Impact of Different Auto-Scaling Strategies on Adaptive Mobile Cloud Computing Systems

Mobile Cloud Computing (MCC) is an emerging paradigm aiming to elastically extend the range of resource-intensive tasks supported by mobile devices, leveraging upon broadband connectivity and cloud-based resources. In literature, almost all MCC models focus on mobile devices, considering the Cloud as a system endowed with unlimited resources. In this paper, we illustrate a novel MCC model characterized by the presence of adaptive loops, i.e., feedback interactions between the mobile device and the Cloud, with the purpose to enforce adaptive behavior on both sides. Indeed, the Cloud adapts its resource allocation (number of activated virtual machines) to the workload provided by mobile devices. On the other hand, feedback from the Cloud allows mobile devices to improve offloading decisions. The performance of the whole system is heavily affected by the auto-scaling strategy adopted by the Cloud. By means of simulations, we have evaluated the impact of two very different auto-scaling strategies. Quantitative results are reported and discussed.