Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., ...computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator.
A Grid is a network of computational resources that may potentially span many continents. Load balancing in a Grid is a hot research issue which affects every aspect of the Grid, including service ...selection and task execution. Thus, it is necessary and significant to solve the load balancing problem in a Grid. In this paper, we propose a dynamic, distributed load balancing scheme for a Grid which provides deadline control for tasks. In our scenario, first, resources check their state and make a request to the Grid Broker according to the change of load state. Then, the Grid Broker assigns Gridlets between resources and scheduling for load balancing under the deadline request. We apply our load balancing strategy into a popular Grid simulation platform GridSim. Experimental results prove that our proposed load balancing mechanism can (1) reduce the makespan, (2) improve the finished rate of the Gridlet, and (3) reduce the resubmitted time.
► We propose a dynamic, distributed load balancing scheme for a Grid. ► The load balancing scheme provides deadline control for tasks. ► It works on three levels: processing entity (PE), machine, and resource. ► Performance evaluation on constant resources and constant Gridlets.
Grid computing is a prominent tool for assembling and incorporating groups of heterogeneous resources scattered around the world, connected through a network. Since load balancing is a challenging ...issue in a distributed environment, Grid computing provides an architecture, which is well suited, low-cost, and consistent. For task execution on suitable resources, grid computing provides efficient load balancing and fault tolerance approaches. But, due to the dynamic nature of grid resources, sometimes tasks can't be completed within given constraints (deadline, cost). To solve these issues, this paper proposes a fault tolerance-based load balancing approach by considering the dynamic nature of resources. First, significant contributions in the field of load balancing are analyzed based on several parameters. Second, a fault tolerance dynamic load balancing model is proposed for task execution based on resource load and fault index value. For fault tolerance, checkpoints are set at various determined intervals to resume tasks at the next possible instance that avoids unnecessary placement of checkpoints. Third, the proposed model is validated and provides improved performance in terms of response time, makespan, and throughput. The model provides up to 12% and 9% performance improvement depending on method of measurement in comparison to other state-of-the-art methods.
In the smart grid, electricity price is a key element for all participants in the electric power industry. To meet the smart grid’s various goals, Demand-Response (DR) control aims to change the ...electricity consumption behavior of consumers based on dynamic pricing or financial benefits. DR methods are divided into centralized and distributed control based on the communication model. In centralized control, consumers communicate directly with the power company, without communicating among themselves. In distributed control, consumer interactions offer data to the power utility about overall consumption. Online auctions are distributed systems with several software agents working on behalf of human buyers and sellers. The coordination model chosen can have a substantial impact on the performance of these software agents. Based on the fair energy scheduling method, we examined Vickrey and Dutch auctions and coordination models in an electronic marketplace both analytically and empirically. The number of software agents and the number of messages exchanged between these agents were all essential indicators. For the simulation, GridSim was used, as it is an open-source software platform that includes capabilities for application composition, resource discovery information services, and interfaces for assigning applications to resources. We concluded that Dutch auctions are better than Vickrey auctions in a supply-driven world where there is an abundance of power. In terms of equity, Dutch auctions are more equitable than Vickrey auctions. This is because Dutch auctions allow all bidders to compete on an equal footing, with each bidder having the same opportunity to win the item at the lowest possible price. In contrast, Vickrey auctions can lead to outcomes that favor certain bidders over others, as bidders may submit bids that are higher than necessary to increase their chances of winning.
Infrastructure as a Service clouds are a flexible and fast way to obtain (virtual) resources as demand varies. Grids, on the other hand, are middleware platforms able to combine resources from ...different administrative domains for task execution. Clouds can be used by grids as providers of devices such as virtual machines, so they only use the resources they need. But this requires grids to be able to decide when to allocate and release those resources. Here we introduce and analyze by simulations an economic mechanism (a) to set resource prices and (b) resolve when to scale resources depending on the users’ demand. This system has a strong emphasis on fairness, so no user hinders the execution of other users’ tasks by getting too many resources.
Our simulator is based on the well-known GridSim software for grid simulation, which we expand to simulate infrastructure clouds. The results show how the proposed system can successfully adapt the amount of allocated resources to the demand, while at the same time ensuring that resources are fairly shared among users.
► We propose an economic model for grids that use resources from clouds. ► The grid uses this model to set prices and to take scaling decisions. ► The main goal is to ensure fairness among grid users. ► The model proposed is evaluated through a modified version of GridSim.
For several special features in the environment of cloud computing, which may be quite different from the centralized computing infrastructure currently available, the existed method of resource ...allocation used in the grid computing environment may not be suitable for these changes. In our paper, a new allocation algorithm based on Ant Colony Optimization (ACO) is proposed to satisfy the needs of Infrastructure as a Service (IaaS) supported by the cloud computing environment. When started, this algorithm first predicts the capability of the potentially available resource nodes; then, it analyzes some factors such as network qualities and response times to acquire a set of optimal compute nodes; finally, the tasks would be allocated to these suitable nodes. This algorithm has shorter response time and better performance than some of other algorithms which are based on Grid environment when running in the simulate cloud environment. This result is verified by the simulation in the Gridsim environment elaborated in the following section.
In this scenario, dynamic and decentralized Load Balancing (LB) considers all the factors pertaining to the characteristics of the Grid computing environment. Dynamic load-balancing algorithms ...attempt to use the run-time state information to make more informative decisions in sharing the system load and in decentralization, algorithm is executed by all nodes in the system and the responsibility of LB is shared among all the nodes in the same pool. For this purpose, in this work, an extensive survey of the existing LB has been done. A detailed classification and gap analysis of the existing techniques is presented based on different parameters. The issue of LB in a Grid has been addressed while maintaining the resource utilization and response time for dynamic and decentralized Grid environment. Here, a hierarchical LB technique has been analyzed based on variable threshold value. The load is divided into different categories, like, lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information. In order to improve response time and to increase throughput of the Grid, a random policy has been introduced to reduce the resource allocation capacity etc. Poisson process has been used for random job arrival and then load calculation has been done for assigning job to the appropriate Processing Entity for balancing the load in the pool. After balancing the load, it comes into the normally loaded pool, and then Job Migration process is executed. The performance of the proposed model, algorithms and techniques has been examined over the GridSim simulator using various parameters, such as response time, resource allocation efficiency, etc. Experimental results prove the superiority of the proposed techniques over the existing techniques.
Load balancing is an important aspect of Grid resource scheduling. This paper attempts to address the issue of load balancing in a Grid, while maintaining the resource utilization and response time ...for dynamic and decentralized Grid environment. Here, to its optimum value, a hierarchical load balancing technique has been analysed based on variable threshold value. The load is divided into different categories, such as lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information. In order to improve response time and to increase throughput of the Grid, a random policy has been introduced to reduce the resource allocation capacity. The proposed model has been rigorously examined over the GridSim simulator using various parameters, such as response time, resource allocation efficiency, etc. Experimental results prove the superiority of the proposed technique over existing techniques, such as without load balancing, load balancing in enhanced GridSim.
Grid is a network of computational resources that may potentially span many continents. Maximization of the resource utilization hinges on the implementation of an efficient load balancing scheme, ...which provides (i) minimization of idle time, (ii) minimization of overloading, and (iii) minimization of control overhead. In this paper, we propose a dynamic and distributed load balancing scheme for grid networks. The distributed nature of the proposed scheme not only reduces the communication overhead of grid resources but also cuts down the idle time of the resources during the process of load balancing. We apply the proposed load balancing approach on Enhanced GridSim in order to gauge the effectiveness in terms of communication overhead and response time reduction. We show that significant savings are delivered by the proposed technique compared to other approaches such as centralized load balancing and no load balancing.
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