Edge nodes host latency-sensitive applications on fixed and resource-limited hardware. Under moderate load increases, contention for shared CPU, network, and I/O resources introduces kernel-level delays that result in tail-latency degradation. In practice, many edge workloads are black-box services, making application-level instrumentation impractical. Existing resource metrics such as CPU utilization and Linux Pressure Stall Information (PSI) often fail to explain latency behavior, especially when they saturate. We present K-Sense, a non-invasive eBPF-based framework that infers QoS degradation from kernel behavior. K-Sense collects a small set of kernel-level delay metrics and represents the system state as a point in a multi-dimensional feature space. It then computes a covariance-aware friction signal using the Mahalanobis distance to quantify deviation from a calibrated normal operating region. We evaluate K-Sense on a Kubernetes testbed using real microservice workloads from DeathStarBench and a sentimentanalysis application. Results show that friction tracks application P99 latency under changing load and continues to reflect latency variations when CPU utilization and PSI saturate. These signals provide a practical basis for higher-level actions such as admission control, scheduling, and workload migration without requiring application modification.
Solid-state drives (SSDs) are deployed in fog, edge, and cloud environments owing to their high performance and reliability, making their lifespan critical concerns. Zoned Namespace SSDs (ZNS SSDs) address garbage collection (GC) overhead caused by out-of-place updates in conventional SSDs, providing stable and high write performance. RocksDB/ZenFS is a prominent system that leverages ZNS SSDs, where RocksDB, an LSM-tree-based key-value store, utilizes ZNS SSDs as storage through the ZenFS filesystem. To fully exploit RocksDB's write performance, multiple compaction threads are typically utilized. However, the existing zone allocation scheme in ZenFS often leads to high write amplification and frequent zone finish operations in multi-threaded compaction environments, thereby shortening the lifespan of ZNS SSDs. To address this issue, we propose Lifetime Ratio-based Zone Allocation (LRZA), a zone allocation scheme that considers the lifetime ratios of files grouped within a zone. LRZA eliminates the need for zone finish operations during allocation and effectively reduces write amplification caused by garbage collection. Experimental results demonstrate that LRZA reduces the number of used zones and zone reset operations by $\mathbf{1 0} \boldsymbol{\%}$ and $\mathbf{1 4} \boldsymbol{\%}$, respectively, compared to ZenFS. These improvements contribute to extending the operational lifespan of ZNS SSDs while maintaining efficient write performance.
Processing real-time data in the cloud has become the basis for various IoT applications. This processing typically incurs a high pricing cost, due to the use of paid messaging services (e.g., AWS SQS, or GCP Pub/Sub) that send real-time data from IoT devices at the edge to IoT applications in the cloud. Alternatively, costly AI-enabled IoT devices are employed for federated learning at the edge, which lowers the amount of data sent to the cloud. To reduce such costs, we propose an approach that sends real-time data from the IoT device to the cloud with reduced sampling frequency. In the cloud, we use AI to predict the missing data, and send both real and predicted data to the IoT application. This way, the IoT application receives the same amount of data, although less data is sent from the edge to the cloud (using messaging services). In addition, we measure the prediction error in the cloud, and adjust the sampling frequency at the edge, so that the error remains low. Thus, this approach lowers the cost of IoT applications by reducing the use of paid messaging services, and avoiding costly AI-enabled IoT devices, while also ensuring a low prediction error. For the evaluation, we build a prototype, and we experiment with real-world IoT data. Our experiments show promising results, including a reduction in costs by about 70% with a prediction error of around 5%.
We address joint task offloading and resource allocation in multi-user multi-server Mobile Edge Computing (MEC) networks, formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem minimizing weighted delay and energy consumption. We propose a bi-level decomposition framework: an outer loop using an intention-enhanced Proximal Policy Optimization (PPO) agent with multi-head self-attention for binary offloading decisions, and an inner convex optimization loop for optimal task division and CPU frequency allocation. Simulations demonstrate superior convergence stability and reduced system costs over DRL and heuristic baselines.
Integrated sensing, computing, and communication (ISCC) networks combine both mobile edge computing (MEC) and sensing functionalities. Joint uncrewed aerial vehicle (UAV) trajectory planning and resource allocation are essential tasks for UAV-enabled ISCC networks. However, existing works primarily rely on bit-level data transmission, which is sensitive to errors in unstable post-disaster channels and incurs excessive offloading delays and limited bandwidth resources. Motivated by this, we incorporate semantic communication to substitute conventional bit-level transmission for robust and lightweight data offloading, referred to as an integrated sensing, semantic computing, and communication (ISSCC) system. An optimization of joint UAV trajectory planning, resource allocation, and semantic compression decision is established to minimize the average age of information (AoI) for both MEC and sensing functionalities. To tackle this problem, we propose a deep reinforcement learning (DRL)-based framework where the state-of-the-art actor-critic schemes are incorporated and a continuous relaxation and quantization strategy is adopted. Experiments are conducted in a synthetic post-disaster smart city scenario, and the results demonstrate the effectiveness and superiority of the proposed algorithms. In addition, the introduced semantic communication in the ISSCC system helps reduce the AoI by $\mathbf{6 8. 0 8} \boldsymbol{\%}$ compared with the conventional ISCC system.
