An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules

Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each task.Multiple task scheduling sequences exist in a workflow application.Different task scheduling sequences have a significant impact on the scheduling performance.It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence.Besides,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a workflow.This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks.An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution.To handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling sequences.The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms.Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.

Modeling and Optimisation of Precedence-Constrained Production Sequencing and Scheduling for Multiple Production Lines Using Genetic Algorithms

This paper presents an integrated methodology for the modelling and optimisation of precedence-constrained production sequencing and scheduling for multiple production lines based on Genetic Algorithms （GA）. The problems in this class are NP-hard combinatorial problems, requiring a triple optimisation at the same time： allocation of resources to each line, production sequencing and production scheduling within each production line. They are ubiquitous to production and manufacturing environments. Due to nature of constraints, the length of solutions for the problem can be variable. To cope with this variability, new strategies for encoding chromosomes, crossover and mutation operations have been developed. Robustness of the proposed GA is demonstrated by a complex and realistic case study.

Pareto-Based Complete Local Search and Combined Timetabling for Multi-objective Job Shop Scheduling Problem with No-Wait Constraint

Job shop scheduling has become the basis and core of advanced manufacturing technology. Various differences exist between academic research and practical production. The majority of previous researches on job shop scheduling problem (JSSP)describe the basic production environment, which have a single objective and limited constraints. However,a practical process of production is characterized by having multiple objectives,no-wait constraint,and limited storage. Thus this research focused on multiobjective,no-wait JSSP. To analyze the problem,it was further divided into two sub-problems, namely, sequencing and timetabling. Hybrid non-order strategy and modified complete local search with memory were used to solve each problem individually. A Pareto-based strategy for performing fitness assessment was presented in this study. Various experiments on benchmark problems proved the feasibility and effectiveness of the proposed algorithm.

MERGING OPTIMALITY CONDITIONS WITH GENETIC ALGORITHM OPERATORS TO SOLVE SINGLE MACHINE TOTAL WEIGHTED TARDINESS PROBLEM

In this paper, a constrained genetic algorithm (CGA) is proposed to solve the single machine total weighted tardiness problem. The proposed CGA incorporates dominance rules for the problem under consideration into the GA operators. This incorporation should enable the proposed CGA to obtain close to optimal solutions with much less deviation and much less computational effort than the conventional GA (UGA). Several experiments were performed to compare the quality of solutions obtained by the three versions of both the CGA and the UGA with the results obtained by a dynamic programming approach. The computational results showed that the CGA was better than the UGA in both quality of solutions obtained and the CPU time needed to obtain the close to optimal solutions. The three versions of the CGA reduced the percentage deviation by 15.6%, 61.95%, and 25% respectively and obtained close to optimal solutions with 59% lower CPU time than what the three versions of the UGA demanded. The CGA performed better than the UGA in terms of quality of solutions and computational effort when the population size and the number of generations are smaller.