Abstract
Cloud computing delivers computing services over virtualized networks to many end-users. Virtualized networks are characterized by such attributes as on-demand self-service, broad network access, resource pooling, rapid and elastic resource provisioning and metered services at various qualities. Cloud networks provide data as well as multimedia and video services. They are classified into private cloud networks, public cloud networks and hybrid cloud networks. Linear video services include broadcasting and in-stream video that may be viewed in a video player whereas non-linear video services include a combination of in-stream video with on-demand services, which are originated from distributed servers in the network and deliver interactive and pay-per view content. Furthermore heterogeneous delivery networks that include fixed and mobile internet infrastructures require that adaptive video streaming should be carried out at network boundaries based on such protocols as HTTP Live Streaming (HLS). Distributed processing of nonlinear video services in cloud environments is addressed in the present work by defining Distributed Acyclic Graphs (DAG) models for multimedia processes executed by a set of non-locally confined virtual machines. A novel discrete multivalue Particle Swarm Optimization (PSO) algorithm is proposed in order to optimize task scheduling and workflow. Numerical simulations regarding such measures as Schedule-Length-Ratio (SLR) and Speedup are given for novel fat-tree cloud architectures.
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Stephanakis, I.M., Chochliouros, I.P., Caridakis, G., Kollias, S. (2013). A Particle Swarm Optimization (PSO) Model for Scheduling Nonlinear Multimedia Services in Multicommodity Fat-Tree Cloud Networks. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41016-1_28
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DOI: https://doi.org/10.1007/978-3-642-41016-1_28
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