Secure Hardware Performance Analysis in Virtualized Cloud Environment

The main obstacle in mass adoption of cloud computing for database operations is the data security issue. In this paper, it is shown that IT services particularly in hardware performance evaluation in virtual machine can be accomplished effectively without IT personnel gaining access to real data for diagnostic and remediation purposes. The proposed mechanisms utilized TPC-H benchmark to achieve 2 objectives. First, the underlying hardware performance and consistency is supervised via a control system, which is constructed using a combination of TPC-H queries, linear regression, and machine learning techniques. Second, linear programming techniques are employed to provide input to the algorithms that construct stress-testing scenarios in the virtual machine, using the combination of TPC-H queries. These stress-testing scenarios serve 2 purposes. They provide the boundary resource threshold verification to the first control system, so that periodic training of the synthetic data sets for performance evaluation is not constrained by hardware inadequacy, particularly when the resources in the virtual machine are scaled up or down which results in the change of the utilization threshold. Secondly, they provide a platform for response time verification on critical transactions, so that the expected Quality of Service (QoS) from these transactions is assured

Building an Emulation Environment for Cyber Security Analyses of Complex Networked Systems

Computer networks are undergoing a phenomenal growth, driven by the rapidly increasing number of nodes constituting the networks. At the same time, the number of security threats on Internet and intranet networks is constantly growing, and the testing and experimentation of cyber defense solutions requires the availability of separate, test environments that best emulate the complexity of a real system. Such environments support the deployment and monitoring of complex mission-driven network scenarios, thus enabling the study of cyber defense strategies under real and controllable traffic and attack scenarios. In this paper, we propose a methodology that makes use of a combination of techniques of network and security assessment, and the use of cloud technologies to build an emulation environment with adjustable degree of affinity with respect to actual reference networks or planned systems. As a byproduct, starting from a specific study case, we collected a dataset consisting of complete network traces comprising benign and malicious traffic, which is feature-rich and publicly available

Methods and Applications of Synthetic Data Generation

The advent of data mining and machine learning has highlighted the value of large and varied sources of data, while increasing the demand for synthetic data captures the structural and statistical characteristics of the original data without revealing personal or proprietary information contained in the original dataset. In this dissertation, we use examples from original research to show that, using appropriate models and input parameters, synthetic data that mimics the characteristics of real data can be generated with sufficient rate and quality to address the volume, structural complexity, and statistical variation requirements of research and development of digital information processing systems. First, we present a progression of research studies using a variety of tools to generate synthetic network traffic patterns, enabling us to observe relationships between network latency and communication pattern benchmarks at all levels of the network stack. We then present a framework for synthesizing large scale IoT data with complex structural characteristics in a scalable extraction and synthesis framework, and demonstrate the use of generated data in the benchmarking of IoT middleware. Finally, we detail research on synthetic image generation for deep learning models using 3D modeling. We find that synthetic images can be an effective technique for augmenting limited sets of real training data, and in use cases that benefit from incremental training or model specialization, we find that pretraining on synthetic images provided a usable base model for transfer learning

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

Enterprise 2.0 – Is The Market Ready?

Enterprise 2.0 family technologies have growing popularity, the cloud computing market is growing rapidly and, as a consequence, companies of all sizes start to evaluate the potential fit. The use of “Software as a Service”, “Platform as a Service” and “Infrastructure as a Service” has been evolving during the past years and has become increasingly popular. As its computing viability and benefits are legitimized, the adoption rate is rapidly increasing. The most popular business model in the abovementioned family is by far “Software as a Service” (also called SaaS), which is a software distribution model assuming the software applications are hosted and maintained by the vendor or the distributor, and user access is granted exclusively by means of the Internet. Based on both literature review and action research, the paper at hand is a synthesis for the results of an empirical study performed during the last two years among Romanian and foreign companies, in order to outline and provide an objective and unbiased answer to the question: “Is the market ready for these technologies or did they come too soon?”. The paper is a part of a larger research performed by the author in the field of Enterprise 2.0 technologies.Enterprise 2.0, Software as a Service, Platform as a Service, Infrastructure as a Service, Empirical study

Exploring Strategies that IT Leaders Use to Adopt Cloud Computing

Information Technology (IT) leaders must leverage cloud computing to maintain competitive advantage. Evidence suggests that IT leaders who have leveraged cloud computing in small and medium sized organizations have saved an average of $1 million in IT services for their organizations. The purpose of this qualitative single case study was to explore strategies that IT leaders use to adopt cloud computing for their organizations. The target population consisted of 15 IT leaders who had experience with designing and deploying cloud computing solutions at their organization in Long Island, New York within the past 2 years. The conceptual framework of this research project was the disruptive innovation theory. Semistructured interviews were conducted and company documents were gathered. Data were inductively analyzed for emergent themes, then subjected to member checking to ensure the trustworthiness of findings. Four main themes emerged from the data: the essential elements for strategies to adopt cloud computing; most effective strategies; leadership essentials; and barriers, critical factors, and ineffective strategies affecting adoption of cloud computing. These findings may contribute to social change by providing insights to IT leaders in small and medium sized organizations to save money while gaining competitive advantage and ensure sustainable business growth that could enhance community standards of living

DevOps for Digital Leaders

DevOps; continuous delivery; software lifecycle; concurrent parallel testing; service management; ITIL; GRC; PaaS; containerization; API management; lean principles; technical debt; end-to-end automation; automatio