TY - JOUR
T1 - Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing
AU - Liu, Chen Feng
AU - Bennis, Mehdi
AU - Debbah, Merouane
AU - Vincent Poor, H.
N1 - Funding Information:
Manuscript received September 3, 2018; revised December 12, 2018; accepted January 29, 2019. Date of publication February 11, 2019; date of current version June 14, 2019. This work was supported in part by the Academy of Finland project CARMA, in part by the Academy of Finland project MISSION, in part by the Academy of Finland project SMARTER, in part by the INFOTECH project NOOR, in part by the Nokia Bell-Labs project FOGGY, in part by the Nokia Foundation, and in part by the U.S. National Science Foundation under Grants CCF-093970 and CCF-1513915. This paper was presented in part at the IEEE Global Communications Conference Workshops, Singapore, December 2017 [1]. The associate editor coordinating the review of this paper and approving it for publication was V. Wong. (Corresponding author: Chen-Feng Liu.) C.-F. Liu and M. Bennis are with the Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland (e-mail: chen-feng.liu@oulu.fi; mehdi.bennis@oulu.fi).
PY - 2019/1/1
Y1 - 2019/1/1
N2 - To overcome devices' limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, the current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users' power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are reassociated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers' computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale, while a dynamic task offloading and resource allocation policy are executed in the short timescale. The simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly reliable task computation and lower delay performance, compared to several baselines.
AB - To overcome devices' limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, the current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users' power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are reassociated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers' computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale, while a dynamic task offloading and resource allocation policy are executed in the short timescale. The simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly reliable task computation and lower delay performance, compared to several baselines.
KW - 5G and beyond
KW - extreme value theory
KW - fog networking and computing
KW - mobile edge computing (MEC)
KW - ultra-reliable low latency communications (URLLC)
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U2 - 10.1109/TCOMM.2019.2898573
DO - 10.1109/TCOMM.2019.2898573
M3 - Article
AN - SCOPUS:85067561043
VL - 67
SP - 4132
EP - 4150
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
SN - 1558-0857
IS - 6
M1 - 8638800
ER -