Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

Abstract

Recently, modern businesses have started to transform into cloud computing platforms to deploy their workflow applications. However, scheduling workflow under resource allocation is significantly challenging due to the computational intensity of the workflow, the dependency between tasks, and the heterogeneity of cloud resources. During resource allocation, the cloud computing environment may encounter considerable problems in terms of execution time and execution cost, which may lead to disruptions in service quality given to users. Therefore, there is a necessity to reduce the makespan and the cost at the same time. Often, this is modeled as a multi-objective optimization problem. In this respect, the fundamental research issue we address in this paper is the potential trade-off between the makespan and the cost of virtual machine usage. We propose a HEFT-ACO approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (ACO) to minimize them. Experimental simulations are performed on three types of realworld science workflows and take into account the properties of the Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than basic ACO, PEFT-ACO, and FR-MOS.

Epigenomics Scientific Big Data Workflow Scheduling for Cancer Diagnosis in Health Care Using Heterogeneous Computing Environment

“…Furthermore, there are many scheduling approaches are available for scheduling workflow applications in which the heuristic-based approach generates results in polynomial time with acceptable performance [9]. Heuristicbased scheduling approaches are further divided as list [10][11][12][13][14][15][16][17], duplication [18][19][20], and clustering based scheduling algorithms [21][22][23]. This section discusses the strengths and weaknesses of different heuristicbased workflow scheduling algorithms in a heterogeneous computing environment.…”

“…Further, it is based on two-phase policy i.e., ranking and task prioritization of list-based scheduling approach. Belgacem and coauthors [16] suggested a method called HEFT-ACO that is a hybrid version of two algorithms that optimizes both cost and time. The ACO approach is used to improve resource allocation, whereas HEFT is used to deal with workflow task dependencies.…”

“…No. of Tasks = [50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000] CCR = [0.1, 0.5, 1, 2, 5, 10] Processors = [2,4,8,16,32,64] Here the term CCR refers to ratio of total edge costs (communication time) to the total task computation costs (compute time) in a particular workflow application. The CCR parameter is generally used to represent a wide range of computing machines.…”

Performance analysis of queueing system model under priority scheduling algorithms within 5G networks slicing framework

“…Since the first release of slicing technology few years ago, the vast majority of researchers, scientists and organizations in the telecommunications industry is focused on developing methods and techniques to flexibly and efficiently share available radio resources within its framework [15]- [19]. In modern wireless networks, one of the possible solutions to meet the QoS requirements is the implementation of priority scheduling algorithms [20]- [23]. Models implementing such algorithms within slicing framework could be described using the mathematical apparatus of retrial queueing theory [24]- [26], where retrial queues, also known as 'orbits', can be used to address service's interruptions problem.…”

Coarse-to-fine evolutionary search for large-scale multi-objective optimization: An application to ratio error estimation of voltage transformers

“…For instance, ratio error (RE) estimation of voltage transformers involves three conflicting objectives, e.g., the total time-varying REs, the sum of the RE variation, and the variance of the phase angle RE variations (Fadaee and Radzi, 2012;He et al, 2020). Scheduling workflows for cloud platforms need to balance makespan and monetary cost (Chen et al, 2021;Belgacem and Beghdad-Bey, 2022). They are mathematically constructed as:…”