Modeling A Green Decision Support System for Data Center Sustainability

The objective of this dissertation is developing more energy efficient data centers while focusing on the environment as well as meeting the increasing computing needs. Reliability of data centers will be the number one priority for management; however, the focus will be to implement a design by incorporating free cooling, applying thermal profiling, utilizing data mining, and continuing virtualization to create more efficient green data centers that are good for the environment. Since the fall of 2009, electrical consumption patterns were measured in the main data center for the servers and the air-conditioners at Montclair State University (MSU) to quantify the carbon footprint and the electrical costs. An important outcome of this work is to build a Decision Support System (DSS) for green computing in data centers. A DSS is a computer based application to assist in providing solutions with respect to decision-making to multifaceted problems. In summary, building on our measurements, the objective is to design a DSS for data centers to enhance energy efficiency, reduce the carbon footprint, and promote sustainability science across disciplines

Management system prototype for intelligent mobile cloud computing for big data

The current challenge of mobile devices is the storage capacity that has led service providers to develop new value-added mobile services. To address these limitations, mobile cloud computing, which offers on-demand is developed. Mobile Cloud Computing (MCC) is developed to augment device capabilities, facilitating to mobile users store, access to a big dataset on the cloud. Even so, given the limitations of bandwidth, latencies, and device battery life, new responses are required to extend the use of mobile devices. This paper presents a novel design and implementation of developing process on intelligent mobile cloud storage management system, also called as Intelligent Mobile Cloud Computing (IMCC) for android based users. IMCC is important for cloud storage user to make their data effectively and efficiently for saving the user time. IMCC provided convenience for user to use multiple cloud storage using one application and easy for users to store their data to any cloud storage. The result shows using IMCC it only took 8 seconds to access the data, which is faster compared with traditional MCC, it took 23.33 seconds. IMCC reduce 65.71% of latency occur using the MCC in managing a user data. The developed IMCC prototype is accessible through the Google Play Store

Towards an interoperable energy efficient Cloud computing architecture-practice & experience

The energy consumption of Cloud computing continues to be an area of significant concern as data center growth continues to increase. This paper reports on an energy efficient interoperable Cloud architecture realized as a Cloud toolbox that focuses on reducing the energy consumption of Cloud applications holistically across all deployments models. The architecture supports energy efficiency at service construction, deployment, and operation and interoperability through the use of the Open Virtualization Format (OVF) standard. We discuss our practical experience during implementation and present an initial performance evaluation of the architecture. The results show that the implementing Cloud provider interoperability is feasible and incurs minimal performance overhead during application deployment in comparison to the time taken to instantiate Virtual Machines

Cloud Computing for Environment-Friendly Data Centers

The purpose of this paper is to analyze the carbon footprint and utilization rates in a data center. The long-term goal of this work is to give data center administrators an enhanced perspective of data center operations to allow for more energy efficient operation, to lower the carbon footprint, and to promote green data centers. Previous literature shows that low utilization rates in data centers are due to the forecasting of demand to meet spikes in data center use. This management policy has led to many servers running idle the majority of the time which is a waste of resources. We argue that a majority of the data centers should be down sized through decommissioning of phantom servers, virtualization, and shifting spikes in demand to a cloud provider. We use data from the operations of a mid-to-large scale data center in a university. We deploy data mining techniques of decision trees and case-based reasoning to conduct analysis for decision support in cloud computing at data centers. We provide recommendations based on a literature search and our own work. This paper describes our work in progress in the area of developing green data centers