Real-time disk scheduling in a mixed-media file system

This paper presents our real-time disk scheduler called the Delta L scheduler, which optimizes unscheduled best-effort disk requests by giving priority to best-effort disk requests while meeting real-time request deadlines. Our scheduler tries to execute real-time disk requests as much as possible in the background. Only when real-time request deadlines are endangered, our scheduler gives priority to real-time disk requests. The Delta L disk scheduler is part of our mixed-media file system called Clockwise. An essential part of our work is extensive and detailed raw disk performance measurements. The Delta L disk scheduler for its real-time schedulability analysis and to decide whether scheduling a best-effort request before a real-time request violates real-time constraints uses these raw performance measurements. Further, a Clockwise off-line simulator uses the raw performance measurements where a number of different disk schedulers are compared. We compare the Delta L scheduler with a prioritizing Latest Start Time (LST) scheduler and non-prioritizing EDF scheduler. The Delta L scheduler is comparable to LST in achieving low latencies for best-effort requests under light to moderate real-time loads and better in achieving low latencies for best-effort requests for extreme real-time loads. The simulator is calibrated to an actual Clockwise. Clockwise runs on a 200MHz Pentium-Pro based PC with PCI bus, multiple SCSI controllers and disks on Linux 2.2.x and the Nemesis kernel. Clockwise performance is dictated by the hardware: all available bandwidth can be committed to real-time streams, provided hardware overloads do not occur

Scheduling Policies in Time and Frequency Domains for LTE Downlink Channel: A Performance Comparison

A key feature of the Long-Term Evolution (LTE) system is that the packet scheduler can make use of the channel quality information (CQI), which is periodically reported by user equipment either in an aggregate form for the whole downlink channel or distinguished for each available subchannel. This mechanism allows for wide discretion in resource allocation, thus promoting the flourishing of several scheduling algorithms, with different purposes. It is therefore of great interest to compare the performance of such algorithms under different scenarios. Here, we carry out a thorough performance analysis of different scheduling algorithms for saturated User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) traffic sources, as well as consider both the time- and frequency-domain versions of the schedulers and for both flat and frequency-selective channels. The analysis makes it possible to appreciate the difference among the scheduling algorithms and to assess the performance gain, in terms of cell capacity, users' fairness, and packet service time, obtained by exploiting the richer, but heavier, information carried by subchannel CQI. An important part of this analysis is a throughput guarantee scheduler, which we propose in this paper. The analysis reveals that the proposed scheduler provides a good tradeoff between cell capacity and fairness both for TCP and UDP traffic sources

Perf&Fair: A Progress-Aware Scheduler to Enhance Performance and Fairness in SMT Multicores

[EN] Nowadays, high performance multicore processors implement multithreading capabilities. The processes running concurrently on these processors are continuously competing for the shared resources, not only among cores, but also within the core. While resource sharing increases the resource utilization, the interference among processes accessing the shared resources can strongly affect the performance of individual processes and its predictability. In this scenario, process scheduling plays a key role to deal with performance and fairness. In this work we present a process scheduler for SMT multicores that simultaneously addresses both performance and fairness. This is a major design issue since scheduling for only one of the two targets tends to damage the other. To address performance, the scheduler tackles bandwidth contention at the L1 cache and main memory. To deal with fairness, the scheduler estimates the progress experienced by the processes, and gives priority to the processes with lower accumulated progress. Experimental results on an Intel Xeon E5645 featuring six dual-threaded SMT cores show that the proposed scheduler improves both performance and fairness over two state-of-the-art schedulers and the Linux OS scheduler. Compared to Linux, unfairness is reduced to a half while still improving performance by 5.6 percent.We thank the anonymous reviewers for their constructive and insightful feedback. This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and Plan E funds, under grants TIN2015-66972-C5-1-R and TIN2014-62246EXP, and by the Intel Early Career Faculty Honor Program Award.Feliu-Pérez, J.; Sahuquillo Borrás, J.; Petit Martí, SV.; Duato Marín, JF. (2017). Perf&Fair: A Progress-Aware Scheduler to Enhance Performance and Fairness in SMT Multicores. IEEE Transactions on Computers. 66(5):905-911. https://doi.org/10.1109/TC.2016.2620977S90591166

Real-Time Virtualization and Cloud Computing

In recent years, we have observed three major trends in the development of complex real-time embedded systems. First, to reduce cost and enhance flexibility, multiple systems are sharing common computing platforms via virtualization technology, instead of being deployed separately on physically isolated hosts. Second, multi-core processors are increasingly being used in real-time systems. Third, developers are exploring the possibilities of deploying real-time applications as virtual machines in a public cloud. The integration of real-time systems as virtual machines (VMs) atop common multi-core platforms in a public cloud raises significant new research challenges in meeting the real-time latency requirements of applications. In order to address the challenges of running real-time VMs in the cloud, we first present RT-Xen, a novel real-time scheduling framework within the popular Xen hypervisor. We start with single-core scheduling in RT-Xen, and present the first work that empirically studies and compares different real-time scheduling schemes on a same platform. We then introduce RT-Xen 2.0, which focuses on multi-core scheduling and spanning multiple design spaces, including priority schemes, server schemes, and scheduling policies. Experimental results demonstrate that when combined with compositional scheduling theory, RT-Xen can deliver real-time performance to an application running in a VM, while the default credit scheduler cannot. After that, we present RT-OpenStack, a cloud management system designed to support co-hosting real-time and non-real-time VMs in a cloud. RT-OpenStack studies the problem of running real-time VMs together with non-real-time VMs in a public cloud. Leveraging the resource interface and real-time scheduling provided by RT-Xen, RT-OpenStack provides real-time performance guarantees to real-time VMs, while achieving high resource utilization by allowing non-real-time VMs to share the remaining CPU resources through a novel VM-to-host mapping scheme. Finally, we present RTCA, a real-time communication architecture for VMs sharing a same host, which maintains low latency for high priority inter-domain communication (IDC) traffic in the face of low priority IDC traffic

Scheduling of data-intensive workloads in a brokered virtualized environment

Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments

Task Activity Vectors: A Novel Metric for Temperature-Aware and Energy-Efficient Scheduling

This thesis introduces the abstraction of the task activity vector to characterize applications by the processor resources they utilize. Based on activity vectors, the thesis introduces scheduling policies for improving the temperature distribution on the processor chip and for increasing energy efficiency by reducing the contention for shared resources of multicore and multithreaded processors

Storage-heterogeneity aware task-based programming models to optimize I/O intensive applications

Task-based programming models have enabled the optimized execution of the computation workloads of applications. These programming models can take advantage of large-scale distributed infrastructures by allowing the parallel and distributed execution of applications in high-level work components called tasks. Nevertheless, in the era of Big Data and Exascale, the amount of data produced by modern scientific applications has already surpassed terabytes and is rapidly increasing. Hence, I/O performance became the bottleneck to overcome in order to achieve more total performance improvement. New storage technologies offer higher bandwidth and faster solutions than traditional Parallel File Systems (PFS). Such storage devices are deployed in modern day infrastructures to boost I/O performance by offering a fast layer that absorbs the generated data. Therefore, it is necessary for any programming model targeting more performance to manage this heterogeneity and take advantage of it to improve the I/O performance of applications. Towards this goal, we propose in this paper a set of programming model capabilities that we refer to as Storage-Heterogeneity Awareness. Such capabilities include: (i) abstracting the heterogeneity of storage systems, and (ii) optimizing I/O performance by supporting dedicated I/O schedulers and an automatic data flushing technique. The evaluation section of this paper presents the performance results of different applications on the MareNostrum CTE-Power heterogeneous storage cluster. Our experiments demonstrate that a storage-heterogeneity aware programming model can achieve up to almost 5x I/O performance speedup and 48% total time improvement compared to the reference PFS-based usage of the execution infrastructure.This work is partially supported by the European Union through the Horizon 2020 research and innovation programme under contracts 721865 (EXPERTISE Project) by the Spanish Government (PID2019-107255GB) and the Generalitat de Catalunya (contract 2014-SGR-1051).Peer ReviewedPostprint (author's final draft