Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading

By Olga Muñoz, Antonio Pascual-Iserte, Josep Vidal. IEEE Transactions on Vehicular Technology, 2014

378 citations recorded by the source corpus.

Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) off-loading of highly demanding applications from smartphones to the so-called femto-cloud. Such off-loading promises to be beneficial in terms of battery savings at the mobile terminal (MT) and/or in latency reduction in the execution of applications. However, for this promise to become a reality, the energy and/or the time required for the communication process must be compensated by the energy and/or the time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency. Multiple antennas are assumed to be available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, and precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. This paper also establishes the conditions under which total or no off-loading is optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes, as a particular case, the minimization of the total consumed energy without latency constraints.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e1639f8d4e80906ea593875a715d4d5e839ea03f

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Citations in the corpus

Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things (2020, background): “[70] set up the conditions under which full or no offloading is optimal”
Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks (2018, methodology): “To address this challenge, the previously used method is to of-ﬂoad the MDs’ computation tasks to the resource-rich cloud by adopting the mobile cloud computing technology, i.e., mobile computation ofﬂoading (MCO) [6]–[8].”
Offloading Schemes in Mobile Edge Computing for Ultra-Reliable Low Latency Communications (2018, background): “When offloading sub-task of Iλi bits to the EN i, the size of the transmitted data will be βIλi, where β(β ≥ 1) represents a ratio of the transmitted data size to the original task data size due to transmission overhead [21].”
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks (2016, background): “To reduce energy cost, the authors in [33] presented a framework of the joint optimization of the radio and computation resource for the”
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks (2018, methodology): “[17] minimized the SMD’s energy consumption by jointly optimizing the transmission time and the amount of data offloaded to a femto access point.”
Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee (2018, background): “, the number of CPU cycles necessary to accomplish the application, as Cn = Dn Appn [17], [18].”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “Several scenarios, such as single-user single-cloud [121], multi-user single-cloud [122], multi-user multi-cloud [123], resource allocation inside of cluster [124], and multi-user [125] are studied.”
Mobile Edge Computing: A Survey on Architecture and Computation Offloading (2017, background): “The study in [95] provides more in-depth theoretical analysis on trade-off between the energy consumption and the”
Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems (2017, background): “In [13], a joint optimization of communication and computational resource allocation for femto-cloud computing systems was proposed, where the cloud server is formed by a set of femto access points.”
A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things (2019, background): “The execution latency [7], [8] and energy assumption [9], [10] are usually two criterions for offloading performance evaluation.”