An integrated security-aware job scheduling strategy for large-scale computational grids

Wu, Chao‐Chin; Sun, Ren-Yi

doi:10.1016/j.future.2009.08.004

Cited by 25 publications

(9 citation statements)

References 31 publications

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“…Makespan and P failure appearing in (2) are the numeric values of the corresponding metrics, and not the metrics themselves. Hence, the fitness function is expressed by an absolute number and not by specific Encoded Solution (18,25,9,11,22,19) f itness = 100 w 1 * makespan + w 2 * P f ailure (2) In the initial population generation step, a predefined number of chromosomes are generated, the collection of which form the initial population. In a typical GA, the initial population is constituted of a pool of solutions, which is randomly formed.…”

Section: Traditional Genetic Algorithmmentioning

confidence: 99%

“…For instance, it is well known that a Grid system is an unreliable environment by nature. The submitted jobs are subject to failures caused by infected hardware, software vulnerability, network failure, overloaded resource conditions, non-availability of required resource and distrusted security policy [22]. A general security architecture of a computational Grid is presented in [23], while a secure architecture specialized in service discovery is studied in [24].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm

Gkoutioudi

Karatza

2012

J Grid Computing

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Section: Traditional Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm

Gkoutioudi

Karatza

2012

J Grid Computing

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“…It allowed both detection of potential failures as well as handling the user's security requirements without the need to constantly update the scheduling policy at local computational nodes. Insecure conditions in on-line job scheduling caused by software vulnerabilities were also analyzed by Wu and Sun (2010) by considering the heterogeneity of the fault-tolerant mechanisms employed in security-assured job scheduling.…”

Section: State Of the Artmentioning

confidence: 99%

Decentralized Job Scheduling in the Cloud Based on a Spatially Generalized Prisoner’s Dilemma Game

Gźsior

Seredyński

2015

International Journal of Applied Mathematics and Computer Science

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We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the assignment of the available resources is governed exclusively by the specialized brokers assigned to individual users submitting their jobs to the system. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and total completion time. Our approach is based on the Pareto dominance relationship and implemented at an individual user level. To select the best scheduling strategies from the resulting Pareto frontiers and construct a global scheduling solution, we developed a decision-making mechanism based on the game-theoretic model of Spatial Prisoner's Dilemma, realized by selfish agents operating in the two-dimensional cellular automata space. Their behavior is conditioned by the objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the scheduler applied is verified by a number of numerical experiments. The related results show the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources involved in the scheduling process.

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“…The results presented in [9] were extended in [13] by considering the heterogeneity of the fault-tolerance mechanism in a security-assured Grid job scheduling. The authors defined four types of GA-based on-line schedulers for the simulation of some fault-tolerance mechanisms, including job retry, job migration (with and without check-pointing), and job replication mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective Security Driven Job Scheduling for Computational Cloud Systems

Gąsior

Seredyński

2013

2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing

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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.

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An integrated security-aware job scheduling strategy for large-scale computational grids

Cited by 25 publications

References 31 publications

Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm

Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm

Decentralized Job Scheduling in the Cloud Based on a Spatially Generalized Prisoner’s Dilemma Game

Multi-objective Security Driven Job Scheduling for Computational Cloud Systems

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