Cost-driven autonomous mobility

Developments in distributed system technology facilitate the sharing of resources, even at a global level. This thesis explores sharing computational resources using mobile computations, agents, and autonomic techniques. We propose autonomous mobile programs (AMPs) which are aware of their resource needs and sensitive to the environment in which they execute. AMPs periodically use a cost model to decide where to execute in a network. Unusually this form of autonomous mobility affects only where the program executes and not what it does. We present a generic AMP cost modei, together with a validated instantiation and comparative performance results for four AMPs. We demonstrate that AMPs are able to dynamically relocate themselves to minimise execution time in the presence of varying network resources. Collections of AMPs effectively perform decentralised dynamic load balancing. Experiments on small LANs show that collections of AMPs quickly obtain and maintain optimal or near-optimal ball1nce. The advantages of our decentralised approach are that it has the potential to scale to very large and dynamic networks, and to achieve improved balance, and offers guarantees to limit overheads under reasonable assumptions. In an autonomous mobile program, the program must contain explicit control of self-aware mobile coordination. To encapsulate this for common patterns of computation over collections, autonomous mobility skeletons (AMSs) are proposed. These are akin to algorithmic skeletons in being polymorphic higher order functions, but where algorithmic skeletons abstract over parallel coordination, AMSs abstract over autonomous mobile coordination. AMS cost models have been built over collection iterations. The automap, autofold and Autolterator AMSs are presented, together with performance measurements for Jocaml, Java Voyager, and JavaGo implementations on LANs. An AMS considers only the cost of the current collective computation, but it is more useful to know the cost of the entire program. We have extended our AMS cost models to be parameterised on the cost of the remainder of the program. A cost calculus to estimate the costs for the remainder of a computation at arbitrary points has been built. An automatic Jocaml cost analyser based oil the calculus produces cost equations parameterised on program variables in context, and may be used to find both cost in higher-order functions and the cost for the remainder of the program. Costed autonomous mobility skeletons (CAMSs) have been built, which not only encapsulate common patterns of autonomous mobility but take additional cost parameters to provide costs for the remainder of the program. Potential performance improvements are assessed by comparing CAMS to AMS programs. The results show that CAMS programs perform more effectively than AMS programs, because they have more accurate cost information. Hence a CAMS program may move to a faster location when the corresponding AMS program does not.EThOS - Electronic Theses Online ServiceGBUnited Kingdo