Static and dynamic job scheduling with communication aware policy in cluster computing

Madheswari, A. Neela; Banu, R. S. D. Wahida

doi:10.1016/j.compeleceng.2013.01.008

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…If the parallel jobs are in need of communicating between themselves, then using the existing techniques [1], [3], there will be performance degradation due to communication overhead. To avoid the communication overhead and to improve the performance of the cluster or any high performance computing environment, communication aware scheduling (CAS) is devised [15]. In CAS, coscheduled job is considered for parallel job.…”

Section: Motivationmentioning

confidence: 99%

“…They are based on the workload consideration as the key point. Communication aware scheduling algorithm is devised by considering the different kinds of workloads such as serial jobs or parallel jobs [15]. The performance is evaluated using two parameters namely: idle time of nodes and wait time of jobs.…”

Section: Scheduling Algorithmsmentioning

confidence: 99%

“…This technique is developed for handling different kinds of workloads such as serial jobs, parallel jobs, mixed jobs and dynamic jobs [15]. For communication aware scheduling, the runtime for sample workload given in Table 1 is given in Figure 8.…”

Section: Communication Aware Schedulingmentioning

confidence: 99%

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Efficient Scheduling Strategy Using Communication Aware Scheduling for Parallel Jobs in Clusters

A¹,

R.S.D²

2013

IJDPS

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In the area of Computer Science, Parallel job scheduling is an important field of research. Finding a best suitable processor on the high performance or cluster computing for user submitted jobs plays an important role in measuring system performance. A new scheduling technique called communication aware scheduling is devised and is capable of handling serial jobs, parallel jobs, mixed jobs and dynamic jobs. This work focuses the comparison of communication aware scheduling with the available parallel job scheduling techniques and the experimental results show that communication aware scheduling performs better when compared to the available parallel job scheduling techniques.

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Section: Motivationmentioning

confidence: 99%

Section: Scheduling Algorithmsmentioning

confidence: 99%

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Efficient Scheduling Strategy Using Communication Aware Scheduling for Parallel Jobs in Clusters

A¹,

R.S.D²

2013

IJDPS

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“…When it comes to tasks such as answering questions, some workers are smarter and faster than others. 2,3…”

mentioning

confidence: 99%

An efficient privacy‐preserving model based on OMFTSA for query optimization in crowdsourcing

Renukadevi¹,

Anita

Geetha³

2021

Concurrency and Computation

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Crowdsourcing is now one of the most important and transformative paradigms, with great success in a variety of application tasks. Crowdsourcing obtains knowledge and information to solve cognitive or intelligence-intensive tasks from an evolving group of participants via the Internet. Unfortunately, providing a hard privacy guarantee and query optimization is incompatible when a higher task acceptance rate needs to be accomplished and this case is common in most existing crowdsourcing solutions. The state of art systems suffered from different complexities such as lack of crowdsourcing optimization techniques, increased cost, latency, security, and scalability issues. In this paper, we have proposed a crowdsourcing model to optimize the cost and latency, issues that occur while query optimization using the Moth Flame and Tunicate Swarm Algorithm (MF-TSA). The TSA algorithm is added to the MF algorithm to enhance its exploitation capability and yield fast convergence. The data privacy concerns of the worker and the requestor are addressed using homomorphic encryption that simultaneously enhances the efficiency of the crowdsourcing framework. The main aim of this work is to optimize the cost and latency for query plan selection along with security. Initially, the homomorphic encryption model is used to encrypt the data. In query design, two kinds of crowd-controlled administrators, that is, Crowd Powered Selection (CSelect) and Crowd Powered Join (CJoin) are connected for assessing query. The proposed framework utilizes MF-TSA to optimize the selection and join queries with low cost and latency. Finally, the experimental results demonstrate better query optimization performance than other existing algorithms such as sequential, parallel, andcrowdsourcing, moth flame-based tunicate swarm algorithm, query, select and join queries INTRODUCTIONRecently, large-scale user representations of extensive collaboration have been made available through the Internet via various types of technologies and mechanisms. The collection of text documents and data source connections is an important part of the World Wide Web (WWW). The emerging crowdsourcing platform has been authorized to collect and distribute human computing for a wide range of tasks. 1 To generate an accurate and efficient solution, crowdsourcing targets a massive audience. The consensus is shared between the crowdsourcing system and implementations, such as employees who participate and delegate tasks that differ. When it comes to tasks such as answering questions, some workers are smarter and faster than others. 2,3

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“…The main issue is how to share the processors available in the existing parallel computing environment among tasks submitted by users or processes. There are different parallel computing environments first, the parallel computers in which the systems come under are desktops and laptops with multi-core chips, second are the grids in which large scale heterogeneous distributed shared computing environment, third are the web servers, in which large scale latency sensitive online services are provided and finally the virtualization, in which the resource management is performed inside and among multiple virtual machines [1][2][3][4][5]. It is easily understood that the parallel computing environments are existing and rather continues to future technologies [3,4].…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy-based Job Scheduling Algorithm for Cluster Computing

Azizpour¹,

Effatparvar²,

Garshasbi³

2013

IJCA

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Scheduling is the process of improving the performance of a parallel and distributed system. Cluster systems are part of distributed systems. Cluster systems refers to the concept of run parallel jobs that can be run simultaneously on several processors. In this paper, introduce a method based on fuzzy logic for scheduling Parallel jobs on cluster systems. The main objective is to achieve performance and power improvement. The results of the simulations indicate our introduced method is better than comparison with the algorithm FCFS and SJF. General TermsCluster Systems, Scheduling, Fuzzy Logic.

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Static and dynamic job scheduling with communication aware policy in cluster computing

Cited by 4 publications

References 12 publications

Efficient Scheduling Strategy Using Communication Aware Scheduling for Parallel Jobs in Clusters

Efficient Scheduling Strategy Using Communication Aware Scheduling for Parallel Jobs in Clusters

An efficient privacy‐preserving model based on OMFTSA for query optimization in crowdsourcing

A New Fuzzy-based Job Scheduling Algorithm for Cluster Computing

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