Transparent and adaptive application partitioning using mobile objects

The dynamic nature and heterogeneity of modern execution environments such as mobile, ubiquitous, and grid computing, present major challenges for the development and efficient execution of the applications targeted for these environments. In particular, applications tailored to run in a specific environment will show different and most likely sub-optimal behaviour when executed on a different and/or dynamic environment. Consequently, there has been growing interests in the area of application adaptation which aims to enable applications to cope with the varying execution environments. Adaptive application partitioning, a specific form of non-functional adaptation involving distribution of mobile objects across multiple host machines, is of particular interest to this thesis due to the diversity of its uses. In this approach, certain runtime information (known as context) is used to allow an object-oriented application to adaptively (re)adjust the placement of its objects during its execution, for purposes such as improving application performance and reliability as well as balancing resource utilisation across machines. Promoting the adoption of such adaptation requires a process that requires minimal human involvement in both the execution and the development of the relevant application. These challenges establish the main goals and contributions of this work, which include: 1) Proposing an effective application partitioning solution via the adoption of a decentralised adaptation strategy known as local adaptation. 2) Enabling adaptive application partitioning which does not require human intervention, through automatic collection of required information/context. 3) Proposing a solution for transparently injecting the required adaptation functionality into regular object-oriented applications allowing significant reduction of the associated development cost/effort. The proposed solutions have been implemented in a Java-based adaptation framework called MobJeX. This implementation, which was used as a test bed for the empirical experiments undertaken in this study, can be used to facilitate future research relevant to this particular study

Improving efficiency, scalability and efficacy of adaptive computation offloading in pervasive computing environments

As computing becomes more mobile and pervasive, there is a growing demand for increasingly rich, and therefore more computationally heavy, applications to run in mobile spaces. However, there exists a disparity between mobile platforms and the desktop environments upon which computationally heavy applications have traditionally run, which is likely to persist as both domains evolve at a competing pace. Consequently, an active research area is Adaptive Computation Offloading or cyber foraging that dynamically distributes application functionality to available peer devices according to resource availability and application behaviour. Integral to any offloading strategy is an adaptive decision making algorithm that computes the optimal placement of application components to remote devices based on changing environmental context. As this decision is typically computed by constrained devices and may occur frequently in dynamic environments, such algorithms should be both resource efficient and yield efficacious adaptation results. However, existing adaptive offloading approaches incur a number of overheads, which limit their applicability in mobile and pervasive spaces. This thesis is concerned with improving upon these limitations by specifically focusing on the efficiency, scalability and efficacy aspects of two major sub processes of adaptation: 1) Adaptive Candidate Device Selection and 2) Adaptive Object Topology Computation. To this end, three novel approaches are proposed. Firstly, a distributed approach to candidate device selection, which reduces the need to communicate collaboration metrics, and allows for the partial distribution of adaptation decision-making, is proposed. The approach is shown to reduce network consumption by over 90% and power consumption by as much as 96%, while maintaining linear memory complexity in contrast to the quadratic complexity of an existing approach. Hence, the approach presents a more efficient and scalable alternative for candidate device selection in mobile and pervasive environments. Secondly, with regards to the efficacy of adaptive object topology computation, a new type of adaptation granularity that combines the efficacy of fine-grained adaptation with the efficiency of coarse level approaches is proposed. The approach is shown to improve the efficacy of adaptation decisions by reducing network overheads by a minimum of 17% to as much 99%, while maintaining comparable decision making efficiency to coarse level adaptation. Thirdly, with regards to efficiency and scalability of object topology computation, a novel distributed approach to computing adaptation decisions is proposed, in which each device maintains a distributed local application sub-graph, consisting only of components in its own memory space. The approach is shown to reduce network cost by 100%, collaboration-wide memory cost by between 37% and 50%, battery usage by between 63% and 93%, and adaptation time by between 19% and 98%. Lastly, since improving the utility of adaptation in mobile and pervasive environments requires the simultaneous improvement of its sub processes, an adaptation engine, which consolidates the individual approaches presented above, is proposed. The consolidated adaptation engine is shown to improve the overall efficiency, scalability and efficacy of adaptation under a varying range of environmental conditions, which simulate dynamic and heterogeneous mobile environments

On the Parallel Programmability of JavaSymphony for Multi-cores and Clusters

This paper explains the programming aspects of a promising Java-based programming and execution framework called JavaSymphony. JavaSymphony provides unified high-level programming constructs for applications related to shared, distributed, hybrid memory parallel computers, and co-processors accelerators. JavaSymphony applications can be executed on multi-/many-core conventional and data-parallel architectures. JavaSymphony is based on the concept of dynamic virtual architectures, which allows programmers to define a hierarchical structure of the underlying computing resources and to control load-balancing and task-locality. In addition to GPU support, JavaSymphony provides a multi-core aware scheduling mechanism capable of mapping parallel applications on large multi-core machines and heterogeneous clusters. Several real applications and benchmarks (on modern multi-core computers, heterogeneous clusters, and machines consisting of a combination of different multi-core CPU and GPU devices) have been used to evaluate the performance. The results demonstrate that the JavaSymphony outperforms the Java implementations, as well as other modern alternative solutions