Multicore processors are being extensively used by real-time systems, mainly because of their demand for increased computing power. However, multicore processors have shared resources that affect the predictability of real-time systems, which is the key to correctly estimate the worst-case execution time of tasks. One of the main factors for unpredictability in a multicore processor is the cache memory hierarchy. Recently, many research works have proposed different techniques to deal with caches in multicore processors in the context of real-time systems. Nevertheless, a review and categorization of these techniques is still an open topic and would be very useful for the real-time community. In this article, we present a survey of cache management techniques for real-time embedded systems, from the first studies of the field in 1990 up to the latest research published in 2014. We categorize the main research works and provide a detailed comparison in terms of similarities and differences. We also identify key challenges and discuss future research directions.

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The uncontrolled use of the cache hierarchy in a multicore processor by real-time tasks may impact their worst-case execution times. Several operating system techniques have been recently proposed to deal with caches in a multiprocessor in order to improve predictability, such as cache partitioning, cache locking, and real-time scheduling. However, the contention caused by the cache coherence protocol and its implication for real-time tasks is still an open problem. In this paper, we present the design and evaluation of a real-time operating system for cache-coherent multicore architectures. The real-time operating system infrastructure includes real-time schedulers, cache partitioning, and cache coherence contention detection through hardware performance counters. We evaluate the real-time operating system in terms of run-time overhead, schedulability of realtime tasks, cache partitioning performance, and hardware performance counters usability. Our results indicate that: (i) a real-time operating system designed from scratch reduces the run-time overhead, and thus improves the realtime schedulability, when compared to a patched operating system; (ii) cache partitioning reduces the contention in the shared cache and provides safe real-time bounds; and (iii) hardware performance counters can detect when real-time tasks interfere with each other at the shared cache level. Scheduling, cache partitioning, and hardware performance counters together are a step-forward to provide real-time bounds in cache-coherent architectures.

Deeply Embedded Systems are designed to perform a determined set of specific tasks, usually on low-cost, highreliability platforms. In order to support on-site firmware updates, such systems are subject to severe resource limitation, like processing power, memory and energy, since the update mechanism itself must share the sparse resources with running applications. This work presents a low-overhead operating system infrastructure for remote code update. By using sophisticate C++ static metaprogramming techniques, the infrastructure and the code update become fully transparent to applications. Moreover, the infrastructure allows the system components to be marked as updatable or not at compilation time, and for those components that are not marked as updatable, no overhead is added. The infrastructure was implemented in EPOS, a multi-platform, component-based, embedded operating system [7], with positive preliminary results that show that the proposed strategy is a feasible solution for software update in deeply embedded systems.

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