2019/12/18 by Mutaz Barika, Saurabh Garg, Barika, Mutaz +5
Computer Science · #Cloud Computing and Resource Management #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1912.08392
openalex publication_date 2019/12/18 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Big data processing applications are becoming more and more complex. They are\nno more monolithic in nature but instead they are composed of decoupled\nanalytical processes in the form of a workflow. One type of such workflow\napplications is stream workflow application, which integrates multiple\nstreaming big data applications to support decision making. Each analytical\ncomponent of these applications runs continuously and processes data streams\nwhose velocity will depend on several factors such as network bandwidth and\nprocessing rate of parent analytical component. As a consequence, the execution\nof these applications on cloud environments requires advanced scheduling\ntechniques that adhere to end user's requirements in terms of data processing\nand deadline for decision making. In this paper, we propose two Multicloud\nscheduling and resource allocation techniques for efficient execution of stream\nworkflow applications on Multicloud environments while adhering to workflow\napplication and user performance requirements and reducing execution cost.\nResults showed that the proposed genetic algorithm is an adequate and effective\nfor all experiments.\n