Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances. (July 2018)

• Record Type: Journal Article
• Authors: Monge, David A.; Pacini, Elina; Mateos, Cristian; García Garino, Carlos
• Is Part Of: Computers & electrical engineering. Volume 69(2018)
• Page Start: 364
• Page End: 377
• Publication Date: 2018-07
• Subjects: Autoscaling -- Parameter sweeping -- Multi-objective optimization -- Evolutive algorithms; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.12.007
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Cloud Computing is the delivery of on-demand computing resources over the Internet on a pay-per-use basis and is very useful to execute scientific experiments such as parameter sweep experiments (PSEs). When PSEs are executed it is important to reduce both the makespan and monetary cost. We propose a novel tri-objective formulation for the PSEs autoscaling problem considering unreliable virtual machines (VM) pursuing the minimization of makespan, monetary cost and probability of failures. We also propose a new autoscaler based on the Non-dominated Sorting Genetic Algorithm II able to automatically determine the right amount for each type of VM and pricing scheme, as well as the bid prices for the spot instances. Experiments show that the proposed autoscaler achieves great improvements in terms of makespan and cost when it is compared against Scaling First and Spot Instances Aware Autoscaling.