Cloud computing offers the elasticity features by dynamically resizing the infrastructure in response to changes in workload demands to meet performance guarantees and minimize costs. In the last decade, a large body of work has been done in the area of horizontal elasticity, while only few research efforts addressed vertical elasticity. This paper develops a vertical elasticity controller for cloud-based applications using control theory principles to guarantee performance requirements by adjusting the memory allocation as a control knob. The novelty of our work lies on applying a controller synthesis technique by guaranteeing robustness and stability of the controlled system, using the application response time as a decision making criterion. The experimental results reveal that the controller is able to efficiently save at least 47% memory usage while keeping an acceptable user experience.
Abstract-Cloud computing revolutionised the industry with its elastic, on-demand approach to computational resources, but has lead to a tremendous impact on the environment. Data centers constitute 1.1-1.5% of total electricity usage in the world. Taking a more informed view of the electrical grid by analysing real-time electricity prices, we set the foundations of a grid-conscious cloud. We propose a scheduling algorithm that predicts electricity price peaks and throttles energy consumption by pausing virtual machines. We evaluate the approach on the OpenStack cloud manager through an empirical approach and show reductions in energy consumption and costs. Finally, we define green instances in which cloud providers can offer such services to their customers under better pricing options.
Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature differences. As a result, a managed virtual machine (VM) may suffer occasional downtimes. Current cloud providers only offer high availability VMs, without enough flexibility to apply such energyaware management. In this paper we show how to analyse past traces of dynamic cloud management actions based on electricity prices and temperatures to estimate VM availability and price values. We propose a novel service level agreement (SLA) specification approach for offering VMs with different availability and price values guaranteed over multiple SLAs to enable flexible energy-aware cloud management. We determine the optimal number of such SLAs as well as their availability and price guaranteed values. We evaluate our approach in a user SLA selection simulation using Wikipedia and Grid'5000 workloads. The results show higher customer conversion and 39% average energy savings per VM.
The rapid cloud computing growth has turned data center energy consumption into a global problem. At the same time, modern cloud providers operate multiple geographically-distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures. Furthermore, to account for emerging technologies surrounding the cloud ecosystem, a maintainable control solution needs to be forward-compatible. Existing cloud controllers are focused on VM consolidation methods suitable only for a single data center or consider migration just in case of workload peaks, not accounting for all the aspects of geographically distributed data centers. In this paper, we propose a pervasive cloud controller for dynamic resource reallocation adapting to volatile time-and location-dependent factors, while considering the QoS impact of too frequent migrations and the data quality limits of time series forecasting methods. The controller is designed with extensible decision support components. We evaluate it in a simulation using historical traces of electricity prices and temperatures. By optimising for these additional factors, we estimate 28.6% energy cost savings compared to baseline dynamic VM consolidation. We provide a range of guidelines for cloud providers, showing the environment conditions necessary to achieve significant cost savings and we validate the controller's extensibility.Dražen Lučanin is a PhD student at the Vienna University of Technology, studying energy efficiency in cloud computing. Previously, he worked as an external associate at the Ruder Bošković Institute on machine learning methods for forecasting financial crises. He graduated with a master's degree in computer science at the Faculty of electrical engineering and computing, University of Zagreb. For more information, please visit http://www.infosys.tuwien.ac.at/staff/ drazen/ Ivona Brandic is Assistant Professor at the Vienna University of Technology. Prior to that, she was Assistant Professor at the Department of Scientific Computing, University of Vienna. She received her PhD degree in 2007 and her venia docendi for practical computer science in 2013, both from Vienna University of Technology. In 2011 she received the Distinguished Young Scientist Award from the Vienna University of Technology for her project on the Holistic Energy Efficient Hybrid Clouds. She published more than 50 scientific journal, magazine and conference publications and she coauthored a text-book on federated and self-manageable Cloud infrastructures. For more information, please visit http://www.infosys.tuwien. ac.at/staff/ivona/
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