Dynamic Environments for Virtual Machine Placement considering Elasticity and Overbooking

Cloud computing datacenters provide millions of virtual machines in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. Considering the on-demand model of cloud computing, the VMP problem should be solved dynamically to efficiently attend typical workload of modern applications. This work proposes a taxonomy in order to understand possible challenges for Cloud Service Providers (CSPs) in dynamic environments, based on the most relevant dynamic parameters studied so far in the VMP literature. Based on the proposed taxonomy, several unexplored environments have been identified. To further study those research opportunities, sample workload traces for each particular environment are required; therefore, basic examples illustrate a preliminary work on dynamic workload trace generation.Comment: arXiv admin note: text overlap with arXiv:1507.0009

Multi-Criteria Virtual Machine Placement in Cloud Computing Environments: A literature Review

Cloud computing is a revolutionary process that has impacted the manner of using networks. It allows a high level of flexibility as Virtual Machines (VMs) run elastically workloads on physical machines in data centers. The issue of placing virtual machines (VMP) in cloud environments is an important challenge that has been thoroughly addressed, although not yet completely resolved. This article discusses the different problems that may disrupt the placement of VMs and Virtual Network Functions (VNFs), and classifies the existing solutions into five major objective functions based on multiple performance metrics such as energy consumption, Quality of Service, Service Level Agreement, and incurred cost. The existing solutions are also classified based on whether they adopt heuristic, deterministic, meta-heuristic or approximation algorithms. The VNF placement in 5G network is also discussed to highlight the convergence toward optimal usage of mobile services by including NFV/Software-Defined-Network technologies