A SECURE ENERGY EFFICIENT VM PREDICTION AND MIGRATION FRAMEWORK FOR OVERCOMMITED CLOUDS

Propose an included, energy efficient, resource allocation framework for overcommitted clouds. The concord makes massive energy investments by 1) minimizing Physical Machine overload occurrences via virtual machine resource usage monitoring and prophecy, and 2) reducing the number of active PMs via efficient VM relocation and residency. Using real Google data consisting of a 29 day traces collected from a crowd together contain more than 12K PMs, we show that our proposed framework outperforms existing overload avoidance techniques and prior VM migration strategies by plummeting the number of unexpected overloads, minimizing migration overhead, increasing resource utilization, and reducing cloud energy consumption.&nbsp

SECURE AND EFFICIENT FAULT NODE DETECTION IN WIRELESS SENSOR NETWORKS

Propose an included, energy efficient, resource allocation framework for overcommitted clouds. The concord makes massive energy investments by 1) minimizing Physical Machine overload occurrences via virtual machine resource usage monitoring and prophecy, and 2) reducing the number of active PMs via efficient VM relocation and residency. Using real Google data consisting of a 29 day traces collected from a crowd together contain more than 12K PMs, we show that our proposed framework outperforms existing overload avoidance techniques and prior VM migration strategies by plummeting the number of unexpected overloads, minimizing migration overhead, increasing resource utilization, and reducing cloud energy consumption.&nbsp

Estimating adaptive setpoint temperatures using weather stations

Reducing both the energy consumption and CO 2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes.Spanish Ministry of Science, Innovation and Universities 00064742/ITC-20133094Spanish Ministry of Economy, Industry and Competitiveness BIA 2017-85657-

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Integrated Green Cloud Computing Architecture

Arbitrary usage of cloud computing, either private or public, can lead to uneconomical energy consumption in data processing, storage and communication. Hence, green cloud computing solutions aim not only to save energy but also reduce operational costs and carbon footprints on the environment. In this paper, an Integrated Green Cloud Architecture (IGCA) is proposed that comprises of a client-oriented Green Cloud Middleware to assist managers in better overseeing and configuring their overall access to cloud services in the greenest or most energy-efficient way. Decision making, whether to use local machine processing, private or public clouds, is smartly handled by the middleware using predefined system specifications such as service level agreement (SLA), Quality of service (QoS), equipment specifications and job description provided by IT department. Analytical model is used to show the feasibility to achieve efficient energy consumption while choosing between local, private and public Cloud service provider (CSP).Comment: 6 pages, International Conference on Advanced Computer Science Applications and Technologies, ACSAT 201