• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.287-307
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• 2018

There are two targets to schedule parallel jobs in the Cloud: (1) scheduling the jobs as many as possible, and (2) reducing the average execution time of the jobs. Most of previous work mainly focuses on the computing speed of resources without considering other attributes, such as bandwidth, memory and so on. Especially, past work does not consider the supply-demand condition from those attributes. Resources have different attributes, considering those attributes together makes the scheduling problem more difficult. This is the problem that we try to solve in this paper. First of all, we propose a new parallel job scheduling method based on a classification method of resources from different attributes, and then a scheduling method-CPLMT (Cloud parallel scheduling based on the lists of multiple attributes) is proposed for the parallel tasks. The classification method categories resources into different kinds according to the number of resources that satisfy the job from different attributes of the resource, such as the speed of the resource, memory and so on. Different kinds have different priorities in the scheduling. For the job that belongs to the same kinds, we propose CPLMT to schedule those jobs. Comparisons between our method, FIFO (First in first out), ASJS (Adaptive Scoring Job Scheduling), Fair and CMMS (Cloud-Minmin) are executed under different environments. The simulation results show that our proposed CPLMT not only reduces the number of unfinished jobs, but also reduces the average execution time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.84-92
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• 2008

Scheduling is one of the most important issues in the planning and operation of production systems. This paper investigates a general job shop scheduling problem with reentrant work flows and sequence dependent setup times. The disjunctive graph representation is used to capture the interactions between machines in job shop. Based on this representation, four two-phase heuristic procedures are proposed to obtain near optimal solutions for this problem. The obtained solutions in the first phase are substantially improved by reversing the direction of some critical disjunctive arcs of the graph in the second phase. A comparative study is conducted to examine the performance of these proposed algorithms.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.7-14
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• 2011

In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled "Shaking Optimization Algorithm" is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.609-612
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• 2000

Job-shop scheduling problem(JSSP) is one of the best-known machine scheduling problems and essentially an ordering problem. A new encoding scheme which always give a feasible schedule is presented, by which a schedule directly corresponds to an assigned-operation ordering string. It is initialized with G&T algorithm and improved using the developed genetic operator; APMX or BPMX crossover operator and mutation operator. and the problem of infeasibility in genetic generation is naturally overcome. Within the framework of the newly designed genetic algorithm, the NP-hard classical job-shop scheduling problem can be efficiently solved with high quality. Moreover the optimal solutions of the famous benchmarks, the Fisher and Thompson's 10${\times}$10 and 20${\times}$5 problems, are found.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.303-306
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• 1996

This paper proposes a heuristic method for job shop scheduling with alternative machines. Our heuristic suggests four machine-selecting rules and two priority dispatching rules for modifying existent ones considering alternative machines, and then it extends existing nondelay/active job shop schedule generation. This heuristic provides good criteria(rules) in the selection of a proper machine among those performing a specific operation and for the dispatch of an operation to a selected machine and thus these rules permit the efficient job shop scheduling with alternative machines. The performances of our four machine-selecting rules in addition to the two priority dispatching rules, applied together with the existing 17 rules, are experimented and evaluated, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.17-23
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• 2004

Changes in manufacturing system are those that occur during production and cause the systems to behave unpredictably. So scheduling problem in this dynamic Industrial environments is very complex. The main concept of this dissertation is to continuously monitor a manufacturing system status(Rate of Prior Job, Rate of Large Job, Rate of Shortest due date Job, Job Interval Time) and detect or predict a change so that scheduling system will react by modifying production schedule(dispaching rule) to lessen the effects of this change.

• Management Science and Financial Engineering
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• v.9 no.2
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• pp.47-68
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• 2003

We consider the customer order scheduling problem with job capacity restriction where the number of jobs in the shop at the same time is fixed. In the customer order scheduling problem, each job is part of some batch (customer order) and the composition of the jobs (product) in the batch is pre-specified. The objective function is associated with the completion time of the batches instead of the completion time of the jobs. We first summarize the known results for the general customer order scheduling problems. Then, we establish some new properties for the problems with job capacity restriction. For the case of unit processing time with the objective of minimizing makespan, we develop a polynomial-time optimal procedure for the two machine case. For the same problem with a variation of no batch alternation, we also develop a polynomial-time optimal procedure. Then, we show that the problems with the objectives of minimizing makespan and minimizing average batch completion time become NP-hard when there exist arbitrary number of machines. Finally, We propose optimal solution procedures for some special cases.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.4
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• pp.107-112
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• 2006

This study presents the new dispatching rules for improving performance measures of job shop scheduling related to completion time and due dates. The proposed dispatching rule considers information, which includes the comparison value of job workload, work remaining, operation time, and operation due dates. Through computer experiments, the performance of the new dispatching rules is compared and analyzed with the existing rules. The results provide a guidance for the researchers to develop new dispatching rules and for practitioners to choose rules of job shop scheduling.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.247-247
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• 1992