Ashish Vulimiri

Publications

• How well can congestion pricing neutralize denial-of-service attacks?, Ashish Vulimiri, Gul A. Agha, P. Brighten Godfrey, Karthik Lakshminarayanan.
  ACM SIGMETRICS 2012 (accepted).
  [abstract] [pdf] [project description]
  Denial of service protection mechanisms usually require classifying malicious traffic, which can be difficult. Another approach is to price scarce resources. However, while congestion pricing has been suggested as a way to combat DoS attacks, it has not been shown quantitatively how much damage a malicious player could cause to the utility of benign participants. In this paper, we quantify the protection that congestion pricing affords against DoS attacks, even for powerful attackers that can control their packets' routes. Specifically, we model the limits on the resources available to the attackers in three different ways and, in each case, quantify the maximum amount of damage they can cause as a function of their resource bounds. In addition, we show that congestion pricing is provably superior to fair queueing in attack resilience.
• Unsupervised and supervised classification of hyperspectral image data using projection pursuit and Markov random field segmentation, Anjan Sarkar, Ashish Vulimiri, Suman Paul, Md. Jawaid Iqbal, Avishek Banerjee, Rahul Chatterjee, Shibendu S. Ray.
  International Journal of Remote Sensing (accepted Aug 2011).
  [abstract] [pdf] [project description]
  This work presents a classification technique for hyperspectral image analysis when concurrent ground-truth is unavailable and available. The method adapts a principal component analysis based projection pursuit (PP) procedure with an entropy index to reduce the dimensionality followed by the Markov Random Field (MRF) model based segmentation. An ordinal optimization approach to PP determines a set of good enough projections with high probability, the best among which is chosen with the help of MRF model based segmentation. When ground-truth is absent, the segmented output obtained is labeled with the desired number of classes so that it resembles the natural scene closely. When the landcover classes are in detailed level, some special reectance characteristics based on the classes of the study area in question are determined. These are later incorporated in MRF model based segmentation stage while minimizing the energy function in the image space. Segments are evaluated with training samples so as to yield a classified image with respect to the type of ground-truth data. Two illustrations are presented with (i) EO-1 Hyperion sensor image with concurrent groundtruth at detailed level classes and (ii) AVIRIS-92AV3C image with concurrent groundtruth - for supervised cases. Comparison of classification accuracies and computational times of some nonparametric approaches with that of the proposed methodology are provided for the illustrations. Experimental results demonstrate that the method provides high classification accuracy and is computationally faster compared to other methods.
  Available on request.
• Application of secondary information for misbehavior detection in VANETs, Ashish Vulimiri, Arobinda Gupta, Pramit Roy, Skanda N. Muthaiah and Arzad A. Kherani.
  9th IFIP TC6 Networking Conference, May 2010. Springer LNCS Vol. 6091, pp. 385-396.
  [abstract] [pdf] [project description]
  Safety applications designed for Vehicular Ad Hoc Networks (VANETs) can be compromised by participating vehicles transmitting false or inaccurate information. Design of mechanisms that detect such misbehaving nodes is an important problem in VANETs. In this paper, we investigate the use of correlated information, called "secondary alerts", generated in response to another alert, called as the "primary alert" to verify the truth or falsity of the primary alert received by a vehicle. We first propose a framework to model how such correlated secondary information observed from more than one source can be integrated to generate a "degree of belief" for the primary alert. We then show an instantiation of the model proposed for the specific case of Post-Crash Notification as the primary alert and Slow/Stopped Vehicle Advisory as the secondary alerts. Finally, we present the design and evaluation of a misbehavior detection scheme (MDS) for PCN application using such correlated information to illustrate that such information can be used efficiently for MDS design.
• Unsupervised hyperspectral image analysis with projection pursuit and MRF segmentation approach, Anjan Sarkar, Ashish Vulimiri, Shantanu Bose, Suman Paul, Shibendu S Ray.
  Proc. of the 2008 International Conference on Artificial Intelligence and Pattern Recognition (AIPR-08), pp. 120-127.
  [abstract] [pdf] [project description]
  This work deals with hyperspectral image analysis in the absence of ground-truth. The method adopts a projection pursuit (PP) procedure with entropy index to reduce the dimensionality followed by Markov Random Field (MRF) model based segmentation. Ordinal optimization approach to PP determines a set of "good enough projections" with high probability the best among which is chosen with the help of MRF model based segmentation. The segmented output so obtained is labeled with desired number of landcover classes in the absence of ground-truth. While comparing with original hyperspectral image the methodology outperforms principal component analysis with respect to class separation as exhibited in the illustration of an archive EO-1 hyperspectral image. The technique is not computationally intensive as is usually the case in hyperspectral image analysis. When training samples are available, the segmented regions yield a classified image with any cluster validation technique.

Other

• Misbehavior detection in Vehicular Ad Hoc Networks, Ashish Vulimiri.
  B.Tech. thesis, Computer Science and Engineering, IIT Kharagpur.
  [pdf] [project description]

Posters and Talks

• Adaptive routing with end-to-end feedback [project description]
  • Poster at the 8th GENI Engineering Conference (GEC8), Jul 2010 [pdf]
  • Talk + demo at the 9th GENI Engineering Conference (GEC9), Nov 2010 [slides]

Projects

Graduate

• Improving datacenter job scheduling
  (with Thomas Karagiannis and Hitesh Ballani at Microsoft Research, Cambridge)
  Job schedulers in distributed execution environments such as Hadoop MapReduce typically make scheduling decisions using simple heuristics that do not incorporate any information about individual jobs. In this project, we show how to design better schedulers that use simple, easily available job-specific information to obtain higher overall throughput and better job completion times.
• Cloud computing and energy efficiency
  While there has been significant interest recently in characterizing the tradeoff between the energy consumption and performance of jobs in cloud computing environments (such as MapReduce implementations), most recent work in this area has been descriptive in nature, with an emphasis on studying the factors contributing to energy consumption for various specific workloads. We focus instead on a predictive capability. Our primary goal is to develop simple analytical models that would allow translating a high-level description of the workload and the execution environment into an energy and performance estimate, and to use these models to investigate system design issues in datacenters.
• Congestion pricing and denial-of-service attacks
  Most conventional DoS protection mechanisms require that the network try to identify and separate out malicious traffic; in contrast, we consider an alternate approach in which the network simply auctions off scarce resources among any competing users. Specifically, we analyze Kelly's proportional allocation mechanism -- in both single-stage and multi-stage settings -- and show that it can allocate resources fairly to the legitimate users of the network even in the presence of arbitrary malicious behavior. We also demonstrate that it is strictly superior to less sophisticated classification-free DoS-resistance methods such a fair queueing.
• Adaptive routing with end-to-end feedback
  Traditional routing protocols rely on (the intermediate nodes in) the network to both collect information about network state and participate in making routing decisions. We demonstrate that acceptable performance can also be obtained when end-hosts make routing decisions solely on the basis of observed end-to-end performance. We have studied the performance of our algorithms on an overlay deployment on PlanetLab, and are currently working on obtaining more accurate performance estimates from an in-network deployment on the GENI OpenFlow testbed.

Undergraduate

• Misbehavior detection in Vehicular Ad Hoc Networks (B.Tech. Project)
  This project dealt with the problem of designing mechanisms enabling vehicles in Vehicular Ad Hoc Networks to detect and work around the presence of malicious entities. My B.Tech. thesis made two primary contributions:

  • A Misbehavior Detection Scheme capable of building misbehavior detection models from experimental or simulation data (these models would then be used independently by each honest node).
  • An algorithm enabling honest nodes to collaborate with each other on the misbehavior detection problem, under the assumption that misbehaving nodes are capable of influencing the collaboration process. This essentially boils down to solving the approximate agreement variant of the Byzantine agreement problem in the VANET scenario.
• P2P file synchronization [Internship, MSR Redmond, Summer 2008]
  (with Dr. Yunnan Wu, then a Researcher in the Communication and Collaboration Systems group at Microsoft Research, Redmond)
  Investigation of delay-optimal scheduling policies for synchronizing updates to a shared filesystem in a P2P network. We used results from a simulation-based study to define an efficient scheduling policy.
• Hyperspectral image classification
  Investigation of techniques for the segmentation and classification of hyperspectral images. In hyperspectral images, high-dimensional vector data are available at each pixel, as opposed to, say, in simple RGB images, where each pixel is completely described by an (R,G,B) triplet. Our approach was to perform dimensionality reduction to make the data more tractable, followed by Markov Random Field based segmentation.

Awards and Achievements

• Was offered the following fellowships in 2009: Gordon Y.S. Wu Fellowship (Princeton University), Cornell Fellowship (Cornell University)
• My B.Tech. thesis was nominated by the Computer Science department at IIT Kharagpur for the IIT Kharagpur Best Project award and the Indian National Academy of Engineering (INAE) Innovative Project award
• Merit scholarship from the university throughout my four years at the Indian Institute of Technology, Kharagpur
• All India Rank 360 in the IIT-JEE (2005). Around 200,000 students appeared for the examination
• Ranked sixteenth in the state of Andhra Pradesh in the Intermediate examination (Class XII)

Coursework

Current

• Random processes
• Optimization by vector space methods
• Concurrent programming languages

Past

• Graduate algorithms [Fall 2010]
• Applied cryptography [Fall 2010]
• Advanced distributed systems [Spring 2010]
• Advanced Internet [Fall 2009]
• Actor languages [Fall 2009]

Professional Experience

• Intern, Microsoft Research, Cambridge (Summer 2011)
• Research Assistant, UIUC (Fall 2009 - present)
• Intern, Microsoft Research, Redmond (Summer 2008)
• Java programmer (part-time), Minekey Inc. (2007-2008)