Security-Aware Resource Allocation for Real-Time Parallel Jobs on Homogeneous and Heterogeneous Clusters

Security is increasingly becoming an important issue in the design of real-time parallel applications, which are widely used in the industry and academic organizations. However, existing resource allocation schemes for real-time parallel jobs on clusters generally do not factor in security requireme...

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Vydáno v:IEEE transactions on parallel and distributed systems Ročník 19; číslo 5; s. 682 - 697
Hlavní autoři: Xie, Tao, Qin, Xiao
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2008
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Allocations
Application software
Clusters
Communication system security
Computer hacking
Construction
Data security
Deadlines
Decisions
Job design
Job shop scheduling
Mathematical models
Parallel processing
Protection
Real time
Real-time distributed
Resource allocation
Resource management
Scheduling and task partitioning
Security
Studies
Timing
Scheduling and task partitioning
Real-time distributed
ISSN:1045-9219, 1558-2183
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Shrnutí:Security is increasingly becoming an important issue in the design of real-time parallel applications, which are widely used in the industry and academic organizations. However, existing resource allocation schemes for real-time parallel jobs on clusters generally do not factor in security requirements when making allocation and scheduling decisions. In this paper, we develop two resource allocation schemes, called task allocation for parallel applications with deadline and security constraints (TAPADS) and security-aware and heterogeneity-aware resource allocation for parallel jobs (SHARP), by taking into account applications' timing and security requirements in addition to precedence constraints. We consider two types of computing platforms: homogeneous clusters and heterogeneous clusters. To facilitate the presentation of the new schemes, we build mathematical models to describe a system framework, security overhead, and parallel applications with deadline and security constraints. The proposed schemes are applied to heuristically find resource allocations that maximize the quality of security and the probability of meeting deadlines for parallel applications running on clusters. Extensive experiments using real-world applications and traces, as well as synthetic benchmarks, demonstrate the effectiveness and practicality of the proposed schemes.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2007.70776