Task Scheduling Algorithm Based on Triangle Module in Grid Computing

Grid computing promotes resource sharing, dynamic computational resource allocation, distributed data access from disjoint application domains and allowing various service providers to meet different demands efficiently. With an increase of user demands to the resources, planning the guaranteed Quality of Service (QoS) is a challenging task. The objective of this paper is to propose an agent based grid framework and QoS Time Based Scheduling (QTBS) algorithm for effective task scheduling in the grid computing paradigm. This algorithm is simulated in the GridSim toolkit and the results shows that the proposed work gives better results in makespan, resource utilization rate and load balancing level than algorithms such as QoS guided Weighted Mean Time Min (QWMTM), QoS guided MinMin, Max-Min and Min-Min heuristic algorithms.

Many applications involve the concepts of scheduling, such as communications, packet routing, production planning [Zhai et al., 2006], classroom arrangement [Mathaisel & Comm, 1991], aircrew scheduling [Chang, 2002], nurse scheduling [Ohki et al., 2006], food industrial [Simeonov & Simeonovova, 2002], control system [Fleming & Fonseca, 1993], resource-constrained scheduling problem [Chen, 2007] and grid computing. There are many different types of scheduling problems such as real-time, job-shop, permutation flow-shop, project scheduling and other scheduling problems have been studied intensively. However, in this work, the studied grid task scheduling problem is much more complex than above stated classic task scheduling problems. Restated, a grid application is regarded as a task scheduling problem involving tasks with inter-communication and distributed homogeneous or heterogeneous resources, and can be represented by a task interaction graph (TIG). Grid is a service for sharing computing power and data storage capacity over the Internet. The grid systems outperform simple communication between computers and aims ultimately to turn the global network of computers into one vast computational resource. Grid computing can be adopted in many applications, such as high-performance applications, large-output applications, data-intensive applications and community-centric applications. These applications major concern to efficiently schedule tasks over the available multi-processor environment provided by the grid. A grid is a collaborative environment in which one or more tasks can be submitted without knowing where the resources are or even who owns the resources [Foster et al., 2001]. The efficiency and effectiveness of grid resource management greatly depend on the scheduling algorithm [Lee et al., 2007]. Generally, in the grid environment, these resources are different over time, and such changes will affect the performance of the tasks running on the grid. In grid computing, tasks are assigned among grid system [Salman, 2002]. The purpose of task scheduling in grid is to find optimal task-processor assignment and hence minimize application completion time (total cost). Most scheduling problems in these applications are categorized into the class of NP-complete problems. This implies that it would take amount of computation time to obtain an optimal solution, especially for a large-scale scheduling problem. A variety of approaches have been applied to solve scheduling problems, such as

Task scheduling is one important job in Grid computing and also a hard and complex problem. We have quite a few algorithm of task scheduling in the past researches. Ant algorithm is a heuristic algorithm. The inherent parallelism and scalability make this algorithm meet the requirement of complex task scheduling in Grid computing.In this paper, we will propose an improved ant algorithm in our power grid environment. We made several improvements on the calculation of pheromone and task issue method as well. The improved algorithm becomes more sensitive with the power grid environment and more robust with heavy workload.

BFRA: bounded flooding routing algorithm for provisioning the globally optimal route in a hierarchical ATM network

Since routers in IP network are connected with ATM virtual path (VP) permanent virtual connection (PVC) and each VP contains bundles of VCs, a VP layer network can serve as a server layer and each IP layer network can be a client layer. Therefore the effective Internet service provisioning completely depends on the reliability of VP server layer network from the perspective of the optimality and the network survivability. This paper proposes the distributed ATM VP PVC restoration framework with the dynamic rerouting schemes in hierarchical transport network. The rerouting algorithm aims to provide rapid ATM VP PVC restoration in the case of fault or performance degradation in terms of maximization of network resource utilization and satisfaction of the end user's QoS requirement. It guarantees the IP network survivability in spite of ATM network faults or performance degradations. In addition, this paper evaluates the rerouting performance for a real network, the High Speed Information Network (HSIN).

Discovery of suitable Web services for a given user requirement is one of the key technology. A mechanism is proposed to support QoS-based semantic web service discovery in the paper. Firstly, to reduce the overhead and enhance the discovery efficiency, genetic algorithm is adopted as a method of service clustering. Furthermore, based on user's QoS requirements, the QoS-based service matchmaker was realized. In the end, simulation results demonstrated the effectiveness of the proposed method.

Relay selection is a challenging issue in cooperative communication networks. In particular, in a multi-user multi- concurrent-transmission network, effects of the resource sharing of a relay by its served users on the relay selection strategy need to be taken into account. There is little research on this investigation in literature. In this paper, we propose a novel efficient and fair relay selection strategy, in which individual user's QoS requirement is inherently taken into account. In terms of efficiency, the proposed strategy is of low computational complexity. In terms of fairness, the system resource is utilized fairly among all relaying nodes so that the system has the best potential to accommodate as many users as possible. Simulation results indicate that the proposed selection strategy outperforms many existing ones, therefore justifying our arguments.

Grid computing comprises of distributed computer systems which are geographically dispersed to share the combination of resources in a heterogeneous environment. Grid computing is one of the latest technologies to solve very complex problems in scientific applications, using the resource sharing concept in the dynamic virtual organizations. The main problem occurs in most of the organization is that the resources are become idle and underutilized. In some organizations, even the server machines can often be relatively idle. Grid computing provides an approach for exploiting these underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage. Effective and Efficient Task scheduling is an important aspect of Grid computing. The objective of this paper is that a grid scheduler first allocates the important(high priority) jobs to the resources and then it allocates the low prioritize job so as to achieve the maximum resource utilization rate, minimize the makespan and avoid the load balancing level problem. In this paper, we propose a new framework and QoS(Quality of Service) Priority Based Scheduling Algorithm for effective task scheduling to the resources in the grid environment. The algorithm is simulated using Java. The results show that our proposed QoS priority based scheduling algorithm gives better results in makespan and resource utilization rate than other algorithms such as QoS guided weighted mean time min (QWMTM), Min-Min and Max-Min heuristic algorithms.

Optical Grid that provides a way to integrate the geographical distributed resources for large- scale grid applications. This paper mainly focuses on the task scheduling issue in Optical Grid and proposes two task scheduling algorithms to mini-mize the total time or scheduling length required for a given task. A simulated optical grid has been addressed to demon-strate the effectiveness of the schedulin g approach. The performance tests and anal ysis of the two algorithms are investi-gated to show the effect of the routing policy on the schedulin g length. A more flexible policy is able to further improve scheduling length. Keywords: Optical Grid, Task Scheduling Algorithm, Communication Contention I INTRODUCTION Distributed computing, also known as computational Grid, is a technology that makes it possible to utilize the geographi-cal distributed computational resources to solve the intensive computation of large-scale data. Most Grid systems were built over the IP packet switched networks in the past. In the situation when some data-intensive Grid applications re-quire moving Gigabytes or even larger of data, the low speed of the network becomes the bottleneck of Grid system, which could not allow the remote resources to be used efficiently. Nowadays the optical network with Synchronous Digital Hierarchy (SDH), Wavelength-D ivision Multiplexing (WDM), and Automa tic Switched Optical Network (ASON) infrastructure can provide a much larger amount of bandwidth than the electronic network, such as IP packet switched network. Therefore, it is significant to use the optical network as the network infrastructure of the computational Grid. We refer to this infrastructure as Optical Grid, which employs optical network to interconnect grid computational re-sources (e.g. supercomputers, computer clusters, storage and display devices) with dynamically provisioned lightpaths [1]. It has been realized that task scheduling is a fundamental issue in achieving high performance in grid environments [6]. Task scheduling aims to optimize the overall performance of a system, e.g., minimizing the average job response time and maximizing the number of jobs exec uted in certain period of time. Optical grid task scheduling is quite different from classic grid task scheduling model. In Optical Grid system, we can treat the optical network as a kind of resource, the scheduling algorithm in Optical Grid system should schedule the tasks to appropriate resources as we ll as scheduling the communications between tasks to appropriate optical links. The objec-tive of task scheduling is to minimize the completion time (we called it as scheduling length or make span ) of an appli-cation. Usually, this problem is modeled and solved under several assumptions. Unless the assumptions are simplified enough, the problem has been proven to be NP-complete [8]. Therefore, many heuristic algorithms have been proposed. But these heuristics either are based on the assumption that the processors are fully connected, or just don’t take into ac-count the optical network’s characters, es pecially the characters of WDM optical network. While actually, in the Optical Grid system, the computational resources are more likely not fully connected, and the communication contention is an important factor that affects the completion time of a Grid application. Thus, a new heuris tic algorithm that can schedule tasks and also the communications are needed. In this paper, we mainly focus on the static scheduling problem, which means the character of the Grid applica tion, including the task execution times, task dependencies, task communications and synchronization are known a priori.

Data-intensive Grid applications require huge data transferring between multiple geographically separated computing nodes where computing tasks are executed. For a future WDM network to efficiently support this type of emerging applications, traditional approaches to establishing lightpaths between given source destination pairs are not sufficient because a computing task may be executed on any one of several computing nodes having the necessary resources. Therefore, lightpath establishment has to be considered jointly with task scheduling to achieve best performance. We study the optimization problems of jointly scheduling both computing resources and network resources. We first present the formulation of two optimization problems with the objectives being the minimization of the completion time of a job and minimization of the resource usage/cost to satisfy a job with a deadline respectively. When the objective is to minimize the completion time, we devise an optimal algorithm for a special type of applications. Furthermore, we propose efficient heuristics to deal with general applications with either optimization objective and demonstrate their good performances via simulation.

Aiming at the sensitive issues of service quality in cloud computing, a task scheduling tactic with multidimensional QoS constraints is studied. Based on cluster service and user QoS preference, this article constructs an immune optimization model to make a description through formulas and quantify the performance constraints; the utility function of multidimensional QoS is given and then the immune optimization operation is performed with the antibodies. It is beneficial to increase the prediction accuracy of the equality evaluation, and the search for a Pareto optimal set of multiobjective optimization problems is implemented. Finally, the optimum node distribution structure with the highest utility value is obtained. It's shown that the approach gives sufficient consideration of multidimensional user QoS requirements. The results from the test show a significant improvement in average rate of equipment utilization, service time and response time compared to similar algorithms.

Most studies of resource allocation mechanisms have used a performance model of the resource, where the very concept of the resource is defined in terms of measurable qualities of the service such as utilization, throughput, response time (delay) and so on. Optimization of resource allocation is defined in terms of these measurable qualities. One novelty introduced by the economic approach is to design a system which takes into account the diverse QoS requirements of users, and therefore use multiobjective (utilities)optimization techniques to characterize and compute optimum allocations. Economic modelling of computer and communication resource sharing uses a uniform paradigm described by two level modelling: QoS requirements as inputs into a performance model that is subject to economic optimization. In a first step, one transforms QoS requirements of users to a performance (example: queueing service model).This model establishes quantifiable parameterization of resource allocation. For example, average delay QoS requirement, when based on a FIFO queueing model, is a function of resources, bandwidth and buffer, and user traffic demands. These parameters are then used to establish an economic optimization model. The question of whether the resource is a piece of hardware, a network link, a software resource such as a database or a server, or a virtual network entity such as a TCP connection is not of primary importance. The first modeling transformation eliminates the details and captures the relevant behaviors and the optimization parameters. We consider a decentralized model of network and server economies , where we show efficient QoS provisioning and Pareto allocation of resources (network and server resources) among agents and suppliers, which are either network routes or servers (content providers)

As the research of grid goes on, users demand increasingly high quality of task completion and high-quality scientific computing tasks continue to increase. This renders QoS a new problem that is to be considered in the grid scheduling algorithm. In this paper, a grid tasks scheduling strategy based on QoS priority grouping is proposed. In this algorithm, the deadline property of task, acceptation rate of tasks and makespan of systems are comprehensively considered. And QD-Sufferage, a grid task scheduling based on task priority grouping and deadline, is presented subsequently. The experiments show that the algorithm overweighs traditional algorithms a lot in makespan, throughout parameters

\n\t\t\t\t\tThis paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.\n\t\t\t\t

The two major problems in grid computing applications are, resource management and job scheduling. These problems do occur due to distributed and heterogeneous nature of the resources. This paper introduces a model in job scheduling in grid computing environments. A dynamic scheduling algorithm is proposed to maximize the resource utilization and minimize processing time of the jobs. The proposed algorithm is based on job grouping. The results show that the proposed scheduling algorithm efficiently reduces the processing time of jobs.