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21
PolynomialTime Approximation Scheme for Data Broadcast
, 2000
"... The data broadcast problem is to nd a schedule for broadcasting a given set of messages over multiple channels. The goal is to minimize the cost of the broadcast plus the expected response time to clients who periodically and probabilistically tune in to wait for particular messages. The problem mod ..."
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Cited by 35 (3 self)
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The data broadcast problem is to nd a schedule for broadcasting a given set of messages over multiple channels. The goal is to minimize the cost of the broadcast plus the expected response time to clients who periodically and probabilistically tune in to wait for particular messages. The problem models disseminating data to clients in asymmetric communication environments, where there is a much larger capacity from the information source to the clients than in the reverse direction. Examples include satellites, cable TV, internet broadcast, and mobile phones. Such environments favor the \pushbased" model where the server broadcasts (pushes) its information on the communication medium and multiple clients simultaneously retrieve the speci c information of individual interest. This sort of environment motivates the study of \broadcast disks" in Information Systems [1; 7]. In this paper we present the rst polynomialtime approximation scheme for the data broadcast problem for the cas...
NPHardness of Broadcast Scheduling and Inapproximability of SingleSource Unsplittable Mincost Flow
 PROCEEDINGS OF THE 13TH ANNUAL ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS (SODA’02), PP. 194–202. C ○ SIAM 2002.
, 2002
"... We consider the version of broadcast scheduling where a server can transmit one message of a given set at each timestep, answering previously made requests for that message. The goal is to minimize the average response time if the amount of requests is known in advance for each timestep and message ..."
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Cited by 35 (3 self)
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We consider the version of broadcast scheduling where a server can transmit one message of a given set at each timestep, answering previously made requests for that message. The goal is to minimize the average response time if the amount of requests is known in advance for each timestep and message. We prove that this problem is NPhard, thus answering an open question stated by Kalyanasundaram, Pruhs and Velauthapillai (Proceedings of ESA 2000, LNCS
Nearly Optimal PerfectlyPeriodic Schedules
 Proc. of the 20th ACM Symp. on Principles of Distr. Comp. (PODC
, 2001
"... We study the problem of scheduling infinitely ¢ often jobs, each with an associated demand probability, under the constraint that each job must be scheduled with a fixed period. That is, the number of time units between two consecutive occurrences of each job is constant (we assume that time is slot ..."
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Cited by 19 (6 self)
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We study the problem of scheduling infinitely ¢ often jobs, each with an associated demand probability, under the constraint that each job must be scheduled with a fixed period. That is, the number of time units between two consecutive occurrences of each job is constant (we assume that time is slotted and that each job can be scheduled in a single timeslot). The goal is to minimize the average time a random arriving client waits until its desired job is executed. This problem is a variant of the broadcast disks problem: the perfect periodicity allows clients to know exactly when their job is scheduled, rather than “busy waiting,” thus saving energy. The problem is known to be NPhard. The best known polynomial algorithm to date guarantees average waiting time of at ¦ §©¨�����������������¢� � most, ¨��© � where is the optimal waiting time. In this paper, we develop a treebased methodology for periodic scheduling, and using new general results, we derive algorithms with better bounds. A key quantity in our �������� � �� � ������������ � � methodology is. We compare the cost of a solution provided by our algorithms to the cost of a solution to a relaxed continuous (nonintegral) version of the problem. Our asymptotic treebased algorithm guarantees cost of ��������� at most times the cost of the relaxed problem; on the other hand, we prove that the cost of any integral solution is bounded from below by the cost of the continuous �������� � � solution times. We also provide three other treebased algorithms with cost bounded by the cost of the continuous solution ���� � times ���������������� � ,,
Approximation Algorithms for Partialinformation based Stochastic Control with Markovian Rewards
"... We consider a variant of the classic multiarmed bandit problem (MAB), which we call FEEDBACK MAB, where the reward obtained by playing each of n independent arms varies according to an underlying on/off Markov process with known parameters. The evolution of the Markov chain happens irrespective of ..."
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Cited by 19 (2 self)
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We consider a variant of the classic multiarmed bandit problem (MAB), which we call FEEDBACK MAB, where the reward obtained by playing each of n independent arms varies according to an underlying on/off Markov process with known parameters. The evolution of the Markov chain happens irrespective of whether the arm is played, and furthermore, the exact state of the Markov chain is only revealed to the player when the arm is played and the reward observed. At most one arm (or in general, M arms) can be played any time step. The goal is to design a policy for playing the arms in order to maximize the infinite horizon time average expected reward. This problem is an instance of a Partially Observable Markov Decision Process (POMDP), and a special case of the notoriously intractable “restless bandit ” problem. Unlike the stochastic MAB problem, the FEEDBACK MAB problem does not admit to greedy indexbased optimal policies. The state of the system at any time step encodes the beliefs about the states of different arms, and the policy decisions change these beliefs – this aspect complicates the design and analysis of simple algorithms. We design a constant factor approximation to the FEEDBACK MAB problem by solving and rounding a natural LP relaxation to this problem. As far as we are aware, this is the first approximation algorithm for a POMDP problem. 1
Pushing dependent data in clientsprovidersservers systems
 Wireless Networks
, 2003
"... In satellite and wireless networks and in advanced traffic information systems in which the uplink bandwidth is very limited, a server broadcasts data files in a roundrobin manner. The data files are provided by different providers and are accessed by many clients. The providers are independent an ..."
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Cited by 18 (3 self)
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In satellite and wireless networks and in advanced traffic information systems in which the uplink bandwidth is very limited, a server broadcasts data files in a roundrobin manner. The data files are provided by different providers and are accessed by many clients. The providers are independent and therefore files may share information. The clients who access these files may have different patterns of access. Some clients may wish to access more than one file at a time in any order, some clients may access one file out of of several files, and some clients may wish to access a second file only after accessing another file. The goal of the server is to order the files in a way that minimizes the access time of the clients given some apriori knowledge of their access patterns. This paper introduces a clientsprovidersservers model that better represents certain environments than the traditional clientsservers model. Then, we show that a random order of the data files performs well, independent of the specific access pattern. Our main technical contribution is derandomizing the procedure that is based on selecting a random order. The resulting algorithm is a polynomialtime deterministic algorithm that finds an order with the same performance bounds as those of the random order.
Multicast Scheduling for List Requests
, 2002
"... Advances in wireless and optical communication, as well as in Internet multicast protocols, make broadcast and multicast methods an effective solution to disseminate data. In particular, repetitive serverinitiated broadcast is an effective technique in wireless systems and is a scalable solution to ..."
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Cited by 15 (2 self)
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Advances in wireless and optical communication, as well as in Internet multicast protocols, make broadcast and multicast methods an effective solution to disseminate data. In particular, repetitive serverinitiated broadcast is an effective technique in wireless systems and is a scalable solution to relieve Internet hot spots. A critical issue for the performance of multicast data dissemination is the multicast schedule. Previous work focused on a model where each data item is requested by clients with a certain probability that is independent of past accesses. In this paper, we consider the more complex scenario where a client accesses pages in blocks (e.g., a HTML file and all its embedded images), thereby introducing dependencies in the pattern of accesses to data. We present a sequence of heuristics that exploit page access dependencies. We measured the resulting clientperceived delay on multiple Web server traces, and observed an average speedup over previous methods ranging from 8% to 91%. We conclude that scheduling for multiitem requests is a critical factor for the performance of repetitive broadcast.
General perfectly periodic scheduling
 In Proc. the 21st Annual Symp. on Principles of Distributed Computing (PODC’02
, 2002
"... In a perfectly periodic schedule, each job must be scheduled precisely every some fixed number of time units after its previous occurrence. Traditionally, motivated by centralized systems, the perfect periodicity requirement is relaxed, the main goal being to attain the requested average rate. Recen ..."
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Cited by 11 (2 self)
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In a perfectly periodic schedule, each job must be scheduled precisely every some fixed number of time units after its previous occurrence. Traditionally, motivated by centralized systems, the perfect periodicity requirement is relaxed, the main goal being to attain the requested average rate. Recently, motivated by mobile clients with limited power supply, perfect periodicity seems to be an attractive alternative that allows clients to save energy by reducing their “busy waiting ” time. In this case, clients may be willing to compromise their requested service rate in order to get perfect periodicity. In this paper, we study a general model of perfectly periodic schedules, where each job has a requested period and a length; we assume that m jobs can be served in parallel for some given m. Job lengths may not be truncated, but granted periods may be different than the requested periods. We present an algorithm which computes schedules such that the worstcase proportion between the requested period and the granted period is guaranteed to be close to the lower bound. This algorithm improves on previous algorithms for perfect schedules in providing a worstcase guarantee rather than an averagecase guarantee, in generalizing unit length jobs to arbitrary length jobs, and in generalizing the singleserver model to multiple servers. 1
Caching and Scheduling for Broadcast Disk Systems
 in Proceedings of the 2nd Workshop on Algorithm Engineering and Experiments (ALENEX
, 1998
"... Unicast connections lead to performance and scalability problems when a large client population attempts to access the same data. Broadcast push and broadcast disk technology address the problem by broadcasting data items from a server to a large number of clients. Broadcast disk performance depends ..."
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Cited by 10 (3 self)
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Unicast connections lead to performance and scalability problems when a large client population attempts to access the same data. Broadcast push and broadcast disk technology address the problem by broadcasting data items from a server to a large number of clients. Broadcast disk performance depends mainly on caching strategies at the client site and on how the broadcast is scheduled at the server site. An online broadcast disk paging strategy makes caching decisions without knowing access probabilities. In this paper, we subject online paging algorithms to extensive empirical investigation. The Gray algorithm [25] always outperformed other online strategies on both synthetic and Web traces. Moreover, caching limited the skewness needed from a broadcast schedule, and led to favor efficient caching algorithms over refined scheduling strategies when the cache was not small. Prior to this paper, no work had empirically investigated online paging algorithms and their relation with serv...
Comparing Push and PullBased Broadcasting  Or: Would . . .
 LECTURE NOTES IN COMPUTER SCIENCE
, 2003
"... The first main goal of this paper is to present Sketchit!, a framework aiming to facilitate development and experimental evaluation of new scheduling algorithms. It comprises many helpful datastructures, a graphical interface with several components and a library with implementations of selected s ..."
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Cited by 8 (0 self)
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The first main goal of this paper is to present Sketchit!, a framework aiming to facilitate development and experimental evaluation of new scheduling algorithms. It comprises many helpful datastructures, a graphical interface with several components and a library with implementations of selected scheduling algorithms. Every scheduling problem covered by the classificationscheme originally proposed by Graham et al. [22] can easily be integrated into the framework. One of the more recent enhancements of this scheme, the so called broadcast scheduling problem, was chosen for an extensive case study of Sketchit!, yielding very interesting experimental results that represent the second main contribution of this paper. In broadcast scheduling many clients listen to a high bandwidth channel on which a server can transmit documents of a given set. Over time the clients request certain documents. In the pullbased setting each client has access to a slow bandwidth channel whereon it notifies the server about its requests. In the pushbased setting no such channel exists. Instead it is assumed that requests for certain documents arrive randomly with probabilities known to the server. The goal in both settings is to generate broadcast schedules for these documents which minimize the average time a client has to wait until a request is answered. We conduct experiments with several algorithms on generated data. We distinguish scenarios for which a slow feedback channel is very advantageous, and others where its benefits are negligible, answering the question posed in the title.
Scheduling for Efficient Data Broadcast over Two Channels
 In Proceedings of International Symposium on Information Theory (ISIT
, 2004
"... Abstract — The broadcast disk provides a way to distribute data to many clients simultaneously. A central server fixes a set of data and a schedule for sending it, and then repeatedly sends the data according to the schedule. Clients listen for data until it is broadcast. We look at the problem of s ..."
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Cited by 4 (3 self)
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Abstract — The broadcast disk provides a way to distribute data to many clients simultaneously. A central server fixes a set of data and a schedule for sending it, and then repeatedly sends the data according to the schedule. Clients listen for data until it is broadcast. We look at the problem of scheduling for two separate channels, where each can have a different broadcast schedule. Our metric for measuring schedule performance is expected delivery time (EDT), the expected value of the total elapsed time between when a client starts listening for data and when the client is completely finished receiving the data. We fix the first channel with a schedule that is optimal for an average case, and look at how to schedule for the second channel. We show two interesting results for sending two items over two channels. The first is that all schedules with equal portions of the two items in the second channel have the same EDT. The second is that for a situation that is symmetric in the two items the optimal schedule is asymmetric with respect to these items. I.