Static heuristics for robust resource allocation of continuously executing applications

Shoukat Ali, Jong-Kook Kim, Howard Jay Siegel, Anthony A. Maciejewski

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. 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 two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found 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 resulting in a QoS violation. 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 (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were "seeded" by the best solution found by using a set of fast greedy heuristics.

Original languageEnglish
Pages (from-to)1070-1080
Number of pages11
JournalJournal of Parallel and Distributed Computing
Volume68
Issue number8
DOIs
Publication statusPublished - 2008 Aug 1

Fingerprint

Resource Allocation
Resource allocation
Quality of service
Quality of Service
Heuristics
Failure Rate
Workload
Sensors
Simulated annealing
Greedy Heuristics
Sensor
Simulated Annealing
Genetic algorithms
Throughput
Percentage
Latency
High Speed
Maximise
Likely
Genetic Algorithm

Keywords

  • Genetic algorithm
  • Heterogeneous distributed computing
  • Resource allocation
  • Robustness
  • Shipboard computing
  • Simulated annealing
  • Static mapping
  • Task scheduling

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Control and Systems Engineering

Cite this

Static heuristics for robust resource allocation of continuously executing applications. / Ali, Shoukat; Kim, Jong-Kook; Siegel, Howard Jay; Maciejewski, Anthony A.

In: Journal of Parallel and Distributed Computing, Vol. 68, No. 8, 01.08.2008, p. 1070-1080.

Research output: Contribution to journalArticle

Ali, Shoukat ; Kim, Jong-Kook ; Siegel, Howard Jay ; Maciejewski, Anthony A. / Static heuristics for robust resource allocation of continuously executing applications. In: Journal of Parallel and Distributed Computing. 2008 ; Vol. 68, No. 8. pp. 1070-1080.
@article{dc92c90aa906460fab87430e0e9d4660,
title = "Static heuristics for robust resource allocation of continuously executing applications",
abstract = "We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. 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 two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found 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 resulting in a QoS violation. 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 (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were {"}seeded{"} by the best solution found by using a set of fast greedy heuristics.",
keywords = "Genetic algorithm, Heterogeneous distributed computing, Resource allocation, Robustness, Shipboard computing, Simulated annealing, Static mapping, Task scheduling",
author = "Shoukat Ali and Jong-Kook Kim and Siegel, {Howard Jay} and Maciejewski, {Anthony A.}",
year = "2008",
month = "8",
day = "1",
doi = "10.1016/j.jpdc.2007.12.007",
language = "English",
volume = "68",
pages = "1070--1080",
journal = "Journal of Parallel and Distributed Computing",
issn = "0743-7315",
publisher = "Academic Press Inc.",
number = "8",

}

TY - JOUR

T1 - Static heuristics for robust resource allocation of continuously executing applications

AU - Ali, Shoukat

AU - Kim, Jong-Kook

AU - Siegel, Howard Jay

AU - Maciejewski, Anthony A.

PY - 2008/8/1

Y1 - 2008/8/1

N2 - We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. 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 two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found 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 resulting in a QoS violation. 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 (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were "seeded" by the best solution found by using a set of fast greedy heuristics.

AB - We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. 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 two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found 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 resulting in a QoS violation. 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 (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were "seeded" by the best solution found by using a set of fast greedy heuristics.

KW - Genetic algorithm

KW - Heterogeneous distributed computing

KW - Resource allocation

KW - Robustness

KW - Shipboard computing

KW - Simulated annealing

KW - Static mapping

KW - Task scheduling

UR - http://www.scopus.com/inward/record.url?scp=46149110480&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=46149110480&partnerID=8YFLogxK

U2 - 10.1016/j.jpdc.2007.12.007

DO - 10.1016/j.jpdc.2007.12.007

M3 - Article

AN - SCOPUS:46149110480

VL - 68

SP - 1070

EP - 1080

JO - Journal of Parallel and Distributed Computing

JF - Journal of Parallel and Distributed Computing

SN - 0743-7315

IS - 8

ER -