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156
Primaldual approximation algorithms for metric facility location and kmedian problems
 Journal of the ACM
, 1999
"... ..."
On the Placement of Web Server Replicas
 In Proceedings of IEEE INFOCOM
, 2001
"... Abstract—Recently there has been an increasing deployment of content distribution networks (CDNs) that offer hosting services to Web content providers. CDNs deploy a set of servers distributed throughout the Internet and replicate provider content across these servers for better performance and avai ..."
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Cited by 288 (8 self)
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Abstract—Recently there has been an increasing deployment of content distribution networks (CDNs) that offer hosting services to Web content providers. CDNs deploy a set of servers distributed throughout the Internet and replicate provider content across these servers for better performance and availability than centralized provider servers. Existing work on CDNs has primarily focused on techniques for efficiently redirecting user requests to appropriate CDN servers to reduce request latency and balance load. However, little attention has been given to the development of placement strategies for Web server replicas to further improve CDN performance. In this paper, we explore the problem of Web server replica placement in detail. We develop several placement algorithms that use workload information, such as client latency and request rates, to make informed placement decisions. We then evaluate the placement algorithms using both synthetic and real network topologies, as well as Web server traces, and show that the placement of Web replicas is crucial to CDN performance. We also address a number of practical issues when using these algorithms, such as their sensitivity to imperfect knowledge about client workload and network topology, the stability of the input data, and methods for obtaining the input. Keywords—World Wide Web, replication, replica placement algorithm, content distribution network (CDN). I.
Local search heuristics for kmedian and facility location problems
, 2001
"... ÔÖÓ��ÙÖ�ØÓØ���ÐÓ��ÐÓÔØ�ÑÙÑ�ÓÖ�Ñ����ÒÛ � Ö�Ø�ÓÓ��ÐÓ�ÐÐÝÓÔØ�ÑÙÑ×ÓÐÙØ�ÓÒÓ�Ø��Ò��Ù×�Ò�Ø�� × ÐÓ�Ð�ØÝ��ÔÓ��ÐÓ�Ð×��Ö�ÔÖÓ��ÙÖ��×Ø��Ñ�Ü�ÑÙÑ �Ñ����Ò�Ò����Ð�ØÝÐÓ�Ø�ÓÒÔÖÓ�Ð�Ñ×Ï���¬Ò�Ø� � ÁÒØ��×Ô�Ô�ÖÛ��Ò�ÐÝÞ�ÐÓ�Ð×��Ö���ÙÖ�×Ø�×�ÓÖØ�� ×�ÓÛØ��ØÐÓ�Ð×��Ö�Û�Ø�×Û�Ô×��×�ÐÓ�Ð�ØÝ��ÔÓ � ×�ÑÙÐØ�Ò�ÓÙ×ÐÝØ��ÒØ��ÐÓ�Ð�ØÝ��ÔÓ�Ø�� ..."
Abstract

Cited by 234 (10 self)
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ÔÖÓ��ÙÖ�ØÓØ���ÐÓ��ÐÓÔØ�ÑÙÑ�ÓÖ�Ñ����ÒÛ � Ö�Ø�ÓÓ��ÐÓ�ÐÐÝÓÔØ�ÑÙÑ×ÓÐÙØ�ÓÒÓ�Ø��Ò��Ù×�Ò�Ø�� × ÐÓ�Ð�ØÝ��ÔÓ��ÐÓ�Ð×��Ö�ÔÖÓ��ÙÖ��×Ø��Ñ�Ü�ÑÙÑ �Ñ����Ò�Ò����Ð�ØÝÐÓ�Ø�ÓÒÔÖÓ�Ð�Ñ×Ï���¬Ò�Ø� � ÁÒØ��×Ô�Ô�ÖÛ��Ò�ÐÝÞ�ÐÓ�Ð×��Ö���ÙÖ�×Ø�×�ÓÖØ�� ×�ÓÛØ��ØÐÓ�Ð×��Ö�Û�Ø�×Û�Ô×��×�ÐÓ�Ð�ØÝ��ÔÓ � ×�ÑÙÐØ�Ò�ÓÙ×ÐÝØ��ÒØ��ÐÓ�Ð�ØÝ��ÔÓ�Ø��ÐÓ�Ð×��Ö � �Ü�ØÐÝ�Ï��ÒÛ�Ô�ÖÑ�ØÔ���Ð�Ø��×ØÓ��×Û�ÔÔ�� �ÑÔÖÓÚ�×Ø��ÔÖ�Ú�ÓÙ×�ÒÓÛÒ��ÔÔÖÓÜ�Ñ�Ø�ÓÒ�ÓÖØ�� × ÔÖÓ�Ð�Ñ�ÓÖÍÒ�Ô��Ø�Ø�����Ð�ØÝÐÓ�Ø�ÓÒÛ�×�ÓÛ ÔÖÓ��ÙÖ��×�Ü�ØÐÝ Ó�ÐÓ�Ð×��Ö��ÓÖ�Ñ����ÒØ��ØÔÖÓÚ���×��ÓÙÒ�� � Ô�Ö�ÓÖÑ�Ò��Ù�Ö�ÒØ��Û�Ø�ÓÒÐÝ�Ñ����Ò×Ì��×�Ð×Ó �ÔÌ��×�×Ø��¬Ö×Ø�Ò�ÐÝ×�× ×Û�ÔÔ�Ò�����Ð�ØÝ��×�ÐÓ�Ð�ØÝ��ÔÓ��Ü�ØÐÝÌ�� × �ÑÔÖÓÚ�×Ø����ÓÙÒ�Ó�ÃÓÖÙÔÓÐÙ�Ø�ÐÏ��Ð×ÓÓÒ ×���Ö��Ô��Ø�Ø�����Ð�ØÝÐÓ�Ø�ÓÒÔÖÓ�Ð�ÑÛ��Ö��� � Ø��ØÐÓ�Ð×��Ö�Û���Ô�ÖÑ�Ø×����Ò��ÖÓÔÔ�Ò��Ò� Ø�ÔÐ�ÓÔ��×Ó�����Ð�ØÝ�ÓÖØ��×ÔÖÓ�Ð�ÑÛ��ÒØÖÓ�Ù � ���Ð�ØÝ��×��Ô��ØÝ�Ò�Û��Ö��ÐÐÓÛ��ØÓÓÔ�ÒÑÙÐ ÐÓ�Ð×��Ö�Û���Ô�ÖÑ�Ø×Ø��×Ò�ÛÓÔ�Ö�Ø�ÓÒ��×�ÐÓ ���Ð�ØÝ�Ò��ÖÓÔ×Þ�ÖÓÓÖÑÓÖ����Ð�Ø��×Ï�ÔÖÓÚ�Ø��Ø �Ò�ÛÓÔ�Ö�Ø�ÓÒÛ���ÓÔ�Ò×ÓÒ�ÓÖÑÓÖ�ÓÔ��×Ó� � �Ð�ØÝ��Ô��ØÛ��Ò�Ò�� ÝÈ�ÖØ��ÐÐÝ×ÙÔÔÓÖØ���Ý���ÐÐÓÛ×��Ô�ÖÓÑÁÒ�Ó×Ý×Ì� � Ê�×��Ö�Ä� � ÒÓÐÓ���×ÄØ���Ò��ÐÓÖ � ÞËÙÔÔÓÖØ���Ý�ÊÇ������� � £È�ÖØ��ÐÐÝ×ÙÔÔÓÖØ���Ý���ÐÐÓÛ×��Ô�ÖÓÑÁ�ÅÁÒ���
Correlation Clustering
 MACHINE LEARNING
, 2002
"... We consider the following clustering problem: we have a complete graph on # vertices (items), where each edge ### ## is labeled either # or depending on whether # and # have been deemed to be similar or different. The goal is to produce a partition of the vertices (a clustering) that agrees as mu ..."
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Cited by 222 (4 self)
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We consider the following clustering problem: we have a complete graph on # vertices (items), where each edge ### ## is labeled either # or depending on whether # and # have been deemed to be similar or different. The goal is to produce a partition of the vertices (a clustering) that agrees as much as possible with the edge labels. That is, we want a clustering that maximizes the number of # edges within clusters, plus the number of edges between clusters (equivalently, minimizes the number of disagreements: the number of edges inside clusters plus the number of # edges between clusters). This formulation is motivated from a document clustering problem in which one has a pairwise similarity function # learned from past data, and the goal is to partition the current set of documents in a way that correlates with # as much as possible; it can also be viewed as a kind of "agnostic learning" problem. An interesting
A constantfactor approximation algorithm for the kmedian problem
 In Proceedings of the 31st Annual ACM Symposium on Theory of Computing
, 1999
"... We present the first constantfactor approximation algorithm for the metric kmedian problem. The kmedian problem is one of the most wellstudied clustering problems, i.e., those problems in which the aim is to partition a given set of points into clusters so that the points within a cluster are re ..."
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Cited by 215 (14 self)
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We present the first constantfactor approximation algorithm for the metric kmedian problem. The kmedian problem is one of the most wellstudied clustering problems, i.e., those problems in which the aim is to partition a given set of points into clusters so that the points within a cluster are relatively close with respect to some measure. For the metric kmedian problem, we are given n points in a metric space. We select k of these to be cluster centers, and then assign each point to its closest selected center. If point j is assigned to a center i, the cost incurred is proportional to the distance between i and j. The goal is to select the k centers that minimize the sum of the assignment costs. We give a 6 2 3approximation algorithm for this problem. This improves upon the best previously known result of O(log k log log k), which was obtained by refining and derandomizing a randomized O(log n log log n)approximation algorithm of Bartal. 1
THE PRIMALDUAL METHOD FOR APPROXIMATION ALGORITHMS AND ITS APPLICATION TO NETWORK DESIGN PROBLEMS
"... The primaldual method is a standard tool in the design of algorithms for combinatorial optimization problems. This chapter shows how the primaldual method can be modified to provide good approximation algorithms for a wide variety of NPhard problems. We concentrate on results from recent researc ..."
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Cited by 123 (7 self)
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The primaldual method is a standard tool in the design of algorithms for combinatorial optimization problems. This chapter shows how the primaldual method can be modified to provide good approximation algorithms for a wide variety of NPhard problems. We concentrate on results from recent research applying the primaldual method to problems in network design.
A new greedy approach for facility location problems
"... We present a simple and natural greedy algorithm for the metric uncapacitated facility location problem achieving an approximation guarantee of 1.61 whereas the best previously known was 1.73. Furthermore, we will show that our algorithm has a property which allows us to apply the technique of Lagra ..."
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Cited by 116 (9 self)
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We present a simple and natural greedy algorithm for the metric uncapacitated facility location problem achieving an approximation guarantee of 1.61 whereas the best previously known was 1.73. Furthermore, we will show that our algorithm has a property which allows us to apply the technique of Lagrangian relaxation. Using this property, we can nd better approximation algorithms for many variants of the facility location problem, such as the capacitated facility location problem with soft capacities and a common generalization of the kmedian and facility location problem. We will also prove a lower bound on the approximability of the kmedian problem.
Improved Approximation Algorithms for Metric Facility Location Problems
 In Proceedings of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization
, 2002
"... In this paper we present a 1.52approximation algorithm for the metric uncapacitated facility location problem, and a 2approximation algorithm for the metric capacitated facility location problem with soft capacities. Both these algorithms improve the best previously known approximation factor for ..."
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Cited by 112 (11 self)
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In this paper we present a 1.52approximation algorithm for the metric uncapacitated facility location problem, and a 2approximation algorithm for the metric capacitated facility location problem with soft capacities. Both these algorithms improve the best previously known approximation factor for the corresponding problem, and our softcapacitated facility location algorithm achieves the integrality gap of the standard LP relaxation of the problem. Furthermore, we will show, using a result of Thorup, that our algorithms can be implemented in quasilinear time.
Clustering data streams: Theory and practice
 IEEE TKDE
, 2003
"... Abstract—The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and clickstreams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little ..."
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Cited by 106 (2 self)
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Abstract—The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and clickstreams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little memory, is crucial. We describe such a streaming algorithm that effectively clusters large data streams. We also provide empirical evidence of the algorithm’s performance on synthetic and real data streams. Index Terms—Clustering, data streams, approximation algorithms. 1