Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling

By T. Dinh, Jianhua Tang, Q. La, Tony Q. S. Quek. 2017

859 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

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Citations in the corpus

Multi-Objective Resource Optimization for Hierarchical Mobile Edge Computing (2018, background): “Multi-level structures have been studied using cost-based models where tasks are shared on the server-level or offloaded to the cloud with an associated cost [3].”
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks (2018, methodology): “Another widely adopted heuristic is through convex relaxation, e.g., by relaxing integer variables to be continuous between 0 and 1 [13] or by approximating the binary constraints with quadratic constraints [14].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “systems: Optimization of different decisions to realize computation offloading were studied in [67] where the authors in [68] considered the selection of the edge device from all the potential candidates within the radio coverage area, the”
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting (2017, methodology): “One critical problem in the computation offloading is the selection of the edge device from all the potential radio device candidates within the radio coverage area [10].”
Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee (2018, methodology): “We adopt the method proposed in [11] and [16], to obtain the offloading decisions in the feasible solution s̃.”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, background): “Most recently, a computation offloading framework that allows a mobile device to offload tasks to multiple MEC servers was proposed in [131], and semidefinite relaxation-based algorithms were proposed to determine the task allocation decisions and CPU frequency scaling.”
Reducing Offloading Latency for Digital Twin Edge Networks in 6G (2020, background): “Dinh et al. [19] developed an ofﬂoading framework that can minimize execution delay and energy consumption by optimizing task allocation and computing resources.”
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks (2018, methodology): “[23] aimed to minimize both total tasks’ execution latency and the SMD’s energy consumption by jointly optimizing the task offloading decision and the SMD’s CPU-cycle frequency, which considered the fixed CPU frequency and elastic CPU frequency.”
Computation Offloading Toward Edge Computing (2019, methodology): “To address these challenges, in the single-user scenario, [110] studied the task allocation in MEC by optimizing both the task execution time and the energy consumption.”
Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things (2020, background): “In [82], the effect when a single mobile device is able to allocate tasks to multiple edge devices is studied, and an optimization framework of offloading is proposed.”
Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks (2018, background): “…and the MD’s energy consumption in the MECO scenario where a single MD can ofﬂoad tasks to multiple wireless access points (APs), Dinh et al. [13] presented a computational ofﬂoading framework by jointly optimizing the task allocation decision and the MD’s CPU frequency, and two semideﬁnite…”
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, background): “Later in [7], the scenario of a UE with multiple tasks was considered, where multiple APs assisted the UE to reduce its total task execution latency and energy consumption.”
Federated Learning via Over-the-Air Computation (2018, methodology): “This emerging technique is termed as edge ML [4], which is supported by mobile edge computing [5], [6] via pushing the cloud computing services to the network edges.”
Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning (2020, methodology): “For all experiments, the data size of each task ranges from 5 KB to 50 KB; the CPU cycles required by each task ranges from 10(7) to 10(8) cycles [5].”
Offloading Schemes in Mobile Edge Computing for Ultra-Reliable Low Latency Communications (2018, methodology): “The complexity of this algorithm is O ( [(N + 1)(N + 4)/2]3 K 2 + N 2 ) , where K is the bit length of the input [15], [32].”