1 research outputs found
Resource allocation in the cloud for video-on-demand applications using multiple cloud service providers
Abstract
Video-on-demand (VoD) applications have become extensively used nowadays. YouTube is one of the most extensively
used VoD application. These applications are used for various purposes like entertainment, education, media, etc., of all
age groups. Earlier, these applications were supported by private data centers and application servers. Sufficient infrastructure had to be bought and maintained, to support the demand even during unexpected peak times. This approach caused
huge loss of resources when the demand is normal as a large portion of the resources remained idle. To overcome this, VoD
application providers moved to the cloud, to host their video content’s. This approach reduced the wastage of resources and
the maintenance cost of the VoD application provider. The problem is to determine the number of resources to handle the
demand while maintaining QoS for every instance. We have designed two algorithms in this paper, namely the multiple
cloud resource allocation (MCRA) algorithm and the hybrid MCRA algorithm. Most of the cloud service providers (CSPs)
basically provide two types of resource allocation schemes: (i) the reservation scheme and (ii) the on-demand scheme. The
reservation scheme provides time-based tariff prices, where the discount is provided for the resources depending on their
quantity and reservation time. This scheme is used in the MCRA algorithm to reduce the cost of the VoD application
provider. In Hybrid MCRA algorithm both the reservation scheme and on-demand scheme are implemented, to overcome
the drawbacks of the MCRA algorithm which are under-subscription and over-subscription. We have analyzed both the
algorithms in terms of cost and allocation of resources. These algorithms can help allocate resources in of cloud for VoD
applications in a cost-effective way and at the same time not compromise on the QoS of the video content