Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling

By Yanting Wang, Min Sheng, Xijun Wang, Liang Wang, Jiandong Li. 2016

833 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “Processing the training operations together with other incumbent applications is another viable solution, in which their operation scheduling is optimized as done in MEC [199] and in the context of exploration–exploitation tradeoff [200].”
Collaborative Cloud and Edge Computing for Latency Minimization (2019, methodology): “In this system, we assume that each computation task can be processed at both the edge node and the cloud server [5], [13], [33].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “Computation resource Computational speed [68], [69] The dynamic voltage frequency scaling technique is applied to control the CPU performance by adjusting the CPU frequency and/or supply voltage.”
Computation Rate Maximization for Wireless Powered Mobile-Edge Computing With Binary Computation Offloading (2017, methodology): “For partial offloading mode, [15] jointly optimizes the offloading ratio, transmission power and CPU frequency to either minimize energy consumption or computation latency.”
Price-Based Distributed Offloading for Mobile-Edge Computing With Computation Capacity Constraints (2017, background): “More specially, the data size of the computed result fed back to user k is αk k, where αk (αk > 0) accounts for the ratio of output to input bits offloaded to the cloud [13], which depends on the applications of the users.”
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks (2019, methodology): “Recent works on MEC include those that reduce energy consumption [7], [8], [10], [12], [14], [15], and those that reduce latency [16], [17] by offloading computation-heavy and latency-sensitive tasks to nearby RSUs and/or base stations for remote execution.”
Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing (2019, methodology): “We consider the data-partitioning based application of [7], where a fraction of the data can be processed locally, while the other part can be off-loaded to the edge node.”
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, methodology): “As in [33], we model the power consumption of CPU in user k as Plocal k [i] = rc(3)2,k, where r denotes the coefficient depending on the chip architecture that is decided by the practical measurement [7].”
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks (2018, methodology): “It is allowed to schedule the CPU-cycle frequency via dynamic voltage and frequency scaling technology [22].”
Offloading Schemes in Mobile Edge Computing for Ultra-Reliable Low Latency Communications (2018, methodology): “For instance, the work in [20], [23]–[25] proposed offloading strategy to improve energy saving or latency separately, while Dinh et al.”
Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach (2020, background): “According to [25], ek,m = ιk,mbk,mηk,mρk,m where ιk,m is the unit cost for the computation resource consumption, bk,m is the size of each image, ηk,m is the effective switched capacitance that depends on the chip architecture, and ρk,m is the number of CPU cycles to process one bit.”
A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things (2019, methodology): “In [19], multiuser computation partitioning problem is formulated as an MILP problem and solved by a greedy heuristic algorithm.”
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks (2018, neutral): “We assume equal computing efficiency ki = 10−26, i = 1, · · · , N , and φ = 100 for all the WDs [32].”
Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, methodology): “Computation Energy Consumption: Similar to [40], we model the power consumption of the CPU in UAV as κ(f UAV i ) 3, where κ denotes the effective switched capacitance depending on the CPU architecture.”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “In Reference [153], the authors involve a small cell cloud manager into the edge computing architecture that manages the computing-related activities of the femto-cloud.”