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Due to the rapid technological advancements, the Grid computing has emerged as a new field, distinguished from conventional distributed computing. The load balancing is considered to be very important in Grid systems. In this paper, we propose a new dynamic and distributed load balancing method called "Enhanced GridSim with Load Balancing based on Cost Estimation" (EGCE) for computational...
Grid computing, was developed by computer scientists in the mid 1990's based on ease of use and access geographically distributed resources which are dynamic and heterogeneous for solving difficult problems. These distributed resources are owned by different organizations. Grid computing provides a framework for parallel/distributed computing. For developing a grid, low-level services (secure access...
Computer grids are systems containing heterogeneous, autonomous and geographically distributed nodes. The management of these resources is the works of the meta-scheduler, who allocates work the nodes that are part of a grid, such as clusters, which in turn, have their own local schedulers. In this work we propose a new multi-agent distributed meta-scheduling model. Our model takes, on one hand, benefit...
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...
Distributed system is a set of resources interconnected by a network. Grid computing systems are distributed system designed by integrating heterogeneous resources with different characteristics. These heterogeneous computing resources are designed for highly complex programs that require high processing power and huge volume of input data. Large scale applications such as meteorological simulations,...
The heterogeneous nature of distributed platforms such as computational Grids is one of the main barriers to effectively deploy tightly-coupled applications. For those applications, one common problem that appears due to the hardware heterogeneity is the load imbalance which slows down the application to the pace of the slower processor. One solution is to distribute the load adequately taking into...
This paper discusses the workload utilization dissemination for grid computing. The CPU is a well-known resource item and it is an integral part in most literatures while other RI's may include memory, network and I/O overhead. The selection of resource variables and the number of RI's involved will result in different definitions of the workload. Various combination of computer RI's have been explored...
In this paper, a hierarchical load balancing technique has been presented, which is based on variable threshold value. Through this paper, an attempt has been made to solve the problem of load balancing while maintaining the resource utilization and response time with the help of sender initiative policy. The proposed technique is suitable for dynamic and decentralized Grid environments. The load...
Grids are a form of distributed computing whereby a 'super virtual computer' is composed of many networked loosely coupled computers acting together to perform very large tasks. This technology has been applied to computationally intensive scientific, mathematical and academic problems through volunteer computing, and it is used in commercial enterprises for many diverse applications. Computational...
Standard high performance clusters (HPC) are being extensively used for solving computationally intensive problems in various scientific fields, one of which is the field of reservoir simulation. In our work, we expand the conventional HPC systems to run larger reservoir simulations on a heterogeneous grid of HPCs. This expansion is accomplished by developing a unique domain decomposition technique...
The current Operating System (OS) kernels calculate the load average value as a lump sum. Also the algorithm for the calculation of load average does not separate CPU load from Disk load. This leads to the presentation of an incorrect measurement when both disk bound tasks and CPU bound tasks run simultaneously. In this paper a new algorithm is proposed to calculate, store and display each user's...
A new era of Cloud Computing has emerged, but the characteristics of Cloud load in data centers is not perfectly clear. Yet this characterization is critical for the design of novel Cloud job and resource management systems. In this paper, we comprehensively characterize the job/task load and host load in a real-world production data center at Google Inc. We use a detailed trace of over 25 million...
A computational grid is a large scale federated infrastructure where users execute several types of applications with different submission rates. On the evaluation of solutions for grids, there are not much effort on using realistic workloads for experiments, and most of the time users' activities and applications are not well represented. In this work, we propose a user-based grid workload model...
The optimal allocation of computational resources for efficient and high performance Grid applications is not a trivial task and mainly arises a challenge for calibration and execution of huge hydrological models. Hydrological models are used to assess the sustainability and vulnerability of different geographical regions. However, the calibration process that is necessary for the SWAT hydrological...
Effective utilization of computing resources and prediction of future resource capabilities are needed to achieve high performance computing in grid Environment. To ensure this, effective and flexible forecasting and prediction method needed to use time-shared resources for large applications which impact greater importance for scheduling. Predicting the available performance on each resource is basic...
Distributed task scheduling in a heterogeneous computing environment is one of the most challenging problems. The optimally mapping of independent tasks onto heterogeneous distributed computing systems is known to be NP-complete. The most common objective function of a distributed task scheduling problem is to reduce the make span and increase the load balancing across the machines. In this paper,...
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...
A constantly increasing number of resource demanding applications from various scientific sectors are finding their way towards adopting Grid technologies in order to take advantage of their computational power. The aim of this paper is to demonstrate how tasks of large-scale distributed simulation can be scheduled based on GA in a Grid environment by taking into account load of computing nodes, network...
The objective of the work is to propose a resource discovery model for distributed resources using grid brokering and dispute solving techniques in a grid environment. The synchronization between local and external schedulers and the effective utilization of the available resources can be achieved by dispute solving with periodical resource auditing processes by an agent technique. The discovery of...
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