A resource allocation mechanism for video mixing as a cloud computing service in multimedia conferencing applications

Abstract

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). Many multimedia conferencing applications use video extensively, thus video mixing in conferencing settings is of critical importance. Cloud computing is a technology that can solve the scalability issue in multimedia conferencing, while bringing other benefits, such as, elasticity, efficient use of resources, rapid development, and introduction of new applications. However, proposed cloud-based multimedia conferencing approaches so far have several deficiencies when it comes to efficient resource usage while meeting Quality of Service (QoS) requirements. We propose a solution to optimize resource allocation for cloud-based video mixing service in multimedia conferencing applications, which can support scalability in terms of number of users, while guaranteeing QoS. We formulate the resource allocation problem mathematically as an Integer Linear Programming (ILP) problem and design a heuristic for it. Simulation results show that our resource allocation model can support more participants compared to the state-of-the-art, while honoring QoS, with respect to end-to-end delay.

Resource Allocation Mechanism for Media Handling Services in Cloud Multimedia Conferencing

“…As it is described in the related work section, to the best of our knowledge, CRAM is the first work on cloud-based resource allocation for media handling services that meets all the requirements of scalability, cost-efficiency, elasticity, and meeting the QoS. However, to compare the CRAM heuristic with other algorithms, we choose VMRA [5], as the baseline. The focus of the VMRA algorithm is minimizing the servers' resource cost while meeting the QoS requirements.…”

“…In fact, the farther two locations are, the higher is the network cost between them. Also, for the media handling time and required resources, we consider our prototype experience in [5]. The simulation parameters and settings are depicted in Table IV.…”

“…Besides efficient resource and network utilization, it caters to the QoS, with respect to media handling response time and network latency. This paper is an extension of our previous cloud-based resource allocation mechanism [5]. Our proposed mechanism in [5] considered conferencing applications which need video mixing.…”

“…This paper is an extension of our previous cloud-based resource allocation mechanism [5]. Our proposed mechanism in [5] considered conferencing applications which need video mixing. Functionalities of Media handling services, such as audio and video mixing and compressing, are of critical importance in conferencing applications.…”

“…We analyze the impact of participants' geographical distribution on the required This paper is an extended version of the paper published in CNSM2015, under compression rate for video streams. In addition, we take into account the servers' resource cost and the network cost in the objective function of our ILP formulation while these were not considered in [5]. Indeed, the cost has a critical impact of the number of participants and their geographical distributions when allocating the resource for media handling services in Cloudbased multimedia conferencing.…”