Resource Allocation Mechanism for Media Handling Services in Cloud Multimedia Conferencing
This addresses resource efficiency and scalability challenges for cloud-based multimedia conferencing applications like MMOGs and distance learning, but it appears incremental as it builds on existing cloud approaches.
The paper tackles the problem of inefficient resource usage and scalability in cloud multimedia conferencing by proposing a resource allocation mechanism that optimizes resource allocation and scales with participants while guaranteeing Quality of Service (QoS), with simulation results showing optimal resource allocation honoring end-to-end delay.
Multimedia conferencing is the conversational exchange of multimedia content between multiple parties. It has a wide range of applications (e.g., Massively Multiplayer Online Games (MMOGs) and distance learning). Media handling services (e.g., video mixing, transcoding, and compressing) are critical to multimedia conferencing. However, efficient resource usage and scalability still remain important challenges. Unfortunately, the cloud-based approaches proposed so far have several deficiencies in terms of efficiency in resource usage and scaling, while meeting Quality of Service (QoS) requirements. This paper proposes a solution which optimizes resource allocation and scales in terms of the number of participants while guaranteeing QoS. Moreover, our solution composes different media handling services to support the participants' demands. We formulate the resource allocation problem mathematically as an Integer Linear Programming (ILP) problem and design a heuristic for it. We evaluate our proposed solution for different numbers of participants and different participants' geographical distributions. Simulation results show that our resource allocation mechanism can compose the media handling services and allocate the required resources in an optimal manner while honoring the QoS in terms of end-to-end delay.