University of California, Irvine, USA.
Ph. D Student in Computer Science (September 2009--Present).
Thesis Title: Qos-Aware Middleware for Optimal Service Allocation in Mobile Cloud Computing.

Abstract: The past two decades of explosive growth of wireless networking, mobile computing and web technologies has profoundly influenced society at large. Almost anyone with access to a mobile device has access to services on the Internet and has reaped the benefits of instant accessibility to Internet-enabled technologies such as social networks, media streaming applications, location-based services, instant messaging, email etc. In this thesis we aim to synergistically exploit mobile and cloud computing to enable services that can enrich the experience and capabilities of mobile users in a pervasive environment. While mobile computing empowers users with anywhere, anytime access to the Internet, cloud computing harnesses the vast storage, computing, and software infrastructure resources of large organizations (e.g Amazon, Google) into a single virtualized infrastructure within reach of the general population. In this work we studied the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. In this thesis we concentrate on three main parts: (i). System modeling and problem formulation, (ii) Service and resource provisioning algorithms, (iii) System performance testing and prototyping. In the first part we proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated and mapped to mobile service usage patterns. We showed that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We proposed efficient and scalable classes of heuristics for solving mentioned problem in the second part. Finally in system performance testing and prototyping we developed a middleware solution which orchestrating all required components for such mobile cloud computing system. We studies each of these contributions by an extensive experimental evaluation and prototyping.

University of Regina, SK, Canada.
Master of Applied Science in Software Engineering (September 2009).
Thesis: Optimizing Multicast Throughput in IP Networks (Best Thesis Award Nominee).

Abstract: Given a fixed network of routers, a set of multicast sources and their corresponding receivers, we investigate the problem of constructing multicast sessions that maximize the multicast throughput of all sessions for different applications. It is known that for problems with only one source node, heuristic algorithms based on packing maximum-rate Steiner trees may achieve throughputs close to network capacity for some networks of interest. We found that direct extension of such successful algorithms for single-source scenarios to multi-source application is inefficient. We have proposed and investigated three classes of tree packing algorithms, the non-cooperative class, the medium cooperative class and the highly cooperative class, that are distinguished by the degree of cooperation between participating routers. We show how better performance can be achieved when the source nodes act more cooperatively (and less selfishly), as they claim bandwidth resources in the network. Through extensive simulations, we have shown that the performance of our best algorithm is very close to the network coding capacity with an average throughput that is about 92% of the theoretical upper bound. We used the rate-distortion framework to quantify the performance of our approach for multimedia multicast. Through simulations, we showed that our best algorithms can achieve average distortions that are just 0.44db higher than what theoretically achievable through network coding. Finally, we show how our proposed algorithms can be implemented on top of standard IP-multicast protocols, with only software updates to existing router technologies.

Sharif University of Technology, Tehran, Iran.
Master of Science in Computer Science (January 2007).
Thesis: Routing and Clustering Algorithms in Wireless Sensor Networks.

Abstract: Due to the wide usage of wireless technology and advances in microelectronics, wireless technology is used in many applications, such as smart homes, monitoring remote area, battle fields etc. There is not standard solution for this new architecture, so there is a new paradigm that attracts many researchers. In this thesis we are concerning both on modeling and simulation of the routing and clustering algorithms in network layer. There has not been any good and solid framework for simulation this kind of network in literature. In this work we used object oriented and UML technology, and listener based pattern for modeling of wireless sensor networks. We developed flexible frame work which is called Xmulator for wireless systems. With this framework we developed, tested and compared some of the routing algorithms for wireless sensor networks.

Sharif University of Technology, Tehran, Iran.
Bachelor of Engineering in Electrical Engineering (September 2001).
Thesis: Fingerprint High Level Classification (Distinguished Thesis).

Abstract: In this thesis a novel algorithm for finger print high level classification is presented. This based on a novel algorithm for detection of singular points, the core and delta points, in fingerprint images. The number and location of singular points,  are used to classify fingerprint images in to five general groups; and therefore to narrow down the search space in large fingerprint databases. Using the proposed directional masks in the first step, detects the neighborhood of the singular points. In the second stage by implementing the proposed algorithm, an adaptive singular point detection method, is designed to extract the exact location of core and delta points. Usage of the proposed directional masks speeds up the process and the proposed adaptive singular point detection method increases the accuracy of the algorithm.

Reza Rahimi

Ph.D Candidate
School of Information and Computer Science,
University of California Irvine, USA.

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