Broadcast on demand: efficient and timely dissemination of data in mobile environments

Xuan, Ping; Sen, Souvik; Gonzalez, O.F.; Fernández, Juan J.; Ramamritham, Krithi

doi:10.1109/rttas.1997.601342

Cited by 113 publications

(98 citation statements)

References 4 publications

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“…Xuan et al [31] evaluated several alternative system designs for timeconstrained data accesses and showed that on-demand broadcast with the earliest deadline first (EDF) scheduling algorithm performs well. They concentrated on the system design aspect of on-demand broadcast, which is complementary to the focus of this paper on the algorithmic aspect of time-critical on-demand broadcast.…”

Section: Related Workmentioning

confidence: 99%

Time-critical on-demand data broadcast: algorithms, analysis, and performance evaluation

Tang

Lee

2006

IEEE Trans. Parallel Distrib. Syst.

147

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On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and deal with dynamic user access patterns. With the rapid growth of time-critical 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 time-critical on-demand data broadcast. We propose a novel scheduling algorithm called SIN-α that takes the urgency and number of outstanding requests into consideration. An efficient implementation of SIN-α is presented. We also analyze the theoretical bound of request drop rate when the request arrival rate rises towards infinity. Trace-driven experiments show that SIN-α significantly outperforms existing algorithms over a wide range of workloads and approaches the analytical bound at high request rates.

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Section: Related Workmentioning

confidence: 99%

Time-critical on-demand data broadcast: algorithms, analysis, and performance evaluation

Tang

Lee

2006

IEEE Trans. Parallel Distrib. Syst.

147

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“…This is true in much scheduling work for broadcast delivery in general, such as in [18], and even when the authors are considering less conventional measures of client waiting, such as [2] ("stretch" as client response time divided by the size of the data item the client is requesting) and [21] (which supposes client requests to have deadlines that may or may not be met). We remove the single-data-itemrequest assumption, and consider subscriber clients.…”

Section: Related Workmentioning

confidence: 99%

Multicasting a changing repository

Lam

García-Molina

Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405)

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Web crawlers generate significant loads on Web servers, and are difficult to operate. Instead of repeatedly running crawlers at many "client" sites, we propose a central crawler and Web repository that multicasts appropriate subsets of the central repository, and their subsequent changes, to subscribing clients. Loads at Web servers are reduced because a single crawler visits the servers, as opposed to all the client crawlers. In this paper we model and evaluate such a central Web multicast facility for subscriber clients, and for mixes of subscriber and one-time downloader clients. We consider different performance metrics and multicast algorithms for such a multicast facility, and develop guidelines for its design under various conditions.

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“…In all four distributions, the overall number of data pages are 1000. In order for a comparison, we choose a work in the literature that is closest to ours, the periodic broadcast (PB) method [19]. However, as the original paper of the PB method only provides an algorithm for a single channel environment [19], we have to extend their idea to cope with multiple channel environments.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In order for a comparison, we choose a work in the literature that is closest to ours, the periodic broadcast (PB) method [19]. However, as the original paper of the PB method only provides an algorithm for a single channel environment [19], we have to extend their idea to cope with multiple channel environments. In order to make fair comparisons, assignment of data to multiple channels is the same as that of the PAMAD algorithm once the broadcast frequency is determined (referring to Algorithm 4 of the PA-MAD method).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…If the waiting time of clients in the broadcast channels can be controlled within a certain expected time, the quality of service of the on-demand channels can also remain unaffected. These practical reasons and real-time requirements motivate the need of new research on the scheduling of broadcast data so as to fulfill clients' needs [8,11,14,19].…”

Section: Introductionmentioning

confidence: 99%

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