Deploying Large Language Models (LLMs) over the edge-cloud continuum faces severe stability challenges due to the conflict between stochastic network topology and complex workflow dependencies. Existing schedulers, relying either on computationally prohibitive Graph Neural Networks (GNNs) or topology-agnostic heuristic...
With the increasing number of end devices and the growing heterogeneity of application demands, task orchestration in dynamic cloud–edge environments faces challenges arising from network fluctuations, resource contention, and decision uncertainty. To address these challenges, this paper proposes a Cost-Optimized Adapt...
Pervasive intelligent applications are increasingly deployed on mobile and Internet of Things (IoT) edge devices. Consequently, Large Language Models (LLMs) are increasingly used to support these applications. Yet, due to their high resource demands, LLMs are mostly deployed in the cloud. Layer-wise edge-cloud inferenc...
A prediction-guided runtime that uses workflow forecasts to construct and optimize a physical execution graph and reduces end-to-end makespan and overall p95 completion latency under burst arrivals by up to 36.8% and 25.8% over state-of-the-art workflow schedulers.
The microservice architecture has been applied broadly in many mainstream computing environments. As one of the most prevalent environments, edge computing also widely employs microservices to handle diverse requests and tasks. In practical scenarios, microservices usually constitute workflows that are built based on s...
Pretrained Foundation Models (PFMs) enable highaccuracy inference services but are typically deployed in remote datacenters, resulting in prohibitively high inference delay. Mobile Edge Computing (MEC) can mitigate such high delays by caching PFMs or their fine-tuned variants on cloudlets located close to end users. Ho...
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