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1. 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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2. 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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3. Optimal Offloading for Dynamic Compute-Intensive Applications in Wireless Networks

   Li, Bin (December 2019, IEEE Conference on 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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4. Data Sharing-Aware Task Allocation in Edge Computing Systems

   https://doi.org/10.1109/EDGE53862.2021.00018  

   Rabinia, Sanaz; Mehryar, Haydar; Brocanelli, Marco; Grosu, Daniel (September 2021, 2021 IEEE International Conference on Edge Computing (EDGE))

   Edge computing allows end-user devices to offload heavy computation to nearby edge servers for reduced latency, maximized profit, and/or minimized energy consumption. Data-dependent tasks that analyze locally-acquired sensing data are one of the most common candidates for task offloading in edge computing. As a result, the total latency and network load are affected by the total amount of data transferred from end-user devices to the selected edge servers. Most existing solutions for task allocation in edge computing do not take into consideration that some user tasks may actually operate on the same data items. Making the task allocation algorithm aware of the existing data sharing characteristics of tasks can help reduce network load at a negligible profit loss by allocating more tasks sharing data on the same server. In this paper, we formulate the data sharing-aware task allocation problem that make decisions on task allocation for maximized profit and minimized network load by taking into account the data-sharing characteristics of tasks. In addition, because the problem is NP-hard, we design the DSTA algorithm, which finds a solution to the problem in polynomial time. We analyze the performance of the proposed algorithm against a state-of-the-art baseline that only maximizes profit. Our extensive analysis shows that DSTA leads to about 8 times lower data load on the network while being within 1.03 times of the total profit on average compared to the state-of-the-art. 

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5. 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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