Virtual Machines In Data Centers Research Articles

Prediction models for Clustered Virtual Machines in Data Centers

In data center (DC) environments, machine learning algorithms play an important role in resource management to increase efficiency by means of proper predictive monitoring workload trends and adjusting jobs accordingly. In this paper, we propose a system to predict the CPU usage of virtual machines (VMs) of a DC. Our proposal performs clustering of VMs based on their historical information (i.e., time series) by evaluating several traditional ML algorithms using common statistical features of VM time series, which facilitates grouping VMs with similar behaviors and establishing clusters based on these features. Then, training of representative models is performed to finally choose the one with the lowest mean error per cluster. The simulation results show that by performing clustering and training the model with representative time series, it is indeed possible to obtain a low mean error while reducing the local training time per individual VM.

A frequency-aware management strategy for virtual machines in DVFS-enabled clouds

It is demanding to curtail energy consumption of virtual-machine-powered data centers, because modern data centers have been significantly scaling up in capacity in past decades. In this study, we propose a frequency-aware management strategy, which controls dynamic power and static power of processors running virtual machines in data centers. Unlike existing dynamic voltage and frequency scaling schemes, our strategy simply incorporates frequency requirements rather than task execution times. This salient feature is practical, because task execution times in a raft of real-world applications are unknown in a priori. We build a frequency-aware model, which is adept at deriving an optimal frequency ratio that minimizes processors’ energy consumption. With our model in place, the energy efficiency of a data center can be maximized by adjusting the processor's frequency to meet the optimal frequency ratio. We design a management approach to judiciously adjust frequency ratio to conserve energy without violating the frequency requirements imposed by virtual machines. After analyzing the correlations between frequency ratio and energy consumption, we show that a small static power proportion gives rise to high energy-saving performance. The results demonstrate that our model lays out a solid theoretical foundation catering to the development of power management software in DVFS-enabled clouds.

EPBLA: energy-efficient consolidation of virtual machines using learning automata in cloud data centers

High demand for computational power by business, science, and applications has led to the creation of large-scale data centers that consume enormous amounts of energy. This high energy consumption not only imposes a significant operating cost but also has a negative impact on the environment (greenhouse gas emissions). A promising solution to reduce the amount of energy used by data centers is the consolidation of virtual machines (VMs) that allows some hosts to enter low consuming sleep modes. Dynamic migration (replacement) of VMs between physical hosts is an effective strategy to achieve VM consolidation. Dynamic migration not only saves energy by migrating the VMs hosted by idle hosts but can also avoid hotspots by migrating VMs from over-utilized hosts. In this paper, we presented a new approach, called extended-placement by learning automata (EPBLA), based on learning automata for dynamic replacement of VMs in data centers to reduce power consumption. EPBLA consists of two parts (i) a linear reward penalty scheme which is a finite action-set learning automata that runs on each host to make a fully distributed VM placement considering CPU utilization as a metric to categorize the hosts, and (ii) a continuous action-set learning automata as a policy for selecting an underload host initiating the migration process. A real-world workload is used to evaluate the proposed method. Simulation results showed the efficiency of EPBLA in terms of reduction of energy consumption by 20% and 30% compared with PBLA and Firefly, respectively.

Energy-efficiency virtual machine placement based on binary gravitational search algorithm

Cloud computing is a remarkable growing paradigm for hosting and offering services through the Internet. It attracted the most notorious business companies and resulted to an exponential increase of its users from simple end users to companies that deploy more and more of their system over the cloud. The amount of resources to provide the demand became tremendous. therefore, a great need energy supply. The world as we know is highly concerned about the environment and the energy-efficiency in all aspect of life and the domain of IT is one them. To deal with energy wastage in data centers, researches use Virtual machine placement as a main key to assure cloud consolidation and reduce power wastage. Several approaches were proposed for Virtual machine placement. This paper proposes a solution based on Binary gravitational search algorithm (BGSA) for the virtual machine placement in the heterogeneous data center. In this work, we compared the BGSA method to fit with virtual machines in data centers with particle swarm optimization, First-fit, Best-fit, and worst-fit. results showed significant difference of energy save comparing to other strategies. The results obtained gave the advantage to our approach and its better response with the increase of number of virtual machines.

Ecope: Task Aware Workload Elastic Scheduling and Customization for Infrastructure

In the past several years, the development in non functional requirement such as CPU and memory has been done. Due to the workload characteristics the energy efficiency of non functional component has made a large coverage. We develop Ecope to attain energy proportionality for different methods of services of virtual machine in data centres’ decrease non functional energy for servers in large data centers. Demonstrate three input methods to illustrate our concept to real world services such as file processing, backend services and content processing. These services are applying on virtual machine in large data centers. In short, our aim is to recognize the preeminent non functional configuration among various workloads.

Fair Network Bandwidth Allocation in IaaS Datacenters via a Cooperative Game Approach

With wide application of virtualization technology, tenants are able to access isolated cloud services by renting the shared resources in Infrastructure-as-a-Service (IaaS) datacenters. Unlike resources such as CPU and memory, datacenter network, which relies on traditional transport-layer protocols, suffers unfairness due to a lack of virtual machine (VM)-level bandwidth guarantees. In this paper, we model the datacenter bandwidth allocation as a cooperative game, toward VM-based fairness across the datacenter with two main objectives: 1) guarantee bandwidth for VMs based on their base bandwidth requirements, and 2) share residual bandwidth in proportion to the weights of VMs. Through a bargaining game approach, we propose a bandwidth allocation algorithm, Falloc, to achieve the asymmetric Nash bargaining solution (NBS) in datacenter networks, which exactly meets our objectives. The cooperative structure of the algorithm is exploited to develop an online algorithm for practical real-world implementation. We validate Falloc with experiments under diverse scenarios and show that by adapting to different network requirements of VMs, Falloc can achieve fairness among VMs and balance the tradeoff between bandwidth guarantee and proportional bandwidth sharing. Our large-scale trace-driven simulations verify that Falloc achieves high utilization while maintaining fairness among VMs in datacenters.

Topology-Aware Virtual Machine Placement in Data Centers

This paper presents the Topology-aware Virtual Machine Placement algorithm, which aims at placing groups of virtual machines in data centers. It was designed to occupy small areas of the data center network in order to consolidate the network flows produced by the virtual machines. Extensive simulation is used to show that the proposed algorithm prevents the formation of network bottlenecks, therefore accepting more requests of allocation of virtual machines. Moreover, these advantages are obtained without compromising energy efficiency. The energy consumption of servers and switches are taken into account, and these are switched off whenever idle.

Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing

In this paper, we developed a dynamic energy-efficient virtual machine (VM) migration and consolidation algorithm based on a multi-resource energy-efficient model. It can minimize energy consumptio...

A Study of Software Defined Networking with OpenFlow

data center complexity has been drastically increased with the widespread of many dynamic services. However, provisioning heterogeneous services to satisfy user's demand is a challenging task for the service providers and as well for the cloud Network administrators. Traditional network architectures were not designed to meet the requirements of today's enterprises and end users. Hence to meet the users demand and to address the difference between market requirements and network capabilities in data centers the industry has come up with the Software-Defined Networking (SDN) architecture and its related standards. With SDN, static network can advance into a wide range of service delivery platform capable of responding rapidly to changing business, end user's demand, and market needs. SDN provides a novel and innovative approach for controlling and managing virtual machines in data centers. In this paper, we discuss the concept of SDN, which can be implemented by the OpenFlow protocol. We discuss the OpenFlow architecture and its components with various OpenFlow versions. Finally we discuss OpenFlow based SDN implementation, testing and present an overview of SDN based applications. KeywordsDefined Networking, Open Flow, Network management

A cloud middleware for assuring performance and high availability of soft real-time applications

Applications are increasingly being deployed in the cloud due to benefits stemming from economy of scale, scalability, flexibility and utility-based pricing model. Although most cloud-based applications have hitherto been enterprise-style, there is an emerging need for hosting real-time streaming applications in the cloud that demand both high availability and low latency. Contemporary cloud computing research has seldom focused on solutions that provide both high availability and real-time assurance to these applications in a way that also optimizes resource consumption in data centers, which is a key consideration for cloud providers. This paper makes three contributions to address this dual challenge. First, it describes an architecture for a fault-tolerant framework that can be used to automatically deploy replicas of virtual machines in data centers in a way that optimizes resources while assuring availability and responsiveness. Second, it describes the design of a pluggable framework within the fault-tolerant architecture that enables plugging in different placement algorithms for VM replica deployment. Third, it illustrates the design of a framework for real-time dissemination of resource utilization information using a real-time publish/subscribe framework, which is required by the replica selection and placement framework. Experimental results using a case study that involves a specific replica placement algorithm are presented to evaluate the effectiveness of our architecture.

A Dynamically-adaptive Resource Aware Load Balancing Scheme for VM migrations in Datacenters

The advancements in the field of networking, network infrastructures and the growing size for processing, storage/data and communication, the demand for Virtualization and Data Center flexible resource management has become significantly important. Virtualization is the single most effective way to reduce IT expenses while boosting efficiency and agility. And with time, the advancements in the field of Virtualization, increasing demands of datacenters and increasing network infrastructure development and maintenance cost lead to the development of Virtual Machines and their migrations across datacenters at large scale now. Though Virtual Machines in datacenter decouples physical machines with Virtual ones for their processing but they are still coupled with hosts for their virtual resources sharing and thereby exerting load on servers further. This paper focuses on balancing the load of the servers with the help of efficient VM migrations and proposes an algorithm that adapts to the dynamic business needs in a better way for datacenter access and request processing.

Effective Technique for VM Provisioning on PaaS Cloud Computing Platform

In a Cloud Computing platform, Virtual Machine Provisioning involves instantiation of one or more Virtual Machines (VMs) that match the hardware and software requirements of an application and plays an important role in performance issues [4]. This paper discusses a VM Provisioning technique to reduce the response time in a cloud computing platform with PaaS type of deployment model when the physical servers hosting Virtual Machines in datacenters vary in their configurations. The method proposed in this paper is application specific and has been implemented on a test bed with VMware environment. Experimental results show that it can deliver better performance compared to random scheduling method for applications with repetitive computations