Analysis of Dynamic Memory Bandwidth Regulation in Multi-core Real-Time Systems

One of the primary sources of unpredictability in modern multi-core embedded systems is contention over shared memory resources, such as caches, interconnects, and DRAM. Despite significant achievements in the design and analysis of multi-core systems, there is a need for a theoretical framework that can be used to reason on the worst-case behavior of real-time workload when both processors and memory resources are subject to scheduling decisions. In this paper, we focus our attention on dynamic allocation of main memory bandwidth. In particular, we study how to determine the worst-case response time of tasks spanning through a sequence of time intervals, each with a different bandwidth-to-core assignment. We show that the response time computation can be reduced to a maximization problem over assignment of memory requests to different time intervals, and we provide an efficient way to solve such problem. As a case study, we then demonstrate how our proposed analysis can be used to improve the schedulability of Integrated Modular Avionics systems in the presence of memory-intensive workload.Comment: Accepted for publication in the IEEE Real-Time Systems Symposium (RTSS) 2018 conferenc

Analysis of Memory-Contention in Heterogeneous COTS MPSoCs

Multiple-Processors Systems-on-Chip (MPSoCs) provide an appealing platform to execute Mixed Criticality Systems (MCS) with both time-sensitive critical tasks and performance-oriented non-critical tasks. Their heterogeneity with a variety of processing elements can address the conflicting requirements of those tasks. Nonetheless, the complex (and hence hard-to-analyze) architecture of Commercial-Off-The-Shelf (COTS) MPSoCs presents a challenge encumbering their adoption for MCS. In this paper, we propose a framework to analyze the memory contention in COTS MPSoCs and provide safe and tight bounds to the delays suffered by any critical task due to this contention. Unlike existing analyses, our solution is based on two main novel approaches. 1) It conducts a hybrid analysis that blends both request-level and task-level analyses into the same framework. 2) It leverages available knowledge about the types of memory requests of the task under analysis as well as contending tasks; specifically, we consider information that is already obtainable by applying existing static analysis tools to each task in isolation. Thanks to these novel techniques, our comparisons with the state-of-the art approaches show that the proposed analysis provides the tightest bounds across all evaluated access scenarios

WCET-Driven Dynamic Data Scratchpad Management With Compiler-Directed Prefetching

In recent years, the real-time community has produced a variety of approaches targeted at managing on-chip memory (scratchpads and caches) in a predictable way. However, to obtain safe WCET bounds, such techniques generally assume that the processor is stalled while waiting to reload the content of the on-chip memory; hence, they are less effective at hiding main memory latency compared to speculation-based techniques, such as hardware prefetching, that are largely used in general-purpose systems. In this work, we introduce a novel compiler-directed prefetching scheme for scratchpad memory that effectively hides the latency of main memory accesses by overlapping data transfers with the program execution. We implement and test an automated program compilation and optimization flow within the LLVM framework, and we show how to obtain improved WCET bounds through static analysis

PREM-Based Optimal Task Segmentation Under Fixed Priority Scheduling

Recently, a large number of works have discussed scheduling tasks consisting of a sequence of memory phases, where code and data are moved between main memory and local memory, and computation phases, where the task executes based on the content of local memory only; the key idea is to prevent main memory contention by scheduling the memory phase of one task in parallel with computation phases of tasks running on other cores. This paper provides two main contributions: (1) we present a compiler-level tool, based on the LLVM intermediate representation, that automatically converts a program into a conditional sequence of segments comprising memory and computation phases; (2) we propose an algorithm to find optimal segmentation decisions for a task set scheduled according to a fixed-priority partitioned scheme. Our evaluation shows that the proposed framework can be feasibly applied to realistic programs, and vastly overperforms a baseline greedy approach

Efficient feasibility analysis of real-time asynchronous task sets

Several schedulability tests for real-time periodic task sets scheduled under the Earliest Deadline First algorithm have been proposed in literature, including analyses for precedence and resource constraints. However, all available tests consider synchronous task sets only, that are task sets in which all tasks are initially activated at the same time. In fact, every necessary and sufficient feasibility condition for asynchronous task sets, also known as task sets with offsets, is proven to be NP-complete in the number of tasks. We propose a new schedulability test for asynchronous task sets that, while being only sufficient, performs extremely better than available tests at the cost of a slight complexity increase. The test is further extended to task sets with resource constraints, and we discuss the importance of task offsets on the problems of feasibility and release jitter. We then show how our methodology can be extended in order to account for precedence constraints and multiprocessor and distributed computation applying holistic response time analysis to a real-time transaction-based model. This analysis is finally applied to asymmetric multiprocessor systems where it is able to achieve a dramatic performance increase over existing schedulability tests

A Novel Side-Channel in Real-Time Schedulers

We demonstrate the presence of a novel scheduler side-channel in preemptive, fixed-priority real-time systems (RTS); examples of such systems can be found in automotive systems, avionic systems, power plants and industrial control systems among others. This side-channel can leak important timing information such as the future arrival times of real-time tasks.This information can then be used to launch devastating attacks, two of which are demonstrated here (on real hardware platforms). Note that it is not easy to capture this timing information due to runtime variations in the schedules, the presence of multiple other tasks in the system and the typical constraints (e.g., deadlines) in the design of RTS. Our ScheduLeak algorithms demonstrate how to effectively exploit this side-channel. A complete implementation is presented on real operating systems (in Real-time Linux and FreeRTOS). Timing information leaked by ScheduLeak can significantly aid other, more advanced, attacks in better accomplishing their goals

A Composable Worst Case Latency Analysis for Multi-Rank DRAM Devices under Open Row Policy

The final publication is available at Springer via http://dx.doi.org/10.1007/s11241-016-9253-4As multi-core systems are becoming more popular in real-time embedded systems, strict timing requirements for accessing shared resources must be met. In particular, a detailed latency analysis for Double Data Rate Dynamic RAM (DDR DRAM) is highly desirable. Several researchers have proposed predictable memory controllers to provide guaranteed memory access latency. However, the performance of such controllers sharply decreases as DDR devices become faster and the width of memory buses is increased. High-performance Commercial-Off-The-Shelf (COTS) memory controllers in general-purpose systems employ open row policy to improve average case access latencies and memory throughput, but the use of such policy is not compatible with existing real-time controllers. In this article, we present a new memory controller design together with a novel, composable worst case analysis for DDR DRAM that provides improved latency bounds compared to existing works by explicitly modeling the DRAM state. In particular, our approach scales better with increasing memory speed by predictably taking advantage of shorter latency for access to open DRAM rows. Furthermore, it can be applied to multi-rank devices, which allow for increased access parallelism. We evaluate our approach based on worst case analysis bounds and simulation results, using both synthetic tasks and a set of realistic benchmarks. In particular, benchmark evaluations show up to 45% improvement in worst case task execution time compared to a competing predictable memory controller for a system with 16 requestors and one rank.NSERC DG || 402369-2011 CMC Microsystem

WCET Derivation under Single Core Equivalence with Explicit Memory Budget Assignment

In the last decade there has been a steady uptrend in the popularity of embedded multi-core platforms. This represents a turning point in the theory and implementation of real-time systems. From a real-time standpoint, however, the extensive sharing of hardware resources (e.g. caches, DRAM subsystem, I/O channels) represents a major source of unpredictability. Budget-based memory regulation (throttling) has been extensively studied to enforce a strict partitioning of the DRAM subsystem’s bandwidth. The common approach to analyze a task under memory bandwidth regulation is to consider the budget of the core where the task is executing, and assume the worst-case about the remaining cores' budgets. In this work, we propose a novel analysis strategy to derive the WCET of a task under memory bandwidth regulation that takes into account the exact distribution of memory budgets to cores. In this sense, the proposed analysis represents a generalization of approaches that consider (i) even budget distribution across cores; and (ii) uneven but unknown (except for the core under analysis) budget assignment. By exploiting the additional piece of information, we show that it is possible to derive a more accurate WCET estimation. Our evaluations highlight that the proposed technique can reduce overestimation by 30% in average, and up to 60%, compared to the state of the art.Accepted manuscrip