Mean Makespan Research Articles

Mean makespan task scheduling approach for the edge computing environment

Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics.

Balancing Total Energy Consumption and Mean Makespan in Data Offloading for Space-Air-Ground Integrated Networks

We study the data offloading problem in space-air-ground integrated networks (SAGINs) by jointly optimizing task scheduling and power control to balance the total energy consumption and mean makespan. We consider a mixed integer nonlinear programming problem to minimize a normalized weighted combination of these two conflicting objectives. We first propose an approximation algorithm to find a high-quality solution, which is shown to be at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{2}$</tex-math></inline-formula> from the optimum to this problem for given power allocation. We further show that optimal power allocation can be obtained in closed form under the assumption that satellite-ground links have low signal-to-noise ratio (SNR). Thus, the proposed approximation algorithm can be directly utilized to obtain a constant-factor solution to the studied problem in low-SNR scenarios. To extend our solution to more general scenarios, we further propose an efficient hybird algorithm based on a genetic framework. Our simulation results demonstrate the near-optimality and correctness of the proposed algorithms, and they unveil the interplay between total energy consumption and mean makespan in SAGINs as well.

Two-objective robust job-shop scheduling with two problem-specific neighborhood structures

This paper discusses a two-objective robust job-shop scheduling problem with uncertain processing times described by discrete scenarios. The two objectives are to minimize the mean makespan and the worst-scenario makespan across all the scenarios. Two hybrid algorithms are developed by combining the elitist nondominated sorting genetic algorithm (NSGA-II) and tabu search (TS) operators. Two kinds of neighborhood structures are constructed based on the problem-specific knowledge for discrete scenarios and used in local TS operators, which are performed for each individual to improve offspring population. The hybrid algorithms with two specialized neighborhood structures are compared with each other, with the state-of-the-art existing algorithms for solving the discussed problem. The computational results verify the original intentions of designation of two neighborhood structures as well as the advantages of the developed hybrid algorithms for the proposed two-objective problem.

A NSGA-II Algorithm Hybridizing Local Simulated-Annealing Operators for a Bi-Criteria Robust Job-Shop Scheduling Problem Under Scenarios

This paper discusses a bi-criteria robust job-shop scheduling problem. Uncertain processing times are described by discrete scenario set. Two objectives are to minimize the mean makespan and to minimize the worst-case makespan among all scenarios, which reflect solution optimality and solution robustness, respectively. The NSGA-II algorithm framework is incorporated with local simulated annealing (SA) operators to solve the proposed problem. The Metropolis criterion is defined by considering two objectives in order to evaluate bi-criteria individuals. United-scenario neighborhood structure is used in local SA operators to adapt to the uncertainty described by scenarios. The developed hybrid algorithm was compared with four alternative algorithms to obtain Pareto solution sets for the proposed bi-criteria problem in an extensive experiment. The computational results show that the developed algorithm obviously outperformed other algorithms in all test instances.

A Makespan Optimization Scheme for NP-Hard Gari Processing Job Scheduling Using Improved Genetic Algorithm

An optimization scheme for minimizing makespan of Gari processing jobs using improved initial population Genetic Algorithm (GA) is proposed. GA with initial population improved by using job sequencing and dispatching rules of First Come First Served (FCFS), Shortest Processing Time (SPT), Longest Processing Time (LPT), and Modified Johnson’s Algorithm form-machines in order to obtain better schedules than is affordable by GA with freely generated initial population and by individual traditional sequencing and dispatching rules was used. The traditional GA crossover and mutation operators as well as a custom-made remedial operator were used together with a hybrid of elitism and roulette wheel algorithms in the selection process based on job completion times. A test problem of 20 jobs with specified job processing and arrival times was simulated through the integral 5-process Gari production routine using the sequencing and dispatching rules, GA with freely generated initial population, and the improved GA. Comparisons based on performance measures such as optimal makespan, mean makespan, execution time, and solution improvement rate established the superiority of the improved initial population GA over the traditional sequencing and dispatching rules and freely generated initial population GA.

Job scheduling in a yam flour producing company in Benin City

A job scheduling mathematical model was applied to a yam flour producing company in Benin City area of Nigeria with a view to develop a framework for the proper scheduling of jobs (orders) in the paint manufacturing company. The problem is addressed by considering customers (n) to be served (where n is large); in what way should customers’ order be processed such that the company’s profit is maximized while the customers are not delayed? The paper addressed the problem by using makespan as a measure of performance while the job orders were sequentially scheduled according to order of priority to achieve optimum results. The mean makespan for the developed CDS heuristic is 35.99, for the developed “A1” is 36.08 while the mean makespan for the usual traditional serial order (USO) method is 40.91. The average gain with the application of CDS heuristic is 4.962 and for “A1” is 4.912. The results show that CDS and “A1” heuristics are preferred to the usual method of USO. The CDS heuristic was fond to give the best makespan results, followed by “A1” heuristic.

클라우드 컴퓨팅에서 다중 워크플로우 어플리케이션을 위한 비용 기반 랭크 스케줄링 알고리즘

클라우드 컴퓨팅은 자원 공유를 위한 새로운 컴퓨팅 패러다임이다. 클라우드 서비스를 위해 사용 하는 다양한 어플리케이션들은 워크플로우들로 표현된다. 이러한 워크플로우 어플리케이션은 클라우드의 자원 또는 서비스들에 적절하게 할당되어야 한다. 본 논문에서는 클라우드 컴퓨팅 환경을 고려하여 다중 워크플로우 어플리케이션을 위한 새로운 스케줄링 알고리즘을 제안한다. 제안하는 알고리즘은 비용 기반 랭크 스케줄링 알고리즘으로서 다중 워크플로우 어플리케이션을 고려할 뿐만 아니라, 서비스 평가를 위한 다양한 QoS 메트릭을 고려한다. 실험결과에서 제안한 알고리즘은 다른 알고리즘들에 비해 평균 총 처리시간과 평균 가용성에서 향상된 결과를 보였다. Cloud computing is a new computing paradigm for sharing resources. Various applications used for cloud services are represented as workflows. These workflow applications must be appropriately allocated to resources or services in cloud. In this paper, a new scheduling algorithm is proposed for multiple workflow applications considering cloud computing environment. The cost-based rank scheduling algorithm considers not only multiple workflow applications, but various QoS metrics for evaluating services. Simulation results show that the proposed algorithm can improve the mean makespan and the availability significantly over two well-known algorithms.

Application of mathematical model to job scheduling in an oil processing firm in Nigeria

The job scheduling mathematical model was applied to small scale oil winning processing in this paper with a view to develop a framework for the proper scheduling of jobs (orders) in oil winning processing firm in Nigeria. The problem is addressed by supposing we have (n) customers to be served (where n is large); in what way should customers’ order be processed such that the firm’s profit is maximized while the customers are not unnecessarily delayed? The paper addressed the problem by using makespan as a measure of performance while the job orders were sequentially scheduled according to order of priority to achieve optimum results. The mean makespan for the CDS heuristic is 35.99, for AI is 36.08 while for the usual traditional serial order (USO) method is 40.91. The average gain with the application of CDS heuristic is 4.962 and for AI is 4.912. The results show that CDS and A1 heuristics are preferred to the usual method of USO. Accordingly, the CDS heuristic, followed by A1 heuristic, gives the best makespan results.