The Convergence of Realistic Distributed Load-Balancing Algorithms

Grids are an emerging infrastructure providing distributed access to computational and storage resources. Handling many incoming requests at the same time and distributing the workload efficiently is a challenge which load balancing algorithms address. Current load balancing implementations for the Grid are central in nature and therefore prone to the single point of failure problem. This paper introduces two distributed artificial life-inspired load balancing algorithms using Ant Colony Optimization and Particle Swarm Optimization. Distributed load balancing stands out as a robust algorithm in regard to any topology changes in the network. The implementation details are given and evaluation results show the efficiency of the two distributed load balancing algorithms.

To solve the load imbalance problem of a solution-adaptive finite element application program on a distributed memory multicomputer, nodes of a refined finite element graph can be remapped to processors or load of a refined finite element graph can be redistributed based on the current load of each processor. For the former case, remapping can be performed by some fast mapping algorithms. For the latter case, a load-balancing algorithm can be applied to balance the computational load of each processor. In this paper, three tree-based parallel load-balancing methods, the MCSTLB method, the BTLB method, and the CBTLB method, were proposed to deal with the load imbalance problems of solution-adaptive finite element application programs. To evaluate the performance of the proposed methods, we have implemented those methods along with three mapping methods, the AE/ORB method, the AE/MC method, and the MLkP method, on an SP2 parallel machine. Three criteria, the execution time of mapping/load-balancing methods, the execution time of a solution-adaptive finite element application program under different mapping/load-balancing methods, and the speedups achieved by mapping/load-balancing methods for a solution-adaptive finite element application program, are used for the performance evaluation. The experimental results show that 1) if the initial mapping is performed by a mapping method and the same mapping method and load-balancing methods were used in each refinement to balance the load of processors, the execution time of an application program under a load-balancing method is always shorter than that of the mapping method, and 2) the execution time of an application program under the CBTLB method is shorter than that of the BTLB method and the MCSTLB method.

This paper discusses the multiblock (MB)-split load bal ancing tool for structured-grid multiblock Navier-Stokes solvers running on parallel computers of MIMD type. Many load-balancing algorithms and tools are available for com putational fluid dynamics solvers using unstructured meshes. Most load-balancing algorithms for block-struc tured meshes only aim at distributing the initial blocks of the mesh over the processors, without splitting any blocks. A more flexible load-balancing technique uses domain decomposition and splits blocks into smaller blocks. One of the problems with this approach is the complexity of updating block connectivity boundary conditions. The three load-balancing algorithms are implemented and some results for realistic test cases are presented.

Novel distributed load balancing algorithms in cloud storage

Workload information management and resource management are two key aspects in grid computing to provide the better services to grid environment users. Grid computing also faced other challenging areas like heterogeneous nature of resources, huge number of computing elements, independency of computing resources, different processing capacities of the nodes, different types of load conditions, overloading of jobs at computing nodes, underutilized of resources, etc. A distributed load balancing algorithm is proposed that can handle any kind of grid structure. The proposed algorithm is divided into two steps: To reduce the time taken for executing the jobs or we can say reduce the response time and communication cost between transferring the jobs from one computing node to another in a grid architecture. In proposed algorithm it is shown that the load will be primarily balanced at local level to reduce the communication cost.

The paper presents a new approach to load balancing for large distributed systems. The approach characterizes the global state uncertainty inherent in a large distributed system in terms of the fuzzy set theory and presents a fuzzy-based distributed load balancing algorithm that explicitly reflects the effect of the uncertainty in the decision making process. The notion of linguistic variables is used to model state variables that have imprecise and uncertain state values and fuzzy control to estimate the amount of consistency relaxation from the states of uncertainty sources. A fuzzy-based consistency model provides a mechanism that allows each node to make flexible scheduling and state update decisions based on the estimated degree of consistency relaxation. Simulation results show that the proposed algorithm yields a better performance, substantially reduces the number of messages required, and generally transfers fewer tasks, compared to two conventional distributed load balancing algorithms. >

Rapid evaluation of potentials in large physical systems plays a crucial role in several fields and has been an intensely studied topic on parallel computers. Computational methods and associated parallel algorithms tend to vary depending on the potential being computed. Real applications often involve multiple potentials, leading to increased complexity and the need to strike a balance between competing data distribution strategies, ultimately resulting in low parallel efficiencies. In this paper, we present a parallel accelerated Cartesian expansion (PACE) method that enables rapid evaluation of multiple forms of potentials using a common Fast Multipole Method (FMM) type framework. In addition, our framework localizes potential dependent computations to one particular operator, allowing reuse of much of the computation across different potentials. We present an implicitly load balanced and communication efficient parallel algorithm and show that it can integrate multiple potentials, multiple time steps and address dynamically evolving physical systems. We demonstrate the applicability of the method by solving particle dynamics simulations using both long-range and Lennard-Jones potentials with parallel efficiencies of 97% on 512 to 1024 processors.

A multiprocessor system is a computer with two or more central processing units (CPUs) with each one sharing the common main memory as well as the peripherals. Multiprocessor system is either homogeneous or heterogeneous system. A homogeneous system is a cluster of processors joined to a high speed network for accomplishing the required task; also it is defined as parallel computing system. Homogeneous is a technique of parallel computing system. A heterogeneous system can be defined as the interconnection of a number of processors, having dissimilar computational speed. Load balance is a method of distributing work between the processors fairly in order to get optimal response time, resource utilization, and throughput. Load balancing is either static or dynamic. In static load balancing, work is distributed among all processors before the execution of the algorithm. In dynamic load balancing, work is distributed among all processors during execution of the algorithm. So problems arise when it cannot statistically divide the tasks among the processors. To use multiprocessor systems efficiently, several load balancing algorithms have been adopted widely. This paper proposes an efficient load balance algorithm which addresses common overheads that may decrease the efficiency of a multiprocessor system. Such overheads are synchronization, data communication, response time, and throughput.

Agent-based simulation of large multicellular biological systems has become a viable option owing to affordable parallel computers, such as Beowulf-style clusters. We describe a scalable modular software platform that (i) provides for an easy integration of different solvers computing internal dynamics of the cells, and (ii) dynamically adapts to the changing loads on the cluster nodes using a load balancing algorithm. Simulations of systems of about 100,000 bacterial cells have been shown to be feasible using the platform.KeywordsBiological CellNode PerformanceSignal Transduction NetworkBrownian Motion ModelList Schedule AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

— A deterministic dynamic nonlinear time-delay system is developed to model load balancing in a cluster of computer nodes used for parallel computations. The model is shown to be self consistent in that the queue lengths cannot go negative and the total number of tasks in all the queues and the network are conserved (i.e., load balancing can neither create nor lose tasks). Further, it is shown that using the proposed load balancing algorithms, the system is stable in the sense of Lyapunov. Experimental results are presented and compared with the predicted results from the analytical model. In particular, simulations of the models are compared with an experimental implementation of the load balancing algorithm on a distributed computing network.

A multi-resolution smoothed particle hydrodynamics with multi-GPUs acceleration for three-dimensional fluid-structure interaction problems

We consider the problem of diffusion-based load balancing on a distributed network with n processors. If the load is arbitrarily divisible, then the convergence is fairly well captured in terms of the second largest eigenvalue of the diffusion matrix. As for many applications load can not be arbitrarily divided, we consider a model where load consists of indivisible, unit-size tokens. Quantifying by how much this integrality assumption worsens the efficiency of load balancing algorithms is a natural question which has been posed by many authors [9, 15, 16, 6, 19, 17]. In this paper we show essentially that discrete load balancing is almost as easy as continuous load balancing. More precisely, we present a fully distributed, randomized algorithm for discrete load balancing that balances the load up to an additive constant error on any graph in time O(log (Kn)/(1-λ2)), where K is the initial imbalance and λ2 is the second largest eigenvalue of the diffusion matrix. This improves and tightens a result of Elsasser, Monien, Schamberger (2006) who proved a runtime bound of O((log (K) + (log n)2) / (1-λ2)). We also develop a load balancing algorithm based on routing that achieves a runtime of O(D ⋅ log n), where D is the diameter of the graph.

Mobile Worldwide Interoperability for Microwave Access (Mobile WiMAX) is a wireless access technology based on IEEE 802.16e. The two most important concepts are introduced in the MAC layer of Mobile WiMAX networks, handover and load balancing. In this paper, we have focused on balancing the load of the system, using handover mechanisms suggested by mobile WiMAX. The proposed algorithm, WQLB, concentrates on controlling the system load and keeping it in a balanced state by using a handover process. Also, the algorithm is designed to utilize resources efficiently, supporting QoS. As load definition is an essential criterion in load balancing algorithms, we have suggested a new load model for mobile WiMAX networks. Simulation results show efficient resource consumption and a higher degree of QoS support through the entire network. In general, as the proposed load balancing algorithm is QoS aware, it can be a very e?cient way to release resources in the network and a fairness load distribution takes place.

Load-balancing problems arise in many applications, but, most importantly, they play a special role in the operation of parallel and distributed computing systems. Load-balancing deals with partitioning a program into smaller tasks that can be executed concurrently and mapping each of these tasks to a computational resource such as a processor (e.g., in a multiprocessor system) or a computer (e.g., in a computer network). By developing strategies that can map these tasks to processors in a way that balances out the load, the total processing time will be reduced with improved processor utilization. Most of the research on load-balancing focused on static scenarios that, in most of the cases, employ heuristic methods. However, genetic algorithms have gained immense popularity over the last few years as a robust and easily adaptable search technique. The work proposed here investigates how a genetic algorithm can be employed to solve the dynamic load-balancing problem. A dynamic load-balancing algorithm is developed whereby optimal or near-optimal task allocations can "evolve" during the operation of the parallel computing system. The algorithm considers other load-balancing issues such as threshold policies, information exchange criteria, and interprocessor communication. The effects of these and other issues on the success of the genetic-based load-balancing algorithm as compared with the first-fit heuristic are outlined.

Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies. Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information.