Revisiting Matrix Product on Master-Worker Platforms

This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous master-worker platforms. While matrix-product is well-understood for homogeneous 2D-arrays of processors (e.g., Cannon algorithm and ScaLAPACK outer product algorithm), there are three key hypotheses that render our work original and innovative: - Centralized data. We assume that all matrix files originate from, and must be returned to, the master. - Heterogeneous star-shaped platforms. We target fully heterogeneous platforms, where computational resources have different computing powers. - Limited memory. Because we investigate the parallelization of large problems, we cannot assume that full matrix panels can be stored in the worker memories and re-used for subsequent updates (as in ScaLAPACK). We have devised efficient algorithms for resource selection (deciding which workers to enroll) and communication ordering (both for input and result messages), and we report a set of numerical experiments on various platforms at Ecole Normale Superieure de Lyon and the University of Tennessee. However, we point out that in this first version of the report, experiments are limited to homogeneous platforms

Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions

Edge and Fog computing paradigms utilise distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of the fast-evolving IoT applications. Due to the fine-grained modularity of the microservices along with their independently deployable and scalable nature, MSA exhibits great potential in harnessing both Fog and Cloud resources to meet diverse QoS requirements of the IoT application services, thus giving rise to novel paradigms like Osmotic computing. However, efficient and scalable scheduling algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. To this end, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications scheduling in Edge and Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the scheduling problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey

MBA: A market-based approach to data allocation and migration for cloud database

With the coming shift to cloud computing, cloud database is emerging to provide database service over the Internet. In the cloud-based environment, data are distributed at internet scale and the system needs to handle a huge number of user queries simultaneously without delay. How data are distributed among the servers has a crucial impact on the query load distribution and the system response time. In this paper, we propose a market-based control method, called MBA, to achieve query load balance via reasonable data distribution. In MBA, database nodes are treated as traders in a market, and certain market rules are used to intelligently decide data allocation and migration. We built a prototype system and conducted extensive experiments. Experimental results show that the MBA method signicantly improves system performance in terms of average query response time and fairness

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