Resource-constrained devices face significant performance challenges when encountering memory pressure situations due to limited hardware resources. Existing approaches mainly focus on reactive and instantaneous approaches, but they often fail to accurately identify the root cause of memory pressure, resulting in delayed and ineffective response strategies. In this paper, we address this limitation by proposing an alternative data-driven approach to proactively detect memory pressure and identify the responsible process in resource-limited devices. Our method enables the activation and deactivation of extended process-level profiling based on the predicted memory pressure, facilitating the identification of the root cause process. Through evaluation, we achieved an 85% accuracy in forecasting memory pressure situations and correctly identified the responsible process in 83% of use-cases. These results demonstrate the effectiveness of our strategy to stream large amount of trace data in mitigating memory pressure issues in resource-constrained systems. This approach has the potential to enhance system performance and improve overall system architecture in such devices.

Memory leaks are of serious concern in software programs written in languages that do not have a dedicated garbage collector. Memory leaks are the result of prolonged unnecessary usage of the system memory resources. This increases the workload, paging, and lowers the response rate leading to performance degradation of the system or software. Such leaks are difficult to detect due to a software’s development testing span and different working environments.This research paper proposes an algorithm to detect memory leaks based on the growth analysis of the memory blocks. The proposed method captures the allocation made in memory blocks via malloc, calloc, and realloc and stores the captured results onto the files. The files are then analyzed using the leak threshold determined by the developer or tester. Unlike other approaches such as ASAN/LSAN which require multiple snapshots to analyze the leaks, the proposed method works without a snapshot for the summary analysis algorithm and only requires one snapshot, collected at the end of the execution. This approach does not need to be integrated within the application to analyze the software and detect memory leaks. Our method provides more accurate results by visualizing the captured and analyzed data for the developers, making it more convenient to detect the root cause of the leakage.

Kernel traces are sequences of low-level events comprising a name and multiple arguments, including a timestamp, a process id, and a return value, depending on the event. Their analysis helps uncover intrusions, identify bugs, and find latency causes. However, their effectiveness is hindered by omitting the event arguments. To remedy this limitation, we introduce a general approach to learning a representation of the event names along with their arguments using both embedding and encoding. The proposed method is readily applicable to most neural networks and is task-agnostic. The benefit is quantified by conducting an ablation study on three groups of arguments: call-related, process-related, and time-related. Experiments were conducted on a novel web request dataset and validated on a second dataset collected on pre-production servers by Ciena, our partnering company. By leveraging additional information, we were able to increase the performance of two widely-used neural networks, an LSTM and a Transformer, by up to 11.3% on two unsupervised language modelling tasks. Such tasks may be used to detect anomalies, pre-train neural networks to improve their performance, and extract a contextual representation of the events.