Minimizing Energy Consumption in Data Centers Using Embedded Sensors and Machine Learning

Cloud Data Centers (DCs) consume extensive amounts of energy, making a significant contribution to environmental concerns. Moreover, with the emergence of 5G and future B5G networks, which are increasingly inclined towards software orientation and reliant on cloud computing, there is an urgent requirement for optimizing the energy consumption of DCs. We address this issue by proposing an energy-aware Virtual Machine (VM) placement solution for energy minimization. In the first part of this study, we propose a highly accurate model for predicting the dynamic power consumption of cloud computing devices. Our proposal takes advantage of the various sensors that are now embedded in physical machines, or more generally in cloud server machines, as well as Performance Monitoring Counters (PMCs) to implement a highly accurate Machine Learning (ML) power prediction model. The core part of this study then integrates the novel feature space of real-time sensors’ measurements and the predictive power model to propose a scalable placement algorithm, enabling proactive and energy-aware Virtual Machine placements. In addition, it utilizes a new set of temperature-related features that enables proactive hotspot avoidance. Our ML predictive models, as well as our proposed placement algorithm, were extensively evaluated on a cluster of real physical machines and demonstrated a significantly higher performance as compared to the implemented reference models and algorithms, reducing energy consumption by up to 7%, CPU temperature by 2%, and overloading by 28%

Network Aware VM Migration using Community Recognition

Cloud Computing is a powerful concept buzzing these days in industry by which we can avail resources as and when we require like electricity and where required softwares and information are provided based on demand. It enables us with large computing power with low-cost and hence removes the hassle of storing and maintaining servers locally. It can be basically divided into three of business i.e. Software as a Service, Platform as a Service and Infrastructure as a Service which helps to transfer service to end user very efficiently. VM placement belongs to the model of the Infrastructure as a Service. Basically it means that all applications have a certain need of computing power, memory storage, network bandwidth, and some power consumption to function which is abstracted as a Virtual Machine and provided by the Data Centers. Virtual Machine Migration is the method of transferring the VMs to the Physical Machines in such a way that there is efficient usage of energy, network bandwidth, etc. I have proposed a new network aware VM Migration scheme using Community Recognition which shows the candidates for migration and takes into account all other factors like energy, migration criteria, etc

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