Multi-objective ACO resource consolidation in cloud computing environment

Cloud computing systems provide services to users based on a pay-as-you-go model. High volume of interest and a number of requests by user in cloud computing has resulted in the creation of data centers with large amounts of physical machines. These data centers consume huge amounts of electrical energy and air emissions. In order to improve Datacenter efficiency, resource consolidation using virtualization technology is becoming important for the reduction of the environmental impact caused by the data centers (e.g. electricity usage and carbon dioxide). By using Virtualization technology multiple VM (logical slices that conceptually called VMs) instances can be initialised on a physical machine. As a result, the amounts of active hardware are reduced and the utilisations of physical resources are increased. The present thesis focuses on problem of virtual machine placement and virtual machine consolidation in cloud computing environment. VM placement is a process of mapping virtual machines (Beloglazov and Buyya) to physical machines (PMs). VM consolidation reallocates and optimizes the mapping of VMs and PMs based on migration technique. The goal is to minimize energy consumption, resource wastage and energy communication cost between network elements within a data center under QoS constraints through VM placement and VM consolidation algorithms. The multi objective algorithms are proposed to control trade-off between energy, performance and quality of services. The algorithms have been analyzed with other approaches using Cloudsim tools. The results demonstrate that the proposed algorithms can seek and find solutions that exhibit balance between different objectives. Our main contributions are the proposal of a multi- objective optimization placement approach in order to minimize the total energy consumption of a data center, resource wastage and energy communication cost. Another contribution is to propose a multiobjective consolidation approach in order to minimize the total energy consumption of a data center, minimize number of migrations, minimize number of PMs and reconfigure resources to satisfy the SLA. Also the results have been compared with other single-objective and multi-objective algorithms

An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.Comment: Submitted for publication consideration for the Journal of Network and Computer Applications (JNCA). Total page: 28. Number of figures: 15 figure

Migration energy aware reconfigurations of virtual network function instances in NFV architectures

Network function virtualization (NFV) is a new network architecture framework that implements network functions in software running on a pool of shared commodity servers. NFV can provide the infrastructure flexibility and agility needed to successfully compete in today's evolving communications landscape. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF instances (VNFIs) that are software modules executed on virtual machines. This paper deals with the migration problem of the VNFIs needed in the low traffic periods to turn OFF servers and consequently to save energy consumption. Though the consolidation allows for energy saving, it has also negative effects as the quality of service degradation or the energy consumption needed for moving the memories associated to the VNFI to be migrated. We focus on cold migration in which virtual machines are redundant and suspended before performing migration. We propose a migration policy that determines when and where to migrate VNFI in response to changes to SFC request intensity. The objective is to minimize the total energy consumption given by the sum of the consolidation and migration energies. We formulate the energy aware VNFI migration problem and after proving that it is NP-hard, we propose a heuristic based on the Viterbi algorithm able to determine the migration policy with low computational complexity. The results obtained by the proposed heuristic show how the introduced policy allows for a reduction of the migration energy and consequently lower total energy consumption with respect to the traditional policies. The energy saving can be on the order of 40% with respect to a policy in which migration is not performed