Resource Allocation for Periodic Applications in a Shipboard Environment

Shestak, Vladimir; Chong, Edwin K. P.; Maciejewski, Anthony A.; Siegel, Howard Jay; Benmohamed, Lotfi; Wang, I.-J.; Daley, Rose A.

doi:10.1109/ipdps.2005.382

Cited by 9 publications

(6 citation statements)

References 28 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These heuristics are probabilistic search techniques that have been widely used in optimization research [34,43,30], artificial intelligence [19], and many other areas. The first two of the heuristics operate with a set of complete resource allocations; whereas the third heuristic iteratively changes a single complete resource allocation.…”

Section: Overviewmentioning

confidence: 99%

Stochastic robustness metric and its use for static resource allocations

Shestak

Smith

Maciejewski

et al. 2008

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

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 a physical environment replete with uncertainty, which causes the amount of processing required to fluctuate substantially over time. 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 proposed in this research is based on a mathematical model where the relationship between uncertainty in system parameters and its impact on system performance are described stochastically.The utility of the established metric is then exploited in the design of optimization techniques based on greedy and iterative approaches that address the problem of resource allocation in a large class of distributed systems operating on periodically updated data sets. The performance results are presented for a simulated environment that replicates a heterogeneous cluster-based radar data processing center. A mathematical performance lower bound is presented for comparison analysis of the heuristic results. The lower bound is derived based on a relaxation of the Integer Linear Programming formulation for a given resource allocation problem.

show abstract

Section: Overviewmentioning

confidence: 99%

Stochastic robustness metric and its use for static resource allocations

Shestak

Smith

Maciejewski

et al. 2008

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

show abstract

“…Portions of a preliminary version of this material were presented at the 14th IEEE Heterogeneous Computing Workshop (HCW 2005) [37]. The authors thank Jay Smith for his valuable comments.…”

Section: Acknowledgmentsmentioning

confidence: 99%

A hybrid Branch-and-Bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systems

Shestak

Chong

Siegel

et al. 2008

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

“…Robust static resource allocations in distributed, multitasking computing systems aboard shipboard environments is studied in [5]. Complete and partial resource allocation schemes were considered.…”

Section: Deterministic Robustnessmentioning

confidence: 99%

Models and Heuristics for Robust Resource Allocation in Parallel and Distributed Computing Systems

Janovy

Smith

Siegel

et al. 2007

2007 IEEE International Parallel and Distributed Processing Symposium

View full text Add to dashboard Cite

This is an overview of the robust resource allocation research efforts that have been and continue to be conducted by the CSU Robustness in Computer Systems Group.Parallel and distributed computing systems, consisting of a (usually heterogeneous) set of machines and networks, frequently operate in environments where delivered performance degrades due to unpredictable circumstances. Such unpredictability can be the result of sudden machine failures, increases in system load, or errors caused by inaccurate initial estimation. The research into developing models and heuristics for parallel and distributed computing systems that create robust resource allocations is presented.

show abstract

Resource Allocation for Periodic Applications in a Shipboard Environment

Abstract: Abstract

Cited by 9 publications

References 28 publications

Stochastic robustness metric and its use for static resource allocations

Stochastic robustness metric and its use for static resource allocations

A hybrid Branch-and-Bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systems

Models and Heuristics for Robust Resource Allocation in Parallel and Distributed Computing Systems

Contact Info

Product

Resources

About