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    Reputation guided genetic scheduling algorithm for independent tasks in inter-clouds environments

    Pop, F. and Cristea, V. and Bessis, N. and Sotiriadis, Stelios (2013) Reputation guided genetic scheduling algorithm for independent tasks in inter-clouds environments. In: UNSPECIFIED (ed.) 2013 27th International Conference on Advanced Information Networking and Applications Workshops. IEEE, pp. 772-776. ISBN 9781467362399.

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    Abstract

    Evolutionary computing offers different methods to solve NP-hard problems, finding a near-optimal solution. Task scheduling is a complex problem for large environments like Clouds. Genetic algorithms are a good method to find a solution for this problem considering multi-criteria constrains. This is also a method used for optimization. In these type of environments service providers want to increase the profit and the customers (end-users) want to minimize the costs. So, its all about money and we have minimum two optimization constrains. On the other hand, a good technique to ensure the QoS is to use the reputation of resources offered. This aspect is very important for service providers because represents a ranking method for them. We present in this paper a reputation guided genetic scheduling algorithm for independent tasks in inter-Clouds environments. The reputation is considered in the selection phase of genetic algorithm as an evolutionary criteria for the algorithm. We evaluate the proposed solution considering load-balancing as a way to measure the optimization impact for providers and maxspan as a metric for user performance.

    Metadata

    Item Type: Book Section
    Keyword(s) / Subject(s): cloud computing, genetic algorithms, resource allocation, scheduling, NP-hard problem, QoS ensurance, cost minimization, end-users, evolutionary computing, evolutionary criteria, independent task, intercloud environment, load balancing, multicriteria constrain, near-optimal solution, optimization constrain, optimization impact, reputation guided genetic scheduling algorithm, resource reputation, service provider ranking method, task scheduling, user performance, Biological cells, Genetic algorithms, Measurement, Optimal scheduling, Processor scheduling, Program processors, Schedules, Cloud Computing, Genetic Algorithm, Independent Tasks, Reputation, Scheduling
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Stelios Sotiriadis
    Date Deposited: 27 Jun 2018 14:21
    Last Modified: 14 Feb 2021 11:05
    URI: https://eprints.bbk.ac.uk/id/eprint/21835

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