Solving Job Scheduling Problem in Computational Grid Systems Using a Hybrid Algorithm

2021 ◽  
pp. 2267-2281
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Hybrid Algorithm ◽  
High Performance ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
System Throughput ◽  
Scheduling Problem ◽  
Grid Systems ◽  
High Performance Distributed Computing ◽  
Job Scheduling Problem

Grid computing is a high performance distributed computing system that consists of different types of resources such as computing, storage, and communication. The main function of the job scheduling problem is to schedule the resource-intensive user jobs to available grid resources efficiently to achieve high system throughput and to satisfy user requirements. The job scheduling problem has become more challenging with the ever-increasing size of grid systems. The optimal job scheduling is an NP-complete problem which can easily be solved by using meta-heuristic techniques. This chapter presents a hybrid algorithm for job scheduling using genetic algorithm (GA) and cuckoo search algorithm (CSA) for efficiently allocating jobs to resources in a grid system so that makespan, flowtime, and job failure rate are minimized. This proposed algorithm combines the advantages of both GA and CSA. The results have been compared with standard GA, CSA, and ant colony optimization (ACO) to show the importance of the proposed algorithm.

Solving Job Scheduling Problem in Computational Grid Systems Using a Hybrid Algorithm

2019 ◽  
pp. 310-324 ◽  
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Hybrid Algorithm ◽  
High Performance ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
System Throughput ◽  
Scheduling Problem ◽  
Grid Systems ◽  
High Performance Distributed Computing ◽  
Job Scheduling Problem

Grid computing is a high performance distributed computing system that consists of different types of resources such as computing, storage, and communication. The main function of the job scheduling problem is to schedule the resource-intensive user jobs to available grid resources efficiently to achieve high system throughput and to satisfy user requirements. The job scheduling problem has become more challenging with the ever-increasing size of grid systems. The optimal job scheduling is an NP-complete problem which can easily be solved by using meta-heuristic techniques. This chapter presents a hybrid algorithm for job scheduling using genetic algorithm (GA) and cuckoo search algorithm (CSA) for efficiently allocating jobs to resources in a grid system so that makespan, flowtime, and job failure rate are minimized. This proposed algorithm combines the advantages of both GA and CSA. The results have been compared with standard GA, CSA, and ant colony optimization (ACO) to show the importance of the proposed algorithm.

A Hybrid Algorithm Using Genetic Algorithm and Cuckoo Search Algorithm to Solve Job Scheduling Problem in Computational Grid Systems

2016 ◽  
Vol 7 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Distributed Resources ◽  
Computing Systems ◽  
Grid Systems ◽  
Job Scheduling Problem

Job scheduling is one of the major challenges in Grid computing systems to efficiently exploit the capabilities of dynamic, autonomous, heterogeneous and distributed resources for execution of different types of jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic techniques. This paper presents a hybrid algorithm for job scheduling using Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for efficiently allocating jobs to resources in a Grid system so that makespan and flowtime are minimized. This proposed algorithm combines the advantages of both GA and CSA. The authors' results have been compared with standard GA, CSA and Ant Colony Optimization (ACO) to show the importance of the proposed algorithm.

A Structured Tabu Search Approach for Scheduling in Parallel Computing Systems

2010 ◽  
pp. 354-377 ◽  
Author(s):  
Tore Ferm ◽  
Albert Y. Zomaya
Keyword(s):  
Parallel Computing ◽  
Tabu Search ◽  
High Performance ◽  
Search Algorithm ◽  
Evaluation Criteria ◽  
System Throughput ◽  
Task Graph ◽  
Scheduling Problem ◽  
Computing Systems ◽  
Task Graphs

Task allocation and scheduling are essential for achieving the high performance expected of parallel computing systems. However, there are serious issues pertaining to the efficient utilization of computational resources in such systems that need to be resolved, such as, achieving a balance between system throughput and execution time. Moreover, many scheduling techniques involve massive task graphs with complex precedence relations, processing costs, and inter-task communication costs. In general, there are two main issues that should be highlighted: problem representation and finding an efficient solution in a timely fashion. In the work proposed here, the authors have attempted to overcome the first problem by using a structured model which offers a systematic method for the representation of the scheduling problem. The model used can encode almost all of the parameters involved in a scheduling problem in a very systematic manner. To address the second problem, a Tabu Search algorithm is used to allocate tasks to processors in a reasonable amount of time. The use of Tabu Search has the advantage of obtaining solutions to more general instances of the scheduling problem in reasonable time spans. The efficiency of the proposed framework is demonstrated by using several case studies. A number of evaluation criteria will be used to optimize the schedules. Communication- and computation-intensive task graphs are analyzed, as are a number of different task graph shapes and sizes.

scholarly journals Solving Job Scheduling Problem Using Fireworks Algorithm

2019 ◽  
Vol 11 (2) ◽  
pp. 1-8 ◽  
Author(s):  
Jamal N. Hasoon ◽  
Rehab Hassan
Keyword(s):  
Job Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Scheduling Problem ◽  
Improve Performance ◽  
Fireworks Algorithm ◽  
Np Hard Problem ◽  
Job Scheduling Problem ◽  
Optimum Solution

Scheduling is critical part in most creation frameworks and information processing as sequencing of tasks or jobs framework executed on a grouping of processors.  One of the NP-hard problem is “Job Shop Scheduling Problem”. In this work, a method of optimization proposed called “Fireworks Algorithm”. The solutions divided into fireworks and each one applied sparks to find the best solution. For some selected spark applied Gaussian mutation to find enhanced solution and find optimum solution.   FWA tested on dataset to improve performance and it do well with respect to some other algorithm like Meerkat Clan Algorithm (MCA), Camel Herds Algorithm) CHA(, and Cukoo Search Algorithm (CSA).

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

scholarly journals Cost sharing in a job scheduling problem

2006 ◽  
Vol 29 (3) ◽  
pp. 369-382 ◽  
Author(s):  
Debasis Mishra ◽  
Bharath Rangarajan
Keyword(s):  
Cost Sharing ◽  
Job Scheduling ◽  
Scheduling Problem ◽  
Job Scheduling Problem

scholarly journals Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

2017 ◽  
Vol 7 (5) ◽  
pp. 2349 ◽  
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Scheduling Problem ◽  
Power Scheduling

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

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