Biblio

High performance computing is widely used for large-scale simulations, designs and analysis of critical problems especially through the use of cloud computing systems nowadays because cloud computing provides ubiquitous, on-demand computing capabilities with large variety of hardware configurations including GPUs and FPGAs that are highly used for high performance computing. However, it is well known that inefficient management of such systems results in excessive power consumption affecting the budget, cooling challenges, as well as reducing reliability due to the overheating and hotspots. Furthermore, considering the latest trends in the attack scenarios and crypto-currency based intrusions, security has become a major problem for high performance computing. Therefore, to address both challenges, in this paper we present an autonomic management methodology for both security and power/performance. Our proposed approach first builds knowledge of the environment in terms of power consumption and the security tools' deployment. Next, it provisions virtual resources so that the power consumption can be reduced while maintaining the required performance and deploy the security tools based on the system behavior. Using this approach, we can utilize a wide range of secure resources efficiently in HPC system, cloud computing systems, servers, embedded systems, etc.

On cloud computing systems consisting of task queuing and resource allocations, it is essential but hard to model and evaluate the global performance. In most of the models, researchers use a stochastic process or several stochastic processes to describe a real system. However, due to the absence of theoretical conclusions of any arbitrary stochastic processes, they approximate the complicated model into simple processes that have mathematical results, such as Markov processes. Our purpose is to give a universal method to deal with common stochastic processes as long as the processes can be expressed in the form of transition matrix. To achieve our purpose, we firstly prove several theorems about the convergence of stochastic matrices to figure out what kind of matrix-defined systems has steady states. Furthermore, we propose two strategies for measuring the rate of convergence which reflects how fast the system would come to its steady state. Finally, we give a method for reducing a stochastic matrix into smaller ones, and perform some experiments to illustrate our strategies in practice.

Hypervisors are the main components for managing virtual machines on cloud computing systems. Thus, the security of hypervisors is very crucial as the whole system could be compromised when just one vulnerability is exploited. In this paper, we assess the vulnerabilities of widely used hypervisors including VMware ESXi, Citrix XenServer and KVM using the NIST 800-115 security testing framework. We perform real experiments to assess the vulnerabilities of those hypervisors using security testing tools. The results are evaluated using weakness information from CWE, and using vulnerability information from CVE. We also compute the severity scores using CVSS information. All vulnerabilities found of three hypervisors will be compared in terms of weaknesses, severity scores and impact. The experimental results showed that ESXi and XenServer have common weaknesses and vulnerabilities whereas KVM has fewer vulnerabilities. In addition, we discover a new vulnerability called HTTP response splitting on ESXi Web interface.

The explosive growth of IT infrastructures, cloud systems, and Internet of Things (IoT) have resulted in complex systems that are extremely difficult to secure and protect against cyberattacks which are growing exponentially in complexity and in number. Overcoming the cybersecurity challenges is even more complicated due to the lack of training and widely available cybersecurity environments to experiment with and evaluate new cybersecurity methods. The goal of our research is to address these challenges by exploiting cloud services. In this paper, we present the design, analysis, and evaluation of a cloud service that we refer to as Cybersecurity Lab as a Service (CLaaS) which offers virtual cybersecurity experiments that can be accessed from anywhere and from any device (desktop, laptop, tablet, smart mobile device, etc.) with Internet connectivity. In CLaaS, we exploit cloud computing systems and virtualization technologies to provide virtual cybersecurity experiments and hands-on experiences on how vulnerabilities are exploited to launch cyberattacks, how they can be removed, and how cyber resources and services can be hardened or better protected. We also present our experimental results and evaluation of CLaaS virtual cybersecurity experiments that have been used by graduate students taking our cybersecurity class as well as by high school students participating in GenCyber camps.