Energy Efficient Virtual Machine Migration in Cloud Data Centers

Cloud computing services have been on the rise over the past few decades, which has led to an increase in the number of data centers worldwide which increasingly consume more and more amount of energy for their operation, leading to high carbon dioxide emissions and also high operation costs. Cloud computing infrastructures are designed to support the accessibility and deployment of various service oriented applications by the users. The resources are the major source of the power consumption in data centers along with air conditioning and cooling equipment. Moreover the energy consumption in the cloud is proportional to the resource utilization and data centers are almost the worlds highest consumers of electricity. It is therefore, the need of the hour to devise efficient consolidation schemes for the cloud model to minimize energy and increase Return of Investment(ROI) for the users by decreasing the operating costs. The consolidation problem is NP-complete in nature, which requires heuristic techniques to get a sub-optimal solution. The complexity of the problem increases with increase in cloud infrastructure. We have proposed a new consolidation scheme for the virtual machines(VMs) by improving the host overload detection phase of the scheme. The resulting scheme is effective in reducing the energy and the level of Service Level Agreement(SLA) violations both, to a considerable extent. For testing the performance of our implementation on cloud we need a simulation environment that can provide us an environment with system and behavioural modelling of the actual cloud computing components, and can generate results that can help us in the analysis so that we can deploy them on actual clouds. CloudSim is one such simulation toolkit that allows us to test and analyse our allocation and selection algorithms. In this thesis we have used CloudSim version 3.0.3 to test and analyse our policies and modifications in the current policies. The advantages of using CloudSim 3.0.3 is that it takes very less effort and time to implement cloud-based application and we can test the performance of application services in heterogeneous Cloud environments. The observations are validated by simulating the experiment using the CLoudSim framework and the data provided by PlanetLab

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

A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure

Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure. The model is based on the classical minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Towards green computing in wireless sensor networks: controlled mobility-aided balanced tree approach

Virtualization technology has revolutionized the mobile network and widely used in 5G innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like SaaS, PaaS, and IaaS. The proliferation of these services among the users led to the establishment of large-scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models namely Median Migration Time (MeMT), Smallest Void Detection (SVD) and Maximum Fill (MF) that can reduce energy consumption with minimal variation in SLAs negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on liner relationship between power consumption and resource utilization. A virtual machine migration technique is considered focusing on synchronization oriented shorter stop-and-copy phase. The complete operational steps as algorithms are developed for energy aware heuristic models including MeMT, SVD and MF. To evaluate proposed heuristic models, we conduct experimentations using PlanetLab server data often ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab Servers. Through evaluation process, we deduce that proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining the high system performance

Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing

Cloud computing becomes increasingly popular for hosting all kinds of applications not only due to their ability to support dynamic provisioning of virtualized resources to handle workload fluctuations but also because of the usage based on pricing. This results in the adoption of data centers which store, process and present the data in a seamless, efficient and easy way. Furthermore, it also consumes an enormous amount of electrical energy, then leads to high using cost and carbon dioxide emission. Therefore, we need a Green computing solution that can not only minimize the using costs and reduce the environment impact but also improve the performance. Dynamic consolidation of Virtual Machines (VMs), using live migration of the VMs and switching idle servers to sleep mode or shutdown, optimizes the energy consumption. We propose an adaptive underloading detection method of hosts, VMs migration selecting method and heuristic algorithm for dynamic consolidation of VMs based on the analysis of the historical data. Through extensive simulation based on random data and real workload data, we show that our method and algorithm observably reduce energy consumption and allow the system to meet the Service Level Agreements (SLAs)