On the Optimality of Task Offloading in Mobile Edge Computing Environments

Abstract

Mobile Edge Computing (MEC) has emerged as new computing paradigm to improve the QoS of users' applications. A challenge in MEC is computation (task/data) offloading, whose goal is to enhance the mobile devices' capabilities to face the requirements of new applications. Computation offloading faces the challenges of where and when to offload data to perform computing (analytics) tasks. In this paper, we tackle this problem by adopting the principles of Optimal Stopping Theory contributing with two time-optimized sequential decision making models. A performance evaluation is provided using real world data sets compared with baseline deterministic and stochastic models. The results show that our approach optimizes such decision in single user and competitive users scenarios. Index Terms-Mobile edge computing, tasks offloading, optimal stopping theory, sequential decision making.

Proactive & Time-Optimized Data Synopsis Management at the Edge

“…1). This supports applications requiring pro-active decision making for: (i) allocating tasks/analytics queries only to relevant ENs based on their synopses studied in [30] and [49], (ii) share and update ML models in Federated Learning [5] for environmental monitoring based on synopses and network load, (iii) transfer tasks for ML models training & inference ENs based on network congestion [36], and (iv) separating context data by distributively gathering similar data to same datasets studied in [28]. As exemplified in Fig.…”

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

“…Common methods are primal-dual optimization [10,11], nonlinear optimization [7,12,13] and mixedinteger linear programming problem [14,15]. In [10], an online truthful mechanism based on the primal-dual optimization framework integrating computation and communication resource allocation is proposed .…”

“…Bai et al [12] conceived an energy-efficient computation offloading technique for UAV-MEC systems and formulated many energy-efficiency problems, which are then transformed into convex problems. In [13], Alghamdi et al tackled the MEC problem by adopting the principles of optimal stopping theory contributing to two time-optimized sequential decision-making models. Guo et al [14] forced on the problem of assigning resources for offloading the computationally intensive tasks of mobile applications.…”

Data quality-aware task offloading in Mobile Edge Computing: An Optimal Stopping Theory approach

“…In our previous work [29,30,31], we proposed a set of lightweight sequential decision making models adopting the principle of OST. In particular, in our work in [29,30], we proposed a Delay Tolerant Offloading (DTO) decision making in mobile edge computing environment.…”