Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty

• Published in: Future generation computer systems Vol. 79; pp. 830 - 848
• Main Authors: López-Pires, Fabio, Barán, Benjamín, Benítez, Leonardo, Zalimben, Saúl, Amarilla, Augusto
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2018
• Subjects: Cloud computing; Elasticity; Incremental VMP; Overbooking; Uncertainty; Virtual machine placement; VMP reconfiguration; Cloud computing; Uncertainty; Incremental VMP; VMP reconfiguration; Elasticity; Overbooking; Virtual machine placement
• DOI: 10.1016/j.future.2017.09.021
• ISSN: 0167-739X

Infrastructure as a Service (IaaS) providers must support requests for virtual resources in highly dynamic cloud computing environments. Due to the randomness of customer requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. This work presents a novel two-phase optimization scheme for the resolution of VMP problems for cloud computing under uncertainty of several relevant parameters, combining advantages of online and offline formulations in dynamic environments considering service elasticity and overbooking of physical resources. In this context, a formulation of a VMP problem is presented, considering the optimization of the following four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. The proposed two-phase optimization scheme includes novel methods to decide when to trigger a placement reconfiguration through migration of virtual machines (VMs) between physical machines (PMs) and what to do with VMs requested during the placement recalculation time. An experimental evaluation against state-of-the-art alternative approaches for VMP problems was performed considering 400 scenarios. Experimental results indicate that the proposed methods outperform other evaluated alternatives, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs. •A first proposal for a complex IaaS environment for VMP problems considering service elasticity, including both vertical and horizontal scaling of cloud services, as well as overbooking of physical resources, including server (CPU and RAM) as well as networking resources (Ortigoza et al., 2016).•A two-phase optimization scheme for VMP problems, combining advantages of both online and offline VMP formulations in the proposed IaaS environment, introducing a prediction-based VMPr Triggering method to decide when to trigger a placement reconfiguration (Research Question 1) as well as an update-based VMPr Recovering method to decide what to do with VMs requested during placement recalculation times (Research Question 2).•A first scenario-based uncertainty approach for modeling the following relevant uncertain parameters of the proposed complex IaaS environment: (i) virtual resources capacities (vertical elasticity), (ii) number of VMs that compose cloud services (horizontal elasticity), (iii) utilization of CPU and RAM memory virtual resources (relevant for overbooking) and (iv) utilization of networking virtual resources (also relevant for overbooking).•A first formulation of a VMP problem considering the above mentioned contributions, for the optimization of the following four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization, as well as (iv) placement reconfiguration time.•An experimental evaluation of the presented two-phase optimization scheme against state-of-the-art alternatives for VMP problems, considering 400 different scenarios.