Packet scheduling, together with classification, is one of the most expensive processing steps in systems providing tight bandwidth and delay guarantees at high packet rates. Schedulers with near-optimal service guarantees and ${ O}({1})$ time complexity have been proposed in the past, using techniques such as timestamp rounding and flow grouping to keep their execution time small. However, even the two best proposals in this family have a per-packet cost component that is linear either in the number of groups or in the length of the packet being transmitted. Furthermore, no studies are available on the actual execution time of these algorithms. In this paper we make two contributions. First, we present Quick Fair Queueing (QFQ), a new ${ O}({ 1})$ scheduler that provides near-optimal guarantees and is the first to achieve that goal with a truly constant cost also with respect to the number of groups and the packet length. The QFQ algorithm has no loops and uses very simple instructions and data structures that contribute to its speed of operation. Second, we have developed production-quality implementations of QFQ and of its closest competitors, which we use to present a detailed comparative performance analysis of the various algorithms. Experiments show that QFQ fulfills our expectations, outperforming the other algorithms in the same class. In absolute terms, even on a low-end workstation, QFQ takes about 110 ns for an enqueue()/dequeue() pair (only twice the time of DRR, but with much better service guarantees)

Abstract-Mainstream applications-such as file copy/transfer, Web, DBMS, or video streaming-typically issue synchronous disk requests. As shown in this paper, this fact may cause workconserving schedulers to fail both to enforce guarantees and to provide a high disk throughput. A high throughput can be however recovered by just idling the disk for a short time interval after the completion of each request. In contrast, guarantees may still be violated by existing timestamp-based schedulers, because of the rules they use to tag requests.Budget Fair Queueing (BFQ), the new disk scheduler presented in this paper, is an example of how disk idling, combined with proper back-shifting of request timestamps, may allow a timestamp-based disk scheduler to preserve both guarantees and a high throughput. Under BFQ each application is always guaranteed-over any time interval and independently of whether it issues synchronous requests-a bounded lag with respect to its reserved fraction of the total number of bytes transferred by the disk device.We show the single-disk performance of our implementation of BFQ in the Linux kernel through experiments with real and emulated mainstream applications.Index Terms-Scheduling, secondary storage, quality of service.

One of the main predictability bottlenecks of modern multi-core embedded systems is contention for access to shared memory resources. Partitioning and software-driven allocation of memory resources is an effective strategy to mitigate contention in the memory hierarchy. Unfortunately, however, many of the strategies adopted so far can have unforeseen side-effects when practically implemented latest-generation, highperformance embedded platforms. Predictability is further jeopardized by cache eviction policies based on random replacement, targeting average performance instead of timing determinism. In this paper, we present a framework of software-based techniques to restore memory access determinism in highperformance embedded systems. Our approach leverages OStransparent and DMA-friendly cache coloring, in combination with an invalidation-driven allocation (IDA) technique. The proposed method allows protecting important cache blocks from (i) external eviction by tasks concurrently executing on different cores, and (ii) internal eviction by tasks running on the same core. A working implementation obtained by extending the Jailhouse partitioning hypervisor is presented and evaluated with a combination of synthetic and real benchmarks.

Abstract

BFQ (Budget Fair Queueing) is a production-quality, proportional-share disk scheduler with a relatively large user base. Part of its success is due to a set of simple heuristics that we added to the original algorithm about one year ago. These heuristics are the main focus of this paper.The first heuristic enriches BFQ with one of the most desirable properties for a desktop or handheld system: responsiveness. The remaining heuristics improve the robustness of BFQ across heterogeneous devices, and help BFQ to preserve a high throughput under demanding workloads. To measure the performance of these heuristics we have implemented a suite of micro and macro benchmarks mimicking several real-world tasks, and have run it on three different systems with a single rotational disk. We have also compared our results against Completely Fair Queueing (CFQ), the default Linux disk scheduler.

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There is an increasing interest among real-time systems architects for multi- and many-core accelerated platforms. The main obstacle towards the adoption of such devices within industrial settings is related to the difficulties in tightly estimating the multiple interferences that may arise among the parallel components of the system. This in particular concerns concurrent accesses to shared memory and communication resources. Existing worst-case execution time analyses are extremely pessimistic, especially when adopted for systems composed of hundreds-tothousands of cores. This significantly limits the potential for the adoption of these platforms in real-time systems. In this paper, we study how the predictable execution model (PREM), a memory-aware approach to enable timing-predictability in realtime systems, can be successfully adopted on multi- and manycore heterogeneous platforms. Using a state-of-the-art multi-core platform as a testbed, we validate that it is possible to obtain an order-of-magnitude improvement in the WCET bounds of parallel applications, if data movements are adequately orchestrated in accordance with PREM. We identify which system parameters mostly affect the tremendous performance opportunities offered by this approach, both on average and in the worst case, moving the first step towards predictable many-core systems