Qualification: 
Ph.D, M.Tech, B-Tech
subhasrid@am.amrita.edu

Dr. Subhari Duttagupta has received her M. Tech. degree in 1993 in the area of Parallel Computing and received the doctoral degree in 2010 in the area of sensor networks, both from Indian Institute of Technology at Mumbai. Between 1994-2002, she worked in various organizations such as IBM, Micron and HP in USA. Before joining Amrita, she was working as a senior scientist in TCS Research Labs, Mumbai in the area of performance engineering.

Areas of interest: Performance Evaluation And Modelling Of Systems And Networks, Distributed Systems, Real-Life Applications Using Sensor Networks And Analysing Iot Applications

Qualification

Year Affiliation
2010 Ph.D, Computer Science and Engineering Indian Institute of Technology, Bombay
1993 M. Tech, Computer Science and Engineering Indian Institute of Technology, Bombay
1991 B. Tech, Computer Science and Engineering Indian Institute of Technology, Bombay

Awards and Achievements

  • 2 USA and 1 EU patents granted, 6 USA patents filed

Publications

Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2017

Conference Proceedings

D. Raj, Dr. Maneesha V. Ramesh, and Duttagupta, S., “Delay Tolerant Routing Protocol For Heterogeneous Marine Vehicular Mobile Ad-Hoc Network”, IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, Kona, HI, USA, pp. 461-466, 2017.[Abstract]


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.

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2016

Conference Proceedings

S. Duttagupta, Kumar, M., and Nambiar, M., “Performance Prediction And Analysis Of Internet Of Things Applications”, Proceedings of the 6th International Conference on the Internet of Things. ACM, New York, USA, pp. 43-51, 2016.[Abstract]


The ubiquitous deployment of mobile and sensor devices is creating a new environment, known as the Internet of Things (IoT). In this new realm, wireless sensor nodes, smart devices along with information and communication systems together constitute the elements of new computing environment. For wide acceptance of IoT applications, guaranteeing their performance is important. However, performance analysis of IoT applications encounters a lot of challenges such as interaction among a number of different technologies, various usage patterns of smart devices, numerous possible transactions, unavailability of suitable testing platforms and so on. In this paper, we deal with performance analysis of a scalable IoT platform that attempts to take a holistic approach for enterprise level data management in the IoT domain as well as development of IoT applications. We predict the performance of specific APIs offered by the platform using queuing network modeling and also validate them through experimental analysis on two deployment platforms. Additionally, we are able to predict performance of a real-life energy monitoring application deployed on this platform in a production environment. Our analysis is done mostly based on data extracted from production environment and requires only limited performance tests

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2015

Conference Proceedings

S. Duttagupta, Virk, R., and Nambiar, M., “Software Bottleneck Analysis During Performance Testing”, International Conference and Workshop on Computing and Communication (IEMCON). IEEE, Vancouver, BC, Canada, pp. 1-7, 2015.[Abstract]


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%.

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2014

Conference Proceedings

S. Duttagupta, Virk, R., and Nambiar, M., “Predicting Performance In The Presence Of Software And Hardware Resource”, Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS). IEEE, Monterey, CA, USA, pp. 542-549, 2014.[Abstract]


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.

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2014

Conference Proceedings

R. Mansharamani, Duttagupta, S., and Nehete, A., “Automatically Determine Load Test Duration Using Confidence Intervals”, CMG India Proceedings. 2014.[Abstract]


Load testing has become the de facto standard to evaluate performance of applications in the IT industry, thanks to the growing popularity of automated load testing tools. These tools report performance metrics such as average response time and throughput, which are sensitive to the test duration specified by the tester. Too short a duration can lead to inaccurate estimates of performance and too long a duration leads to reduced number of cycles of load testing. Currently, no scientific methodology is followed by load testers to specify run duration. In this paper, we present a simple methodology, using confidence intervals, such that a load test can automatically determine when to converge. We demonstrate the methodology using five lab applications and three real world applications.

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2012

Conference Proceedings

S. Duttagupta and Nambiar, M., “Performance Extrapolation Using Load Testing Results”, International Journal of Simulation, Systems, Science and Technology, vol. 13. IEEE, Madrid, Spain, pp. 424-429, 2012.[Abstract]


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.

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2011

Conference Proceedings

S. Duttagupta, Ramamritham, K., and Kulkarni, P., “Tracking Dynamic Boundary Fronts Using Sensor Networks”, IEEE Transactions on Parallel and Distributed Systems, vol. 22. IEEE, pp. 1766-1774, 2011.

2011

Conference Proceedings

S. Duttagupta and Mansharamani, R., “Extrapolation Tool For Load Testing Results”, International Symposium on Performance Evaluation of Computer & Telecommunication Systems (SPECTS). IEEE, The Hague, Netherlands, pp. 69-76, 2011.[Abstract]


Load testing of IT applications is fraught with the challenges of time to market, quality of results, high cost of commercial tools, and accurately representing production like scenarios. 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. This in turn will cut down cycle times and costs and allow for a variety of extrapolations closer to production. We present a simple extrapolation technique based on statistical empirical modeling, which we have found to be more than 90% accurate across a range of applications running across a number of hardware servers. The technique has currently been validated for scenarios where the hardware is the bottleneck and is extensible to a wider range of scenarios as well.

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2008

Conference Proceedings

S. Duttagupta, Ramamritham, K., and Kulkarni, P., “Tracking Dynamic Boundary Fronts Using Range Sensors”, The fifth European Conference on Wireless Sensor Networks. Springer Berlin/Heidelberg, Bologna, Italy, pp. 125-140, 2008.[Abstract]


We examine the problem of tracking dynamic boundaries occurring in natural phenomena using sensor networks. Remotely
placed sensor nodes produce noisy measurements of various points on the boundary using range-sensing. 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 discrete estimations of points on the boundary using a regression-based
spatial estimation technique and a smoothing interpolation scheme to estimate a confidence band around the entire boundary. In
addition, a Kalman Filter-based temporal estimation is used to help selectively refresh the estimated boundary at a point only
if the boundary is predicted to move out of the previous estimated intervals at that point. An algorithm for dynamic boundary
tracking (DBTR), the combination of temporal estimation with an aperiodically updated spatial estimation, allows us to provide
a low overhead solution to track dynamic boundaries that does not require prior knowledge about the nature of the dynamics.
Experimental results demonstrate the effectiveness of our algorithm and estimated confidence bands achieve loss of coverage of
less than 2% for smooth boundaries.

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2006

Conference Proceedings

S. Duttagupta, Ramamritham, K., and Ramanathan, P., “Distributed Boundary Tracking Using Sensor Networks”, The 3rd IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS). IEEE, Vancouver, Canada, pp. 316-325, 2006.[Abstract]


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

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2006

Conference Proceedings

S. Duttagupta, Ramamritham, K., and B, A., “aAQUA: A Database-Backended Multilingual, Multimedia Community Forum”, Proceedings of the 2006 ACM SIGMOD international conference on Management of data. ACM, New York, USA, pp. 784-786, 2006.[Abstract]


aAQUA is an online multilingual, multimedia Agricultural portal for disseminating information from and to rural communities. 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. aAQUA’s 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 in education, healthcare and other domains of interest to a developing population. This demonstration showcases the utility of various component DB/IR technologies built into aAQUA to enhance the QoS delivered to rural populations.

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