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16
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 37 (5 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.
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...
Exponential index: A parameterized distributed indexing scheme for data on air
 In Proceedings of the 2nd ACM/USENIX International Conference on Mobile Systems, Applications, and Services (MobiSys ’04
, 2004
"... Wireless data broadcast has received a lot of attention from industries and academia in recent years. Access efficiency and energy conservation are two critical performance concerns in a wireless data broadcast environment. To improve the efficiency of energy consumption on mobile devices, tradition ..."
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Cited by 22 (6 self)
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Wireless data broadcast has received a lot of attention from industries and academia in recent years. Access efficiency and energy conservation are two critical performance concerns in a wireless data broadcast environment. To improve the efficiency of energy consumption on mobile devices, traditional diskbased indexing techniques such as B +tree have been extended to index broadcast data on a wireless channel. However, existing designs are mostly based on centralized tree structures. Most of these indexing techniques are not flexible in the sense that the tradeoff between access efficiency and energy conservation is not adjustable based on application specific requirements. We propose in this paper a novel parameterized index, called the exponential index, which can be tuned to optimize the access latency with the tuning time bounded by a given limit, and vice versa. The proposed index is very efficient because it facilitates replication naturally by sharing links in multiple search trees and thus minimizes storage overhead. Experimental results show that the exponential index not only achieves better performance than the stateoftheart indexes but also enables great flexibility in tradeoffs between access latency and tuning time.
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 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.
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 13 (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
 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...
Middleware Support for Multicastbased Data Dissemination: A Working Reality
, 2003
"... Multicasting is an effective method to guarantee scalability of data transfer. Multicast applications range from the relief of Internet hot spots to healthcare alert systems. Much research has focused on isolated data management issues that arise in a multicast environment, including our previous ..."
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Cited by 9 (6 self)
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Multicasting is an effective method to guarantee scalability of data transfer. Multicast applications range from the relief of Internet hot spots to healthcare alert systems. Much research has focused on isolated data management issues that arise in a multicast environment, including our previous work on caching, scheduling, indexing, hybrid schemes, and consistency maintenance. This paper discusses the integration of these research contributions and the transition to a working software distribution that provides the middleware support of a data management layer to applications. Our middleware is flexible, can be shared across applications, and operates on top of existing and upcoming implementations of multicast protocols. The middleware benefits distributed applications with a uniform, efficient, scalable, and stateoftheart support for critical data management functionality.