Configuration and deployment derivation strategies for distributed real-time and embedded systems

Abstract

Distributed real-time and embedded (DRE) systems are constructed by allocating software tasks to hardware. This allocation, called a deployment plan, must ensure that design constraints, such as quality of service (QoS) demands and resource requirements, are satisfied. Further, the financial cost and performance of these systems may differ greatly based on software allocation decisions, auto-scaling strategy, and execution schedule. This dissertation describes techniques for addressing the challenges of deriving DRE system configurations and deployments. First, we show how heuristic algorithms can be utilized to determine system deployments that meet QoS demands and resource requirements. Second, we use metaheuristic algorithms to optimize system-wide deployment properties. Third, we describe a Model-Driven Architecture (MDA) based methodology for constructing a DRE system configuration modeling tool. Fourth, we demonstrate a methodology for evolving DRE systems as new components become available. Next, we provide a technique for configuring virtual machine instances to create greener cloud-computing environments. Finally, we present a metric for assessing and increasing performance gains due to processor caching.