Scalable Resource Augmentation for Mobile Devices

This thesis focuses on scalability of a resource augmentation environment when a large number of mobile devices and multiple service nodes are present.To deal with congestion, a scanning method was proposed to get information on users' density in an area such that the service nodes and access points could be placed at strategic points.To lower communication overhead, a centralized broker-node architecture was proposed, which manages resource monitoring on behalf of all mobile devices.In the centralized architecture, mathematical models for the task scheduling problem in the local resources case and the mobile cloud computing case were proposed to optimally minimize the total energy consumption across all mobile devices.A generalized model for the task scheduling problem was proposed.The model optimally minimized the total energy and monetary cost when evaluated in two environments for mobile cloud computing, one using a local private cloud and the other using public clouds.The models found optimal solutions for the centralized task scheduling problems, and an improvement in the total costs was observed when offloading with optimization compared to when offloading without optimization using the centralized task scheduler.xiii 5.5 Two Linux containers representing a service node and a mobile device connected through ns-3 WiFi network. . . . . . . . . . . . . . . . . .5.6 Effect of number of servers in the serverApp() service on resource monitoring time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.7 (a) Comparison of resource monitoring time and scalability, (b) collisions in the WiFi channel, in baseline and broker scenarios. . . . . . .6.1 Graphical representation of the task scheduler model. . . . . . . . . .6.2 Total energy consumption across all mobile devices. . . . . . . . . . .7.1 Resource augmentation environment for MCC. . . . . . . . . . . . . .7.2 Total energy consumption across all mobile devices. . . . . . . . . . .7.3 Total energy consumption across all mobile devices. . . . . . . . . . .7.4 The effect of delay tolerance (λ m ) on the total energy consumption. .8.1 Resource augmentation environments for MCC. . . . . . . . . . . . .8.2 The effect of finite and infinite resources on the percentage saving in the total energy consumption when offloading with optimization. . . .8.3 The total energy consumption when offloading with and without optimization in RAE using a local private cloud. . . . . . . . . . . . . . .xiv 8.4 The total energy consumption and the total monetary cost when offloading with and without optimization in RAE using public clouds. .157 8.5 Percentage saving in the total energy consumption when offloading with optimization at different data sizes, in both RAEs. . . . . . . . .159 8.6 Selecting different cloud providers for data intensive tasks based on monetary costs in RAE using public clouds. . . . . . . . . . . . . . .161 8.7 The effect of delay tolerance on the total energy consumption when data sizes (in MB) are in a range U(0, 40). . . . . . . . . . . . . . . .1628.8 The effect on the total energy consumption, the total monetary cost, and the number of offloaded tasks, when only small or small, large, and xlarge VM instances are available in RAE using public clouds. . .163 xv constraints intrinsic to their size and weight [22].Consequently, the available computing power, memory capacity, or battery energy are not enough for resource intensive applications [95].Thus, mobile devices either cannot run these applications, or, even if able to run them, find that the required application fidelity cannot be achieved, and/or that the battery will not last as long compared to normal usage. ContributionsIn this research work, resource augmentation of mobile devices through task offloading is considered in an environment having a large number of mobile devices and multiple service nodes.In this environment, the objective is to investigate and reduce the congestion and communication overhead caused by the presence of and task scheduling by a large number of mobile devices.A scanning method is presented for the placement of service nodes and Access Points (APs) in an area according to the density distribution of the users.The aim of this approach is to reduce congestion created due to the presence of a large number of mobile devices.Further, a centralized architecture for a large RAE is proposed: (i) to reduce the communication overhead due to repeated resource monitoring performed by a large number of mobile devices, -Manjinder Nir and Ashraf Matrawy, "Centralized Management of Scalable Cyber Foraging Systems", in Proceedings of the 4th International Conference on

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