PROJECT TITLE:

Innovative Schemes for Resource Allocation in the Cloud for Media Streaming Applications - 2015

ABSTRACT:

Media streaming applications have recently attracted a massive variety of users within the Internet. With the advent of these bandwidth-intensive applications, it's economically inefficient to provide streaming distribution with guaranteed QoS relying solely on central resources at a media content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., Video on Demand (VoD) suppliers) will use to obtain streaming resources that match the demand. Media content suppliers are charged for the number of resources allocated (reserved) within the cloud. Most of the present cloud providers employ a pricing model for the reserved resources that is based on non-linear time-discount tariffs (e.g., Amazon CloudFront and Amazon EC2). Such a pricing scheme offers discount rates depending non-linearly on the period of your time during which the resources are reserved in the cloud. During this case, an open downside is to make your mind up on both the right amount of resources reserved in the cloud, and their reservation time such that the financial price on the media content supplier is minimized. We tend to propose a easy-easy to implement-algorithm for resource reservation that maximally exploits discounted rates offered within the tariffs, whereas guaranteeing that sufficient resources are reserved within the cloud. Based mostly on the prediction of demand for streaming capacity, our algorithm is rigorously designed to cut back the danger of constructing wrong resource allocation decisions. The results of our numerical evaluations and simulations show that the proposed algorithm significantly reduces the monetary cost of resource allocations in the cloud as compared to alternative standard schemes.


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