Results 1  10
of
17
Approximation Algorithms for Data Placement in Arbitrary Networks
, 2001
"... We study approximation algorithms for placing replicated data in arbitrary networks. Consider a network of nodes with individual storage capacities and a metric communication cost function, in which each node periodically issues a request for an object drawn from a collection of uniformlength objec ..."
Abstract

Cited by 84 (4 self)
 Add to MetaCart
(Show Context)
We study approximation algorithms for placing replicated data in arbitrary networks. Consider a network of nodes with individual storage capacities and a metric communication cost function, in which each node periodically issues a request for an object drawn from a collection of uniformlength objects. We consider the problem of placing copies of the objects among the nodes such that the average access cost is minimized. Our main result is a polynomialtime constantfactor approximation algorithm for this placement problem. Our algorithm is based on a careful rounding of a linear programming relaxation of the problem. We also show that the data placement problem is MAXSNPhard. We extend our approximation result to a generalization of the data placement problem that models additional costs such as the cost of realizing the placement. We also show that when object lengths are nonuniform, a constantfactor approximation is achievable if the capacity at each node in the approximate solution is allowed to exceed that in the optimal solution by the length of the largest object.
Hedging uncertainty: Approximation algorithms for stochastic optimization problems
 In Proceedings of the 10th International Conference on Integer Programming and Combinatorial Optimization
, 2004
"... We initiate the design of approximation algorithms for stochastic combinatorial optimization problems; we formulate the problems in the framework of twostage stochastic optimization, and provide nearly tight approximation algorithms. Our problems range from the simple (shortest path, vertex cover, ..."
Abstract

Cited by 77 (13 self)
 Add to MetaCart
(Show Context)
We initiate the design of approximation algorithms for stochastic combinatorial optimization problems; we formulate the problems in the framework of twostage stochastic optimization, and provide nearly tight approximation algorithms. Our problems range from the simple (shortest path, vertex cover, bin packing) to complex (facility location, set cover), and contain representatives with different approximation ratios. The approximation ratio of the stochastic variant of a typical problem is of the same order of magnitude as its deterministic counterpart. Furthermore, common techniques for designing approximation algorithms such as LP rounding, the primaldual method, and the greedy algorithm, can be carefully adapted to obtain these results. 1
Making Routers Last Longer with ViAggre
"... This paper presents ViAggre (Virtual Aggregation), a “configurationonly ” approach to shrinking the routing table on routers. ViAggre does not require any changes to router software and routing protocols and can be deployed independently and autonomously by any ISP. ViAggre is effectively a scalabi ..."
Abstract

Cited by 41 (2 self)
 Add to MetaCart
(Show Context)
This paper presents ViAggre (Virtual Aggregation), a “configurationonly ” approach to shrinking the routing table on routers. ViAggre does not require any changes to router software and routing protocols and can be deployed independently and autonomously by any ISP. ViAggre is effectively a scalability technique that allows an ISP to modify its internal routing such that individual routers in the ISP’s network only maintain a part of the global routing table. We evaluate the application of ViAggre to a few tier1 and tier2 ISPs and show that it can reduce the routing table on routers by an order of magnitude while imposing almost no traffic stretch and negligible load increase across the routers. We also deploy Virtual Aggregation on a testbed comprising of Cisco routers and benchmark this deployment. Finally, to understand and address concerns regarding the configuration overhead that our proposal entails, we implement a configuration tool that automates ViAggre configuration. While it remains to be seen whether most, if not all, of the management concerns can be eliminated through such automated tools, we believe that the simplicity of the proposal and its possible shortterm impact on routing scalability suggest that it is an alternative worth considering. I.
Approximation Algorithms for Clustering Problems
, 2004
"... Clustering is a ubiquitous problem that arises in many applications in different fields such as data mining, image processing, machine learning, and bioinformatics. Clustering problems have been extensively studied as optimization problems with various objective functions in the Operations Research ..."
Abstract

Cited by 12 (5 self)
 Add to MetaCart
(Show Context)
Clustering is a ubiquitous problem that arises in many applications in different fields such as data mining, image processing, machine learning, and bioinformatics. Clustering problems have been extensively studied as optimization problems with various objective functions in the Operations Research and Computer Science literature. We focus on a class of objective functions more commonly referred to as facility location problems. These problems arise in a wide range of applications such as, plant or warehouse location problems, cache placement problems, and network design problems where the costs obey economies of scale. In the simplest of these problems, the uncapacitated facility location (UFL) problem, we want to open facilities at some subset of a given set of locations and assign each client in a given set D to an open facility so as to minimize the sum of the facility opening costs and client assignment costs. This is a very wellstudied problem; however it fails to address many of the requirements of real applications. In this thesis we consider various problems that build upon UFL and capture additional issues that arise in applications such as, uncertainties in the data, clients with different service needs, and facilities with interconnectivity requirements. By focusing initially on facility location problems in these new models, we develop new algorithmic techniques that will find application in a wide range of settings. We consider a widely used paradigm in stochastic programming to model settings where the underlying data, for example, the locations or demands of the clients, may be uncertain: the 2stage with recourse model that involves making some initial decisions, observing additional information, and then augmenting the initial decisions, if necessary, by taking recourse actions. We present a randomized polynomial time
Playing push vs pull: Models and algorithms for disseminating dynamic data in networks
 In Proceedings of the ACM Symposium on Parallelism in Algorithms and Architectures
, 2006
"... Consider a network in which a collection of source nodes maintain and periodically update data objects for a collection of sink nodes, each of which periodically accesses the data originating from some specified subset of the source nodes. We consider the task of efficiently relaying the dynamically ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
(Show Context)
Consider a network in which a collection of source nodes maintain and periodically update data objects for a collection of sink nodes, each of which periodically accesses the data originating from some specified subset of the source nodes. We consider the task of efficiently relaying the dynamically changing data objects to the sinks from their sources of interest. Our focus is on the following “pushpull” approach for this data dissemination problem. Whenever a data object is updated, its source relays the update to a designated subset of nodes, its push set; similarly, whenever a sink requires an update, it propagates its query to a designated subset of nodes, its pull set. The push and pull sets need to be chosen such that every pull set of a sink intersects the push sets of all its sources of interest. We study the problem of choosing push sets and pull sets to minimize total global communication while satisfying all communication requirements. We formulate and study several variants of the above data dissemination problem, that take into account different paradigms for routing between sources (resp., sinks) and their push sets (resp., pull sets) – multicast, unicast, and controlled broadcast – as well as the aggregability of the data objects. Under the multicast model, we present an optimal polynomial time algorithm for tree networks, which yields a randomized O(log n)approximation algorithm for nnode general networks, for which the problem is hard to approximate within a constant factor. Under the unicast ∗ Chakinala, Kumarasubramanian, and Manokaran were partially supported by a generous gift from Northeastern
Content replication and placement in mobile networks
, 2011
"... Performance and reliability of content access in mobile networks is conditioned by the number and location of content replicas deployed at the network nodes. Location theory has been the traditional, centralized approach to study content replication: computing the number and placement of replicas in ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
(Show Context)
Performance and reliability of content access in mobile networks is conditioned by the number and location of content replicas deployed at the network nodes. Location theory has been the traditional, centralized approach to study content replication: computing the number and placement of replicas in a static network can be cast as a facility location problem. The endeavor of this work is to design a practical solution to the above joint optimization problem that is suitable for mobile wireless environments. We thus seek a replication algorithm that is lightweight, distributed, and reactive to network dynamics. We devise a solution that lets nodes (i) share the burden of storing and providing content, so as to achieve load balancing, and (ii) autonomously decide whether to replicate or drop the information, so as to adapt the content availability to dynamic demands and timevarying network topologies. We evaluate our mechanism through simulation, by exploring a wide range
1Content Replication in Mobile Networks
"... Abstract—Performance and reliability of content access in mobile networks is conditioned by the number and location of content replicas deployed at the network nodes. In this work, we design a practical, distributed solution to content replication that is suitable for dynamic environments and achiev ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract—Performance and reliability of content access in mobile networks is conditioned by the number and location of content replicas deployed at the network nodes. In this work, we design a practical, distributed solution to content replication that is suitable for dynamic environments and achieves load balancing. Simulation results show that our mechanism, which uses local measurements only, approximates well an optimal solution while being robust against network and demand dynamics. Also, our scheme outperforms alternative approaches in terms of both content access delay and access congestion. Index Terms—Content replication, mobile networks, node cooperation, distributed algorithms. I.
Multicommodity Facility Location under Group Steiner Access Cost
"... Motivated by publishsubscribe mechanisms in networks, we introduce a new class of multicommodity facility location problems: Multicommodity Group Steiner Facility Location (MGSFL). The input to MGSFL consists of a metric space over a given set of locations, a cost function which provides a building ..."
Abstract
 Add to MetaCart
(Show Context)
Motivated by publishsubscribe mechanisms in networks, we introduce a new class of multicommodity facility location problems: Multicommodity Group Steiner Facility Location (MGSFL). The input to MGSFL consists of a metric space over a given set of locations, a cost function which provides a building cost for each commodity at each location, a set of clients located at various points in the metric, and the set of commodities that each client is interested in reaching. A solution to MGSFL consists of (a) for each commodity, the locations where facilities are built, and (b) for each client, a tree connecting the client to at least one facility for each commodity in its interest set. The goal is to minimize the sum of the total facility building costs and the metric cost of the client trees. MGSFL is a natural generalization of the wellstudied Group Steiner Tree problem, which is equivalent to the special case of MGSFL in which every building cost is either 0 or ∞ and there is only one client. We also note that given the facility locations, the best client tree is an optimal solution to an appropriate Group Steiner Tree instance. Since the Group Steiner Tree problem is hard to approximate to within a factor of Ω(log 2−ɛ m) times optimum unless NP has quasipolynomial Las Vegas algorithms, where m is the number of commodities, the same hardness result immediately extends to MGSFL. Our main result is a randomized 2 O( √ log n log log n) approximation algorithm for MGSFL, where n is the number of clients. We also present deterministic polylogarithmic approximations for three special cases. We give an O(log n)approximation algorithm when the facility building costs differ only by commodity, not by location. We present an O(log 4 n log m)approximation algorithm when the interest sets are laminar — i.e., for each pair of clients, either their interest sets do not intersect or else one client’s interest set is contained within the other client’s interest set. We end with an O(log n)approximation algorithm when there are no building costs but each commodity must be built exactly once. 1
Network Design for Information Networks (Extended Abstract)
"... ... O(ln n)approximation algorithm for the Single Sink Information Network Design problem. We show that the Stochastic Steiner Tree problem can be approximated by dependent may be cast, and using this we obtain an O(1)approximation algorithm for the kstage stochastic Steinertree problem for any f ..."
Abstract
 Add to MetaCart
(Show Context)
... O(ln n)approximation algorithm for the Single Sink Information Network Design problem. We show that the Stochastic Steiner Tree problem can be approximated by dependent may be cast, and using this we obtain an O(1)approximation algorithm for the kstage stochastic Steinertree problem for any fixed k. Our algorithm allows scenarios to have different inflation factors, and works for any distribution provided that we can sample the distribution. This is the first approximation algorithm for the multistage problem in this general setting.