Parimala Thulasiraman

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.

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.

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.

PSO based neural network for time series forecasting

Artificial neural networks are being widely used for time series forecasting. In recent years much effort has been made for the development of particle swarm algorithm for the optimization of neural networks. In this paper, the performance of two variants of particle swarm optimization algorithm (Trelea I and Trelea II) for training neural network has been examined with a real data for financial time series forecasting. Results clearly indicated the superiority of swarm based algorithms over the standard backpropagation training algorithm with respect to common performance measures across three forecasting horizons. In particular, with the Trelea II trained model, we obtained 92.48 %, 56.64 %, and 44.66 % decrease in terms of MSE over the standard back-propagation trained neural network for 10 days, 30 days and 60 days ahead forecasts respectively.

Performance Evaluation of a Multithreaded Fast Fourier Transform Algorithm for Derivative Pricing

Pricing of derivatives is one of the central problems in computational finance. Since the theory of derivative pricing is highly mathematical, numerical techniques such as lattice approach, finite-difference and finite-element among others have been employed. Recently fast Fourier transform (FFT) has been employed for derivative pricing in sequential computers. In this paper, we report development of a multithreaded FFT pricing algorithm and performance evaluation on a multithreaded platform. The focus of this study is on the effectiveness of using a parallel computer for financial problems and performance evaluation of a multithreaded algorithm for finance applications such as derivative pricing. In general, a parallel algorithm for FFT, with blocked data distribution of N elements on P processors, involves communication for log P iterations and terminates after log N iterations. The first (log N − log P) iterations therefore, require no communication and a sequential algorithm can be used in each processor. We call this a local algorithm. At the end of the (log N − log P) iterations, the processors switch to a multithreaded algorithm where sending and receiving of threads is through message passing. We call this a remote algorithm. The algorithm has been implemented on the EARTH (Efficient Architecture for Running THreads) multithreaded platform. Our results indicate that the FFT multithreaded algorithm for option pricing is very efficient giving a relative speedup of 50% on 64 processors. This study reveals an important commercial application for High Performance Computing.

PACONET: imProved Ant Colony Optimization Routing Algorithm for Mobile Ad Hoc NETworks

Mobile ad hoc networks (MANETS) are infrastructureless network consisting of mobile nodes, with constantly changing topologies, that communicate via a wireless medium. Therefore, routing is a challenging issue in MANETs. Recently, nature inspired algorithms have been explored as means of finding an efficient solution to this routing problem. In this paper, we develop an improved routing algorithm for MANETs based on ant colony optimization (ACO) inspired by real ants. The performance of the routing algorithm is evaluated through simulation and is compared to an existing well known MANET routing protocol, ad hoc on-demand distance vector (AODV). Several performance metrics are considered in different scenarios with varying mobility levels and traffic load.

MAZACORNET: Mobility aware zone based ant colony optimization routing for VANET

Vehicular Ad hoc Networks (VANET) exhibit highly dynamic behavior with high mobility and random network topologies. The performance of Transmission Control Protocols (TCP) in such wireless ad hoc networks is plagued by a number of problems: frequent link failures, scalability, multi-hop data transmission and data loss. In this work, we make use of the vehicles movement pattern, vehicle density, vehicle velocity and vehicle fading conditions to develop a hybrid, multi-path ant colony based routing algorithm, Mobility Aware Zone based Ant Colony Optimization Routing for VANET (MAZACORNET). that exhibits locality and scalability. We use ACO to find multiple routes between nodes in the network to aid in link failures. To achieve scalability we partition the network into multiple zones. We use proactive approach to find a route within a zone and reactive approach to find routes between zones using the local information stored in each zone thereby trying to reduce broadcasting and congestion. Our proposed algorithm makes effective use of the network bandwidth, is scalable and is robust to link failures. The results show that the algorithm works well for dense networks. The algorithm produces better delivery ratio and is scalable for zones beyond four. When compared to other existing VANET algorithms, the hybrid algorithm proved to be more efficient in terms of packet delivery ratio and end to end delay. To our knowledge this is the first ant based routing algorithm for VANET that uses the concept of zones.