Yue-E Zhou

We consider the energy-constrained task allocation problem in large-scale Aerial Edge Computing (AEC) systems, which encompasses a series of tightly coupled decision-making processes, including which tasks need to be processed by unmanned aerial vehicles (UAVs), how to allocate these tasks and balance energy across UAVs for delay-sensitive requirements. However, little attention has been devoted to exploring the above coupled decision-making problem in AEC with various resource and energy constraints, which is further complicated by energy dynamics (UAV battery states), task-specific consumption, and allocation-feedback balance. In this paper, we formulate a multi-dimensional joint optimization problem, simultaneously optimizing task allocation and energy rewarding to maximize long-term system rewards while balancing service quality and energy efficiency. To this end, we propose a green aerial edge computing framework where partial UAVs are equipped with energy harvesting modules to collect ambient energy. To circumvent the intractable computational complexity arising from the coupled energy states of massive UAVs, we design a distributed solution method based on the mean field game, which decouples the dense multi-agent interactions into a game between an individual UAV and the aggregate population state, thereby transforming the complex global optimization problem into a set of equivalent scalable subproblems. We develop an optimal energy valuation scheme to guide UAV behavior. Numerical results show that our mechanism can effectively ensure sustainable system operation while maintaining high quality of service for metaverse users, outperforming existing methods in both system sustainability and service responsiveness.

We consider the collaborative inference acceleration task via cloud-edge-end collaboration, which involves a series of tightly coupled decision-making steps, including which DNN model to be selected, how much to compress model, how to partition model, and where to offload partitioned submodels. In practical deployments, these decisions jointly affect both fine-tuning and inference performance, and must jointly account for such aspects as the model being used, the computational resources and local datasets available at each device, as well as network latencies, which significantly increases the complexity of optimizing the problem. Yet, no existing studies focus on such joint optimization problem for these tightly coupled decisions. In this paper, we model this problem as a multi-dimensional optimization problem, jointly optimizing collaborative inference and fine-tuning by selecting the DNN model, compression level, partition strategy, and computational resource allocation, with the objective of minimizing the overall energy consumption of the learning-inference process, subject to accuracy and latency constraints. To this end, we propose an algorithmic framework called JQODI combining a time-energy tree diagram to represent the learning process, a dynamic programming solution strategy, and a data-driven theoretical approach to predict the expected total number of training epochs that meet the accuracy requirements. We prove that JQODI approximates the optimal solution with polynomial complexity. Numerical results demonstrate that JQODI surpasses state-of-the-art methods in both energy efficiency and latency.

We consider the efficient resource allocation task in the blockchain-based mobile edge computing (MEC) system that requires decentralized transaction management to validate transactions between edge servers (ESs) and mobile devices (MDs).In such task allocation process (where MDs’ resources are limited and privacy-sensitive), it is a significant challenge to guarantee individual rationality with satisfactory system stability while enabling flexible task offloading under various locality constraints (e.g., communication distance, bandwidth and delay). In this paper, we formulate the target problem as a blockchain-assisted task-resource matching model, and then propose a near optimal locality-aware resource allocation mechanism over smart contract to enable automatic and efficient transactions in MEC system. More specifically, for the service agents selection, we design the preference-based selection strategy to get highest estimated profit. For the flexible task offloading, we develop the minimum delay task graph partitioning algorithm to determine the optimal task offloading solution for MD under different resource bundles. For the task-resource matching, we propose a task-resource matching game (based on potential game) with the second lowest cost strategy to determine the matching of task-resource and decide the price of resource bundle. For the transaction verification and block allocation, we propose a social welfare-driven consensus mechanism to enable verified transaction and fair block allocation in a reward-free way. Strict theoretical analysis and extensive simulations demonstrate that our mechanism guarantees individual rationality, Nash Equilibrium, and stable near optimal solution.

One-shot neural architecture search (NAS) trains an over-parameterized network (termed as supernet) that assembles all the architectures as its subnets by using weight sharing for computational budget reduction. However, there is an issue of multi-model forgetting during supernet training that some weights of the previously well-trained architecture will be overwritten by that of the newly sampled architecture which has overlapped structures with the old one. To overcome the issue, we propose an orthogonal gradient learning (OGL) guided supernet training paradigm, where the novelty lies in the fact that the weights of the overlapped structures of current architecture are updated in the orthogonal direction to the gradient space of these overlapped structures of all previously trained architectures. Moreover, a new approach of calculating the projection is designed to effectively find the base vectors of the gradient space to acquire the orthogonal direction. We have theoretically and experimentally proved the effectiveness of the proposed paradigm in overcoming the multi-model forgetting. Besides, we apply the proposed paradigm to two one-shot NAS baselines, and experimental results demonstrate that our approach is able to mitigate the multi-model forgetting and enhance the predictive ability of the supernet with remarkable efficiency on popular test datasets.