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Rapid growth of internet and other technologies seems more challenging to develop the high speed networks with powerful capabilities in lower computational cost. To cover the above problem grid computing has emerged rapidly. The previous technologies such as distributed computing cluster computing, parallel computing, etc., facing the problem of space utilization. Using Grid Computing technologies...
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...
A load balanced cloud system can bring substantial performance improvement. This can be achieved by making decisions of Virtual Machine (VM) allocation at new request time and the run time. This paper proposes a novel approach for load management in cloud which proves to be beneficial to migrate entire VM from overloaded physical machine to underloaded ones with an unnoticed migration time and without...
Since high performance computing sustained petaflops in 2008, numerical simulation entered a new era to use 10K to 100K processor cores in one single run of parallel computing. In pursuit of petascale computing, the challenges of scalability must be addressed. Petapar is a highly scalable simulation framework which implements two popular meshfree/particle methods, the smoothed particle hydrodynamics...
In this paper, we present a novel in-domain neighborhood approach to clarify the dynamic load balancing problem on a heterogeneous network and to solve many practical problems. The distributed system consists of a network of workstations with different domains, speeds and capacities. Since the number of workstations and the diameter of the network affect the convergences rate, our approach introduces...
In distributed computing environment, divisible load technique is used to speedup the completion time of a parallel task by splitting a huge task into a smaller grain size jobs where jobs can be executed remotely by other nodes. Due to the heterogeneity of computing nodes, load balancing technique is employed to distribute workload evenly across distributed nodes in order to reduce the overall response...
While the difficulty and complexity of simulation system increasing, the development efficiency and execution performance of the large-scale distributed simulation system (LSDSS) must be improved effectively. Furthermore, while the multi-core has become popular hardware, yet the traditional federate mechanism couldn't utilize and schedule the CPU resource, which would limit the simulation performance...
Clustered heterogeneous computing environment is used to execute parallel applications that require significant amount of computing resources either in the form of computational processing resources or data storage. A cluster, comprising of heterogeneous nodes, requires careful load balancing strategies in order to result in a good processing response time for parallel applications. The workload for...
Grid computing is the combination of computer resources from multiple administrative domains for a common goal. Grid computing (or the use of a computational grid) is applying the resources of many computers in a network to a single problem at the same time — usually to solve a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of...
Weather forecasting models are computationally intensive applications. These models are typically executed in parallel machines and a major obstacle for their scalability is load imbalance. The causes of such imbalance are either static (e.g. topography) or dynamic (e.g. shortwave radiation, moving thunderstorms). Various techniques, often embedded in the application's source code, have been used...
Dynamic load balancing holds the potential to scale virtual worlds flexibly by dynamic allocation of hardware to match load. In this paper, we study the benefits and overheads of space based load partitioning, in particular, distributed binary space partitioning (BSP). Our evaluation is based on Open Simulator, a virtual world system compatible with Second Life® viewers. Our work reveals that although...
Load balancing is an important technique for improving distributed system performance by considering the group of hosts in the system to share their workloads. It results in a better utilization of host resources, a high system throughput and quick response time of user requests. In distributed database, it is important to take data locality into account, since they have big impact on the communication...
In cellular networks, mobile users in hot cells may suffer from low throughput due to the load imbalance problem. Different approaches such as channel borrowing and cell breathing have been proposed to accommodate this problem. Meanwhile, relay stations, which can extend cell coverage and enhance signal strength for boundary users, appear to be important components in next generation networks. In...
As the computer industry moves toward large-scale multi-core processors (also called Chip Multi-Processor, CMP) ,the quantity of cores on a chip increases dramatically. In order to fully utilize these processing cores, load balancing has become one of the most important factors that affect the performance of multi-cores. Based on further research on dynamic load balancing scheduling model of multiprocessor...
We re-examine the problem of load balancing in conservatively synchronized parallel, discrete- event simulations executed on high-performance computing clusters, focusing on simulations where computational and messaging load tend to be spatially clustered. Such domains are frequently characterized by the presence of geographic "hot-spots'' - regions that generate significantly more simulation...
As a consequence of Moore's law, the size of integrated circuits has grown extensively, resulting in simulation becoming the major bottleneck in the circuit design process. In this paper, we examine the performance of a parallel Verilog simulator on large, real designs. As previous work has made use of either relatively small benchmarks or synthetic circuits, the use of these circuits is far more...
HLA-based simulations, as any distributed computing application, can undergo critical performance issues due to load imbalances on large-scale, heterogeneous, non-dedicated distributed systems. Such imbalances are produced by HLA simulation entities that can dynamically change their computation and communication load during their execution time, so an initial static load deployment is incapable of...
Parallel and distributed computing has been adapted to many scientific applications for aggregate computational power and memory capacity. To utilize resources efficiently and speed up application execution, tasks are split and dispatched across multiple computing elements. The ideal case is that all subtasks can finish roughly at the same time. However, this is not always achievable due to different...
In this paper we present load balancing techniques for Cluster-based pub/sub framework that include both static and dynamic load balancing. The static load balancing is done through a multi-cluster architecture for broker overlay network which is based on subscription distribution knowledge in the event space. The dynamic load balancing,on the other hand, is achieved through exploiting multiple inter-cluster...
Balanced load distribution is especially important to attain optimal use of existing computational resources in distributed and parallel applications. In dynamic load balancing (DLB), surplus workload in nodes overwhelmed with work is transferred to relatively free nodes during run time. While in iterative DLB methods, the load reaches to its final execution node through several iteration steps, the...
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