Multi-access Edge Computing (MEC) deploys computation and storage resources at the network edge, enabling devices to process data and requests on nearby edge services. This reduces data transmission latency and network congestion. However, due to edge servers' volatile running status and limited resources, the reliability of edge services deployed on them fluctuates over time. This may lead to concept drifts in edge services' real-time reliability streaming data. A severe negative drift may indicate a runtime reliability anomaly in an edge service, which often impacts users' Quality of Experience (QoE). To ensure edge services' reliability, this paper proposes CS-Detection, a hierarchical approach for detecting runtime reliability anomalies based on concept drift. CS-Detection employs the compressed sensing technique to sample complex and large-scale reliability streaming data. It employs a new technique that combines Variational AutoEncoder and Energy-Based Generative Adversarial Network (E2BGAN) to estimate the anomaly level of edge services by calculating the reconstruction error and discriminant error of compressed real-time reliability streaming data. To demonstrate the usefulness of CS-Detection in ensuring the QoE of MEC systems, we present CPRest, a coordinated checkpoint-based effective rejuvenation approach for restoring the normal operation of edge services affected by runtime reliability anomalies. CPRest classifies detection results into four levels and adjusts the edge services' restart trigger time accordingly. Comprehensive experiments conducted on real-world datasets demonstrate the effectiveness and efficiency of CS-Detection compared to state-of-the-art approaches.

By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC services are challenged by the risks of runtime reliability anomalies. Detecting runtime reliability anomalies for MEC services is challenging yet critical to ensuring the stability of MEC systems. The effectiveness of existing anomaly detection methods suffers from poor performance when handling MEC services’ large-volume, continuous, and volatile reliability streaming data. The key is to identify significant changes in the distribution of MEC services’ current reliability streaming data compared with their historical performance. Inspired by concept drift, this paper proposes B-Detection, a boosting Long Short-Term Memory (LSTM) Autoencoder for detecting MEC services’ runtime reliability anomalies based on distribution dissimilarity evaluation. B-Detection employs a deep learning method named LSTM Autoencoder to characterize the MEC services’ historical reliability data distribution. To cope with the challenge of modeling complex distribution characteristics of MEC services’ historical reliability streaming data and guarantee the real-time performance of B-Detection, we enhance LSTM Autoencoder with a weight-based reservoir sampling technique and an LSTM boosting algorithm. The reconstruction loss of the trained LSTM Autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services’ runtime reliability anomalies. The performance of B-Detection is verified through a series of experiments conducted on a real-world dataset.

As a nascent technique, Mobile Edge Computing (MEC) is mushrooming with a broad application prospect. By transferring abundant computing and storage resources from cloud to edge servers close to users, it allows services to be hosted on edge servers, which greatly reduces service latency. However, due to environmental dynamics, the edge services' reliability fluctuates in real-time. When the reliability of an edge service degrades severely, it may be suffering an anomaly, which can seriously impact its real-time performance and users' quality of experience. To ensure the real-time performance of edge services, this paper presents CDCrest, a concept drift-based checkpoint restart approach for edge service rejuvenation. CDCrest employs L-Detection, a concept drift-based approach to detect edge services' runtime reliability anomalies. L-Detection leverages a sliding window and Locality Sensitive Hashing (LSH)-based sampling to extract features from an edge service's real-time and historic reliability data streams. Then, it calculates the real-time Distribution Change Degree (DCD) based on Jensen-Shannon (JS) divergence to infer whether the edge service is suffering a reliability anomaly. Once an anomaly for an edge service is identified, CDCrest employs a checkpoint restart mechanism to ensure the rapid rejuvenation of the edge service. Extensive experiments conducted based on a popular real-world dataset demonstrate the effectiveness and efficiency of CDCrest against the state-of-the-art approaches.