ADS: Adaptive and Dynamic Scaling Mechanism for Multimedia Conferencing Services in the Cloud

TL;DR

This paper introduces ADS, an adaptive and dynamic scaling mechanism for cloud-based multimedia conferencing that efficiently manages resources while meeting QoS requirements and anticipating future demand.

Contribution

The paper presents a novel ILP-based formulation and heuristic for scalable resource allocation in multimedia conferencing services in the cloud.

Findings

ADS elastically scales conferencing services in simulations.

The heuristic outperforms greedy algorithms in resource efficiency.

ADS maintains QoS while optimizing resource usage.

Abstract

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.