A Survey of Job Scheduling in Grids

Jiang, Congfeng; Wang, Cheng; Liu, Xiaohu; Zhao, Yinghui

doi:10.1007/978-3-540-72524-4_44

Cited by 26 publications

(21 citation statements)

References 24 publications

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“…The key of the above proof is based on the Inequality (16) (14) holds, then under the resource allocation r * E , the bound of execution time must conform to Inequality (20).…”

Section: Ikmentioning

confidence: 99%

“…Similarly, According to Inequality (16) Remark: Let us review the Theorem 4 and discuss its significance. Inequality (20) implies that task t i 's execution time based on the optimal resource allocation of Algorithm 1 under inaccurate workload ratios has an upper bound, which is only determined by the lower bound of the inaccurate ratio α. In principle, by leveraging this theoretical result, we can always provide the strict guarantee for user-preset deadline even with the wrong prediction of task's property, as long as there are relatively sufficient resources.…”

Section: Ikmentioning

confidence: 99%

“…Traditional job scheduling [20] is often formulated as a kind of combinatorial optimization problem (or queuebased multi-processor scheduling problem [21], [22], [12]), due to the non-guaranteed performance isolation for multiple tasks running on the same machines. That is, most of the existing deadline-driven task scheduling solutions (from single cluster environment confined in LAN [23], [24] to the Grid computing environment suitable for WAN [25], [26]) are also strictly subject to the queueing model under which a single machine's multiple resources cannot be further split to smaller fractions at will.…”

Section: Related Workmentioning

confidence: 99%

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Error-Tolerant Resource Allocation and Payment Minimization for Cloud System

Wang

2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-With virtual machine (VM) technology being increasingly mature, compute resources in Cloud systems can be partitioned in fine granularity and allocated on demand. We make three contributions in this paper: (1) We formulate a deadline-driven resource allocation problem based on the Cloud environment facilitated with VM resource isolation technology, and also propose a novel solution with polynomial time, which could minimize users' payment in terms of their expected deadlines. (2) By analyzing the upper bound of task execution length based on the possibly inaccurate workload prediction, we further propose an error-tolerant method to guarantee task's completion within its deadline. (3) We validate its effectiveness over a real VM-facilitated cluster environment under different levels of competition. In our experiment, by tuning algorithmic input deadline based on our derived bound, task execution length can always be limited within its deadline in the sufficient-supply situation; the mean execution length still keeps 70% as high as user-specified deadline under the severe competition. Under the original-deadline-based solution, about 52.5% of tasks are completed within 0.95∼1.0 as high as their deadlines, which still conforms to the deadline-guaranteed requirement. Only 20% of tasks violate deadlines, yet most (17.5%) are still finished within 1.05 times of deadlines.

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“…The key of the above proof is based on the Inequality (16) (14) holds, then under the resource allocation r * E , the bound of execution time must conform to Inequality (20).…”

Section: Ikmentioning

confidence: 99%

Section: Ikmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Error-Tolerant Resource Allocation and Payment Minimization for Cloud System

Wang

2013

IEEE Trans. Parallel Distrib. Syst.

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“…Although job scheduling problem [13] in Grid computing [14] has been extensively studied for years, most of them (such as [15], [16]) are not suited for our cloud composite service processing environment. Grid jobs are often with long execution length, while Cloud tasks are often short based on [17].…”

Section: Related Workmentioning

confidence: 99%

Optimization and stabilization of composite service processing in a cloud system

Kondo

Wang

2013

2013 IEEE/ACM 21st International Symposium on Quality of Service (IWQoS)

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Abstract-With increasingly mature virtual machine (VM) technology, the compute resources provided by Cloud systems can be divided or isolated on demand under a payment model. By leveraging such a feature, we design and implement a cloud system that can optimize the overall performance of processing user requests which are made up of composite services. Specifically, we aim to minimize the response time for each user request, and also maximize the fairness of the treatment for the competitive situation in short supply. We first design an optimal VM resource allocation scheme with a minimized VMM operation cost for each task. Then, for maximizing the fairness of the treatment in the competitive situation, we design a best-suited queuing policy and a resource sharing scheme adjusted based on Proportional-Share model, which can effectively disperse the resource contention. Experiments confirm two points: (1) the mean task response time is close to the theoretically optimal value in the non-competitive situation; (2) when the system runs in short supply, each request could still be processed efficiently, with just a slight extension on their response times compared to their ideal values. The solution that combines Lightest Workload First (LWF) queuing policy with our designed Adjusted Proportional-Share Model (LWF+APSM) exhibits the best and stable performance. It outperforms other solutions in the competitive situation, by 38% w.r.t. the worst-case response time and by 12% w.r.t. the fairness of the treatment.

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“…The activity of each server can vary over time, the network activity can vary over time and the activity of each client can vary over time. In some cases network status information is coupled with server information through a broker service to guide the client to the best server [8]. However, these services require each server to publish the necessary information in order for the clients to make decisions.…”

Section: The Data Distribution Problemmentioning

confidence: 99%

A Swarm Inspired Method for Efficient Data Transfer

Kawai

Hasan

Iwai

et al. 2012

IEICE Trans. Inf. & Syst.

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Yutaka KAWAI†a) , Student Member, Adil HASAN † †b) , Go IWAI †c) , Takashi SASAKI †d) , and Yoshiyuki WATASE †e) , Nonmembers SUMMARYIn this paper we report on an approach inspired by Ant Colony Optimization (ACO) to provide a fault tolerant and efficient means of transferring data in dynamic environments. We investigate the problem of distributing data between a client and server by using pheromone equations. Ants choose the best source of food by selecting the strongest pheromone trail leaving the nest. The pheromone decays over-time and needs to be continually reinforced to define the optimum route in a dynamic environment. This resembles the dynamic environment for the distribution of data between clients and servers. Our approach uses readily available network and server information to construct a pheromone that determines the best server from which to download data. We demonstrate that the approach is self-optimizing and capable of adapting to dynamic changes in the environment.

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A Survey of Job Scheduling in Grids

Cited by 26 publications

References 24 publications

Error-Tolerant Resource Allocation and Payment Minimization for Cloud System

Error-Tolerant Resource Allocation and Payment Minimization for Cloud System

Optimization and stabilization of composite service processing in a cloud system

A Swarm Inspired Method for Efficient Data Transfer

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