GAMESH: A grid architecture for scalable monitoring and enhanced dependable job scheduling

Bellavista, Paolo; Cinque, Marcello; Corradi, Antonio; Frattini, Flavio; Povedano-Molina, Javier

doi:10.1016/j.future.2016.10.023

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…The check-pointing parts of the mechanism aim to decrease the total effort lost during grid failure by instantly saving the status of the grid computing system [ 24 ]. A grid architecture for scalable monitoring and enhanced, dependable job scheduling is presented in [ 45 ]. The proposed scalable monitor architecture focuses on two critical distributed heterogeneous and multi-domain grid computing features: the scalable distribution of control and administration data and system recovery of when job failures occur [ 45 ].…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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“…In online mode, scheduling the tasks are performed as soon as the task will arrives. Second one, Batch mode the tasks are scheduled with batch wise [14].…”

Section: Literature Surveymentioning

confidence: 99%

Potential Finish Time and Min-mean Algorithm for allocating Meta-Tasks on distributed Computational Grid

Sentamilselvan*¹,

Kamalam²

2019

IJRTE

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Grid is widely distributed and promising technology that enables the integrated and heterogeneous resource sharing for solving computationally challenging scientific engineering problems. In a distributed grid environment, allocating the tasks to the available computing resources proves complex and it is an NP-Complete problem as resources are geo-graphically distributed. In this paper presents a new task scheduling algorithm, called Potential Finish Time Min-mean Task Scheduling Algorithm (PFTSA), to enhance the selection of the suitable resources, which is responsible for the scheduling process. The proposed algorithm (PFTSA) schedules the tasks to the suitable resources by considering the potential finish time of the tasks, average execution time and waiting time of the tasks and average completion time of the resources. The proposed algorithm (PFTSA) results in minimum makespan as well as improved resource utilization. The experimental results indicate the PFTSA is a promising algorithm than the existing Min-min algorithm.

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“…The heuristic scheduling algorithm aims to minimize the makespan, i.e., completion time of the metatask as soon as possible [10,11]. The proposed algorithm (WSSLVA) schedules the task in the order specified in the Worth Set (WS) to the resource until the completion time of the tasks is less than the Scheduling Limit Value of the resources.…”

Section: Related Workmentioning

confidence: 99%

Heuristic Algorithm for Independent Task Scheduling in Grid Computing

2019

IJRTE

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Grid enables the integration of large number of geographically distributed heterogeneous resources owned by different organizations for resource sharing and collaboration in solving advanced sciences and engineering applications. In a distributed heterogeneous computing grid environment, scheduling independent tasks on the grid resources is more complicated and is an NP-Complete problem. Scheduling is the process of mapping the tasks to the available resources. In order to utilize the essence of grid efficiently, this paper presents a heuristic technique for scheduling/mapping the tasks to the resources. The efficiency of the proposed algorithm (WSSLVA) in terms of reduced makespan as well as better resource utilization is achieved. The experimental results indicate that the proposed WSSLVA algorithm is a promising algorithm than the Min-min heuristic scheduling algorithm in terms of makespan and resource utilization.

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GAMESH: A grid architecture for scalable monitoring and enhanced dependable job scheduling

Cited by 9 publications

References 22 publications

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Potential Finish Time and Min-mean Algorithm for allocating Meta-Tasks on distributed Computational Grid

Heuristic Algorithm for Independent Task Scheduling in Grid Computing

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