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.