ADS: Adaptive and Dynamic Scaling Mechanism for Multimedia Conferencing Services in the Cloud
This addresses scalability issues for cloud-based multimedia conferencing applications like online games and distance learning, though it appears incremental by improving on existing cloud solutions.
The paper tackles the problem of efficiently scaling multimedia conferencing services in the cloud as participant numbers fluctuate, proposing an Adaptive and Dynamic Scaling (ADS) mechanism that uses Integer Linear Programming and a heuristic to achieve cost-efficient scaling while meeting QoS requirements, with simulation results showing it outperforms a greedy algorithm in resource efficiency.
Multimedia conferencing is used extensively in a wide range of applications, such as online games and distance learning. These applications need to efficiently scale the conference size as the number of participants fluctuates. Cloud is a technology that addresses the scalability issue. However, the proposed cloud-based solutions have several shortcomings in considering the future demand of applications while meeting both Quality of Service (QoS) requirements and efficiency in resource usage. In this paper, we propose an Adaptive and Dynamic Scaling mechanism (ADS) for multimedia conferencing services in the cloud. This mechanism enables scalable and elastic resource allocation with respect to the number of participants. ADS produces a cost-efficient scaling schedule while considering the QoS requirements and the future demand of the conferencing service. We formulate the problem using Integer Linear Programming (ILP) and design a heuristic for it. Simulation results show that ADS mechanism elastically scales conferencing services. Moreover, the ADS heuristic is shown to outperform a greedy algorithm from a resource-efficiency perspective.