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eal-time systems with hard timing constrains require known upper bounds on each task’s worst-case execution time (WCET) to determine if all deadlines can be met. One challenge in predictable execution is that Dynamic Random Access Memory (DRAM) cells must be refreshed periodically to maintain data validity, yet memory remains blocked during refresh, which results in overly pessimistic WCET bounds. This work contributes “Colored Refresh” to hide DRAM refresh overhead while preserving real-time schedulability for cyclic executives, which are widely used in highly critical systems. Colored Refresh partitions DRAM memory at rank granularity such that refreshes rotate round-robin from rank to rank. Real-time tasks are assigned different ranks via colored memory allocation. By cooperatively scheduling real-time tasks and refresh operations, memory requests no longer suffer from refresh interference. This reduces memory access latencies for tasks irrespective of DRAM density and size. Hence, Colored Refresh reduces a task’s WCET and makes its execution more predictable.
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DNA: Dynamic Resource Allocation for Soft Real-Time Multicore Systems
Modern latency-sensitive and real-time systems often use multi-core platforms; thus, tasks on different cores share certain hardware resources, such as the memory bus and certain cache levels. This has two undesirable consequences: (1) tasks can interfere with each other, causing high latency for the system as a whole, and (2) it becomes difficult to meet deadlines, since the worst-case timing of a given task depends on the worst task it might have to compete with. Static partitioning isolates tasks from each other by allocating a certain fraction of the resources to each; however, many tasks execute in different phases (e.g., memory-intensive and CPU-intensive) that have different requirements. Thus, system designers are left with a choice between overprovisioning, based on the most demanding phase, or suboptimal performance. In this paper, we propose a pair of techniques, called DNA and DADNA, to address the above challenge. DNA increases throughput and decreases latency, by building an execution profile of each task to identify the phases, and then dynamically allocating resources based on which task can benefit the most; DADNA further adds support for soft real-time workloads by taking deadlines into account. We have built a prototype of both techniques in the Xen hypervisor; our experimental results show that, compared to a state-of-the-art solution, DNA and DADNA can substantially improve schedulability, reduce job deadline miss ratios, and cut latencies by more than a factor of two even in extremely overloaded situations.
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- PAR ID:
- 10282461
- Date Published:
- Journal Name:
- Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '21)
- Page Range / eLocation ID:
- 196 to 209
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RTAS52030.2021.00024
