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Longest wait first for broadcast scheduling
 IN WAOA ’09: PROCEEDINGS OF 7TH WORKSHOP ON APPROXIMATION AND ONLINE ALGORITHMS
, 2009
"... We consider online algorithms for broadcast scheduling. In the pullbased broadcast model there are n unitsized pages of information at a server and requests arrive online for pages. When the server transmits a page p, all outstanding requests for that page are satisfied. There is a lower bound of ..."
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Cited by 8 (4 self)
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We consider online algorithms for broadcast scheduling. In the pullbased broadcast model there are n unitsized pages of information at a server and requests arrive online for pages. When the server transmits a page p, all outstanding requests for that page are satisfied. There is a lower bound of Ω(n) on the competitiveness of online algorithms to minimize average flowtime [27]; therefore we consider resource augmentation analysis in which the online algorithm is given extra speed over the adversary. The longestwaitfirst (LWF) algorithm is a natural algorithm that has been shown to have good empirical performance [2]. Edmonds and Pruhs showed that LWF is 6speed O(1)competitive using a very complex analysis; they also showed that LWF is not O(1)competitive with less than 1.618speed. In this paper we make several contributions to the analysis of LWF and broadcast scheduling. – An intuitive and easy to understand analysis of LWF that shows that it is O(1/ɛ 2) competitive for average flowtime with 4+ɛ speed. – LWF is O(1/ɛ 3) competitive for average flowtime with 3.4+ɛ speed. We use our insights to prove that a natural extension of LWF is O(1)speed O(1) competitive for more general objective functions such as average delayfactor and Lk norms of delayfactor (for fixed k). These metrics generalize average flowtime and Lk norms of flowtime respectively and ours are the first nontrivial results for these objective functions.
An Online Scalable Algorithm for Average Flowtime in Broadcast Scheduling
 In SODA 10: Proceedings of the twentyfirst annual ACMSIAM symposium on Discrete algorithms
, 2010
"... In this paper the online pullbased broadcast model is considered. In this model, there are n pages of data stored at a server and requests arrive for pages online. When the server broadcasts page p, all outstanding requests for the same page p are simultaneously satisfied. We consider the problem o ..."
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Cited by 7 (6 self)
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In this paper the online pullbased broadcast model is considered. In this model, there are n pages of data stored at a server and requests arrive for pages online. When the server broadcasts page p, all outstanding requests for the same page p are simultaneously satisfied. We consider the problem of minimizing average (total) flow time online where all pages are unitsized. For this problem, there has been a decadelong search for an online algorithm which is scalable, i.e. (1 + ɛ)speed O(1)competitive for any fixed ɛ> 0. In this paper, we give the first analysis of an online scalable algorithm. 1
Online Scalable Scheduling for the ℓknorms of Flow Time Without Conservation of Work
"... We address the scheduling model of arbitrary speedup curves and the broadcast scheduling model. The former occurs when jobs are scheduled in a multicore system or on a cloud of machines. Here jobs can be sped up when given more processors or machines. However, the parallelizability of the jobs may ..."
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Cited by 3 (3 self)
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We address the scheduling model of arbitrary speedup curves and the broadcast scheduling model. The former occurs when jobs are scheduled in a multicore system or on a cloud of machines. Here jobs can be sped up when given more processors or machines. However, the parallelizability of the jobs may vary and the algorithm is required to be oblivious of the parallelizability of a job. The latter model is natural in wireless and LAN networks where requests (or jobs) can be simultaneously satisfied together. Both settings are similar in that two schedules can do different amounts of work to satisfy all the jobs. We focus on optimizing the ℓk norms of flow time. Recently, Gupta et al. [24] gave a (k + ɛ)speed O(1)competitive algorithm for the ℓk norms of flow time in both scheduling settings for fixed k. Inspired by this work, we give the first analysis of a scalable algorithm, i.e. (1 + ɛ)speed O(1)competitive, for all ℓknorms of flow time in both settings for fixed k and 0 < ɛ ≤ 1. Both problems have a strong lower bound without resource augmentation, so this is the best result that can be shown in the worst case setting up to a constant factor in the competitive ratio.
To Broadcast Push or Not and What
 In Proc. of MDM2006
, 2006
"... A major problem in mobile web applications as well as the wireless Internet is the scalable delivery of data. The most popular solution for this problem is a hybrid system that uses broadcast push to scalably deliver the most popular data, and reserves broadcast pull for delivery of less popular dat ..."
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Cited by 3 (1 self)
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A major problem in mobile web applications as well as the wireless Internet is the scalable delivery of data. The most popular solution for this problem is a hybrid system that uses broadcast push to scalably deliver the most popular data, and reserves broadcast pull for delivery of less popular data. Such a hybrid scheme introduces a variety of data management problems at the broadcast server. In this paper, we examine three of these problems: the push popularity problem, the document classification problem, and the bandwidth division problem. The push popularity problem is to estimate the popularity of the documents in the web site. The document classification problem is to determine which documents should be pushed and which documents must be pulled. The bandwidth division problem is to determine how much of the server bandwidth to devote to pushed documents and how much of the server bandwidth should be reserved for pulled documents. We propose simple and elegant solutions for these problems. We report on experiments with our system that validate our algorithms. 1.
Scalable Dissemination: What's Hot and What's Not
 Growth and Poverty Reduction in Indochina and Myanmar
, 2004
"... A major problem in web database applications and on the Internet in general is the scalable delivery of data. One proposed solution for this problem is a hybrid system that uses multicast push to scalably deliver the most popular data, and reserves traditional unicast pull for delivery of less popul ..."
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Cited by 3 (1 self)
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A major problem in web database applications and on the Internet in general is the scalable delivery of data. One proposed solution for this problem is a hybrid system that uses multicast push to scalably deliver the most popular data, and reserves traditional unicast pull for delivery of less popular data. However, such a hybrid scheme introduces a variety of data management problems at the server. In this paper we examine three of these problems: the push popularity problem, the document classification problem, and the bandwidth division problem. The push popularity problem is to estimate the popularity of the documents in the web site. The document classification problem is to determine which documents should be pushed and which documents must be pulled. The bandwidth division problem is to determine how much of the server bandwidth to devote to pushed documents and how much of the server bandwidth should be reserved for pulled documents. We propose simple and elegant solutions for these problems. We report on experiments with our system that validate our algorithms.
The Multicast Pull Advantage in Disseminationbased Data Delivery
"... A major problem in web database applications and on the Internet in general is the scalable delivery of data. Multicast is one of the standard techniques to achieve scalable data dissemination. However the use of multicast introduces a variety of data management issues at the server. In this paper ..."
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A major problem in web database applications and on the Internet in general is the scalable delivery of data. Multicast is one of the standard techniques to achieve scalable data dissemination. However the use of multicast introduces a variety of data management issues at the server. In this paper we examine three major problems namely, the push popularity problem, the document classification problem and the bandwidth division problem, that arise in the design of a hybrid data dissemination scheme. We propose solutions to these problems and argue that these are essentially the best possible solutions. In particular, we argue for having a multicast pull mode, in addition to the traditional unicast pull mode and the commonly proposed multicast push mode. We give a simple method for estimating the current popularities of pushed documents. We give an algorithm for determining which documents should be pushed/pulled, and for determining how much of the server bandwidth should be devoted to push/pull. We report on experiments with our system that validate our algorithms. 1.
Hybrid Dissemination Based Scalable and Adaptive Context Delivery for Ubiquitous Computing
"... Abstract. Context delivery is an inevitable issue for ubiquitous computing. Contextaware middlewares perform all the functions of context sensing, inferring and delivery to contextaware applications. But one of the major issues for these middlewares is to devise a context delivery scheme that is s ..."
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Abstract. Context delivery is an inevitable issue for ubiquitous computing. Contextaware middlewares perform all the functions of context sensing, inferring and delivery to contextaware applications. But one of the major issues for these middlewares is to devise a context delivery scheme that is scalable as well as efficient. Pure unicast or pure broadcast based dissemination can not provide scalability as well as less average latency. In this paper we present a scalable context delivery mechanism for contextaware middlewares based on hybrid data dissemination technique where the most requested data are broadcasted and the rest are delivered through unicast. Our scheme is adaptive in the sense that it dynamically differentiates hot (most requested) and cold (less requested) data according to request rate and waiting time. Inclusion of lease mechanism and bandwidth division further allows us to reduce network traffic and average latency. We validated our claim through extensive simulation. 1
Contents lists available at ScienceDirect Performance Evaluation
"... journal homepage: www.elsevier.com/locate/peva Simulation studies on scheduling requests for multiple data items in ..."
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journal homepage: www.elsevier.com/locate/peva Simulation studies on scheduling requests for multiple data items in
requests in multichannel broadcast environments
"... analysis of data scheduling algorithms for multiitem ..."
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Longest Wait First for Broadcast Scheduling
, 2009
"... We consider online algorithms for broadcast scheduling. In the pullbased broadcast model there are n unitsized pages of information at a server and requests arrive online for pages. When the server transmits a page p, all outstanding requests for that page are satisfied. There is a lower bound of ..."
Abstract
 Add to MetaCart
(Show Context)
We consider online algorithms for broadcast scheduling. In the pullbased broadcast model there are n unitsized pages of information at a server and requests arrive online for pages. When the server transmits a page p, all outstanding requests for that page are satisfied. There is a lower bound of Ω(n) on the competitiveness of online algorithms to minimize average flowtime; therefore we consider resource augmentation analysis in which the online algorithm is given extra speed over the adversary. The longestwaitfirst (LWF) algorithm is a natural algorithm that has been shown to have good empirical performance [2]. Edmonds and Pruhs showed that LWF is 6speed O(1)competitive using a very complex analysis; they also showed that LWF is not O(1)competitive with less than 1.618speed. In this paper we make two main contributions to the analysis of LWF and broadcast scheduling. • We give an intuitive and easy to understand analysis of LWF which shows that it is O(1/2)competitive for average flowtime with (4 + ) speed. Using a more involved analysis, we show that LWF is O(1/3)competitive for average flowtime with (3.4 + ) speed. • We show that a natural extension of LWF is O(1)speed O(1)competitive for more general objective functions such as average delayfactor and Lk norms of delayfactor (for fixed k). These metrics generalize average flowtime and Lk norms of flowtime respectively and ours are the first nontrivial results for these objective functions in broadcast scheduling.