Grid computing: a STOPE view

Arafah, Mohammad; Al-Harbi, Hazza S.; Bakry, Saad Haj

doi:10.1002/nem.634

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network [ 4 , 5 ]. Grid computing is a large-scale computational environment consisting of a huge number of heterogeneous resources combined to perform complex processes [ 6 ]. From a communication viewpoint, the IoT is identified as a system of interconnected things, each with a unique address to deliver computational services and connect using specified protocols.…”

Section: Introductionmentioning

confidence: 99%

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

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Section: Introductionmentioning

confidence: 99%

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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“…Grid computing aims to handle data and process intensive applications. Grid environments typically include a large number of nodes each of which owns one or more resources to be shared and used by the applications [1,2]. With regard to the continuous increasing of networks' bandwidth and resource varieties, grid computing is emerging as the next-generation computing platform in government, science, and business [3].…”

Section: Introductionmentioning

confidence: 99%

A Weight-based Query Forwarding Technique for Super-peer-based Grid Resource Discovery

Mollamotalebi

Maghami

Ismail

2017

Eng. Technol. Appl. Sci. Res.

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Grid computing environments include heterogeneous resources shared by a large number of computers to handle data and process intensive applications. The required resources must be accessible for the grid applications on demand, which makes resource discovery a critical service. In recent years, different techniques are provided to index and discover grid resources. Response time and message load during the search process highly affect the efficiency of resource discovery. This paper proposes a technique to forward the queries based on the resource types accessible through each neighbor in super-peer-based grid resource discovery approaches. The proposed technique is simulated in GridSim and the experimental results indicated that it is able to reduce the response time and message load during the search process especially when the grid environment contains a large number of nodes.

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“…The sharing mentioned in the definition is not a simple file sharing but rather direct contact and access to resources and services in a secure manner [1][2]. A virtual Organization (VO) typically comprises of a set of individuals or organizations, those individuals or organizations physically dispersed, but they work as one general organization, with a general concern [3].…”

Section: Introductionmentioning

confidence: 99%

A Bidding-based Grid Resource Selection Algorithm using Single Reservation Mechanism

Yousif¹,

Abdullah²,

Ahmed³

2011

IJCA

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The selection of resources that best fitting tasks in grid environments is an essential and critical factor to system performance. Current resource selection methods apply traditional mechanisms for the process of task allocation to resources, which cause performance problems. This paper proposes a resource selection algorithm in bidding based grid environment to minimize the total time for task completion in effective and efficient way. To avoid unexpected completion time arises when using non-reserved bidding process due to the competition between grid clients on resources; our algorithm proposes the single reservation mechanism to reserve the best resource for the task as a commitment and hence guarantees the task completion time will be as expected. This algorithm uses single reservation mechanism to reserve the best resource for the task. Unlike traditional reserved algorithms this algorithm reserves only the best resource and hence it allows other resources to participate in other bidding processes. We believe the proposed algorithm can select the most fitting resources for tasks execution and achieve a good performance in terms of effectiveness and efficiency.

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Grid computing: a STOPE view

Cited by 10 publications

References 18 publications

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

A Weight-based Query Forwarding Technique for Super-peer-based Grid Resource Discovery

A Bidding-based Grid Resource Selection Algorithm using Single Reservation Mechanism

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