A Distance-Heuristic Tree Building Approach in Application Layer Multicast

In the application layer multicast (ALM), clustering nearby nodes can effectively improve the multicast performance. However, it is difficult for the ALM solution to quickly and accurately position the newcomer, because group members have no direct knowledge of underlying network topology. Additionally, ALM delivery trees with different performances are built when group members join the group in different join sequences. To alleviate the above problems, this paper proposes a distance-heuristic tree building protocol (called DHTB). DHTB uses our proposed distance-constrained cluster model and close-member-first-receive (CF) rule. In the model, most nearby nodes are grouped into some distance-constrained clusters, with little cluster organization and maintenance overhead. The CF rule arranges or rearranges the locations of group members according to related distances, and effectively positions the newcomer with the help of on-demand landmarks. Both the distance-constrained cluster model and CF rule are distance-heuristic. Therefore DHTB can alleviate the join sequence problem, and build the ALM tree with desirable performance

Service Quality Assessment for Cloud-based Distributed Data Services

The issue of less-than-100% reliability and trust-worthiness of third-party controlled cloud components (e.g., IaaS and SaaS components from different vendors) may lead to laxity in the QoS guarantees offered by a service-support system S to various applications. An example of S is a replicated data service to handle customer queries with fault-tolerance and performance goals. QoS laxity (i.e., SLA violations) may be inadvertent: say, due to the inability of system designers to model the impact of sub-system behaviors onto a deliverable QoS. Sometimes, QoS laxity may even be intentional: say, to reap revenue-oriented benefits by cheating on resource allocations and/or excessive statistical-sharing of system resources (e.g., VM cycles, number of servers). Our goal is to assess how well the internal mechanisms of S are geared to offer a required level of service to the applications. We use computational models of S to determine the optimal feasible resource schedules and verify how close is the actual system behavior to a model-computed \u27gold-standard\u27. Our QoS assessment methods allow comparing different service vendors (possibly with different business policies) in terms of canonical properties: such as elasticity, linearity, isolation, and fairness (analogical to a comparative rating of restaurants). Case studies of cloud-based distributed applications are described to illustrate our QoS assessment methods. Specific systems studied in the thesis are: i) replicated data services where the servers may be hosted on multiple data-centers for fault-tolerance and performance reasons; and ii) content delivery networks to geographically distributed clients where the content data caches may reside on different data-centers. The methods studied in the thesis are useful in various contexts of QoS management and self-configurations in large-scale cloud-based distributed systems that are inherently complex due to size, diversity, and environment dynamicity

Acceleration of Computational Geometry Algorithms for High Performance Computing Based Geo-Spatial Big Data Analysis

Geo-Spatial computing and data analysis is the branch of computer science that deals with real world location-based data. Computational geometry algorithms are algorithms that process geometry/shapes and is one of the pillars of geo-spatial computing. Real world map and location-based data can be huge in size and the data structures used to process them extremely big leading to huge computational costs. Furthermore, Geo-Spatial datasets are growing on all V’s (Volume, Variety, Value, etc.) and are becoming larger and more complex to process in-turn demanding more computational resources. High Performance Computing is a way to breakdown the problem in ways that it can run in parallel on big computers with massive processing power and hence reduce the computing time delivering the same results but much faster.This dissertation explores different techniques to accelerate the processing of computational geometry algorithms and geo-spatial computing like using Many-core Graphics Processing Units (GPU), Multi-core Central Processing Units (CPU), Multi-node setup with Message Passing Interface (MPI), Cache optimizations, Memory and Communication optimizations, load balancing, Algorithmic Modifications, Directive based parallelization with OpenMP or OpenACC and Vectorization with compiler intrinsic (AVX). This dissertation has applied at least one of the mentioned techniques to the following problems. Novel method to parallelize plane sweep based geometric intersection for GPU with directives is presented. Parallelization of plane sweep based Voronoi construction, parallelization of Segment tree construction, Segment tree queries and Segment tree-based operations has been presented. Spatial autocorrelation, computation of getis-ord hotspots are also presented. Acceleration performance and speedup results are presented in each corresponding chapter

Running parallel applications on a heterogeneous environment with accessible development practices and automatic scalability

Grid computing makes it possible to gather large quantities of resources to work on a problem. In order to exploit this potential, a framework that presents the resources to the user programmer in a form that maintains productivity is necessary. The framework must not only provide accessible development, but it must make efficient use of the resources. The Seeds framework is proposed. It uses the current Grid and distributed computing middleware to provide a parallel programming environment to a wider community of programmers. The framework was used to investigate the feasibility of scaling skeleton/pattern parallel programming into Grid computing. The research accomplished two goals: it made parallel programming on the Grid more accessible to domain­specific programmers, and it made parallel programs scale on a heterogeneous resource environ­ ment. Programming is made easier to the programmer by using skeleton and pat­ tern­based programming approaches that effectively isolate the program from the envi­ ronment. To extend the pattern approach, the pattern adder operator is proposed, imple­ mented and tested. The results show the pattern operator can reduce the number of lines of code when compared with an MPJ­Express implementation for a stencil algorithm while having an overhead of at most ten microseconds per iteration. The research in scal­ ability involved adapting existing load­balancing techniques to skeletons and patterns re­ quiring little additional configuration on the part of the programmer. The hierarchical de­ pendency concept is proposed as well, which uses a streamed data flow programming model. The concept introduces data flow computation hibernation and dependencies that can split to accommodate additional processors. The results from implementing skeleton/patterns on hierarchical dependencies show an 18.23% increase in code is neces­ sary to enable automatic scalability. The concept can increase speedup depending on the algorithm and grain size

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model