Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 201

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

The final publication is available at Springer via http://dx.doi.org/10.1007/s11590-016-1065-xReducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO2 footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.Peer ReviewedPostprint (author's final draft

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

Ресурсозберігаючий розподіл навантаження для ефективного управління центром обробки даних із хмарними обчисленнями

Survey of research in resource-efficient computing and architectural principles forresource-efficient management of Clouds are offered in this article. Resource-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices are defined.Определены  архитектурные рамки и принципы энергосберегающих облачных вычислений. Рассмотрены алгоритмы распределения для энергоэффективного управления в Cloud-вычислительных средах. Показаны ресурсосберегающие возможности центров обработки данных для предоставления эвристики распределения клиентских приложений, чтобы повысить эффективность энергопотребления центра обработки данных и определить согласованное качество обслуживания QoS. Приведен обзор исследований ресурсоэффективних вычислений. Предложены архитектурные принципы энергосберегающего управления облаками, политика распределения ресурсоэффективних возможностей и алгоритмы планирования с учетом ожидания QoS, особенности характеристик использования устройств, научно-исследовательские задачи, используя которые можно получить существенные выгоды для поставщиков и потребителей ресурсов.Визначено архітектурні рамки і принципи енергозберігаючих хмарних обчислень. Розглянуто алгоритми розподілу для енергоефективного управління в Cloud-обчислювальних середовищах. Показано ресурсозберігаючі можливості центрів обробки даних для надання евристики розподілу клієнтських додатків, щоб підвищити ефективність енергоспоживання центру обробки даних і визначити узгоджену якість обслуговування QoS. Подано огляд досліджень iз ресурсоефективних обчислень. Запропоновано архітектурні принципи енергозберігаючого управління хмарами, політика розподілу ресурсоефективних можливостей і алгоритми планування з урахуванням очікування QoS, особливості характеристик використання пристроїв, науково-дослідні завдання, використовуючи які можна отримати істотні вигоди для постачальників і споживачів ресурсів

Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future

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

Advances in Dynamic Virtualized Cloud Management

Cloud computing continues to gain in popularity, with more and more applications being deployed into public and private clouds. Deploying an application in the cloud allows application owners to provision computing resources on-demand, and scale quickly to meet demand. An Infrastructure as a Service (IaaS) cloud provides low-level resources, in the form of virtual machines (VMs), to clients on a pay-per-use basis. The cloud provider (owner) can reduce costs by lowering power consumption. As a typical server can consume 50% or more of its peak power consumption when idle, this can be accomplished by consolidating client VMs onto as few hosts (servers) as possible. This, however, can lead to resource contention, and degraded VM performance. As such, VM placements must be dynamically adapted to meet changing workload demands. We refer to this process as dynamic management. Clients should also take advantage of the cloud environment by scaling their applications up and down (adding and removing VMs) to match current workload demands. This thesis proposes a number of contributions to the field of dynamic cloud management. First, we propose a method of dynamically switching between management strategies at run-time in order to achieve more than one management goal. In order to increase the scalability of dynamic management algorithms, we introduce a distributed version of our management algorithm. We then consider deploying applications which consist of multiple VMs, and automatically scale their deployment to match their workload. We present an integrated management algorithm which handles both dynamic management and application scaling. When dealing with multi-VM applications, the placement of communicating VMs within the data centre topology should be taken into account. To address this consideration, we propose a topology-aware version of our dynamic management algorithm. Finally, we describe a simulation tool, DCSim, which we have developed to help evaluate dynamic management algorithms and techniques

Performance Controlled Power Optimization for Virtualized Internet Datacenters

Modern data centers must provide performance assurance for complex system software such as web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. In recent years, more and more data centers start to adopt server virtualization strategies for resource sharing to reduce hardware and operating costs by consolidating applications previously running on multiple physical servers onto a single physical server. In this dissertation, several power efficient algorithms are proposed to effectively reduce server power consumption while achieving the required application-level performance for virtualized servers. First, at the server level this dissertation proposes two control solutions based on dynamic voltage and frequency scaling (DVFS) technology and request batching technology. The two solutions share a performance balancing technique that maintains performance balancing among all virtual machines so that they can have approximately the same performance level relative to their allowed peak values. Then, when the workload intensity is light, we adopt the request batching technology by using a controller to determine the time length for periodically batching incoming requests and putting the processor into sleep mode. When the workload intensity changes from light to moderate, request batching is automatically switched to DVFS to increase the processor frequency for performance guarantees. Second, at the datacenter level, this dissertation proposes a performance-controlled power optimization solution for virtualized server clusters with multi-tier applications. The solution utilizes both DVFS and server consolidation strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the cluster level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Finally, this dissertation proposes a VM scheduling algorithm that exploits core performance heterogeneity to optimize the overall system energy efficiency. The four algorithms at the three different levels are demonstrated with empirical results on hardware testbeds and trace-driven simulations and compared against state-of-the-art baselines