Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms

By Jia Yu, Rajkumar Buyya. Scientific Programming, 2006

423 citations recorded by the source corpus.

Grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users' QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.

Subfields: Computer Networks and Communications, Information Systems and Management, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/c5951773fd6dcd9b387c6c758385472bef830162

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In Computer Networks and Communications

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In Ocean Engineering

Citations in the corpus

Auto-scaling to minimize cost and meet application deadlines in cloud workflows (2011, positive): “Yu and Buyya [16][17][18] introduce deadline assignment ideas and use genetic algorithms to find optimal scheduling plans by defining the fitness function as the ratio of performance over cost.”
A market-oriented hierarchical scheduling strategy in cloud workflow systems (2011, methodology): “In recent years, GA has been adopted to address large complex scheduling problems and proved to be quite effective in many distributed and dynamic resource environments, such as parallel processor systems and grid workflow systems [27, 28, 30, 32, 49].”
A review of metaheuristic scheduling techniques in cloud computing (2015, methodology): “As [43,45,48,52] applied GA to solve workflow scheduling problem, precedence of tasks was also considered while generating initial population.”
Cloud auto-scaling with deadline and budget constraints (2010, methodology): “[45] present a workflow scheduling technique using genetic algorithms.”
HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds (2011, background): “Yu and Buyya present in [34] a study showing how to schedule scientific workflow applications with budget and deadline constraints onto computational grids using basic genetic algorithms.”