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38
The data broadcast problem with nonuniform time
 In Proc. of the 10th Symp. on Discrete Algorithms (SODA ’99
, 1999
"... Abstract. The Data Broadcast Problem consists of finding an infinite schedule to broadcast a given set of messages so as to minimize a linear combination of the average service time to clients requesting messages, and of the cost of the broadcast. This problem also models the Maintenance Scheduling ..."
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Cited by 40 (4 self)
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Abstract. The Data Broadcast Problem consists of finding an infinite schedule to broadcast a given set of messages so as to minimize a linear combination of the average service time to clients requesting messages, and of the cost of the broadcast. This problem also models the Maintenance Scheduling Problem and the MultiItem Replenishment Problem. Previous work concentrated on a discretetime restriction where all messages have transmission time equal to 1. Here, we study a generalization of the model to a setting of continuous time and messages of nonuniform transmission times. We prove that the Data Broadcast Problem is strongly NPhard, even if the broadcast costs are all zero, and give 3approximation algorithms. Key Words. broadcasting.
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 37 (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
TimeCritical OnDemand Data Broadcast: Algorithms, Analysis, and Performance Evaluation
 IEEE Trans. Parallel and Distributed Systems
, 2006
"... Ondemand broadcast is an effective wireless data dissemination technique to enhance system scalability and deal with dynamic user access patterns. With the rapid growth of timecritical information services in emerging applications, there is an increasing need for the system to support timely data ..."
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Cited by 36 (1 self)
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Ondemand broadcast is an effective wireless data dissemination technique to enhance system scalability and deal with dynamic user access patterns. With the rapid growth of timecritical information services in emerging applications, there is an increasing need for the system to support timely data dissemination. This paper investigates online scheduling algorithms for timecritical ondemand data broadcast. We propose a novel scheduling algorithm called SINα that takes the urgency and number of outstanding requests into consideration. An efficient implementation of SINα is presented. We also analyze the theoretical bound of request drop rate when the request arrival rate rises towards infinity. Tracedriven experiments show that SINα significantly outperforms existing algorithms over a wide range of workloads and approaches the analytical bound at high request rates. Index Terms: Mobile computing, ondemand data broadcast, scheduling, content delivery, time constraint.
A maiden analysis of Longest Wait First
 Proc. of the 15th Annual ACMSIAM Symposium on Discrete Algorithms
, 2004
"... We consider server scheduling strategies to minimize average flow time in a multicast pull system where data items have uniform size. The algorithm Longest Wait First (LWF) always services the page where the aggregate waiting times of the outstanding requests for that page is maximized. We provide t ..."
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Cited by 22 (2 self)
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We consider server scheduling strategies to minimize average flow time in a multicast pull system where data items have uniform size. The algorithm Longest Wait First (LWF) always services the page where the aggregate waiting times of the outstanding requests for that page is maximized. We provide the first nontrivial analysis of the worst case performance of LWF. On the negative side, we show that LWF is not sspeed O(1)competitive for s < 1+ √ 5 2. On the positive side, we show that LWF is 6speed O(1)competitive. 1
Broadcast Scheduling: When Fairness is Fine
 in SODA
, 2002
"... We investigate server scheduling policies to minimize user perceived latency in a clientserver system where the server uses broadcast communication. We show that no O(1)competitive online algorithms exist for this problem. We consider the intuitive algorithm BEQUI that broadcasts all requested ..."
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Cited by 19 (6 self)
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We investigate server scheduling policies to minimize user perceived latency in a clientserver system where the server uses broadcast communication. We show that no O(1)competitive online algorithms exist for this problem. We consider the intuitive algorithm BEQUI that broadcasts all requested files at a rate proportional to the number of outstanding requests for that file. We show that BEQUI is an O(1)speed O(1)approximation algorithm. We give another algorithm BEQUIEDF, and show that BEQUIEDF is also an O(1)speed O(1)approximation algorithm. However, BEQUIEDF has the advantage that it preempts each broadcast on average at most once and will never preempt if the data items have unit size.
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 ���������������� � ,,
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 18 (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.
Efficient Periodic Scheduling by Trees
 Proc. of the IEEE INFOCOM
, 2002
"... Abstract — In a perfectlyperiodic schedule, time is divided into timeslots, and each client gets a time slot precisely every predefined number of time slots. The input to a schedule design algorithm is a frequency request for each client, and its task is to construct a perfectly periodic schedule ..."
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Cited by 14 (3 self)
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Abstract — In a perfectlyperiodic schedule, time is divided into timeslots, and each client gets a time slot precisely every predefined number of time slots. The input to a schedule design algorithm is a frequency request for each client, and its task is to construct a perfectly periodic schedule that matches the requests as “closely ” as possible. The quality of the schedule is measured by the ratios between the requested frequency and the allocated frequency for each client (either by the weighted average or by the maximum of these ratios over all clients). Periodic schedules enjoy maximal fairness, and are very useful in many contexts of asymmetric communication, e.g., push systems and Bluetooth networks. However, finding an optimal periodic schedule is NPhard in general. Tree scheduling is a methodology for developing perfectly periodic schedules with quality guarantees by constructing trees that correspond to periodic schedules. We explore a few aspects of tree scheduling. First, noting that a complete schedule table may be exponential in size, and that using the tree for scheduling directly may require logarithmic time on average, we give algorithms that find the next client to schedule in constant amortized time, using only polynomial space in most practical cases. Second, we present a few heuristic algorithms for generating schedules, based on analysis of optimal treescheduling algorithms, for both the average and maximum measures. Simulation results indicate that some of these heuristics produce excellent schedules in practice, sometimes even beating the best known nonperiodic schedules. Index Terms — periodic schedules, fair scheduling, broadcast disks, Bluetooth, push systems I.
General Perfectly Periodic Scheduling
, 2006
"... 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. Rece ..."
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Cited by 13 (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.