Deadline-constrained and bi-objective workflow scheduling with fuzziness in cloud computing systems

Abstract

Deploying intelligent expert systems on the cloud is a cost-effective solution for handling compute-intensive service requests, where each request is treated as a workflow processing procedure. Cloud workflows break down complex service requests into smaller tasks and take advantage of automatic maintenance services provided by the cloud workflow management system (CWMS). CWMS can orchestrate task execution, handle dependencies, and direct dynamic scaling of resources based on workload demands. For a CWMS, the main challenge lies in the uncertainty of workflow scheduling, i.e., the processing time, the data transmission time, and the due date are not crisp values. This paper investigates the problem of bi-objective workflow scheduling under fuzziness, aiming to minimize both the total rental cost and the degree of user dissatisfaction. Triangular fuzzy numbers are used to represent the uncertainty of temporal parameters. Two general pricing models in cloud systems are considered: on-demand and reserved price structures. A bi-objective fuzzy workflow scheduling framework is proposed, which consists of workflow sequencing, fuzzy solution generation and solution improvement components. The workflow sequencing component determines the priorities of the workflows. The fuzzy solution generation component assigns tasks to appropriate resources. The Simulated Annealing with Variable Neighborhood Search (SAVNS) method is developed for the solution improvement component. The experimental results demonstrate that the proposal can achieve better effectiveness and robust performance than the baseline algorithms compared. The proposed method can offer practical solutions for CWMS to optimize cost-performance trade-offs in uncertain environments.