Edge computing facilitates the development and implementation of new mechanisms for data storage on edge servers located near users, thereby enabling low-latency edge data retrieval services. However, individual edge servers have limited storage capacity, which restricts their ability to meet edge users' diverse data demands. They often need to cooperate with neighbors. Edge data indexing systems play a crucial role in helping edge servers locate the requested data. Existing edge data indexing systems typically rely on static indexing structures, which may lead to significantly reduced query accuracy or excessive system memory overhead in dynamic edge data query (EDQ) scenarios, making it difficult to meet the high scalability requirements of edge computing systems. In this paper, we address these challenges and propose a novel adaptive edge data indexing system called Balloon, which dynamically scales memory usage to balance query accuracy and memory overhead as data volume varies at runtime. Specifically, each edge server maintains a resizable Adaptive Bloom Filter (ABF) for its local data, and builds a Balloon tree that aggregates the ABFs of neighboring servers within a latency constraint. The size of the Balloon tree dynamically scales with data volume to balance query accuracy and memory overhead. To validate Balloon, we implement and conduct comprehensive experiments using an edge storage system with 50 edge servers. The results indicate that, compared to the state-of-the-art edge indexing system, Balloon improves query accuracy by 26.74% and reduces query time by 18.16%. In the meantime, it reduces up to 35.66% memory overhead.

Mobile edge computing (MEC) can pre-cache deep neural networks (DNNs) near end-users, providing low-latency services and improving users’ quality of experience (QoE). However, caching all DNN models at capacity-limited edge servers is difficult, and the impact of model loading time on QoE remains underexplored. We explore dynamic DNNs by disassembling a complete DNN model into interrelated submodels to enable fine-grained joint optimization of submodel caching and request routing to balance inference precision and loading latency. In this paper, we study the joint dynamic model caching and request routing problem in MEC networks, aiming to maximize user request inference precision under constraints of server resources, latency, and model loading time. We propose CoCaR, an offline algorithm based on linear programming and random rounding that optimizes joint decisions with a provable performance bound. Furthermore, we develop an online extension, CoCaROL, to adapt to dynamic and unpredictable request patterns. The simulation results demonstrate that CoCaR improves the average inference precision for user requests by 40.1% over state-of-the-art baselines. In addition, CoCaR-OL achieves an improvement of at least 32.3% in users’ QoE over competitive baselines.

With the explosive increase in mobile data traffic and stringent quality-of-experience requirements of users, mobile edge caching is a promising paradigm to reduce delivery latency and network congestions by serving content requests locally. However, it is extremely challenging to conduct cache replacement when the cache is full and the future request pattern is unknown subject to enormous content volume but limited cache capacity at the network edge. In this paper, we propose a cache replacement algorithm based on the oracle approximation named OA-Cache in an end-to-end manner to maximize the cache hit rate. Specifically, we construct a complex model that uses a temporal convolutional network to capture the long and short dependencies between content requests. Then, an attention mechanism is adopted to find out the correlations between the requests in the sliding window and cached contents. Instead of training a policy to mimic Belady that evicts the content with the longest reuse distance, we cast the learning task into a classification model to distinguish unpopular contents from popular ones. Finally, we apply the knowledge distillation approach to assist in transferring knowledge from a large pre-trained complex network to a lightweight network to readily accommodate to the network edge scenario. To validate the effectiveness of OA-Cache, we conduct extensive experiments on real-world datasets. The evaluation results demonstrate that OA-Cache can achieve the superior performance compared to candidate algorithms.