Ruppa K. Thulasiram

HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network

Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for MANETs. Swarm intelligence is a computational intelligence technique that involves collective behavior of autonomous agents that locally interact with each other in a distributed environment to solve a given problem in the hope of finding a global solution to the problem. In this paper, we propose a hybrid routing algorithm for MANETs based on ACO and zone routing framework of bordercasting. The algorithm, HOPNET, based on ants hopping from one zone to the next, consists of the local proactive route discovery within a nodes neighborhood and reactive communication between the neighborhoods. The algorithm has features extracted from ZRP and DSR protocols and is simulated on GlomoSim and is compared to AODV routing protocol. The algorithm is also compared to the well known hybrid routing algorithm, AntHocNet, which is not based on zone routing framework. Results indicate that HOPNET is highly scalable for large networks compared to AntHocNet. The results also indicate that the selection of the zone radius has considerable impact on the delivery packet ratio and HOPNET performs significantly better than AntHocNet for high and low mobility. The algorithm has been compared to random way point model and random drunken model and the results show the efficiency and inefficiency of bordercasting. Finally, HOPNET is compared to ZRP and the strength of nature-inspired algorithm is shown.

Resource Provisioning Policies to Increase IaaS Provider's Profit in a Federated Cloud Environment

Cloud Federation is a recent paradigm that helps Infrastructure as a Service (IaaS) providers to overcome resource limitation during spikes in demand for Virtual Machines (VMs) by outsourcing requests to other federation members. IaaS providers also have the option of terminating spot VMs, i.e, cheaper VMs that can be canceled to free resources for more profitable VM requests. By both approaches, providers can expect to reject less profitable requests. For IaaS providers, pricing and profit are two important factors, in addition to maintaining a high Quality of Service (QoS) and utilization of their resources to remain in the business. For this, a clear understanding of the usage pattern, types of requests, and infrastructure costs are necessary while making decisions to terminate spot VMs, outsourcing or contributing to the federation. In this paper, we propose policies that help in the decision-making process to increase resources utilization and profit. Simulation results indicate that the proposed policies enhance the profit, utilization, and QoS (smaller number of rejected VM requests) in a Cloud federation environment.

Pricing Cloud Compute Commodities: A Novel Financial Economic Model

In this study, we design, develop, and simulate a cloud resources pricing model that satisfies two important constraints: the dynamic ability of the model to provide a high satisfaction guarantee measured as Quality of Service (QoS) - from users perspectives, profitability constraints - from the cloud service providers perspectives We employ financial option theory and treat the cloud resources as underlying assets to capture the realistic value of the cloud compute commodities (C3). We then price the cloud resources using our model. We discuss the results for four different metrics that we introduce to guarantee the quality of service and price as follows: (a) Moores law based depreciation of asset values, (b) new technology based volatility measures in capturing price changes, (c) a new financial option pricing based model combining the above two concepts, and (d) the effect of age of resources and depreciation of cloud resource on QoS. We show that the cloud parameters can be mapped to financial economic model and we discuss the results of cloud compute commodity pricing for various parameters, such as the age of the resource, quality of service, and contract period.

Proceedings of the SSDSN, UPWN, WISH, SGC, ParDMCom, HiPCoMB, and IST-AWSN international workshops held at ISPA 2007 on Frontiers of High Performance Computing and Networking

Workshop on Security and Survivability in Distributed Sensor Networks.- To Increase Survivability with Software Rejuvenation by Having Dual Base Station in WSN Environment.- DoS Attack Mining in Sensor Node Replacement.- Secure Cluster Header Election Techniques in Sensor Network.- A Secure Data Aggregation Scheme for Wireless Sensor Networks.- A Key Revocation Scheme for Mobile Sensor Networks.- Workshop on Ubiquitous Processing for Wireless Networks.- Adaptive Binding Update Schemes in NEMO.- An Information Aggregation Scheme of Multi-node in Ubiquitous Sensor Networks.- QLP-LBS: Quantization and Location Prediction-Based LBS for Reduction of Location Update Costs.- Positioning System in Taipei Childrens Museum of Transportation and Communication.- Portable Patient Information Integration System for Patient Safety Improvement.- Secure Session Management Mechanism in VoIP Service.- An Effective Local Repair Scheme Using Candidate Node and Hello Message in AODV.- A Frame-Based Selective Encryption Method for Real Time Video Transmission on VoIP.- Workshop on Intelligent Systems and Smart Home.- ECG Anomaly Detection via Time Series Analysis.- Semantics-Based Event-Driven Web News Classification.- A Study on Application of Cyber Shopping Service with Utilization of Context Awareness in Smart Home Environment.- A Transparent Protocol Scheme Based on UPnP AV for Ubiquitous Home.- Learning Fuzzy Concept Hierarchy and Measurement with Node Labeling.- Prepositions and Conjunctions in a Natural Language Interfaces to Databases.- Zigbee Positioning System for Smart Home Application.- Solving Unbounded Knapsack Problem Using an Adaptive Genetic Algorithm with Elitism Strategy.- Automatic Lexico-Semantic Frames Acquisition from Syntactic Parsed Tree by Using Clustering and Combining Techniques.- Intelligent Home Network Authentication: S/Key-Based Home Device Authentication.- GA Based Optimal Keyword Extraction in an Automatic Chinese Web Document Classification System.- Design and Implementation of Context-Aware Security Management System for Ubiquitous Computing Environment.- Forward Secure Privacy Protection Scheme for RFID System Using Advanced Encryption Standard.- Workshop on Semantic and Grid Computing.- Polygon-Based Similarity Aggregation for Ontology Matching.- A User-Controlled VoiceXML Application Based on Dynamic Voice Anchor and Node.- Grid Computing in New York State, USA.- Workshop on Parallel and Distributed Multimedia Computing.- A Data Allocation Method for Efficient Content-Based Retrieval in Parallel Multimedia Databases.- Optimization of VoD Streaming Scheduling for IPTV Multi-channel Support.- Continuous Kernel-Based Outlier Detection over Distributed Data Streams.- A Resource Discovery and Allocation Mechanism in Large Computational Grids for Media Applications.- Impact of Dynamic Growing on the Internet Degree Distribution.- Simulation-Based Evaluation of Distributed Mesh Allocation Algorithms.- A New Method for Describing the Syntax and Semantics of VIEWCHARTS.- A New Formalism for Describing Concurrent Systems.- Distributed Multi-source Regular Path Queries.- Parallel Matrix Multiplication Based on Dynamic SMP Clusters in SoC Technology.- Multi-Agent Design of Urban Oriented Traffic Integration Control System.- Register File Management and Compiler Optimization on EDSMT.- Workshop on High Performance Computing in Medicine and Biology.- Services, Standards, and Technologies for High Performance Computational Proteomics.- High Throughput Protein Similarity Searches in the LIBI Grid Problem Solving Environment.- Grid and Distributed Public Computing Schemes for Structural Proteomics: A Short Overview.- Distributed Processing of Clinical Practice Data in Grid Environment for Pharmacotherapy Personalization and Evidence-Based Pharmacology.- Workshop on Intelligent Systems Techniques for Ad Hoc and Wireless Sensor Networks.- Fault Tolerance of Connectivity Performance in CDMA-Based Wireless Sensor Networks.- Lifetime Performance of an Energy Efficient Clustering Algorithm for Cluster-Based Wireless Sensor Networks.- Balancing Energy Dissipation in Clustered Wireless Sensor Networks.- Dynamic Key Management Schemes for Secure Group Communication Based on Hierarchical Clustering in Mobile AdHocNetworks.- Privacy Preserving Monitoring and Surveillance in Sensor Networks.- A Distributed Clustering Algorithm for Fault-Tolerant Event Region Detection in Wireless Sensor Networks.- Optimal Multicast Multichannel Routing in Computer Networks.- A Secure On-Demand Source Routing Scheme Using Hierarchical Clustering in Mobile Ad Hoc Networks.- A Hybrid Location-Semantic Approach to Routing Assisted by Agents in a Virtual Network.

Characterizing spot price dynamics in public cloud environments

The surge in demand for utilizing public Cloud resources has introduced many trade-offs between price, performance and recently reliability. Amazons Spot Instances (SIs) create a competitive bidding option for public Cloud users at lower prices without providing reliability on services. It is generally believed that SIs reduce monetary cost to the Cloud users, however it appears from the literature that their characteristics have not been explored and reported. We believe that characterization of SIs is fundamental in the design of stochastic scheduling algorithms and fault tolerant mechanisms in public Cloud environments for the spot market. In this paper, we have done a comprehensive analysis of SIs based on one year price history in four data centers of Amazons EC2. For this purpose, we have analyzed all different types of SIs in terms of spot price and the inter-price time (time between price changes) and determined the time dynamics for spot price in hour-in-day and day-of-week. Moreover, we have proposed a statistical model that fits well these two data series. The results reveal that we are able to model spot price dynamics as well as the inter-price time of each SI by a mixture of Gaussians distribution with three or four components. The proposed model is validated through extensive simulations, which demonstrate that our model exhibits a good degree of accuracy under realistic working conditions.

Multithreaded algorithms for pricing a class of complex options

In this paper, we study multithreaded algorithms for pricing American Style options. We describe the algorithms, explain their relative complexities, and study their performance. The binomial lattice problem has been formulated in two distinct ways. In the first approach, the recursive algorithm, we establish a parent-child relationship between threads while fully exploiting the inherent parallelism. The second approach, the iterative algorithm, follows a data-flow model based on the producer-consumer style of programming. We implement the algorithms on the EARTH platform. The limitations posed by the problem size on the recursive algorithm and the solution to overcome this problem by the iterative algorithm are explained through the performance results. We have then extended these algorithms to study complicated options with dividend paying underlying assets and reported the performance results.

Collaborative multi-swarm PSO for task matching using graphics processing units

We investigate the performance of a highly parallel Particle Swarm Optimization (PSO) algorithm implemented on the GPU. In order to achieve this high degree of parallelism we implement a collaborative multi-swarm PSO algorithm on the GPU which relies on the use of many swarms rather than just one. We choose to apply our PSO algorithm against a real-world application: the task matching problem in a heterogeneous distributed computing environment. Due to the potential for large problem sizes with high dimensionality, the task matching problem proves to be very thorough in testing the GPUs capabilities for handling PSO. Our results show that the GPU offers a high degree of performance and achieves a maximum of 37 times speedup over a sequential implementation when the problem size in terms of tasks is large and many swarms are used.

Option Pricing on the GPU

In recent years, Graphics Processing Units (GPUs) have been opened to general purpose programming. As a result, researchers and developers have access to the massively parallel GPU architecture for applications beyond that of graphics rendering and gaming. We first investigate a design and implementation of the trinomial lattice strategy for the pricing of simple European options on the GPU. This implementation serves to allow a comparison to be made to an existing, alternative implementation on the GPU. Following this introduction, we design an algorithm for pricing an exotic American look back option and analyze its pricing performance on the GPU. This look back option pricing algorithm showcases tremendous speedup over a sequential CPU implementation and hence suitable for real-time application.

A parallel ant colony optimization algorithm for all-pair routing in MANETs

A mobile ad hoc network (MANET) consists of mobile wireless nodes that communicate in a distributed fashion without any centralized administration. The nodes instantaneously and dynamically form a network on the fly when it is needed. We define an irregular application as one that changes the network dynamically during runtime, exhibits chaotic load balancing among the processors and unpredictable communication behavior among the nodes during runtime. An ad hoc network has all these characteristics and hence could be considered as an irregular application from the parallel computing perspective. In this paper, we design an on-demand routing algorithm called source update for MANET using a meta-heuristic based on the ant colony optimization (ACO) search technique. We develop a mechanism to detect cycles, parallelize this algorithm on a distributed memory machine using MPI, and study the performance of the parallel algorithm. On a distributed network of workstations, we obtain a relative speedup of 7 with 10 processors.

Parallel algorithm for pricing American Asian options with multi-dimensional assets

In this paper, we develop parallel algorithms for pricing American-style Asian options employing binomial tree method. We describe the algorithm, explain the complexities, and study the performance. We have extended our algorithm to handle Asian options with up to 10 underlying assets and shown that the multi-asset Asian options offer a better problem for parallel computation.