With the increasing use of multicore platforms to realize mixed-criticality systems, understanding the underlying shared resources, such as the memory hierarchy shared among cores, and achieving isolation between co-executing tasks running on the same platform with different criticality levels becomes relevant. In addition to safety considerations, a malicious entity can exploit shared resources to create timing attacks on critical applications. In this paper, we focus on understanding the shared DRAM dual in-line memory module and created a timing attack, that we named the "bank & row conflict bomb", to target a victim task in a multicore platform. We also created a "navigate" algorithm to understand how victim requests are managed by the Memory Controller and provide valuable inputs for designing the bank & row conflict bomb. We performed experimental tests on a 2nd Gen Intel Xeon Processor with an 8GB DDR4-2666 DRAM module to show that such an attack can produce a significant increase in the execution time of the victim task by about 150%, motivating the need for proper countermeasures to help ensure the safety and security of critical applications.

The improvements in network latency and the advent of Edge computing have inspired industries to explore providing Real-time (RT) applications as cloud-based services and benefit from the availability, scalability, and efficient hardware resource utilization of clouds. It is crucial to improve the entire stack, including the applications’ containerization, container deployment, and orchestration across nodes to host RT applications in the cloud. However, state-of-the-art container orchestrators, e.g., Kubernetes (K8s), and the underlying Linux and containerization layer ignore orchestration and management of shared resources (e.g., memory bandwidth, cache); thus, rendering them unsuitable for RT use cases due unpredictability as a result of shared resource contention. We propose K8s extensions inspired by existing RT resource management frameworks to the underlying Linux kernel and containerization layer of each node for shared resource monitoring to help K8s maintain a cloud-wide view and allocate and dynamically orchestrate shared resources to enforce the guarantees required by the RT containers. Additionally, as a proof-of-concept, we design and implement (1) new K8s shared resource orchestration extensions to support memory bandwidth and last-level cache allocation and (2) a shared-resource controller in Linux based on a new algorithm to combine approximate but throttling-free memory bandwidth allocation by simple and efficient hardware controllers (e.g., Intel MBA) together with strict but pessimistic guarantees offered by software budget allocation and throttling (e.g., Memguard). We performed experiments to evaluate and demonstrate the newly implemented extensions on server-grade hardware.

The increasing interest in adopting cloud technologies for Industry 4.0 and mixed-criticality environments is paving the way for compelling new opportunities and challenges. However, cloud deployments use multicore processors that introduce interference between co-executing applications on different cores due to contention in shared resources, such as the memory subsystem. Such interference can cause critical applications to miss their deadlines. To enable the co-execution of critical and non-critical applications on the same multicore processor, we propose an approach that guarantees memory access time isolation for critical cores, while not jeopardizing the memory bandwidth of the non-critical ones. We prove the viability of our approach using Intel’s resource director technology for memory access detection and regulation. Experiments show that queue occupancy is an excellent metric to estimate the number of interfering cores co-accessing the memory. We also assess the indirect memory bandwidth limitation achievable by applying Intel’s Memory Bandwidth Allocation technology.

The automotive industry is looking into integrated architecture to combine multiple application subsystems of different criticalities on the readily available low-cost multicore platforms as they promise several benefits. However, it is difficult to achieve the required isolation and guarantees in such an architecture due to contention in shared resources, e.g., CPU, shared-bus, and memory (controller). This can cause unpredictable delays leading to deadline misses in real-time applications. We propose a low overhead modular monitoring framework to provide support for ensuring that the real-time applications meet their deadline when considering shared resource accesses, and helping to improve resource utilization so that best-effort applications can achieve a better Quality-of-Service despite pessimistic resource allocation assumptions of real-time applications. Our framework keeps the monitoring overheads to a minimum and triggered reaction meaningful by operating on the basis of low-level hardware and software signals, strategically checking resources, and triggering actions based on abstract availability of resources. We propose a Domain-Specific Language (DSL) to relieve the system designers from the tedious and error-prone job of configuring platform-specific parameters for the framework. Finally, this paper evaluates our monitoring framework based on an instantiation on a Xilinx Zynq UltraSacle multicore SoC running Linux and a simple industry-inspired use case.

Industries are recently considering the adoption of cloud computing for hosting safety critical applications. However, the use of multicore processors usually adopted in the cloud introduces temporal anomalies due to contention for shared resources, such as the memory subsystem. In this paper we explore the potential of Intel’s Memory Bandwidth Allocation (MBA) technology, available on Xeon Scalable processors. By adopting a systematic measurement approach on real hardware, we assess the indirect memory bandwidth limitation achievable by applying MBA delays, showing that only given delay values (namely 70, 80 and 90) are effective in our setting. We also test the derived bandwidth assured to a hypothetical critical core when interfering cores (e.g., generating a concurrent memory access workload) are present on the same machine. Our results can support designers by providing understanding of impact of the shared memory to enable predictable progress of safety critical applications in cloud environments.

This paper explores virtualization technologies and cloud computing for migrating an existing real-time safety-critical railway use-case from dedicated hardware solutions. Cloud computing is rapidly gaining popularity in many domains as they provide benefits such as higher availability, scalability, and efficient hardware resource utilization. We examine existing virtualization technologies for deploying a (private) Real-Time(RT)-Cloud on COTS server hardware to run an existing railway use-case while meeting stringent safety and security requirements. We base our migration review on comparison and relevant benchmarking of KVM and Xen virtualization technologies for the specific railway requirements. Based on the insights gained, we provide suggestions for using existing virtualization technologies with new RT-cloud components to safely and securely run the railway use-case applications.

Safety-Critical (SC) applications require high availability, possibility of run-time reconfiguration, and significant resource over-provisioning. Furthermore, they suffer from hardware obsolescence due to the use of custom or specialized hardware. Cloud computing could be used to resolve these issues. Moreover, they could improve SC systems suffering from scalability issues, e.g., the every growing SC railway network. However, SC applications require low latencies and guarantees that are currently not possible on clouds. In this paper, we explore the possibility of enhancing the current cloud computing paradigm by adding a resource management layer to support the deterministic execution of SC applications while providing the benefits of cloud computing principles.We provide a cloud-wide global resource manager that monitors, controls, and coordinates node-level Local Resource Managers (LRMs) placed on each private cloud node. In addition, we give guarantees to SC Virtual Machines (VMs) on each node via a novel CPU-and memory bandwidth-aware Time-Triggered (TT) offline scheduling algorithm that generates a scheduling table for use by an LRM. For improving the utilization of the cloud resources, the LRMs provide flexibility to schedule Event-Triggered (ET) SC and non-critical VMs at run-time without regenerating the offline scheduling table. We implemented our approach in a KVM-based private cloud and performed experiments to determine the relevant overheads.

Existing global resource management approaches for real-time and safety-critical applications deployed on distributed systems consisting of multicore and many -core nodes don't consider safety and security together for the global resource management itself. In this work, we propose an approach considering safety and security using distributed global resource re-orchestration decision-making to complement the existing approaches. The implemetation of our framework is based on Hyperledger Sawtooth blockchain and smart contracts.