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This paper presents for the first time a formulation of the Virtual Machine Placement as a Many-Objective problem (MaVMP), considering the simultaneous optimization of the following five objective functions for dynamic environments: (1) power consumption, (2) inter-VM network traffic, (3) economical revenue, (4) number of VM migrations and (5) network traffic overhead for VM migrations. To solve the formulated MaVMP problem, a novel Memetic Algorithm is proposed. As a potentially large number of feasible solutions at any time is one of the challenges of MaVMP, five selection strategies are evaluated in order to automatically select one solution at each time. The proposed algorithm with the considered selection strategies were evaluated in two different scenarios.
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.
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.
Resource allocation in cloud computing datacenters presents several research challenges, where the Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations considering a large number of existing optimization criteria. This chapter presents the main contributions that studied for the first time Many-Objective VMP (MaVMP) problems for cloud computing environments. In this context, two variants ofMaVMP problems were formulated and different algorithms were designed to effectively address existing research challenges associated to the resolution of Many-Objective Optimization Problems (MaOPs). Experimental results proved the correctness of the presented algorithms, its effectiveness in solving particular associated challenges and its capabilities to solve problem instances with large numbers of physical and virtual machines for: (1) MaVMP for initial placement of VMs (static) and (2) MaVMP with reconfiguration of VMs (semi-dynamic). Finally, open research problems for the formulation and resolution of MaVMP problems for cloud computing (dynamic) are discussed.
Cloud computing datacenters dynamically provide millions of virtual machines in real-world cloud computing environments. A large number of research challenges have to be addressed toward an efficient resource management of these cloud computing infrastructures. In the resource allocation field, Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations and a large number of existing optimization criteria, considering solutions with high economical and ecological impact. Based on systematic reviews of the VMP literature, a taxonomy of VMP problem environments is presented to understand different possible environments where a VMP problem could be considered, from both provider and broker perspectives in different deployment architectures. Additionally, another taxonomy for VMP problems is presented to identify existing approaches for the formulation and resolution of the VMP as an optimization problem. Finally a detailed view of the VMP problem is presented, identifying research opportunities to further advance in cloud computing resource allocation areas.
Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. This work presents a formulation of a VMP problem considering the optimization of the following objective functions: (1) power consumption, (2) economical revenue, (3) quality of service and (4) resource utilization. To analyze alternatives to solve the formulated problem, an experimental comparison of fi�ve diff�erent online deterministic heuristics against an offl�ine memetic algorithm with migration of VMs was performed, considering several experimental workloads. Simulations indicate that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics on average. Experimental results prove that an offl�ine memetic algorithm improves the quality of the solutions with migrations of VMs at the expense of placement recon�gurations.
Infrastructure as a Service (IaaS) providers must support requests for virtual resources in complex dynamic cloud computing environments, taking into account service elasticity and overbooking of physical resources. Due to the randomness of customer requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. This work proposes an experimental evaluation of a two-phase optimization scheme for VMP problems, studying different (i) online heuristics, (ii) overbooking protection factors and (iii) objective function scalarization methods. The proposed experimental evaluation considers an uncertain VMP formulation for the optimization of the following three objective functions: (i) power consumption, (ii) economical revenue, and (iii) resource utilization. Experiments were performed considering 96 different scenarios, representing complex cloud computing environments. Experimental results shows that Best-Fit and Best-Fit Decreasing heuris-tics are recommended in the incremental VMP (iVMP) phase working with the considered Memetic Algorithm in the VMP reconfiguration (VMPr) phase, adjusting protection factors to 0.00 and 0.75 in low and high CPU load scenarios respectively, while scalarazing the proposed three objective functions considering an Euclidean distance to the origin.