Maximizing Service Uptime of Smartphone-based Distributed Real-time and Embedded Systems

Abstract

This thesis presents, SmartDeploy, the deployment technique for maximizing service uptime in distributed applications over a network of smartphones. It takes into account the power consumption rate of the software components as a key factor affecting service uptime besides hardware resource constraints like memory, CPU, etc. The problem becomes more challenging with heterogeneity of devices and when system scale consists of hundreds of software components deployed on to hundreds of devices. The work suggests a hybrid deployment optimization technique by intelligently placing the software components onto the devices where they obtain maximum battery power and sufficient hardware resources like memory, CPU, etc. SmartDeploy provides a framework that can be strategized with the desired bin packing heuristic along with a strategizable framework to plug in the desired evolutionary algorithm so that a variation of a hybrid algorithm can be synthesized. To solve the service uptime maximization problem, SmartDeploy is strategized with the worst-fit bin packer which ensures that services are load balanced across the collection of smartphones used in the mission in a way that minimizes battery drain while also delivering the QoS. The evolutionary algorithm (particle swarm optimization or genetic algorithm) generates initial and evolved random vectors and evaluates them using a fitness function.