An Improved dynamic Load Balancing Algorithm applied to a Cafeteria System in a University Campus

Load-balancing algorithms play a key role in improving the performance of practical distributed systems that consist of heterogeneous nodes. The performance of any load-balancing algorithms and its convergence-rate is affected by the structural factors of the network that executes the algorithm. The performance deteriorated as the number of system nodes, the network-diameter, the communication-overhead increased. Moreover, additional technical-factors of the algorithm itself significantly affect the performance of rebalancing the load among nodes. Therefore, this paper proposes an approach that improves the performance of load-balancing algorithms by considering the load-balancing technical-factors and the structure of the network executes the algorithm. We applied the proposed method to a cafeteria system in a university campus and compared our approach with two significant methods presented in the literature. Results indicate that our approach considerably outperformed the original neighborhood approach and the nearest neighbor approach in terms of response time, throughput, communication overhead, and movements cost

Recent Advances in Fully Dynamic Graph Algorithms

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms

Doctor of Philosophy

dissertationSolutions to Partial Di erential Equations (PDEs) are often computed by discretizing the domain into a collection of computational elements referred to as a mesh. This solution is an approximation with an error that decreases as the mesh spacing decreases. However, decreasing the mesh spacing also increases the computational requirements. Adaptive mesh re nement (AMR) attempts to reduce the error while limiting the increase in computational requirements by re ning the mesh locally in regions of the domain that have large error while maintaining a coarse mesh in other portions of the domain. This approach often provides a solution that is as accurate as that obtained from a much larger xed mesh simulation, thus saving on both computational time and memory. However, historically, these AMR operations often limit the overall scalability of the application. Adapting the mesh at runtime necessitates scalable regridding and load balancing algorithms. This dissertation analyzes the performance bottlenecks for a widely used regridding algorithm and presents two new algorithms which exhibit ideal scalability. In addition, a scalable space- lling curve generation algorithm for dynamic load balancing is also presented. The performance of these algorithms is analyzed by determining their theoretical complexity, deriving performance models, and comparing the observed performance to those performance models. The models are then used to predict performance on larger numbers of processors. This analysis demonstrates the necessity of these algorithms at larger numbers of processors. This dissertation also investigates methods to more accurately predict workloads based on measurements taken at runtime. While the methods used are not new, the application of these methods to the load balancing process is. These methods are shown to be highly accurate and able to predict the workload within 3% error. By improving the accuracy of these estimations, the load imbalance of the simulation can be reduced, thereby increasing the overall performance

Decentralized load balancing in heterogeneous computational grids

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research

Runtime support for load balancing of parallel adaptive and irregular applications

Applications critical to today\u27s engineering research often must make use of the increased memory and processing power of a parallel machine. While advances in architecture design are leading to more and more powerful parallel systems, the software tools needed to realize their full potential are in a much less advanced state. In particular, efficient, robust, and high-performance runtime support software is critical in the area of dynamic load balancing. While the load balancing of loosely synchronous codes, such as field solvers, has been studied extensively for the past 15 years, there exists a class of problems, known as asynchronous and highly adaptive , for which the dynamic load balancing problem remains open. as we discuss, characteristics of this class of problems render compile-time or static analysis of little benefit, and complicate the dynamic load balancing task immensely.;We make two contributions to this area of research. The first is the design and development of a runtime software toolkit, known as the Parallel Runtime Environment for Multi-computer Applications, or PREMA, which provides interprocessor communication, a global namespace, a framework for the implementation of customized scheduling policies, and several such policies which are prevalent in the load balancing literature. The PREMA system is designed to support coarse-grained domain decompositions with the goals of portability, flexibility, and maintainability in mind, so that developers will quickly feel comfortable incorporating it into existing codes and developing new codes which make use of its functionality. We demonstrate that the programming model and implementation are efficient and lead to the development of robust and high-performance applications.;Our second contribution is in the area of performance modeling. In order to make the most effective use of the PREMA runtime software, certain parameters governing its execution must be set off-line. Optimal values for these parameters may be determined through repeated executions of the target application; however, this is not always possible, particularly in large-scale environments and long-running applications. We present an analytic model that allows the user to quickly and inexpensively predict application performance and fine-tune applications built on the PREMA platform

Um mecanismo para distribuição de carga em ambientes virtuais de computação maciçamente paralela

Orientador: Marco Aurelio Amaral HenriquesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad