2 Search Results for "George, Betsy"

Document
DOI: 10.4230/LIPIcs.OPODIS.2024.24
Hash & Adjust: Competitive Demand-Aware Consistent Hashing

Authors: Arash Pourdamghani, Chen Avin, Robert Sama, Maryam Shiran, and Stefan Schmid

Published in: LIPIcs, Volume 324, 28th International Conference on Principles of Distributed Systems (OPODIS 2024)

Abstract
Distributed systems often serve dynamic workloads and resource demands evolve over time. Such a temporal behavior stands in contrast to the static and demand-oblivious nature of most data structures used by these systems. In this paper, we are particularly interested in consistent hashing, a fundamental building block in many large distributed systems. Our work is motivated by the hypothesis that a more adaptive approach to consistent hashing can leverage structure in the demand, and hence improve storage utilization and reduce access time. We initiate the study of demand-aware consistent hashing. Our main contribution is H&A, a constant-competitive online algorithm (i.e., it comes with provable performance guarantees over time). H&A is demand-aware and optimizes its internal structure to enable faster access times, while offering a high utilization of storage. We further evaluate H&A empirically.
Cite as

Arash Pourdamghani, Chen Avin, Robert Sama, Maryam Shiran, and Stefan Schmid. Hash & Adjust: Competitive Demand-Aware Consistent Hashing. In 28th International Conference on Principles of Distributed Systems (OPODIS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 324, pp. 24:1-24:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{pourdamghani_et_al:LIPIcs.OPODIS.2024.24,
  author =	{Pourdamghani, Arash and Avin, Chen and Sama, Robert and Shiran, Maryam and Schmid, Stefan},
  title =	{{Hash \& Adjust: Competitive Demand-Aware Consistent Hashing}},
  booktitle =	{28th International Conference on Principles of Distributed Systems (OPODIS 2024)},
  pages =	{24:1--24:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-360-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{324},
  editor =	{Bonomi, Silvia and Galletta, Letterio and Rivi\`{e}re, Etienne and Schiavoni, Valerio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2024.24},
  URN =		{urn:nbn:de:0030-drops-225607},
  doi =		{10.4230/LIPIcs.OPODIS.2024.24},
  annote =	{Keywords: Consistent hashing, demand-awareness, online algorithms}
}
Document
DOI: 10.4230/DagSemProc.10121.3
Capacity Constrained Routing Algorithms for Evacuation Route Planning

Authors: Shashi Shekhar, Betsy George, and Qingsong Lu

Published in: Dagstuhl Seminar Proceedings, Volume 10121, Computational Transportation Science (2010)

Abstract
Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in face of natural disasters or terrorist attacks. Challenges arise due to violation of key assumptions (e.g. stationary ranking of alternative routes, Wardrop equilibrium) behind popular shortest path algorithms (e.g. Dijktra's, A*) and microscopic traffic simulators (e.g. DYNASMART). Time-expanded graphs (TEG) based mathematical programming paradigm does not scale up to large urban scenarios due to excessive duplication of transportation network across time-points. We present a new approach, namely Capacity Constrained Route Planner (CCRP), advancing ideas such as Time-Aggregated Graph (TAG) and an ATST function to provide earliest-Arrival-Time given any Start-Time. Laboratory experiments and field use in Twincities for DHS scenarios (e.g. Nuclear power plant, terrorism) show that CCRP is much faster than the state of the art. A key Transportation Science insight suggests that walking the first mile, when appropriate, may speed-up evacuation by a factor of 2 to 3 for many scenarios. Geographic Information Science (e.g. Time Geography) contributions include a novel representation (e.g. TAG) for spatio-temporal networks. Computer Science contributions include graph theory limitations (e.g. non-stationary ranking of routes, non-FIFO behavior) and scalable algorithms for traditional routing problems in time-varying networks, as well as new problems such as identifying the best start-time (for a given arrival-time deadline) to minimize travel-time.
Cite as

Shashi Shekhar, Betsy George, and Qingsong Lu. Capacity Constrained Routing Algorithms for Evacuation Route Planning. In Computational Transportation Science. Dagstuhl Seminar Proceedings, Volume 10121, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{shekhar_et_al:DagSemProc.10121.3,
  author =	{Shekhar, Shashi and George, Betsy and Lu, Qingsong},
  title =	{{Capacity Constrained Routing Algorithms for Evacuation Route Planning}},
  booktitle =	{Computational Transportation Science},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10121},
  editor =	{Glenn Geers and Monika Sester and Stephan Winter and Ouri E. Wolfson},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10121.3},
  URN =		{urn:nbn:de:0030-drops-27216},
  doi =		{10.4230/DagSemProc.10121.3},
  annote =	{Keywords: Evacuation, routes, spatio-temporal networks}
}
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