodically updated data sets under an imposed quality of service (QoS) constraint. Such systems are expected to function in an environment replete with uncertainty where the workload is likely to fluctuate substantially. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of QoS is achieved is an important research problem. First, this paper defines a methodology for quantifiably determining a resource allocation’s ability to satisfy QoS constraint in the midst of uncertainty in system parameters. Uncertainty in system parameters and its impact on system performance are modeled stochastically. Second, the established stochastic model is employed to develop greedy resource allocation heuristics. Finally, the utility of the proposed stochastic robustness metric and the performance of the heuristics are evaluated in a simulated environment that replicates a heterogeneous cluster-based radar system. Index Terms — heterogeneous distributed systems, resource allocation, stochastic optimization, greedy heuristics. I.

into subtasks, then each subtask assigned to the machine where it is best suited for execution. Data relocation is defined as selecting the sources for needed data items. It is assumed that multiple independent subtasks of an application program can be executed concurrently on different machines whenever possible. A theoretical stochastic model for HC is proposed, in which the computation times of subtasks and communication times for intermachine data transfers can be random variables. The optimization problem for finding the optimal matching, scheduling, and data relocation schemes to minimize the total execution time of an application program is defined based on this stochastic HC model. The global optimization criterion and search space for the above optimization problem are described. It is validated that a greedy algorithm-based approach can establish a local optimization criterion for developing data relocation heuristics. The validation is provided by a theoretical proof based on a set of common assumptions about the underlying HC system and application program. The local optimization criterion established by the greedy approach, coupled with the search space defined for choosing valid data relocation schemes, can help developers of future practical data relocation heuristics.

This research investigates the problem of robust dynamic resource allocation for heterogeneous distributed computing systems operating under imposed 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 an application 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 service is achieved—is an important area of research. We define a mathematical model of stochastic robustness appropriate for a dynamic environment that can be used during resource allocation to aid heuristic decision making. In addition, we design a novel technique for maximizing stochastic robustness in this environment. Our performance results for this technique are compared with several well known resource allocation techniques in a simulated environment that models a heterogeneous distributed computing system.

This research investigates the problem of robust static resource allocation for a large class of clusters processing periodically updated data sets under an imposed quality of service constraint. The target hardware platform consists of a number of sensors generating input for heterogeneous applications continuously executing on a set of heterogeneous compute nodes. In practice such systems are expected to function in a physical environment replete with uncertainty, which causes the amount of processing required over time to fluctuate substantially. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of QoS is achieved is an important research problem. The stochastic robustness metric is based on a mathematical model where the relationship between uncertainty in system parameters and its impact on system performance is described stochastically. The established metric is then used in the design of several resource allocation algorithms utilizing evolutionary approaches. The performance results and comparison analysis are presented for a simulated environment that replicates a heterogeneous cluster-based processing center for a radar system. KEY WORDS Heterogeneous systems, resource allocation, stochastic optimization, iterative algorithms, computer clusters. 1

Abstract—Heterogeneous, parallel and distributed computing systems frequently must operate in environments where uncertainty in system parameters is common. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. In such an environment, the amount of processing required to complete any given task may fluctuate substantially due to variations in data size and content. Determining a resource allocation that accounts for this uncertainty is an important area of research. In this study, we define a stochastic robustness measure to facilitate batchmode resource allocation decisions in a dynamic environment where tasks are subject to individual deadlines and design a novel resource allocation technique that attempts to maximize our new stochastic robustness measure. We compare the performance of our technique against some commonly used approaches taken from the literature and adapted to our environment. Our performance results demonstrate the viability of our new technique in a dynamic heterogeneous computing system.

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Abstract 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 uncertainties 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 present two techniques for maximizing the probability that a given level of QoS is achieved. The performance results for our techniques are presented for a simulated environment that models a heterogeneous clusterbased radar data processing center.

In parallel and distributed computing, multiple computers are collectively used to process a set of tasks simultaneously to improve performance over that of a single processor (1). Often, such computing systems are constructed from a