A segmentation approach for file broadcast scheduling

We study the broadcast scheduling problem in which clients send their requests to a server in order to receive some files available on the server. The server may be scheduled in a way that several requests are satisfied in one broadcast. When files are transmitted over computer networks, broadcasting the files by fragmenting them provides flexibility in broadcast scheduling that allows the optimization of per user response time. The broadcast scheduling algorithm, then, is in charge of determining the number of segments of each file and their order of transmission in each round of transmission. In this paper, we obtain a closed form approximation formula which approximates the optimal number of segments for each file, aiming at minimizing the total response time of requests. The obtained formula is a function of different parameters including those of underlying network as well as those of requests arrived at the server. Based on the obtained approximation formula we propose an algorithm for file broadcast scheduling which leads to total response time which closely conforms to the optimum one. We use extensive simulation and numerical study in order to evaluate the proposed algorithm which reveals high accuracy of obtained analytical approximation. We also investigate the impact of various headers that different network protocols add to each file segment. Our segmentation approach is examined for scenarios with different file sizes at the range of 100 KB to 1 GB. Our results show that for this range of file sizes the segmentation approach shows on average 13% tolerance from that of optimum in terms of total response time and the accuracy of the proposed approach is growing by increasing file size. Besides, using proposed segmentation in this work leads to a high Goodput of the scheduling algorithm.

[1]  Kirk Pruhs Competitive online scheduling for server systems , 2007, PERV.

[2]  Rajmohan Rajaraman,et al.  Improved algorithms for stretch scheduling , 2002, SODA '02.

[3]  Benjamin Moseley,et al.  Online scheduling to minimize the maximum delay factor , 2008, SODA.

[4]  Samir Khuller,et al.  New Models and Algorithms for Throughput Maximization in Broadcast Scheduling - (Extended Abstract) , 2010, WAOA.

[5]  Benjamin Moseley,et al.  Minimizing Maximum Response Time and Delay Factor in Broadcast Scheduling , 2009, ESA.

[6]  Rajiv Gandhi,et al.  Algorithms for Minimizing Response Time in Broadcast Scheduling , 2002, IPCO.

[7]  S. Muthukrishnan,et al.  Minimizing maximum response time in scheduling broadcasts , 2000, SODA '00.

[8]  Samir Khuller,et al.  Broadcast scheduling: Algorithms and complexity , 2008, TALG.

[9]  Kirk Pruhs,et al.  Broadcast scheduling: when fairness is fine , 2002, SODA '02.

[10]  Marek Chrobak,et al.  A Note on Scheduling Equal-Length Jobs to Maximize Throughput , 2006, J. Sched..

[11]  Rajmohan Rajaraman,et al.  Online scheduling to minimize average stretch , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[12]  Rafael Alonso,et al.  Broadcast disks: data management for asymmetric communication environments , 1995, SIGMOD '95.

[13]  Joseph Naor,et al.  Approximating the average response time in broadcast scheduling , 2005, SODA '05.

[14]  Stanley B. Zdonik,et al.  A framework for scalable dissemination-based systems , 1997, OOPSLA '97.