Antonio Fernández Anta
Abstract
We study a dynamic resource-allocation problem that arises in various parallel computing scenarios, such as mobile cloud computing, cloud computing systems, Internet of Things systems, and others. Generically, we model the architecture as client mobile devices and static base stations. Each client “arrives” to the system to upload data to base stations by radio transmissions and then “leaves.” The problem, called Station Assignment, is to assign clients to stations so that every client uploads their data under some restrictions, including a target subset of stations, a maximum delay between transmissions, a volume of data to upload, and a maximum bandwidth for each station. We study the solvability of Station Assignment under an adversary that controls the arrival and departure of clients, limited to maximum rate and burstiness of such arrivals. We show upper and lower bounds on the rate and burstiness for various client arrival schedules and protocol classes. To the best of our knowledge, this is the first time that Station Assignment is studied under adversarial arrivals and departures.
- Noga Alon, Yossi Azar, Gerhard J. Woeginger, and Tal Yadid. 1997. Approximation schemes for scheduling. In Proceedings of the 8th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’97). 493--500. Google Scholar
Digital Library
- L. Anantharamu, Bogdan S. Chlebus, and Mariusz A. Rokicki. 2009. Adversarial multiple access channel with individual injection rates. In Proceedings of the 13th International Conference on Principles of Distributed Systems (OPODIS’09) (LNCS 5923). Springer-Verlag, 174--188. Google ScholarDigital Library
- Matthew Andrews, Baruch Awerbuch, Antonio Fernández, Frank Thomson Leighton, Zhiyong Liu, and Jon M. Kleinberg. 2001. Universal-stability results and performance bounds for greedy contention-resolution protocols. J. ACM 48, 1 (2001), 39--69. Google ScholarDigital Library
- Matthew Andrews and Lisa Zhang. 2005. Scheduling over a time-varying user-dependent channel with applications to high-speed wireless data. J. ACM 52, 5 (2005), 809--834. Google ScholarDigital Library
- James Aspnes, Yossi Azar, Amos Fiat, Serge A. Plotkin, and Orli Waarts. 1993. On-line load balancing with applications to machine scheduling and virtual circuit routing. In Proceedings of the 25th Annual ACM Symposium on Theory of Computing (STOC’93). 623--631. Google ScholarDigital Library
- Yossi Azar. 1996. On-line load balancing. In Online Algorithms: The State of the Art (Lecture Notes in Computer Science), Amos Fiat and Gerhard J. Woeginger (Eds.), Vol. 1442. Springer, 178--195. Google ScholarDigital Library
- Yossi Azar, Andrei Z. Broder, and Anna R. Karlin. 1994. On-line load balancing. Theor. Comput. Sci. 130, 1 (1994), 73--84. Google ScholarDigital Library
- Yossi Azar, Bala Kalyanasundaram, Serge A. Plotkin, Kirk Pruhs, and Orli Waarts. 1997. On-line load balancing of temporary tasks. J. Algor. 22, 1 (1997), 93--110. Google ScholarDigital Library
- Amotz Bar-Noy and Richard E. Ladner. 2003. Windows-scheduling problems for broadcast systems. SIAM J. Comput. 32, 4 (2003), 1091--1113. Google ScholarDigital Library
- Amotz Bar-Noy, Richard E. Ladner, and Tami Tamir. 2007. Windows scheduling as a restricted version of bin packing. ACM Trans. Algor. 3, 3 (2007), 28. Google ScholarDigital Library
- Marcin Bienkowski, Tomasz Jurdzinski, Miroslaw Korzeniowski, and Dariusz R. Kowalski. 2012. Distributed online and stochastic queuing on a multiple access channel. In Proceedings of the 26th International Symposium on Distributed Computing (DISC’12). 121--135. Google ScholarDigital Library
- Maria J. Blesa, Daniel Calzada, Antonio Fernández, Luis López, Andrés L. Martínez, Agustín Santos, Maria J. Serna, and Christopher Thraves. 2009. Adversarial queueing model for continuous network dynamics. Theory Comput. Syst. 44, 3 (2009), 304--331. Google ScholarDigital Library
- Allan Borodin, Jon M. Kleinberg, Prabhakar Raghavan, Madhu Sudan, and David P. Williamson. 2001. Adversarial queuing theory. J. ACM 48, 1 (2001), 13--38. Google ScholarDigital Library
- Joseph Wun-Tat Chan, Tak Wah Lam, and Prudence W. H. Wong. 2008. Dynamic bin packing of unit fractions items. Theor. Comput. Sci. 409, 3 (2008), 172--206. Google ScholarDigital Library
- Bogdan S. Chlebus, Dariusz R. Kowalski, and Mariusz A. Rokicki. 2012. Adversarial queuing on the multiple access channel. ACM Trans. Algor. 8, 1, Article 5 (Jan. 2012), 31 pages. Google ScholarDigital Library
- R. L. Graham. 1966. Bounds on multiprocessing timing anomalies. Bell Syst. Tech. J. 45 (1966), 1563--1581.Google ScholarCross Ref
- David R. Karger, Steven J. Phillips, and Eric Torng. 1996. A better algorithm for an ancient scheduling problem. J. Algor. 20 (1996), 400--430. Google ScholarDigital Library
- Thomas Kesselheim. 2012. Dynamic packet scheduling in wireless networks. In Proceedings of the 31st ACM Symposium on Principles of Distributed Computing (PODC’12). 281--290. Google ScholarDigital Library
- Kirk Pruhs, Jirí Sgall, and Eric Torng. 2004. Online scheduling. In Handbook of Scheduling—Algorithms, Models, and Performance Analysis., Joseph Y.-T. Leung (Ed.). Chapman and Hall/CRC, Chapter 15, pp. 15--1 to 15--41. Retrieved from http://www.crcnetbase.com/isbn/978-1-58488-397-5.Google Scholar
Index Terms
- Scheduling Dynamic Parallel Workload of Mobile Devices with Access Guarantees
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