Driven by the exponential growth of latency-sensitive applications, mobile edge computing (MEC) has emerged as a pivotal paradigm, yet mitigating its substantial energy consumption remains critical. This paper explores a movable-antenna (MA) assisted energy minimization scheme in an uncrewed aerial vehicle (UAV)-enabled MEC system, where a UAV equipped with an MA array serves as an edge server to process tasks offloaded from terrestrial consumer electronics (CE) devices. To minimize the total system energy consumption, we jointly optimize computation resource allocation, CE transmit power, receive beamforming, and MA positions. To tackle the resulting non-convex problem with coupled variables, a robust alternating optimization algorithm based on the block coordinate descent method is developed. The problem is iteratively decomposed into three subproblems. In particular, the subproblem of transmit power and receive beamforming is reformulated and optimized using the quadratic transform technique, while the MA array positions are optimized via the particle swarm optimization algorithm. Numerical simulations verify that the proposed scheme achieves substantial energy savings over conventional benchmarks.
Jiang Chen, Chunjie Wang, Xuhui Zhang et al.· IEEE transactions on consume...· 0 citations
CoScale is proposed, a layer-wise dynamic scaling mechanism that selectively expands the parallelism of hot layers onto idle resources reclaimed from underutilized devices, enabling elastic data parallelism without altering model architectures or adding hardware overhead.
Jingfeng Wu, Yiyuan He, Minxian Xu et al.· 0 citations
Large language model (LLM) deployment at the network edge faces a fundamental paradox: applications require full-scale models for sophisticated reasoning, yet edge devices impose severe resource constraints across computation, memory, and network. Existing approaches fail to effectively orchestrate resources across the edge-cloud continuum, leaving capacity underutilized while struggling with heterogeneous and volatile distributed environments. We present DynoPipe, an adaptive edge-cloud system that addresses these constraints through dynamic pipeline parallelism with shifting computational boundaries. DynoPipe tackles three core challenges: structural heterogeneity causing 94% pipeline idle time, temporal resource volatility invalidating static partitioning, and boundary migration overhead trapping systems in suboptimal configurations. Through boundary-constrained pipeline construction, proactive multi-configuration orchestration, and hierarchical state management, DynoPipe eliminates the memory wall while preserving data locality, achieving $\mathbf{1 0. 1} \times$ throughput improvement over edge-only baselines and $\mathbf{1. 6} \times$ over cloud-only execution, with 99.2% latency reduction.
Yanying Lin, Baicheng Chen, Xinyu Zhang et al.· International Symposium on C...· 0 citations