Publication

  • S. K. Monga, S. K. R, and Y. Simmhan, “ElfStore: A Resilient Data Storage Service for Federated Edge and Fog Resources,” in IEEE International Conference on Web Services (ICWS), 2019, pp. 1-9.
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  • S. D. Jaiswal and Y. Simmhan, “A Partition-centric Distributed Algorithm for Identifying Euler Circuits in Large Graphs,” in IEEE International Workshop on High-Performance Big Data, Deep Learning, and Cloud Computing (HPBDC), Co-located with IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2019, pp. 1-8.
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  • R. Dindokar and Y. Simmhan, “Adaptive Partition Migration for Irregular Graph Algorithms on Elastic Resources,” in IEEE International Conference on Cloud Computing (CLOUD), 2019.
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  • A. Khochare, S. Ramachandra, S. Ramesh, and Y. Simmhan, “Dynamic Scaling of Video Analytics for Wide-area Tracking in Urban Spaces,” in IEEE International Scalable Computing Challenge (SCALE), 2019.
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  •  R. Buyya, S. N. Srirama, G. Casale, R. Calheiros, Y. Simmhan, B. Varghese, E. Gelenbe, B. Javadi, L. M. Vaquero, M. A. S. Netto, A. N. Toosi, M. A. Rodriguez, I. M. Llorente, S. D. C. D. Vimercati, P. Samarati, D. Milojicic, C. Varela, R. Bahsoon, M. D. D. Assuncao, O. Rana, W. Zhou, H. Jin, W. Gentzsch, A. Y. Zomaya, and H. Shen, “A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade,” ACM Computing Surveys (CSUR), vol. 51, iss. 5, pp. 1-38, 2019.
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  • P. Varshney and Y. Simmhan, “Characterizing Application Scheduling on Edge, Fog and Cloud Computing Resources,” Software: Practice and Experience, pp. 1-40, 2019.
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2018

  • S. Badiger, S. Baheti, and Y. Simmhan, “VIoLET: A Large-scale Virtual Environment for Internet of Things,” in International European Conference on Parallel and Distributed Computing (EuroPar), 2018, pp. 1-16.
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  • A. Shukla and Y. Simmhan, “Toward Reliable and Rapid Elasticity for Streaming Dataflows on Clouds,” in IEEE International Conference on Distributed Computing Systems (ICDCS), 2018, pp. 1-11.
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  • R. Ghosh, S. P. Reddy, and Y. Simmhan, “Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources,” in IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2018, pp. 1-11.
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  •  S. Heidari, Y. Simmhan, R. N. Calheiros, and R. Buyya, “Scalable Graph Processing Frameworks: A Taxonomy and Open Challenges,” ACM Computing Surveys (CSUR), vol. 51, iss. 3, pp. 1-53, 2018.
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  •  A. Shukla and Y. Simmhan, “Model-driven Scheduling for Distributed Stream Processing Systems,” Journal of Parallel and Distributed Computing (JPDC), vol. 117, pp. 98-114, 2018.
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  •  Y. Simmhan, P. Ravindra, S. Chaturvedi, M. Hegde, and R. Ballamajalu, “Towards a Data-driven IoT Software Architecture for Smart City Utilities,” Software: Practice and Experience, vol. 48, iss. 7, pp. 1390-1416, 2018.
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  •  P. Varshney and Y. Simmhan, “AutoBoT: Resilient and Cost-effective Scheduling of a Bag of Tasks on Spot VMs,” IEEE Transactions on Parallel and Distributed Systems (TPDS), pp. 1-16, 2018.
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  •  Y. Simmhan, “Encyclopedia of Big Data Technologies,” , S. Sakr and A. Y. Zomaya, Eds., Springer, 2018.
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2017

  •  P. Varshney and Y. Simmhan, “Demystifying Fog Computing: Characterizing Architectures, Applications and Abstractions,” in IEEE International Conference on Fog and Edge Computing (ICFEC), 2017, pp. 1-10.
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  • A. Khochare, P. Ravindra, S. P. Reddy, and Y. Simmhan, “Distributed Video Analytics across Edge and Cloud using ECHO,” in International Conference on Service-Oriented Computing (ICSOC) Demo, 2017, pp. 1-6.
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  •  P. Ravindra, A. Khochare, S. P. Reddy, S. Sharma, P. Varshney, and Y. Simmhan, “ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge,” in International Conference on Service-Oriented Computing (ICSOC), 2017, pp. 1-16.
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  •  R. Dindokar and Y. Simmhan, “Characterization of Vertex-centric Breadth First Search for Lattice Graphs,” in IEEE International Workshop on Foundations in Big Data Computing (BigDF), Co-located with HiPC, 2017, pp. 1-8.
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  •  J. Kalyanasundaram and Y. Simmhan, “ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads,” in IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC), 2017, pp. 1-10.
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  •  S. Chaturvedi, S. Tyagi, and Y. Simmhan, “Collaborative Reuse of Streaming Dataflows in IoT Applications,” in IEEE International Conference on eScience, 2017, pp. 1-10.
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  •  A. Shukla, S. Chaturvedi, and Y. Simmhan, “RIoTBench: An IoT Benchmark for Distributed Stream Processing Systems,” Concurrency and Computation: Practice and Experience, vol. 29, iss. 21, pp. 1-22, 2017.
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  • R. Ghosh and Y. Simmhan, “Distributed Scheduling of Event Analytics across Edge and Cloud,” ACM Transactions on Cyber-Physical Systems (TCPS), 2017.
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  • Y. Simmhan, “IoT Analytics Across Edge and Cloud Platforms,” IEEE Internet of Things Newsletter, 2017.
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2016

  •  N. Jamadagni and Y. Simmhan, “GoDB: From Batch Processing to Distributed Querying over Property Graphs,” in IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid), 2016, pp. 281-290.
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  •  R. Dindokar and Y. Simmhan, “Elastic Partition Placement for Non-stationary Graph Algorithms,” in IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid), 2016, pp. 90-93.
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  •  A. Shukla and Y. Simmhan, “Benchmarking Distributed Stream Processing Platforms for IoT Applications,” in TPC Technology Conference on Performance Evaluation & Benchmarking (TPCTC), 2016, pp. 90-106.
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  •  R. Dindokar, N. Choudhury, and Y. Simmhan, “A Meta-graph Approach to Analyze Subgraph-centric Distributed Programming Models,” in IEEE International Conference on Big Data (Big Data), 2016, pp. 37-47.
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  •  S. Jha, D. K. A. S. Luckow, O. Rana, and Y. S. N. C. amd Hong, “Introducing Distributed Dynamic Data-intensive (D3) Science: Understanding Applications and Infrastructure,” Concurrency and Computation: Practice and Experience, vol. 28, iss. 8, pp. 1-25, 2016.
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  •  Q. Zhou, Y. Simmhan, and V. Prasanna, “Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams,” Future Generation Computer Systems, vol. 76, pp. 391-406, 2016.
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  •  Y. Simmhan and S. Perera, “Big Data Analytics: Methods and Applications,” , S. Pyne, P. B. L. S. Rao, and S. B. Rao, Eds., Springer India, 2016, pp. 115-135.
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  •  Y. Simmhan, L. Ramakrishnan, G. Antoniu, and C. Goble, “Editorial: Cloud computing for data-driven science and engineering,” Concurrency and Computation: Practice and Experience, 2016.
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2015

  •  R. Dindokar, N. Choudhury, and Y. Simmhan, “Analysis of Subgraph-centric Distributed Shortest Path Algorithm,” in IEEE International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning), 2015, pp. 1185-1190.
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  •  Y. Simmhan, N. Choudhury, C. Wickramaarachchi, A. Kumbhare, M. Frincu, C. Raghavendra, and V. Prasanna, “Distributed Programming over Time-series Graphs,” in IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2015, pp. 809-818.
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  •  A. Shukla, T. Sharma, and Y. Simmhan, “Characterizing Distributed Stream Processing Systems for IoT Applications,” in Workshop on Architectural Support and Middleware for InfoSymbiotics/ Dynamic Data Driven Applications Systems (DDDAS), co-located with High Performance Computing Conference (HiPC), 2015, p. 61.
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  •  S. Aman, M. Frincu, C. Chelmis, M. Noor, Y. Simmhan, and V. K. Prasanna, “Prediction Models for Dynamic Demand Response: Requirements, Challenges, and Insights,” in IEEE International Conference on Smart Grid Communications (SmartGridComm), 2015, pp. 1-6.
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  •  Y. Simmhan, A. Shukla, and A. Verma, “Benchmarking Fast Data Platforms for the Aadhaar Biometric Database,” in Workshop on Big Data Benchmarking (WBDB), 2015, pp. 21-39.
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  •  A. Kumbhare, M. Frincu, Y. Simmhan, and V. K. Prasanna, “Fault-Tolerant and Elastic Streaming MapReduce with Decentralized Coordination,” in IEEE International Conference on Distributed Computing Systems (ICDCS), 2015, pp. 328-338.
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  •  A. G. Kumbhare, Y. Simmhan, M. Frincu, and V. K. Prasanna, “Reactive Resource Provisioning Heuristics for Dynamic Dataflows on Cloud Infrastructure,” IEEE Transactions on Cloud Computing (TCC), vol. 3, iss. 2, pp. 105-118, 2015.
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  •  S. Aman, Y. Simmhan, and V. Prasanna, “Holistic Measures for Evaluating Prediction Models in Smart Grids,” IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 27, iss. 2, pp. 475-488, 2015.
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  • S. Aluru and Y. Simmhan, “Editorial: Scalable Systems for Big Data Management and Analytics,” Journal of Parallel and Distributed Systems (JPDC), 2015.
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  • P. Misra, Y. Simmhan, and J. Warrior, “Towards a Practical Architecture for Internet of Things: An India-centric View,” IEEE Internet of Things Newsletter, pp. 1-2, 2015.
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2014

  •  H. Chu and Y. Simmhan, “Cost-efficient and Resilient Job Life-cycle Management on Hybrid Clouds,” in IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2014, pp. 327-336.
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  •  Y. Simmhan, A. Kumbhare, C. Wickramaarachchi, S. Nagarkar, S. Ravi, C. Raghavendra, and V. Prasanna, “GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics,” in International European Conference on Parallel Processing (EuroPar), 2014, pp. 451-462.
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  •  A. Kumbhare, Y. Simmhan, and V. K. Prasanna, “PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds,” in IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2014, pp. 344-353.
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  •  V. Kushwaha and Y. Simmhan, “An Analysis of Spot-Priced Clouds for Practical Job Scheduling,” in IEEE Cloud Computing for Emerging Markets (CCEM), 2014, pp. 1-8.
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  • N. Govindarajan, Y. Simmhan, N. Jamadagni, and P. Misra, “Event Processing across Edge and the Cloud for Internet of Things Applications,” in International Conference on Management of Data (COMAD), 2014, pp. 101-104.
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  • N. C. Badam and Y. Simmhan, “Subgraph Rank: PageRank for SubgraphCentric Distributed Graph Processing,” in International Conference on Management of Data (COMAD), 2014, pp. 38-49.
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  • N. Choudhury and Y. Simmhan, “Towards Scalable, Sequentially-Dependent Algorithms on Time-Series Graphs,” in HiPC Student Research Symposium, 2014.
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  • T. Sharma and Y. Simmhan, “Online Update Strategies for Distributed Stream Processing in Apache Storm,” in HiPC Student Research Symposium, 2014.
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