Towards energy aware cloud computing application construction

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 realised as a cloud toolbox that focuses on reducing the energy consumption of cloud applications holistically across all deployment models. The architecture supports energy efficiency at service construction, deployment and operation. We discuss our practical experience during implementation of an architectural component, the Virtual Machine Image Constructor (VMIC), required to facilitate construction of energy aware cloud applications. We carry out a performance evaluation of the component on a cloud testbed. The results show the performance of Virtual Machine construction, primarily limited by available I/O, to be adequate for agile, energy aware software development. We conclude that the implementation of the VMIC is feasible, incurs minimal performance overhead comparatively to the time taken by other aspects of the cloud application construction life-cycle, and make recommendations on enhancing its performance

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

Harmony: Towards automated self-adaptive consistency in cloud storage

In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consistency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid?5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80% while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra

An enhancement of toe model by investigating the influential factors of cloud adoption security objectives

Cloud computing (CC) is a future technological trend for technological infrastructure development. And it is growing strongly as the backbone of industrial future technological infrastructure. As CC service has a lot to offer, it also has some major downside that clients cannot ignore. For CC service adoption, the potential candidates are SMEs but due to lack of resources, experience, expertise and low financial structure scenario CC can be most helpful. CC faces a major issue in term of cloud security, an organization doesn’t understand the cloud security factors in the organization and data owner doubts about their data. In the research paper, an investigation on the cloud security objectives to find out the influential factors for cloud adoption in SMEs by proposing an enhancement of Technology-Organization-Environment (TOE) model with some positive influential factor like cloud security, relative advantages, cost saving, availability, SLA, capability, top management, organizational readiness, IS knowledge, malicious insiders, government regulatory support, competitive pressure, size and type. Some negative influencing factors like technological readiness, cloud trust and lack of standards in cloud security. Data were collected by questionnaires from a selected IT company based on SaaS and public cloud. Case study method has been used for validating the enhance TOE model. The IBM Statistics SPSS v22 tool was used for data analysis. The results of data analysis support the enhancement as well as all the proposed hypotheses. In summary, the results of the analysis show that all the enhancement factors were found to have a significant cloud security influence on adoption of cloud computing for SMEs

Mitigating Insider Threat in Relational Database Systems

The dissertation concentrates on addressing the factors and capabilities that enable insiders to violate systems security. It focuses on modeling the accumulative knowledge that insiders get throughout legal accesses, and it concentrates on analyzing the dependencies and constraints among data items and represents them using graph-based methods. The dissertation proposes new types of Knowledge Graphs (KGs) to represent insiders\u27 knowledgebases. Furthermore, it introduces the Neural Dependency and Inference Graph (NDIG) and Constraints and Dependencies Graph (CDG) to demonstrate the dependencies and constraints among data items. The dissertation discusses in detail how insiders use knowledgebases and dependencies and constraints to get unauthorized knowledge. It suggests new approaches to predict and prevent the aforementioned threat. The proposed models use KGs, NDIG and CDG in analyzing the threat status, and leverage the effect of updates on the lifetimes of data items in insiders\u27 knowledgebases to prevent the threat without affecting the availability of data items. Furthermore, the dissertation uses the aforementioned idea in ordering the operations of concurrent tasks such that write operations that update risky data items in knowledgebases are executed before the risky data items can be used in unauthorized inferences. In addition to unauthorized knowledge, the dissertation discusses how insiders can make unauthorized modifications in sensitive data items. It introduces new approaches to build Modification Graphs that demonstrate the authorized and unauthorized data items which insiders are able to update. To prevent this threat, the dissertation provides two methods, which are hiding sensitive dependencies and denying risky write requests. In addition to traditional RDBMS, the dissertation investigates insider threat in cloud relational database systems (cloud RDMS). It discusses the vulnerabilities in the cloud computing structure that may enable insiders to launch attacks. To prevent such threats, the dissertation suggests three models and addresses the advantages and limitations of each one. To prove the correctness and the effectiveness of the proposed approaches, the dissertation uses well stated algorithms, theorems, proofs and simulations. The simulations have been executed according to various parameters that represent the different conditions and environments of executing tasks

Agent-Based System for Mobile Service Adaptation Using Online Machine Learning and Mobile Cloud Computing Paradigm

An important aspect of modern computer systems is their ability to adapt. This is particularly important in the context of the use of mobile devices, which have limited resources and are able to work longer and more efficiently through adaptation. One possibility for the adaptation of mobile service execution is the use of the Mobile Cloud Computing (MCC) paradigm, which allows such services to run in computational clouds and only return the result to the mobile device. At the same time, the importance of machine learning used to optimize various computer systems is increasing. The novel concept proposed by the authors extends the MCC paradigm to add the ability to run services on a PC (e.g. at home). The solution proposed utilizes agent-based concepts in order to create a system that operates in a heterogeneous environment. Machine learning algorithms are used to optimize the performance of mobile services online on mobile devices. This guarantees scalability and privacy. As a result, the solution makes it possible to reduce service execution time and power consumption by mobile devices. In order to evaluate the proposed concept, an agent-based system for mobile service adaptation was implemented and experiments were performed. The solution developed demonstrates that extending the MCC paradigm with the simultaneous use of machine learning and agent-based concepts allows for the effective adaptation and optimization of mobile services