Workload-aware Reliability Evaluation Model in Grid Computing

Hu, Zhigang

doi:10.4304/jcp.7.1.141-146

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…The reliability formulation for distributed computing system by Shatz et al 10 considered the failure caused by node and link fault only. We formulate the reliability by introducing the deadline-miss fault 37 and the steady-state user-perceived availability 38 of resources. The reliability formulation expressed by Shatz et al 10 is given as…”

Section: Reliability Modelmentioning

confidence: 99%

Balanced task allocation in the on‐demand computing‐based transaction processing system using social spider optimization

Mahato

Singh

2017

Concurrency and Computation

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Summary Balanced task allocation is one of the methods that can be used to maximize the performance and reliability in the on‐demand computing‐based transaction processing system. On‐demand computing is an increasingly popular enterprise model. It provides computing resources to the user as needed, which may be maintained within the user's enterprise, or made available by a service provider. The balanced task allocation in such environment is known to be an NP hard. The reliability is a measure of trustworthiness of the system while executing the task. So we derive the reliability formula for the on‐demand computing‐based transaction processing system considering resource availability. We propose the balanced task allocation based on social spider optimization (LBTA_SSO) method for this problem. The LBTA_SSO is based on the cooperative behavior of social‐spiders to find a collection of task allocation solutions. We modified five existing algorithms to obtain the task allocation algorithms; Honey Bee Optimization (HBO), Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized Algorithm respectively. Then, we compared the proposed algorithm with these modified algorithms. The results show that our algorithm works better than the modified existing algorithms.

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Section: Reliability Modelmentioning

confidence: 99%

Balanced task allocation in the on‐demand computing‐based transaction processing system using social spider optimization

Mahato

Singh

2017

Concurrency and Computation

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“…Professor Siewiorek of Carnegie Mellon University proposed the definition of robustness testing benchmark to test system handling error input or unexpected input in different levels, distinguish robustness testing and traditional performance testing for the first time; Professor Koopman described a portable testing benchmark to test the robustness of UNIX operating systems, and proposed an approach to classify the testing results and compare between different operating systems. Mukherjee and Siewiorek proposed a hierarchical approach to building robust testing benchmarks [2] .A workload-aware reliability evaluation model is introduced in [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-layered System Robustness Testing Strategy Based on Abnormal Parameter

Xiang¹,

Zhang

Zuo

et al. 2013

JCP

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A multi-layered fault injection mode is explored and a multi-layered system robustness testing strategy based on abnormal parameter is put forward. Fault injection methods for three layers (API, DPI and system calls based on workloads) of Linux operation system are designed. And an integrated fault injection platform to multi-layered fault injection for testing robustness of operating system is implemented. Using the multi-layered system robustness testing strategy based on abnormal parameter under the platform, various layers of system robustness evaluation are achieved. Finally, the results under different layers of fault injection are compared and analyzed in order to evaluate the robustness of target system.

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“…Energy‐aware scheduling in computational grids based on genetic algorithms was studied in . Workload‐aware reliability evaluation model for grid systems was studied in .…”

Section: Introductionmentioning

confidence: 99%

Cost minimization in utility computing systems

Penmatsa

Chronopoulos

2012

Concurrency and Computation

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Utility computing is a form of computer service whereby the company providing the service charges the users for using the system resources. In this paper, we present system-optimal and user-optimal price-based job allocation schemes for utility computing systems whose objective is to minimize the cost for the users. The system-optimal scheme provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The user-optimal scheme provides an allocation that minimizes the cost for individual users in the system for providing fairness. The system-optimal scheme is formulated as a constraint minimization problem, and the user-optimal scheme is formulated as a noncooperative game. The prices charged by the computing resource owners for executing the users jobs are obtained using a pricing model based on a non-cooperative bargaining game theory framework. The performance of the studied job allocation schemes is evaluated using simulations with various system loads and configurations.The software agents of the user and the resource owner play an incomplete information, alternatingoffer and non-cooperative bargaining game to obtain an agreed price-per-unit-resource. Contribution and related workIn this paper, we present system-optimal and user-optimal price-based job allocation schemes for utility computing systems whose objective is to minimize the cost (or price) for the users. The price-based global-optimal (system-optimal) scheme (GOSP) provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The price-based user-optimal scheme (NASHP) provides an allocation that minimizes the cost for individual users in the system. The system-optimal scheme is formulated as a constraint minimization problem, and the user-optimal scheme is formulated as a non-cooperative game. The objective of NASHP is to provide fairness to all the users, that is, all the users should have to pay approximately the same price, independent of the computers allocated for the execution of their jobs (of approximately the same size). Fairness is very important in modern distributed systems such as the utility computing systems, grid computing systems, and cloud computing systems.Preliminary work on GOSP and NASHP can be found in [2]. In this paper, more experimental results are presented evaluating the performance of the proposed schemes. The proportional job allocation scheme (PROP) [3] is also implemented for comparison purposes. New results presented are the following: variation of expected price with system utilization for various price vectors based on the allocation of NASHP; effect of system utilization on the expected prices provided by GOSP, NASHP, and PROP; effect of system utilization on the fairness indices obtained by GOSP, NASHP, and PROP; variation of expected price with speed skewness for various price vectors based on the allocation of NASHP; effect of speed skewness on the expected prices provide...

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Workload-aware Reliability Evaluation Model in Grid Computing

Cited by 12 publications

References 21 publications

Balanced task allocation in the on‐demand computing‐based transaction processing system using social spider optimization

Balanced task allocation in the on‐demand computing‐based transaction processing system using social spider optimization

Multi-layered System Robustness Testing Strategy Based on Abnormal Parameter

Cost minimization in utility computing systems

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