Subhasri Duttagupta

Distributed Boundary Estimation using Sensor Networks

We examine the problem of determining boundaries occurring in natural phenomena using sensor networks. Sensor nodes remotely collect data about various points on the boundary. From this data, we estimate the boundary along with the confidence intervals using a regression relationship among sensor locations and the distances to the boundary. The confidence intervals are guaranteed to be narrower than a specified maximum width. Our distributed boundary estimation strategy uses a hierarchical structure of clusters of sensor nodes and requires 20-50% less messages as compared to a centralized scheme. The computed intervals show desired coverage of the true boundary points. Further, motivated by the practical need to estimate the boundary with a minimum number of sensors, we develop an adaptive approach for turning sensors on and off. The number of ON sensors in this scheme is only about 15% more than what a practical Oracle needs, to evaluate the boundary and confidence intervals around it. Our algorithms are also evaluated using data from real sensors on a testbed

Tracking dynamic boundary fronts using range sensors

We examine the problem of tracking dynamic boundaries occurring in natural phenomena using range sensors. Two main challenges of the boundary tracking problem are energy-efficient boundary estimations from noisy observations and continuous tracking of the boundary. We propose a novel approach which uses a regression-based spatial estimation technique to determine discrete points on the boundary and estimates a confidence band around the entire boundary. In addition, a Kalman Filter-based temporal estimation technique is used to selectively refresh the estimated boundary to meet the accuracy requirements. Our algorithm for dynamic boundary tracking (DBTR) combines temporal estimation with an aperiodically updated spatial estimation and provides a low overhead solution to track boundaries without requiring prior knowledge about the dynamics of the boundary. Experimental results demonstrate the effectiveness of our algorithm and estimated confidence bands achieve loss of coverage of less than 2 - 5% for a variety of boundaries with different spatial characteristics.

Predicting performance in the presence of software and hardware resource bottlenecks

Scalability of a multi-tier enterprise system is limited by the presence of software and hardware resource bottlenecks. These bottlenecks typically occur at larger number of users. It would help enterprise applications significantly if these bottlenecks are known a-priori during the performance testing itself. This paper deals with predicting the performance of such systems and models an application in terms of a two layer queuing network consisting of software resources and hardware resources. The software modules which require exclusive access by a thread are modeled as a queuing resource and other modules are treated as delay resources in the software queuing network. This network in turn uses a hardware queuing network consisting of resources such as CPU, disk and network. The proposed solution is augmented with additional constraints to ensure that the solution converges at a large number of users. Further, the proposed solution is capable of modeling multi-class requests with critical section and pooling of resources e.g., connection pool or thread pool. We validate the proposed solution with actual experimental results using sample programs and observe that the model is able to predict throughput and resource utilization with close to 90% accuracy.

Innovative ICT Tools for Information Provision in Agricultural Extension (December 2005)

aAQUA is an online multilingual, multimedia agricultural portal for disseminating information from and to the grassroots of the Indian agricultural community. aAQUA simultaneously addresses two major challenges in farmer outreach programs - geographic reach and customized delivery. It answers farmers queries based on the location, season, crop and other information provided by farmers. aAQUA makes use of novel database systems and information retrieval techniques like intelligent caching, offline access with intermittent synchronization, semantic-based search, etc. Agricultural content repositories (digital library), Agri-price information (Bhav Puchiye), farmer schemes and various operations support databases (aAQUA-QoS) have also emerged from the experience of aAQUA deployments. aAQUAs large scale deployment provides avenues for researchers to contribute in the areas of knowledge management, cross-lingual information retrieval, and providing accessible content for rural populations. Apart from agriculture, aAQUA can be configured and customized for expert advice over mobile networks and the Internet in education, Healthcare and other domains of interest to a developing population. This paper will showcase the utility of various component databases built into aAQUA to enhance the QoS delivered to rural populations

Performance Extrapolation for Load Testing Results of Mixture of Applications

Load testing of IT applications faces the challenge of providing high quality test results that would represent the performance in production like scenarios, without incurring high cost of commercial load testing tools. It would help IT projects to be able to test with a small number of users and extrapolate to scenarios with much larger number of users. Such an extrapolation strategy when applied to mixture of application workloads running on a shared server environment must take into consideration application characteristics (CPU/IO intensive, memory bound) as well the server capabilities. The goal is to predict the performance of mixture workload, the maximum throughput offered by the application mix and the maximum number of users supported by the system before the throughput starts degrading. In this paper, we propose an extrapolation strategy that analyses a system workload mix based on its service demand on various resources and extrapolates its performance using simple empirical modeling techniques. Moreover, its ability to extrapolate throughput of an application mixture even if there is a change in the mixture, can help in capacity planning of the system.

Developmental informatics at IIT Bombay

IIT Bombays Developmental Informatics Lab is a cross disciplinary group consisting of 6 faculty, 30 research staff and several students. The lab is working towards increasing access to information -- through the use of internet and communication technologies -- to communities in the developing world especially rural and small town India. The lab is supported by Indian Government funding sources as well as corporate and multi-lateral agencies to solve technical problems in local communities in sustainable ways. This paper focuses on two mature projects of the lab -- one caters to Indian farmers while another helps with the education of tribal populations.

Delay tolerant routing protocol for heterogeneous marine vehicular mobile ad-hoc network

Delay tolerant networks (DTN) are characterized by lack of end-to-end communications and stable infrastructures. This paper deals with DTN networks consisting of a number of heterogeneous mobile fishing vessels where some nodes, referred to as adaptive nodes, are capable of communicating through long-range Wi-Fi whereas other nodes are having simple Wi-Fi access network. The nodes form different clusters consisting of adaptive nodes and access nodes. Message routing in this heterogeneous network happens through adaptive nodes if the source and destination nodes belong to different clusters. Real data from field study reflects that mobile nodes in this network follow Gaussian-Markov mobility model and may have high inter-meeting arrival time based on deployment and node density. Our proposed DTN routing protocol incorporates simple encounter-based message forwarding and achieves lower latency and high delivery probability in the range of 90–98% for most of the scenarios. The proposed protocol is verified through a realistic mobile ad-hoc wireless simulator.

Model Driven Software Performance Engineering: Current Challenges and Way Ahead

Performance model solvers and simulation engines have been around for more than two decades. Yet, performance modeling has not received wide acceptance in the software industry, unlike pervasion of modeling and simulation tools in other industries. This paper explores underlying causes and looks at challenges that need to be overcome to increase utility of performance modeling, in order to make critical decisions on software based products and services. Multiple real-world case studies and examples are included to highlight our viewpoints on performance engineering. Finally, we conclude with some possible directions the performance modeling community could take, for better predictive capabilities required for industrial use.

Performance Mimicking Benchmarks for Multi-tier Applications

Predicting performance of multi-tier enterprise applications for a target platform is of significant importance to IT industries especially when target environment is unavailable for deployment. Performance modeling techniques depend on accurate estimation of resource demands for a specific application. This paper proposes a methodology for deriving Performance Mimicking Benchmarks (PMBs) that can predict resource demand of application server of multi-tier on-line transaction processing applications on a target environment. PMBs do not require the actual application to be deployed on the target itself. These benchmarks invoke similar method calls as the application at different layers in the technology stack that contribute significantly to CPU utilization. Further, they mimic all send and receive interactions with external servers (e.g., database server) and web clients. Ability of PMBs for service demand prediction is validated with a number of sample multi-tier applications including SPECjEnterprise2010 on disparate hardware configurations. These service demands when used in a modified version of Mean Value Analysis algorithm, can predict throughput and response time with accuracy close to 90%.

Software bottleneck analysis during performance testing

Scalability of a multi-tier enterprise system is limited resources that becomes a bottleneck, by the presence of software and hardware resource bottlenecks. These bottlenecks typically occur at larger number of users. From an IT industry point of view, deployment process of enterprise applications becomes simpler if these bottlenecks are known apriori during the performance testing itself. This paper uses an analytical model based technique for analyzing performance of such a system where the model consists of two layers of queuing networks for software resources and hardware resources. Proposed solution strategy involves identifying all the software resources in the application, estimating their service demands along with service demands of hardware resources, incorporating these parameters into the model and finally solving it. The paper describes a methodology that uses these steps to identify software and hardware bottlenecks for a given enterprise application. The paper further presents two case studies dealing with real-life multi-tier enterprise applications that encounter software resource bottlenecks. The case studies show that the model is able to predict throughput and utilization of servers with accuracy close to 90%.