Saurav Nanda

PhD Research

Dissertation Topic: Intelligent System Towards Maintaining Service Level Agreements Related to Application Performance in Cloud Infrastructure.

Advisor: Dr. Thomas J. Hacker

My research interest is in the area of dynamic resource provisioning in cloud computing environment using machine learning techniques.
Application owners are aggressively moving to cloud platforms because of two major reasons: 1) a pay-as-you-go model for resource utilization; and 2) performance-based SLAs for cloud applications. Hence, application owners are not worried about the overhead of hardware maintenance and they do not have to pay for the idle resources. They can dynamically adjust the system resources, such as CPU, memory, disk I/O and network, to handle highly dynamic workload, and technical complexity to ensure an acceptable application performance. Kohavi and Longbotham (2007) described the significance of application performance as their experimental results related to a well-known e-business portal and a most common search portal demonstrates that their financial loss is directly proportional to the response time of the application. In the same report, Kohavi and Longbotham (2007) also described that a well-known e-business portal experienced 1 percent drop in their sales due to 100ms delay in the response time of their portal, and a prominent search portal experienced 20 percent drop in their sales due to 500ms delay their search results. Moreover, it is challenging for cloud infrastructure providers to offer a strict service level agreements (SLAs) related to application performance as explained by Islam, Keung, Lee, and Liu (2012). The ultimate goal of our research is to ensure strict SLAs based on the response time of web/mobile applications hosted in a cloud environment, primarily by using different control theoretic approaches. To achieve this broad goal we break down the following problem into sub-problems: How can performance-based service level agreements (SLAs) for web/mobile applications hosted in cloud environment be maintained?

- How to characterize and model the dynamic nature of application workload and tune elastic system resources (CPU, memory, network) accordingly?
- What are different resource scaling techniques that can minimize the resource allocation and maximize the resource utilization?
- How to filter noise from the resource utilization data that is collected from the virtual and physical hosts?
- How can cloud providers maximize the resource usage and at the same time ensure no SLA violations occur related to response time of the application?
- Can cloud providers enhance the performance of web/mobile applications using global live migration of VMs?
- Which prediction technique would be more accurate to predict the resource utilization on the virtualized infrastructure?
- How to use an ensemble approach to have multiple prediction techniques in a single control system that can choose the best prediction technique for different environmental scenarios?

Masters Research

Masters Thesis: Prediction of Optimal Attack Path using Soft Computing Techniques.

In my masters thesis project, I concentrated on finding an optimal risk-prone attack path to penetrate a wireless network, which is complex due to the inherent dynamic nature of the network. I compared the results of using two soft-computing techniques namely, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) which used attack vector metric to find the optimal attack path. My thesis work was greatly appreciated by the faculty. I was invited to present my research at the IEEE and ICDN conferences and it was published in their proceedings.

Bachelors Research

Bachelors Thesis: Implementation of Periodic Group Re-keying Methods for Secure Multicast Communication.

In my bachelor’s thesis project, I designed a data-driven framework to compare two pre-existing rekeying techniques for secure multicast communication using network socket programming, with a new technique based on hashing. I thoroughly enjoyed working on this research problem and got greatly motivated to pursue advanced research.

• Cloud Computing
• High Performance Computing
• Dynamic Resource Provisioning
• Control Systems
• Optimization Techniques
• Virtualized Environment
• Hypervisor (KVM)
• OpenStack
• Docker

Chief Technology Officer at Abhitech IT Solutions

Lucknow, India (Jan 2010 - Jul 2014)

■ Led the technical front for more than four years, and handled clients across the globe.

■ Delivered more than 10 big and 40 small projects with a team of 20 professionals.

■ Expertise in Customized Web Application and Mobile Application (iPhone, iPad, Android, Blackberry) Development.