Distributed Computing and Networking

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A key challenge in building next-generation data networks is to satisfy the increasing demand for data traffic and extended coverage for people and places without a corresponding rise in energy and carbon emissions.  There will be an explosion in wireless connectivity due to Internet-of-Things (IoT) devices, and these devices consume more energy for communication than for local processing. 

One approach for prolonging the life of these devices is to increase the amount of computation at each node, such as by adding signal processing at the nodes, in order to reduce the number of bits that need to be communicated.  Similarly, in distributed computing frameworks such as MapReduce, one can increase the computation load of the Map functions in order to reduce the communication overhead.  

Multihop wireless transmissions allow for nodes to transmit at low power levels and reuse the same frequency in different physical locations.  In situations where battery life is important and communication to distant nodes should be avoided, distributed solutions are feasible. Distributed algorithms perform suboptimally when compared to a global centralized algorithm but can be implemented efficiently.  Our research group’s focus is to investigate the design and performance of distributed systems and networks. 

Prof. Ashwin Ganesan’s research focus is to investigate the fundamental limits to the performance that is achievable with some given amount of resources. More specifically, if each node in the network has information about only its local neighborhood, then what are the limits to performance?  Given that each link in a wireless network has a certain minimum-bandwidth quality-of-service requirement, is it possible to determine in a distributed fashion whether the given set of flow rates is feasible?

Prof. Ganesan has proposed new distributed algorithms for these problems and has obtained results that quantify the tradeoffs in performance between the level of decentralization and the performance of these distributed algorithms.

Despite the large volume of prior work on this topic, Prof. Ganesan’s work has succeeded in cutting new ground – in analyzing models that have not been considered in the previous literature and in obtaining interesting and nontrivial performance bounds. 

Prof. Ganesan’s theoretical contributions were published in IEEE/ACM Transactions on Networking in February 2020 and will be presented at the International Conference on Distributed Computing and Networking in January 2021. 

If you would like to collaborate with us or join our doctoral program, feel free to contact us on LinkedIn.

Our Team

Principal Investigators:

Research Objectives

  • Conduct research in all areas of networking, including wireless networks, sensor networks, interconnection networks, and IoT
  • Investigate the design and performance of distributed algorithms and resource allocation and scheduling algorithms
  • Conduct research in data science, with a focus on graph algorithms in data mining and networked structures in economic and social networks.
References
  • A. Ganesan, “Performance guarantees of distributed algorithms for QoS in wireless ad hoc networks,” IEEE/ACM Transactions on Networking, vol. 28, pp. 182-195, February 2020. https://doi.org/10.1109/TNET.2019.2959797
  • A. Ganesan, “On some distributed scheduling algorithms for wireless networks with hypergraph interference models,” submitted for publication in October 2019, revised in July 2020. Preprint: https://arxiv.org/abs/1910.01909 
  • A. Ganesan, “Distributed algorithms for QoS in wireless ad hoc networks under the primary interference model,” Proceedings of the International Conference on Wireless Communications, Signal Processing, and Networking (WiSPNET), Chennai, India, August 2020. 
  • A. Ganesan, “Performance analysis of a distributed algorithm for admission control in wireless networks under the 2-hop interference model,” Proceedings of the 22nd International Conference on Distributed Computing and Networking (ICDCN), accepted/to appear, Nara, Japan, January 2021, ACM. Preprint: https://arxiv.org/abs/2007.07921

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