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1. Optimal Offloading for Dynamic Compute-Intensive Applications in Wireless Networks

   https://doi.org/10.1109/GLOBECOM38437.2019.9013327  

   Li, Bin (December 2019, 2019 IEEE Global Communications Conference (GLOBECOM))

   With the rapid growth of wireless compute-intensive services (such as image recognition, real-time language translation, or other artificial intelligence applications), efficient wireless algorithm design should not only address when and which users should transmit at each time instance (referred to as wireless scheduling) but also determine where the computation should be executed (referred to as offloading decision) with the goal of minimizing both computing latency and energy consumption. Despite the presence of a variety of earlier works on the efficient offloading design in wireless networks, to the best of our knowledge, there does not exist a work on the realistic user- level dynamic model, where each incoming user demands a heavy computation and leaves the system once its computing request is completed. To this end, we formulate a problem of an optimal offloading design in the presence of dynamic compute-intensive applications in wireless networks. Then, we show that there exists a fundamental logarithmic energy- workload tradeoff for any feasible offloading algorithm, and develop an optimal threshold-based offloading algorithm that achieves this fundamental logarithmic bound. 

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2. On-Request Wireless Charging and Partial Computation Offloading In Multi-Access Edge Computing Systems

   https://doi.org/10.1109/TWC.2021.3075920  

   Malik, Rafia; Vu, Mai (May 2021, IEEE Transactions on Wireless Communications)

   null (Ed.)

   Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user-devices. We consider a multi-access edge computing (MEC) system with collocated MEC server and base-station/access point (AP), each equipped with a massive MIMO antenna array, supporting multiple users requesting data computation and wireless charging. The goal is to minimize the energy consumption for computation offloading and maximize the received energy at the user from wireless charging. The proposed solution is a novel two-stage algorithm employing nested descent algorithm, primal-dual subgradient and linear programming techniques to perform data partitioning and time allocation for computation offloading and design the optimal energy beamforming for wireless charging, all within MEC-AP transmit power and latency constraints. Algorithm results show that optimal energy beamforming significantly outperforms other schemes such as isotropic or directed charging without beam power allocation. Compared to binary offloading, data partition in partial offloading leads to lower energy consumption and more charging time, leading to better wireless charging performance. The charged energy over an extended period of multiple time-slots both with and without computation offloading can be substantial. Wireless charging from MEC-AP thus offers a viable untethered approach for supplying energy to user-devices. 

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3. Energy-efficient Joint Wireless Charging and Computation Offloading In MEC Systems

   https://doi.org/10.1109/JSTSP.2021.3098963  

   Malik, Rafia; Vu, Mai (July 2021, IEEE Journal of Selected Topics in Signal Processing)

   null (Ed.)

   Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user devices. We consider a mutil-access edge computing (MEC) system with collocated MEC servers and base-stations/access points (BS/AP) supporting multiple users requesting data computation and wireless charging. We propose an integrated solution for wireless charging with computation offloading to satisfy the largest feasible proportion of requested wireless charging while keeping the total energy consumption at the minimum, subject to the MEC-AP transmit power and latency constraints. We propose a novel nested algorithm to jointly perform data partitioning, time allocation, transmit power control and design the optimal energy beamforming for wireless charging. Our resource allocation scheme offers a minimal energy consumption solution compared to other schemes while also delivering a higher amount of wirelessly transferred charge to the users. Even with data offloading, our proposed solution shows significant charging performance, comparable to the case of charging alone, hence showing the effectiveness of performing partial offloading jointly with wireless charging. 

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4. Distributed Threshold-based Offloading for Heterogeneous Mobile Edge Computing

   Qin, Xudong; Xie, Qiaomin; Li, Bin (July 2023, 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS))

   In this paper, we consider a large-scale heterogeneous mobile edge computing system, where each device’s mean computing task arrival rate, mean service rate, mean energy consumption, and mean offloading latency are drawn from different bounded continuous probability distributions to reflect the diverse compute-intensive applications, mobile devices with different computing capabilities and battery efficiencies, and different types of wireless access networks (e.g., 4G/5G cellular networks, WiFi). We consider a class of distributed threshold-based randomized offloading policies and develop a threshold update algorithm based on its computational load, average offloading latency, average energy consumption, and edge server processing time, depending on the server utilization. We show that there always exists a unique Mean-Field Nash Equilibrium (MFNE) in the large-system limit when the task processing times of mobile devices follow an exponential distribution. This is achieved by carefully partitioning the space of mean arrival rates to account for the discrete structure of each device’s optimal threshold. Moreover, we show that our proposed threshold update algorithm converges to the MFNE. Finally, we perform simulations to corroborate our theoretical results and demonstrate that our proposed algorithm still performs well in more general setups based on the collected real-world data and outperforms the well-known probabilistic offloading policy. 

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5. Optimal Joint Offloading and Wireless Scheduling for Parallel Computing with Deadlines

   Qin, Xudong; Xu, Weijian; Li, Bin (June 2019, International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt))

   In this paper, we consider the problem of joint offloading and wireless scheduling design for parallel computing applications with hard deadlines. This is motivated by the rapid growth of compute-intensive mobile parallel computing applications (e.g., real-time video analysis, language translation) that require to be processed within a hard deadline. While there are many works on joint computing and communication algorithm design, most of them focused on the minimization of average computing time and may not be applicable for mobile applications with hard deadlines. In this work, we explicitly take hard deadlines for computing tasks into account and develop a joint offloading and scheduling algorithm based on the stochastic network optimization framework. The proposed algorithm is shown to achieve average energy consumption arbitrarily close to the optimal one. However, this algorithm involves a strong coupling between offloading and scheduling decisions, which yields significant challenges on its implementation. Towards this end, we first successfully decouple the offloading and scheduling decisions in the case with one time slot deadline by exploring the intrinsic structure of the proposed algorithm. Based on this, we further implement the proposed algorithm in the general setups. Simulations are provided to corroborate our findings. 

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