In this paper, we introduce DobLIX, a dual-objective learned index (LI) specifically designed for Log-Structured Merge (LSM) tree-based key-value stores. Traditional LIs primarily focus on optimizing index lookups, often overlooking the critical role of data access from storage, which can become a significant performance bottleneck. In LSM-based systems, a considerable portion of the index is stored on disk, making lookups highly dependent on the efficient coordination between in-memory structures and disk-resident data. Poorly optimized access patterns can lead to excessive I/O operations, negatively impacting read latency and overall system performance. DobLIX addresses this by incorporating a second objective, data access optimization, into the LI training process. This dual-objective approach ensures that both index lookup efficiency and data access costs are minimized, leading to significant improvements in read performance while maintaining write efficiency in real-world LSM systems. Additionally, DobLIX features a reinforcement learning agent that dynamically tunes the system parameters, allowing it to adapt to varying workloads in real-time. Experimental results using real-world datasets demonstrate that DobLIX reduces indexing overhead and improves throughput by 1.19x to 2.21x compared to state-of-the-art methods within RocksDB, a widely used LSM-based storage engine.

Heterogeneous edge platforms enable the efficient execution of machine learning inference applications. These applications often have a critical constraint (such as meeting a deadline) and an optimization goal (such as minimizing energy consumption). To navigate this space, existing optimization frameworks adjust the platform's frequency configuration for the CPU, the GPU and/or the memory controller. However, existing optimization frameworks have two limitations. First, edge applications are frequently deployed in environments where they are exposed to ambient temperature variations. Second, a recent study has shown that temperature has a significant impact on edge platform characteristics. In this context, today's frequency optimization frameworks (which are thermal-oblivious) select frequency configurations that either violate the application's constraints, or are sub-optimal in terms of the optimization goal.

As power systems are gradually evolving into more efficient and intelligent cyber–physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant upon more accurate and complex forecasting. The accuracy and generalizability of the forecasting rest, to a great extent, upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This article proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resiliency of energy forecasting systems. The proposed mechanism is based on cross-validation, least-squares, andz-score metric providing accurate detections with low computational cost and high scalability without utilizing either system’s models or parameters. The effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models. Experiments indicate that corrupted data injected into input, output, and input–output data is properly located and removed, whereby the accuracy and generalizability of the final forecasts are recovered.

This study offers a methodology to characterize intra- and inter-node variability and applies it on two heterogeneous edge platforms (the NVIDIA Jetson AGX and Nano) for performance and power consumption. Firstly, we explore intra-node variability: investigate to what degree deployment decisions can limit it, highlight that it is unavoidable, and offer a scale so that one can compare to what other studies report. Secondly, we characterize inter-node variability by answering two questions: (i) Are the platforms we study statistically different in terms of the applications' power draw and runtime? and (ii) What is the magnitude of these differences? Finally, we attempt to answer the question of why is it paramount to characterize variability and take it into account? to achieve this, we discuss examples from the compiler and runtime optimization domains.