Contention-Aware Dynamic Memory Bandwidth Isolation with Predictability in COTS Multicores: An Avionics Case Study

Airbus is investigating COTS multicore platforms for safety-critical avionics applications, pursuing helicopter-style autonomous and electric aircraft. These aircraft need to be ultra-lightweight for future mobility in the urban city landscape. As a step towards certification, Airbus identified the need for new methods that preserve the ARINC 653 single core schedule of a Helicopter Terrain Awareness and Warning System (HTAWS) application while scheduling additional safety-critical partitions on the other cores. As some partitions in the HTAWS application are memory-intensive, static memory bandwidth throttling may lead to slow down of such partitions or provide only little remaining bandwidth to the other cores. Thus, there is a need for dynamic memory bandwidth isolation. This poses new challenges for scheduling, as execution times and scheduling become interdependent: scheduling requires execution times as input, which depends on memory latencies and contention from memory accesses of other cores - which are determined by scheduling. Furthermore, execution times depend on memory access patterns. In this paper, we propose a method to solve this problem for slot-based time-triggered systems without requiring application source-code modifications using a number of dynamic memory bandwidth levels. It is NoC and DRAM controller contention-aware and based on the existing interference-sensitive WCET computation and the memory bandwidth throttling mechanism. It constructs schedule tables by assigning partitions and dynamic memory bandwidth to each slot on each core, considering worst case memory access patterns. Then at runtime, two servers - for processing time and memory bandwidth - run on each core, jointly controlling the contention between the cores and the amount of memory accesses per slot. As a proof-of-concept, we use a constraint solver to construct tables. Experiments on the P4080 COTS multicore platform, using a research OS from Airbus and EEMBC benchmarks, demonstrate that our proposed method enables preserving existing schedules on a core while scheduling additional safety-critical partitions on other cores, and meets dynamic memory bandwidth isolation requirements

Design and Implementation of a Time Predictable Processor: Evaluation With a Space Case Study

Embedded real-time systems like those found in automotive, rail and aerospace, steadily require higher levels of guaranteed computing performance (and hence time predictability) motivated by the increasing number of functionalities provided by software. However, high-performance processor design is driven by the average-performance needs of mainstream market. To make things worse, changing those designs is hard since the embedded real-time market is comparatively a small market. A path to address this mismatch is designing low-complexity hardware features that favor time predictability and can be enabled/disabled not to affect average performance when performance guarantees are not required. In this line, we present the lessons learned designing and implementing LEOPARD, a four-core processor facilitating measurement-based timing analysis (widely used in most domains). LEOPARD has been designed adding low-overhead hardware mechanisms to a LEON3 processor baseline that allow capturing the impact of jittery resources (i.e. with variable latency) in the measurements performed at analysis time. In particular, at core level we handle the jitter of caches, TLBs and variable-latency floating point units; and at the chip level, we deal with contention so that time-composable timing guarantees can be obtained. The result of our applied study with a Space application shows how per-resource jitter is controlled facilitating the computation of high-quality WCET estimates

Memory-processor co-scheduling in fixed priority systems

A major obstacle towards the adoption of multi-core platforms for real-time systems is given by the difficulties in characterizing the interference due to memory contention. The simple fact that multiple cores may simultaneously access shared memory and communication resources introduces a significant pessimism in the timing and schedulability analysis. To counter this problem, predictable execution models have been proposed splitting task executions into two consecutive phases: a memory phase in which the required instruction and data are pre-fetched to local memory (M-phase), and an execution phase in which the task is executed with no memory contention (C-phase). Decoupling memory and execution phases not only simplifies the timing analysis, but it also allows a more efficient (and predictable) pipelining of memory and execution phases through proper co-scheduling algorithms. In this paper, we take a further step towards the design of smart co-scheduling algorithms for sporadic real-time tasks complying with the M/C (memory-computation) model. We provide a theoretical framework that aims at tightly characterizing the schedulability improvement obtainable with the adopted M/C task model on a single-core systems. We identify a tight critical instant for M/C tasks scheduled with fixed priority, providing an exact response-time analysis with pseudo-polynomial complexity. We show in our experiments that a significant schedulability improvement may be obtained with respect to classic execution models, placing an important building block towards the design of more efficient partitioned multi-core systems

Analysis and Implementation of Global Preemptive Fixed-Priority Scheduling with Dynamic Cache Allocation

We introduce gFPca, a cache-aware global pre-emptive fixed-priority (FP) scheduling algorithm with dynamic cache allocation for multicore systems, and we present its analysis and implementation. We introduce a new overhead-aware analysis that integrates several novel ideas to safely and tightly account for the cache overhead. Our evaluation shows that the proposed overhead-accounting approach is highly accurate, and that gFPca improves the schedulability of cache-intensive tasksets substantially compared to the cache-agnostic global FP algorithm. Our evaluation also shows that gFPca outperforms the existing cache-aware non- preemptive global FP algorithm in most cases. Through our implementation and empirical evaluation, we demonstrate the feasibility of cache-aware global scheduling with dynamic cache allocation and highlight scenarios in which gFPca is especially useful in practice

A Multi-Core Interference-Aware Schedulability Test for IMA Systems, as a Guide for SW/HW Integration

International audienceIn this paper we propose a framework for the automated integration and timing analysis of IMA (Integrated Modular Avionics) applications on multi-core environments. To do so, we present a derivation of the response time analysis formulation by Kim et al. in [12] that takes into account inter-task interference due to sharing the access to the main memory. We adapt the work in [12] to propose a sufficient schedulability test that is adapted both to IMA systems and heterogeneous multi-core platforms. We then exploit this test to guide the design space exploration during the SW/HW integration phase, to select a partition-to-core allocation so that all deadlines are met despite the existence of hardware interference

Schedulability Analysis for Multi-Core Systems Accounting for Resource Stress and Sensitivity

Timing verification of multi-core systems is complicated by contention for shared hardware resources between co-running tasks on different cores. This paper introduces the Multi-core Resource Stress and Sensitivity (MRSS) task model that characterizes how much stress each task places on resources and how much it is sensitive to such resource stress. This model facilitates a separation of concerns, thus retaining the advantages of the traditional two-step approach to timing verification (i.e. timing analysis followed by schedulability analysis). Response time analysis is derived for the MRSS task model, providing efficient context-dependent and context independent schedulability tests for both fixed priority preemptive and fixed priority non-preemptive scheduling. Dominance relations are derived between the tests, and proofs of optimal priority assignment provided. The MRSS task model is underpinned by a proof-of-concept industrial case study

Feedback-Based Admission Control for Firm Real-Time Task Allocation with Dynamic Voltage and Frequency Scaling

Feedback-based mechanisms can be employed to monitor the performance of Multiprocessor Systems-on-Chips (MPSoCs) and steer the task execution even if the exact knowledge of the workload is unknown a priori. In particular, traditional proportional-integral controllers can be used with firm real-time tasks to either admit them to the processing cores or reject in order not to violate the timeliness of the already admitted tasks. During periods with a lower computational power demand, dynamic voltage and frequency scaling (DVFS) can be used to reduce the dissipation of energy in the cores while still not violating the tasks’ time constraints. Depending on the workload pattern and weight, platform size and the granularity of DVFS, energy savings can reach even 60% at the cost of a slight performance degradation