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Documents authored by Munagala, Kamesh

Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2023.2
Probabilistic Metric Embedding via Metric Labeling

Authors: Kamesh Munagala, Govind S. Sankar, and Erin Taylor

Published in: LIPIcs, Volume 275, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)

Abstract
We consider probabilistic embedding of metric spaces into ultra-metrics (or equivalently to a constant factor, into hierarchically separated trees) to minimize the expected distortion of any pairwise distance. Such embeddings have been widely used in network design and online algorithms. Our main result is a polynomial time algorithm that approximates the optimal distortion on any instance to within a constant factor. We achieve this via a novel LP formulation that reduces this problem to a probabilistic version of uniform metric labeling.
Cite as

Kamesh Munagala, Govind S. Sankar, and Erin Taylor. Probabilistic Metric Embedding via Metric Labeling. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 2:1-2:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{munagala_et_al:LIPIcs.APPROX/RANDOM.2023.2,
  author =	{Munagala, Kamesh and Sankar, Govind S. and Taylor, Erin},
  title =	{{Probabilistic Metric Embedding via Metric Labeling}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)},
  pages =	{2:1--2:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-296-9},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{275},
  editor =	{Megow, Nicole and Smith, Adam},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023.2},
  URN =		{urn:nbn:de:0030-drops-188279},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2023.2},
  annote =	{Keywords: Metric Embedding, Approximation Algorithms, Ultrametrics}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2023.16
Online Learning and Bandits with Queried Hints

Authors: Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im, Kostas Kollias, and Kamesh Munagala

Published in: LIPIcs, Volume 251, 14th Innovations in Theoretical Computer Science Conference (ITCS 2023)

Abstract
We consider the classic online learning and stochastic multi-armed bandit (MAB) problems, when at each step, the online policy can probe and find out which of a small number (k) of choices has better reward (or loss) before making its choice. In this model, we derive algorithms whose regret bounds have exponentially better dependence on the time horizon compared to the classic regret bounds. In particular, we show that probing with k = 2 suffices to achieve time-independent regret bounds for online linear and convex optimization. The same number of probes improve the regret bound of stochastic MAB with independent arms from O(√{nT}) to O(n² log T), where n is the number of arms and T is the horizon length. For stochastic MAB, we also consider a stronger model where a probe reveals the reward values of the probed arms, and show that in this case, k = 3 probes suffice to achieve parameter-independent constant regret, O(n²). Such regret bounds cannot be achieved even with full feedback after the play, showcasing the power of limited "advice" via probing before making the play. We also present extensions to the setting where the hints can be imperfect, and to the case of stochastic MAB where the rewards of the arms can be correlated.
Cite as

Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im, Kostas Kollias, and Kamesh Munagala. Online Learning and Bandits with Queried Hints. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 16:1-16:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{bhaskara_et_al:LIPIcs.ITCS.2023.16,
  author =	{Bhaskara, Aditya and Gollapudi, Sreenivas and Im, Sungjin and Kollias, Kostas and Munagala, Kamesh},
  title =	{{Online Learning and Bandits with Queried Hints}},
  booktitle =	{14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
  pages =	{16:1--16:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-263-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{251},
  editor =	{Tauman Kalai, Yael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.16},
  URN =		{urn:nbn:de:0030-drops-175197},
  doi =		{10.4230/LIPIcs.ITCS.2023.16},
  annote =	{Keywords: Online learning, multi-armed bandits, regret}
}
Document
DOI: 10.4230/LIPIcs.FSTTCS.2020.8
Clustering Under Perturbation Stability in Near-Linear Time

Authors: Pankaj K. Agarwal, Hsien-Chih Chang, Kamesh Munagala, Erin Taylor, and Emo Welzl

Published in: LIPIcs, Volume 182, 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2020)

Abstract
We consider the problem of center-based clustering in low-dimensional Euclidean spaces under the perturbation stability assumption. An instance is α-stable if the underlying optimal clustering continues to remain optimal even when all pairwise distances are arbitrarily perturbed by a factor of at most α. Our main contribution is in presenting efficient exact algorithms for α-stable clustering instances whose running times depend near-linearly on the size of the data set when α ≥ 2 + √3. For k-center and k-means problems, our algorithms also achieve polynomial dependence on the number of clusters, k, when α ≥ 2 + √3 + ε for any constant ε > 0 in any fixed dimension. For k-median, our algorithms have polynomial dependence on k for α > 5 in any fixed dimension; and for α ≥ 2 + √3 in two dimensions. Our algorithms are simple, and only require applying techniques such as local search or dynamic programming to a suitably modified metric space, combined with careful choice of data structures.
Cite as

Pankaj K. Agarwal, Hsien-Chih Chang, Kamesh Munagala, Erin Taylor, and Emo Welzl. Clustering Under Perturbation Stability in Near-Linear Time. In 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 182, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{agarwal_et_al:LIPIcs.FSTTCS.2020.8,
  author =	{Agarwal, Pankaj K. and Chang, Hsien-Chih and Munagala, Kamesh and Taylor, Erin and Welzl, Emo},
  title =	{{Clustering Under Perturbation Stability in Near-Linear Time}},
  booktitle =	{40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2020)},
  pages =	{8:1--8:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-174-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{182},
  editor =	{Saxena, Nitin and Simon, Sunil},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2020.8},
  URN =		{urn:nbn:de:0030-drops-132492},
  doi =		{10.4230/LIPIcs.FSTTCS.2020.8},
  annote =	{Keywords: clustering, stability, local search, dynamic programming, coreset, polyhedral metric, trapezoid decomposition, range query}
}
Document
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2016.10
A Competitive Flow Time Algorithm for Heterogeneous Clusters Under Polytope Constraints

Authors: Sungjin Im, Janardhan Kulkarni, Benjamin Moseley, and Kamesh Munagala

Published in: LIPIcs, Volume 60, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016)

Abstract
Modern data centers consist of a large number of heterogeneous resources such as CPU, memory, network bandwidth, etc. The resources are pooled into clusters for various reasons such as scalability, resource consolidation, and privacy. Clusters are often heterogeneous so that they can better serve jobs with different characteristics submitted from clients. Each job benefits differently depending on how much resource is allocated to the job, which in turn translates to how quickly the job gets completed. In this paper, we formulate this setting, which we term Multi-Cluster Polytope Scheduling (MCPS). In MCPS, a set of n jobs arrive over time to be executed on m clusters. Each cluster i is associated with a polytope P_i, which constrains how fast one can process jobs assigned to the cluster. For MCPS, we seek to optimize the popular objective of minimizing average weighted flow time of jobs in the online setting. We give a constant competitive algorithm with small constant resource augmentation for a large class of polytopes, which capture many interesting problems that arise in practice. Further, our algorithm is non-clairvoyant. Our algorithm and analysis combine and generalize techniques developed in the recent results for the classical unrelated machines scheduling and the polytope scheduling problem [10,12,11].
Cite as

Sungjin Im, Janardhan Kulkarni, Benjamin Moseley, and Kamesh Munagala. A Competitive Flow Time Algorithm for Heterogeneous Clusters Under Polytope Constraints. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 60, pp. 10:1-10:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{im_et_al:LIPIcs.APPROX-RANDOM.2016.10,
  author =	{Im, Sungjin and Kulkarni, Janardhan and Moseley, Benjamin and Munagala, Kamesh},
  title =	{{A Competitive Flow Time Algorithm for Heterogeneous Clusters Under Polytope Constraints}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016)},
  pages =	{10:1--10:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-018-7},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{60},
  editor =	{Jansen, Klaus and Mathieu, Claire and Rolim, Jos\'{e} D. P. and Umans, Chris},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2016.10},
  URN =		{urn:nbn:de:0030-drops-66336},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2016.10},
  annote =	{Keywords: Polytope constraints, average flow time, multi-clusters, online scheduling, and competitive analysis}
}
Document
DOI: 10.4230/LIPIcs.ICALP.2016.143
Competitive Analysis of Constrained Queueing Systems

Authors: Sungjin Im, Janardhan Kulkarni, and Kamesh Munagala

Published in: LIPIcs, Volume 55, 43rd International Colloquium on Automata, Languages, and Programming (ICALP 2016)

Abstract
We consider the classical problem of constrained queueing (or switched networks): There is a set of N queues to which unit sized packets arrive. The queues are interdependent, so that at any time step, only a subset of the queues can be activated. One packet from each activated queue can be transmitted, and leaves the system. The set of feasible subsets that can be activated, denoted S, is downward closed and is known in advance. The goal is to find a scheduling policy that minimizes average delay (or flow time) of the packets. The constrained queueing problem models several practical settings including packet transmission in wireless networks and scheduling cross-bar switches. In this paper, we study this problem using the the competitive analysis: The packet arrivals can be adversarial and the scheduling policy only uses information about packets currently queued in the system. We present an online algorithm, that for any epsilon > 0, has average flow time at most O(R^2/epsilon^3*OPT+NR) when given (1+epsilon) speed, i.e., the ability to schedule (1+epsilon) packets on average per time step. Here, R is the maximum number of queues that can be simultaneously scheduled, and OPT is the average flow time of the optimal policy. This asymptotic competitive ratio O(R^3/epsilon^3) improves upon the previous O(N/epsilon^2) which was obtained in the context of multi-dimensional scheduling [Im/Kulkarni/Munagala, FOCS 2015]. In the full general model where N can be exponentially larger than R, this is an exponential improvement. The algorithm presented in this paper is based on Makespan estimates which is very different from that in [Im/Kulkarni/Munagala, FOCS 2015], a variation of the Max-Weight algorithm. Further, our policy is myopic, meaning that scheduling decisions at any step are based only on the current composition of the queues. We finally show that speed augmentation is necessary to achieve any bounded competitive ratio.
Cite as

Sungjin Im, Janardhan Kulkarni, and Kamesh Munagala. Competitive Analysis of Constrained Queueing Systems. In 43rd International Colloquium on Automata, Languages, and Programming (ICALP 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 55, pp. 143:1-143:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{im_et_al:LIPIcs.ICALP.2016.143,
  author =	{Im, Sungjin and Kulkarni, Janardhan and Munagala, Kamesh},
  title =	{{Competitive Analysis of Constrained Queueing Systems}},
  booktitle =	{43rd International Colloquium on Automata, Languages, and Programming (ICALP 2016)},
  pages =	{143:1--143:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-013-2},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{55},
  editor =	{Chatzigiannakis, Ioannis and Mitzenmacher, Michael and Rabani, Yuval and Sangiorgi, Davide},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2016.143},
  URN =		{urn:nbn:de:0030-drops-62876},
  doi =		{10.4230/LIPIcs.ICALP.2016.143},
  annote =	{Keywords: Online scheduling, Average flow time, Switch network, Adversarial}
}
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