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Modern real-time embedded systems are equipped with multi-core processors to execute computationally intensive tasks. In multi-core architecture, last-level cache memory is shared by cores. The shared cache becomes a non-deterministic resource, which affects the independent execution of real-time tasks. We propose a solution to remedy a variation in execution time when interference happens in a shared cache. Current solutions have relied on memory scheduling approaches that avoid concurrent memory access to guarantee deterministic execution time. However, these methods required complex analysis to accurately estimate the worst-case execution time and to schedule tasks in an overly conservative manner. Unlike existing works, the proposed method prevents simultaneous memory access using the side effect of memory barriers rather than the complicated analysis. A memory barrier is inserted based on a simple code analysis that is performed in units of basic blocks using the LLVM compiler. The proposed method not only does not require the modification of the operating system or task execution flow but also relatively shows fast analysis time. Experimental results show that the proposed basic block-based memory barrier insertion method can reduce the variation in execution time by up to 80% when interference occurs.INDEX TERMS code analysis, embedded systems, multi-core architecture, real-time systems, shared cache interference

The increasing number of daily notifications generated by smartphones and wearable devices increases mental burdens, deteriorates productivity, and results in energy waste. These phenomena are exacerbated by emerging use cases in which users are wearing and using an increasing number of personal mobile devices, such as smartphones, smartwatches, AirPods, or tablets because all the devices can generate redundant notifications simultaneously. Therefore, in addition to distraction, redundant notifications triggered by multiple devices result in energy waste. Prior work proposed a notification management system called PASS, which automatically manipulates the occurrence of notifications based on personalized models. However, machine-learning-based models work poorly against new incoming notifications because prior work has not investigated behavior changes over time. To reduce the gap between modeling and real deployment when the model is to be used long-term, we conducted a longitudinal study with data collection over long-term periods. We collected an additional 11,258 notifications and analyzed 18,407 notifications, including the original dataset. The total study spans two years. Through a statistical test, we identified time-invariant features that can be fully used for training. To overcome the accuracy drop caused by newly occurring data, we design windowing time-invariant online learning (WTOL). In the newly collected dataset, WTOL improves the F-score of the original models based on batch learning from 44.3% to 69.0% by combining online learning and windowing features depending on time sensitivity.