Abstract-This paper addresses the problem of scheduling tasks with different criticality levels in the presence of I/O requests. In mixed-criticality scheduling, higher criticality tasks are given precedence over those of lower criticality when it is impossible to guarantee the schedulability of all tasks. While mixed-criticality scheduling has gained attention in recent years, most approaches typically assume a periodic task model. This assumption does not always hold in practice, especially for realtime and embedded systems that perform I/O. In prior work, we developed a scheduling technique in the Quest real-time operating system, which integrates the time-budgeted management of I/O operations with Sporadic Server scheduling of tasks. This paper extends our previous scheduling approach with support for mixed-criticality tasks and I/O requests on the same processing core. Results show that in a real implementation the mixedcriticality scheduling method introduced in this paper outperforms a scheduling approach consisting of only Sporadic Servers.

This paper addresses the problem of guaranteeing performance and predictability of NAND flash memory in a real-time storage system. Our approach implements a new flash translation layer scheme that exploits internal parallelism within solid state storage devices. We describe the Partitioned Real-Time Flash Translation Layer (PaRT-FTL), which splits a set of flash chips into separate read and write sets. This ensures reads and writes to separate chips proceed in parallel. However, PaRT-FTL is also able to rebuild the data for a read request from a flash chip that is busy servicing a write request or performing garbage collection. Consequently, reads are never blocked by writes or storage space reclamation. PaRT-FTL is compared to previous real-time approaches including scheduling, over-provisioning and partial garbage collection. We show that by isolating read and write requests using encoding techniques, PaRT-FTL provides better latency guarantees for real-time applications.

Modern solid-state disks achieve high data transfer rates due to their massive internal parallelism. However, out-of-place updates for flash memory incur garbage collection costs when valid data needs to be copied during space reclamation. The root cause of this extra cost is that solid-state disks are not always able to accurately determine data lifetime and group together data that expires before the space needs to be reclaimed. Real-time systems found in autonomous vehicles, industrial control systems, and assembly-line robots store data from hundreds of sensors and often have predictable data lifetimes. These systems require guaranteed high storage bandwidth for read and write operations by mission-critical real-time tasks. In this article, we depart from the traditional block device interface to guarantee the high throughput needed to process large volumes of data. Using data lifetime information from the application layer, our proposed real-time design, called Telomere , is able to intelligently lay out data in NAND flash memory and eliminate valid page copies during garbage collection. Telomere’s real-time admission control is able to guarantee tasks their required read and write operations within their periods. Under randomly generated tasksets containing 500 tasks, Telomere achieves 30% higher throughput with a 5% storage cost compared to pre-existing techniques.