Maximizing Service Uptime of Smartphone-Based Distributed Real-Time and Embedded Systems

Smart phones are starting to find use in mission critical applications, such as search-and-rescue operations, wherein the mission capabilities are realized by deploying a collaborating set of services across a group of smart phones involved in the mission. Since these missions are deployed in environments where replenishing resources, such as smart phone batteries, is hard, it is necessary to maximize the lifespan of the mission while also maintaining its real-time quality of service (QoS) requirements. To address these requirements, this paper presents a deployment framework called Smart Deploy, which integrates bin packing heuristics with evolutionary algorithms to produce near-optimal deployment solutions that are computationally inexpensive to compute for maximizing the lifespan of smart phone-based mission critical applications. The paper evaluates the merits of deployments produced by Smart Deploy for a search-and-rescue mission comprising a heterogeneous mix of smart phones by integrating a worst-fit bin packing heuristic with particle swarm optimization and genetic algorithm. Results of our experiments indicate that the missions deployed using Smart Deploy have a lifespan that is 20% to 162% greater than those deployed using just the bin packing heuristic or evolutionary algorithms. Although Smart Deploy is slightly slower than the other algorithms, the slower speed is acceptable for offline computations of deployment.