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  • 1.
    Casas, Israel
    et al.
    Australia.
    Taheri, Javid
    Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).
    Ranjan, Rajiv
    Australia, UK.
    Wang, Lizhe
    China.
    Zomaya, Albert Y.
    Australia.
    A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems2017Inngår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 74, s. 168-178Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cloud computing provides substantial opportunities to researchers who demand pay-as-you-go computing systems. Although cloud provider (e.g., Amazon Web Services) and application provider (e.g., biologists, physicists, and online gaming companies) both have specific performance requirements (e.g. application response time), it is the cloud scheduler’s responsibility to map the application to underlying cloud resources. This article presents a Balanced and file Reuse-Replication Scheduling (BaRRS) algorithm for cloud computing environments to optimally schedule scientific application workflows. BaRRS splits scientific workflows into multiple sub-workflows to balance system utilization via parallelization. It also exploits data reuse and replication techniques to optimize the amount of data that needs to be transferred among tasks at run-time. BaRRS analyzes the key application features (e.g., task execution times, dependency patterns and file sizes) of scientific workflows for adapting existing data reuse and replication techniques to cloud systems. Further, BaRRS performs a trade-off analysis to select the optimal solution based on two optimization constraints: execution time and monetary cost of running scientific workflows. BaRRS is compared with a state-of-the-art scheduling approach; experiments prove its superior performance. Experiments include four well known scientific workflows with different dependency patterns and data file sizes. Results were promising and also highlighted most critical factors affecting execution of scientific applications on clouds. 

  • 2.
    Geiger, Matthias
    et al.
    University of Bamberg.
    Harrer, Simon
    University of Bamberg.
    Lenhard, Jörg
    Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).
    Wirtz, Guido
    University of Bamberg.
    BPMN2.0: The state of support and implementation2018Inngår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 80, s. 250-262Artikkel i tidsskrift (Fagfellevurdert)
  • 3.
    Taheri, Javid
    et al.
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Bouvry, Pascal
    Department of Electrical and Computer Engineering, University of Luxembourg.
    Khan, Samee U.
    Department of Electrical Engineering, North Dakota State University, Fargo.
    Hopfield Neural Network for Simultaneous Job Scheduling and Data Replication in Grids2013Inngår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 29, s. 1885-1900Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel heuristic approach, named JDS-HNN, to simultaneously schedule jobs and replicate data files to different entities of a grid system so that the overall makespan of executing all jobs as well as the overall delivery time of all data files to their dependent jobs is concurrently minimized. JDS-HNN is inspired by a natural distribution of a variety of stones among different jars and utilizes a Hopfield Neural Network in one of its optimization stages to achieve its goals. The performance of JDS-HNN has been measured by using several benchmarks varying from medium- to very-large-sized systems. JDS-HNN's results are compared against the performance of other algorithms to show its superiority under different working conditions. These results also provide invaluable insights into scheduling and replicating dependent jobs and data files as well as their performance related issues for various grid environments.

  • 4.
    Taheri, Javid
    et al.
    The University of Sydney, Australia.
    Zomaya, Albert
    The University of Sydney, Sydney, Australia.
    Siegel, Howard Jay
    USA.
    Tari, Zahir
    Australia.
    Pareto frontier for job execution and data transfer time in hybrid clouds2014Inngår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 37, nr 0, s. 321-334Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper proposes a solution to calculate the Pareto frontier for the execution of a batch of jobs versus data transfer time for hybrid clouds. Based on the nature of the cloud application, jobs are assumed to require a number of data-files from either public or private clouds. For example, gene probes can be used to identify various infection agents such as bacteria, viruses, etc. The heavy computational task of aligning probes of a patient's DNA (private-data) with normal sequences (public-data) with various data sizes is the key to this process. Such files have different characteristics depends on their nature and could be either allowed for replication or not in the cloud. Files could be too big to replicate (big data), others might be small enough to be replicated but they cannot be replicated as they contain sensitive information (private data). To show the relationship between the execution time of a batch of jobs and the transfer time needed for their required data in hybrid cloud, we first model this problem as a bi-objective optimization problem, and then propose a Particle Swarm Optimization (PSO)-based approach, called here PSO-ParFnt, to find the relevant Pareto frontier. The results are promising and provide new insights into this complex problem.

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