Paper Info
Title
Multi-objective Security Driven Job Scheduling for Computational Cloud Systems
Abstract
This paper proposes a multi-objective parallel job scheduling algorithm for a Computational Cloud environment. We present a fault-tolerant, scalable and efficient solution for optimizing scheduling of N independent jobs on M parallel machines that minimizes two objectives simultaneously, namely the failure probability and the total completion time of all the jobs. Obtaining an optimal solution for this type of complex, large-sized problem in a reasonable computational time using traditional approaches or optimization tools is extremely difficult. As this problem is NP-hard in the strong sense, a meta-heuristic method which is the second version of the non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve this problem. This approach is based on the Pareto dominance relationship, providing no single optimal solution, but a set of solutions which are not dominated by each other. The performance of the presented model and the applied GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and medium-sized problems.
Year
DOI
Venue
2013
10.1109/3PGCIC.2013.101
P2P, Parallel, Grid, Cloud and Internet Computing
Keywords
Field
DocType
multi-objective security driven job,computational cloud systems,single optimal solution,efficient solution,optimal solution,m parallel machine,medium-sized problem,genetic algorithm,large-sized problem,optimizing scheduling,multi-objective parallel job scheduling,cloud computing,genetic algorithms,scheduling
Mathematical optimization,Multiprocessor scheduling,Job shop scheduling,Fair-share scheduling,Computer science,Flow shop scheduling,Nurse scheduling problem,Job scheduler,Rate-monotonic scheduling,Dynamic priority scheduling,Distributed computing
Conference
Citations 
PageRank 
References 
2
0.37
9
Authors
2
Name
Order
Citations
PageRank
Jakub Gasior1104.58
Franciszek Seredynski236655.06