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Admission control to minimize rejections and online set cover with repetitions
 proceedings of the 17th ACM Symposium on Parallelism in Algorithms and Architectures, Las Vegas
"... We study the admission control problem in general networks. Communication requests arrive over time, and the online algorithm accepts or rejects each request while maintaining the capacity limitations of the network. The admission control problem has been usually analyzed as a benefit problem, where ..."
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We study the admission control problem in general networks. Communication requests arrive over time, and the online algorithm accepts or rejects each request while maintaining the capacity limitations of the network. The admission control problem has been usually analyzed as a benefit problem, where the goal is to devise an online algorithm that accepts the maximum number of requests possible. The problem with this objective function is that even algorithms with optimal competitive ratios may reject almost all of the requests, when it would have been possible to reject only a few. This could be inappropriate for settings in which rejections are intended to be rare events. In this paper, we consider preemptive online algorithms whose goal is to minimize the number of rejected requests. Each request arrives together with the path it should be routed on. We show an O(log 2 (mc))competitive randomized algorithm for the weighted case, where m is the number of edges in the graph and c is the maximum edge capacity. For the unweighted case, we give an O(log m log c)competitive randomized algorithm. This settles an open question of Blum, Kalai and Kleinberg raised in [10]. We note that allowing preemption and handling requests with given paths are essential for avoiding trivial lower bounds. The admission control problem is a generalization of the online set cover with repetitions problem, whose input is a family of m subsets of a ground set of n elements. Elements
Call control in rings
 In Proceedings of the 29th International Colloquium on Automata, Languages and Programming ICALP 2002, LNCS 2380
, 2002
"... Abstract. The call control problem is an important optimization problem encountered in the design and operation of communication networks. The goal of the call control problem in rings is to compute, for a given ring network with edge capacities and a set of paths in the ring, a maximum cardinality ..."
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Abstract. The call control problem is an important optimization problem encountered in the design and operation of communication networks. The goal of the call control problem in rings is to compute, for a given ring network with edge capacities and a set of paths in the ring, a maximum cardinality subset of the paths such that no edge capacity is violated. We give a polynomialtime algorithm to solve the problem optimally. The algorithm is based on a decision procedure that checks whether a solution with at least � paths exists, which is in turn implemented by an iterative greedy approach operating in rounds. We show that the algorithm can be implemented efficiently and, as a byproduct, obtain a lineartime algorithm to solve the call control problem in chains optimally. 1
Combining Online Algorithms for Rejection and Acceptance
 on Foundations of Computer Science
, 1994
"... Resource allocation and admission control are critical tasks in a communication network, that often must be performed online. Algorithms for these types of problems have been considered both under benefit models (e.g., with a goal of approximately maximizing the number of calls accepted) and under c ..."
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Resource allocation and admission control are critical tasks in a communication network, that often must be performed online. Algorithms for these types of problems have been considered both under benefit models (e.g., with a goal of approximately maximizing the number of calls accepted) and under cost models (e.g., with a goal of approximately minimizing the number of calls rejected). Unfortunately, algorithms designed for these two measures can often be quite different, even polar opposites (e.g., [1, 8]). In this work we consider the problem of combining algorithms designed for each of these objectives in a way that simultaneously is good under both measures. More formally, we are given an algorithm A which is cA competitive w.r.t. the number of accepted calls and an algorithm R which is cR competitive w.r.t. the number of rejected calls. We derive a combined algorithm whose competitive ratio is O(cRcA) for rejection A ) for acceptance. We also show building on known techniques that given a collection of k algorithms, we can construct one master algorithm which performs similar to the best algorithm among the k for the acceptance problem and another master algorithm which performs similar to the best algorithm among the k for the rejection problem. Using our main result we can combine the two master algorithms to a single algorithm which guarantees both rejection and acceptance competitiveness.
Approximation algorithms for edgedisjoint paths and unsplittable flow
 Efficient Approximation and Online Algorithms
, 2006
"... Abstract. In the maximum edgedisjoint paths problem (MEDP) the input consists of a graph and a set of requests (pairs of vertices), and the goal is to connect as many requests as possible along edgedisjoint paths. We give a survey of known results about the complexity and approximability of MEDP ..."
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Abstract. In the maximum edgedisjoint paths problem (MEDP) the input consists of a graph and a set of requests (pairs of vertices), and the goal is to connect as many requests as possible along edgedisjoint paths. We give a survey of known results about the complexity and approximability of MEDP and sketch some of the main ideas that have been used to obtain approximation algorithms for the problem. We consider also the generalization of MEDP where the edges of the graph have capacities and each request has a profit and a demand, called the unsplittable flow problem.
Call Control with k Rejections
 JOURNAL OF COMPUTER AND SYSTEM SCIENCES
, 2002
"... Given a set of connection requests (calls) in a communication network, the call control problem is to accept a subset of the requests and route them along paths in the network such that no edge capacity is violated, with the goal of rejecting as few requests as possible. We investigate the comple ..."
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Given a set of connection requests (calls) in a communication network, the call control problem is to accept a subset of the requests and route them along paths in the network such that no edge capacity is violated, with the goal of rejecting as few requests as possible. We investigate the complexity of parameterized versions of this problem, where the number of rejected requests is taken as the parameter. For the
Routing and Call Control Algorithms for Ring Networks
, 2003
"... A vast majority of communications in a network occurs between pairs of nodes, each such interaction is termed a call. The job of call control algorithm is to decide which of a set of calls to accept in the network so as to maximize a certain objective, viz., the number of accepted calls or the profi ..."
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A vast majority of communications in a network occurs between pairs of nodes, each such interaction is termed a call. The job of call control algorithm is to decide which of a set of calls to accept in the network so as to maximize a certain objective, viz., the number of accepted calls or the profit associated with the accepted calls. When a call is accepted it uses up some network resources, like bandwidth, along the path through which it is routed. Thus, the call control algorithm needs to make intelligent tradeoffs between resource constraints and profits. In this paper, we investigate two variants of call control problems on ring networks; in the first, the algorithm is allowed to determine the route connecting the end nodes of a call, while in the second, the route is specified as part of the input. For the first variant, we show an efficient algorithm that achieves the objective of routing and maximizing the nrnber of accepted calls within an additive constant of at most 3 to an optimal algorithm. This yields byproduct. For the fixed path variant, we derive a 2approximation for maximizing the profits (which could be arbitrary) of accepted calls.
SCHEDULING AND ADMISSION CONTROL
, 2006
"... We present algorithms and hardness results for three resource allocation problems. The first is an abstract admission control problem where the system receives a series of requests and wants to satisfy as many as possible, but has bounded resources. This occurs, for example, when allocating network ..."
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We present algorithms and hardness results for three resource allocation problems. The first is an abstract admission control problem where the system receives a series of requests and wants to satisfy as many as possible, but has bounded resources. This occurs, for example, when allocating network bandwidth to incoming calls so the calls receive guaranteed quality of service. Algorithms can have performance guarantees for this problem either with respect to acceptances or with respect to rejections. These types of guarantees are incomparable and algorithms having different types of guarantee can have nearly opposite behavior. We give two procedures for combining one algorithm of each type into a single algorithm having both types of guarantee simultaneously. Specifically, if we combine an algorithm that is cAcompetitive for acceptances with an algorithm that is cRcompetitive for rejections, the combined algorithm is O(cA)competitive for acceptance and O(cAcR)competitive for rejections. If both the input algorithms are deterministic, then so is the combined algorithm. In addition, one of the combining procedures does not need to know the value of cA and neither needs to know the value of cR. The second problem we consider is scheduling with rejections, a combination of scheduling
A Data Aware Admission Control Technique for Social Live Streams (SOLISs)
, 2012
"... A SOcial LIve Stream, SOLIS, is a live stream produced by a device whose owner is sharing the stream with her friends, granting each friend to perform time shifted viewing for a prespecified duration. The system buffers this chase data to facilitate its browsing and display. In the presence of many ..."
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A SOcial LIve Stream, SOLIS, is a live stream produced by a device whose owner is sharing the stream with her friends, granting each friend to perform time shifted viewing for a prespecified duration. The system buffers this chase data to facilitate its browsing and display. In the presence of many SOLISs, memory may overflow and prevent display of some chase data. This paper presents a novel dataaware admission control, DAAdmCtrl, technique that summarizes chase data proactively to maximize the number of admissible SOLISs with no memory overflow. It is designed for use with multicore CPUs and maximizes utility of data whenever the user’s level of satisfaction (utility) with different data formats is available. We use analytical models and simulation studies to quantify the tradeoff associated with DAAdmCtrl.