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Grid Resource Prediction based on Support Vector Regression and Simulated Annealing Algorithms  [cached]
Ying Zheng
Modern Applied Science , 2010, DOI: 10.5539/mas.v4n11p97
Abstract: Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is a novel and effective regression algorithm, is applied to grid resources prediction. In order to build an effective SVR model, SVR’s parameters must be selected carefully. Therefore, we develop a simulated annealing algorithm-based SVR (SA-SVR) model that can automatically determine the optimal parameters of SVR with higher predictive accuracy and generalization ability simultaneously. The performance of the hybrid model (SA-SVR), the back-propagation neural network (BPNN) and traditional SVR model whose parameters are obtained by trial-and-error procedure (T-SVR) have been compared with benchmark data set. Experimental results demonstrate that SA-SVR model works better than the other two models.
BEYOND SIMULATED ANNEALING IN GRID SCHEDULING  [PDF]
R. Joshua Samuel,Dr. V. Vasudevan
International Journal on Computer Science and Engineering , 2011,
Abstract: In Grid Environment the number of resources and tasks to be scheduled is usually variable and dynamic in nature. This characteristic emphasizes the scheduling approach as a complex optimization problem. Scheduling is a key issue which must be solved in grid computing study and a better scheduling scheme can greatly improve the efficiency.The objective of this paper is to explore and investigate Simulated Annealing with limited iterations to promote compute intensive grid applications to maximize the Job Completion Ratio based on the comprehensive understanding of the challenges and thestate of the art of current research. Experimental results emonstrate the effectiveness and robustness of the proposed algorithm. Further the comparative evaluation with other scheduling algorithms such as First Come First Serve (FCFS), Earliest Deadline First (EDF) is plotted.
The Simulated Annealing Algorithm and Its Application on Resource-saving Society Construction  [cached]
Shaomei Yang,Qian Zhu,Zhibin Liu
Journal of Software , 2012, DOI: 10.4304/jsw.7.3.620-625
Abstract: Construct the resource-saving society, which not only help to implement the scientific development concept, change the economic growth mode, but also contribute to implement the sustainable development strategy. Evaluation index system construction is part and parcel of building a resource-saving society; a scientific and rational evaluation index system not only can evaluate the resource-saving society construction standard, but also can guide the resource-saving society construction. Based on the analysis of the resource-saving society evaluation status quo, this paper established an evaluation index system including economic, social, environmental and technological, described the optimization ideas, algorithms and implementation of Simulated Annealing(SA). In this paper, the Hebei region as a case, which verified the SA results accuracy compared to the traditional BP network algorithm, and applied to North China, Central China and Northeast China, including 11 provinces and municipalities, the results showed that in line with the actual situation and have certain guiding significance, and the model’s commonality is very good.
A Bargaining based Scheduling for Resources Advanced Reservation Using Simulated Annealing into Grid System  [PDF]
Seyedeh Yasaman Rashida,Hamidreza Navidi
International Journal of Computer Science Issues , 2012,
Abstract: The concept of grid computing is getting popular day to day with the emergence of the Internet as a ubiquitous media and the wide spread availability of powerful computers and networks as low-cost commodity components. In these environments requests are served from external users along with local users. Since there are a limited number of resources to be used in the grid system, in spite of vast requests, resources management and scheduling is a complex undertaking. The resource consumers adopt the strategy of solving their problems at low cost with in a required time frame and also the resource providers adopt the strategy of obtaining best possible return on their investment while trying to maximize their resource utilization by offering a competitive service access cost in order to attract consumers. In this paper, we propose a bargaining based scheduling for resource advanced reservation using Simulated Annealing such that consumers can choose providers that best meet their requirements with low price. To achieve the goals, we use a maximum conflict algorithm that we presented in 2010. The simulation results indicate that the scheduling lead to maximize number of reserved requests in their deadline and both consumers and providers obtain maximum profits.
Floridian high-voltage power-grid network partitioning and cluster optimization using simulated annealing  [PDF]
Ibrahim Abou Hamad,Per Arne Rikvold,Svetlana V. Poroseva
Computer Science , 2011, DOI: 10.1016/j.phpro.2011.05.051
Abstract: Many partitioning methods may be used to partition a network into smaller clusters while minimizing the number of cuts needed. However, other considerations must also be taken into account when a network represents a real system such as a power grid. In this paper we use a simulated annealing Monte Carlo (MC) method to optimize initial clusters on the Florida high-voltage power-grid network that were formed by associating each load with its "closest" generator. The clusters are optimized to maximize internal connectivity within the individual clusters and minimize the power deficiency or surplus that clusters may otherwise have.
Resolution of Resource Contentions in the CCPM-MPL Using Simulated Annealing and Genetic Algorithm  [PDF]
Hajime Yokoyama, Hiroyuki Goto
American Journal of Operations Research (AJOR) , 2016, DOI: 10.4236/ajor.2016.66044
Abstract: This research aims to plan a “good-enough” schedule with leveling of resource contentions. We use the existing critical chain project management-max-plus linear framework. Critical chain project management is known as a technique used to both shorten the makespan and observe the due date under limited resources; the max-plus linear representation is an approach for modeling discrete event systems as production systems and project scheduling. If a contention arises within a single resource, we must resolve it by appending precedence relations. Thus, the resolution framework is reduced to a combinatorial optimization. If we aim to obtain the exact optimal solution, the maximum computation time is longer than 10 hours for 20 jobs. We thus experiment with Simulated Annealing (SA) and Genetic Algorithm (GA) to obtain an approximate solution within a practical time. Comparing the two methods, the former was beneficial in computation time, whereas the latter was better in terms of the performance of the solution. If the number of tasks is 50, the solution using SA is better than that using GA.
Energy Management in Storage-Augmented, Grid-Connected Prosumer Buildings and Neighbourhoods Using a Modified Simulated Annealing Optimization  [PDF]
Rosemarie Velik,Pascal Nicolay
Computer Science , 2015,
Abstract: This article introduces a modified simulated annealing optimization approach for automatically determining optimal energy management strategies in grid-connected, storage-augmented, photovoltaics-supplied prosumer buildings and neighbourhoods based on user-specific goals. For evaluating the modified simulated annealing optimizer, a number of test scenarios in the field of energy self-consumption maximization are defined and results are compared to a gradient descent and a total state space search approach. The benchmarking against these two reference methods demonstrates that the modified simulated annealing approach is able to find significantly better solutions than the gradient descent algorithm - being equal or very close to the global optimum - with significantly less computational effort and processing time than the total state space search approach.
Resource Scheduling in Hybrid Grid Environment  [PDF]
Dr. N. Malarvizhi,Dr. N. Sankar Ram,Dr. V. Rhymend Uthariaraj
International Journal on Computer Science and Engineering , 2012,
Abstract: This paper deals with the resource scheduling algorithm for multi cluster hybrid grid environment. The combination of both the centralized and decentralized grid environments is collectivelycalled as the hybrid grid environment. In this environment, each cluster has its own scheduler and cluster information system, and the whole organization is managed by one global grid scheduler with a gridinformation system. This mixed approach has the benefit of the shared management and administration of local and global schedulers. The local schedulers are responsible for the management of their ownresources and the global scheduler manages the local schedulers. In the hybrid approach, as in the decentralized model, users submit their jobs to the appropriate cluster, and the cluster information isupdated at a specific interval. The scheduler in the cluster then searches the nodes for executing the jobs in the originating cluster itself. If the number of nodes in the cluster is not satisfied, then the job will be transferred to the grid level scheduler. As in the centralized model, the grid scheduler schedules the jobs coming into it. The experimental results demonstrate that, the proposed hybrid algorithm effectively schedule the grid jobs thereby reduces total time of the jobs submitted in the grid. Also, it increases thepercentage of jobs completed within the specified deadline and making the grid trustworthy.
Effective Reformulation for Resource Allocation in Computational Grid  [PDF]
Xiaoshe Dong,Yiduo Mei,Siyuan Ma,Guannan Gong,Zhengdong Zhu
International Journal of Distributed Sensor Networks , 2009, DOI: 10.1080/15501320802575070
Abstract: Grid enables resource sharing and dynamic allocation of computational resources. It is a great challenge to make numerous resources available on-demand to guarantee the Quality-of-Service for jobs. This paper presents a two-stage optimization model for resource allocation in grid. Job constraints are classified into mandatory constraints and negotiated constraints. In the first stage, a preprocessing procedure such as resource discovery deals with the mandatory constraints. This has been fulfilled as our previous work. In the second stage, negotiated constraints are treated as Knapsack Problem-based optimization problem. Centralized scheduling and decentralized scheduling have been considered in this paper. This work is fulfilled as part of the Constellation Model for grid resource management.
Hybrid-Policy Co-allocation Model in Computational Grid  [cached]
Peng Xiao,Zhigang Hu,Xilong Qu
Journal of Software , 2012, DOI: 10.4304/jsw.7.2.382-388
Abstract: To address the issue of resource co-allocation with deadline constraint in grids, a novel approach is proposed to evaluate the deadline-guarantee of co-allocation schemes that obtained from conventional co-allocation policies. Based on this approach, a hybrid-policy co-allocation model is also proposed to address the issue of deadline-constrained resource co-allocation in grid environments. The proposed model combines multiple co-allocation policies and selects the one with optimal deadline-guarantee for scheduling. In this way, the hybrid-policy model combines the merits of different policies, and overcomes the shortcomings of those policies. Extensive simulations are conducted to verify the effectiveness and the performance of the proposed model in terms of deadline-miss rate. Experimental results show that it can provide co-allocation scheme with improved deadline-guarantee and lower down the deadline-miss rate for real-time applications.
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