Author:
Cañizares Pablo C.,Núñez Alberto,Bernal Adrián,Cambronero M. Emilia,Barker Adam
Abstract
AbstractCloud computing is an evolving paradigm whose adoption has been increasing over the last few years. This fact has led to the growth of the cloud computing market, together with fierce competition for the leading market share, with an increase in the number of cloud service providers. Novel techniques are continuously being proposed to increase the cloud service provider’s profitability. However, only those techniques that are proven not to hinder the service agreements are considered for production clouds. Analysing the expected behaviour and performance of the cloud infrastructure is challenging, as the repeatability and reproducibility of experiments on these systems are made difficult by the large number of users concurrently accessing the infrastructure. To this, must be added the complications of using different provisioning policies, managing several workloads, and applying different resource configurations. Therefore, in order to alleviate these issues, we present Simcan2Cloud, a discrete-event-based simulator for modelling and simulating cloud computing environments. Simcan2Cloud focuses on modelling and simulating the behaviour of the cloud provider with a high level of detail, where both the cloud infrastructure and the interactions of the users with the cloud are integrated in the simulated scenarios. For this purpose, Simcan2Cloud supports different resource allocation policies, service level agreements (SLAs), and an intuitive and complete API for including new management policies. Finally, a thorough experimental study to measure the suitability and applicability of Simcan2Cloud, using both real-world traces and synthetic workloads, is presented.
Funder
MINECO/FEDER
Comunidad de Madrid
Comunidad de Madrid - Universidad Complutense
University of Castilla-La Mancha
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Reference37 articles.
1. Flexera (2019) RightScale 2019 State of the Cloud Report. Tech. rep
2. Perumal K, Mohan S, Frnda J, Divakarachari PB (2022) Dynamic resource provisioning and secured file sharing using virtualization in cloud azure. J Cloud Comput Adv Syst Appl 11(46):1–12
3. Oren T, Yilmaz L (2012) Synergies of simulation, agents, and systems engineering. Expert Syst Appl 39(1):81–88
4. Khani H, Khanmirza H (2019) Randomized routing of virtual machines in IaaS data centers. PeerJ Comput Sci 5:211
5. Arzymatov K, Sapronov A, Belavin V, Gremyachikh L, Karpov M, Ustyuzhanin A, Tchoub I, Ikoev A (2020) SANgo: A storage infrastructure simulator with reinforcement learning support. PeerJ Comput Sci 6:271
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献