TY - JOUR
T1 - Scheduling dynamic parallel workload of mobile devices with access guarantees
AU - Anta, Antonio Fernández
AU - Kowalski, Dariusz R.
AU - Mosteiro, Miguel A.
AU - Wong, Prudence W.H.
N1 - Funding Information:
This research was partially supported by the UK Royal Society International Exchanges 2017 Round 3 Grant No. 170293, Pace University SRC and Kenan Fund, the initiative Networks Sciences & Technologies (NeST) by School of EEE & CS, University of Liverpool, the Polish National Science Center (NCN) Grant No. UMO-2017/25/B/ST6/02553, the Regional Government of Madrid (CM) grant Cloud4BigData (S2013/ICE-2894), cofunded by FSE & FEDER, the Spanish Ministry of Science, Innovation and Universities grant DiscoEdge (TIN2017-88749-R), the EU Commission H2020 project RECAP (RIA 732667), and the NSF of China Grant No. 61520106005. Authors’ addresses: A. F. Anta, IMDEA Networks Institute, Av. Mar Mediterráneo 22, Leganés, 28918 Madrid, Spain; email: antonio.fernandez@imdea.org; D. R. Kowalski and P. W. H. Wong, Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK; emails: {D.Kowalski, pwong}@liverpool.ac.uk; M. A. Mosteiro, Department of Computer Science, Pace University, 163 William St. #232, New York, NY 10038, USA; email: mmosteiro@pace.edu. Authors Current Address: Dariusz R. Kowalski is currently affiliated also with SWPS University of Social Sciences and Humanities, Warsaw, Poland. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. © 2018 Association for Computing Machinery. 2329-4949/2018/12-ART10 $15.00 https://doi.org/10.1145/3291529
PY - 2018/1
Y1 - 2018/1
N2 - 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.
AB - 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.
KW - Continuous adversarial dynamics
KW - Health monitoring systems
KW - Internet of things
KW - Mobile cloud computing
KW - Radio networks
KW - Station assignment
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U2 - 10.1145/3291529
DO - 10.1145/3291529
M3 - Article
AN - SCOPUS:85061201347
SN - 2329-4949
VL - 5
JO - ACM Transactions on Parallel Computing
JF - ACM Transactions on Parallel Computing
IS - 2
M1 - 10
ER -