Real-Time Performance and Middleware on Multicore Linux Platforms

An increasing number of distributed real-time applications are running on multicore platforms. However, existing real-time middleware (e.g., Real-Time CORBA) lacks support for scheduling soft real-time tasks on multicore platforms while guaranteeing their time constraints will be satisfied. This paper makes three contributions to the state of the art in real-time system software for multicore platforms. First, it offers what is to our knowledge the first experimental analysis of real-time performance for vanilla Linux primitives on multicore platforms. Second, it presents MC-ORB, the first real-time object request broker (ORB), designed to exploit the features of multicore platforms, with admission control and task allocation services that can provide schedulability guarantees for soft real-time tasks on multicore platforms. Third, it gives a performance evaluation of MC-ORB on a Linux multicore testbed, the results of which demonstrate the efficiency and effectiveness of MC-ORB

vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing

As an important application in the busy world today, mobile video conferencing facilitates virtual face-to-face communication with friends, families and colleagues, via their mobile devices on the move. However, how to provision high-quality, multi-party video conferencing experiences over mobile devices is still an open challenge. The fundamental reason behind is the lack of computation and communication capacities on the mobile devices, to scale to large conferencing sessions. In this paper, we present vSkyConf, a cloud-assisted mobile video conferencing system to fundamentally improve the quality and scale of multi-party mobile video conferencing. By novelly employing a surrogate virtual machine in the cloud for each mobile user, we allow fully scalable communication among the conference participants via their surrogates, rather than directly. The surrogates exchange conferencing streams among each other, transcode the streams to the most appropriate bit rates, and buffer the streams for the most efficient delivery to the mobile recipients. A fully decentralized, optimal algorithm is designed to decide the best paths of streams and the most suitable surrogates for video transcoding along the paths, such that the limited bandwidth is fully utilized to deliver streams of the highest possible quality to the mobile recipients. We also carefully tailor a buffering mechanism on each surrogate to cooperate with optimal stream distribution. We have implemented vSkyConf based on Amazon EC2 and verified the excellent performance of our design, as compared to the widely adopted unicast solutions.Comment: 10 page

Efficient memory management in VOD disk array servers usingPer-Storage-Device buffering

We present a buffering technique that reduces video-on-demand server memory requirements in more than one order of magnitude. This technique, Per-Storage-Device Buffering (PSDB), is based on the allocation of a fixed number of buffers per storage device, as opposed to existing solutions based on per-stream buffering allocation. The combination of this technique with disk array servers is studied in detail, as well as the influence of Variable Bit Streams. We also present an interleaved data placement strategy, Constant Time Length Declustering, that results in optimal performance in the service of VBR streams. PSDB is evaluated by extensive simulation of a disk array server model that incorporates a simulation based admission test.This research was supported in part by the National R&D Program of Spain, Project Number TIC97-0438.Publicad

SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper, we propose SPINN, a distributed inference system that employs synergistic device-cloud computation together with a progressive inference method to deliver fast and robust CNN inference across diverse settings. The proposed system introduces a novel scheduler that co-optimises the early-exit policy and the CNN splitting at run time, in order to adapt to dynamic conditions and meet user-defined service-level requirements. Quantitative evaluation illustrates that SPINN outperforms its state-of-the-art collaborative inference counterparts by up to 2x in achieved throughput under varying network conditions, reduces the server cost by up to 6.8x and improves accuracy by 20.7% under latency constraints, while providing robust operation under uncertain connectivity conditions and significant energy savings compared to cloud-centric execution.Comment: Accepted at the 26th Annual International Conference on Mobile Computing and Networking (MobiCom), 202

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

A New Approach to Manage QoS in Distributed Multimedia Systems

Dealing with network congestion is a criterion used to enhance quality of service (QoS) in distributed multimedia systems. The existing solutions for the problem of network congestion ignore scalability considerations because they maintain a separate classification for each video stream. In this paper, we propose a new method allowing to control QoS provided to clients according to the network congestion, by discarding some frames when needed. The technique proposed, called (m,k)-frame, is scalable with little degradation in application performances. (m,k)-frame method is issued from the notion of (m,k)-firm realtime constraints which means that among k invocations of a task, m invocations must meet their deadline. Our simulation studies show the usefulness of (m,k)-frame method to adapt the QoS to the real conditions in a multimedia application, according to the current system load. Notably, the system must adjust the QoS provided to active clients1 when their number varies, i.e. dynamic arrival of clients.Comment: 10 pages, International Journal of Computer Science and Information Security (IJCSIS

End-To-End Deadlines over Dynamic Topologies

Despite the creativity of the scientific community and the funding agencies, the underlying model of computation behind IoT, WSN, cloud, edge, fog, and mist is fundamentally the same; Computational nodes which are dynamically interconnected to form a system in where both processing capacity and connectivity may vary over time. On top of such a system, we consider applications that need packets to flow along a path and adhere to end-to-end deadlines. This application model is motivated by both control and automation systems, as well as telecom systems. The challenge is to guarantee end-to-end deadlines when allowing nodes and applications to join or leave. The mainstream, and to some extent natural, approach to this is to relax the stringency of the constraint (e.g. use probabilistic guarantees, soft deadlines). In this paper we take a different approach and keep the end-to-end deadlines as hard constraints and instead partially limit the freedom of how nodes and applications are allowed to leave and join. We present a theoretical framework for modeling such systems along with proofs that deadlines are always honored

Adaptive Quality of Service Control in Distributed Real-Time Embedded Systems

An increasing number of distributed real-time embedded systems face the critical challenge of providing Quality of Service (QoS) guarantees in open and unpredictable environments. For example, such systems often need to enforce CPU utilization bounds on multiple processors in order to avoid overload and meet end-to-end dead-lines, even when task execution times deviate significantly from their estimated values or change dynamically at run-time. This dissertation presents an adaptive QoS control framework which includes a set of control design methodologies to provide robust QoS assurance for systems at different scales. To demonstrate its effectiveness, we have applied the framework to the end-to-end CPU utilization control problem for a common class of distributed real-time embedded systems with end-to-end tasks. We formulate the utilization control problem as a constrained multi-input-multi-output control model. We then present a centralized control algorithm for small or medium size systems, and a decentralized control algorithm for large-scale systems. Both algorithms are designed systematically based on model predictive control theory to dynamically enforce desired utilizations. We also introduce novel task allocation algorithms to ensure that the system is controllable and feasible for utilization control. Furthermore, we integrate our control algorithms with fault-tolerance mechanisms as an effective way to develop robust middleware systems, which maintain both system reliability and real-time performance even when the system is in face of malicious external resource contentions and permanent processor failures. Both control analysis and extensive experiments demonstrate that our control algorithms and middleware systems can achieve robust utilization guarantees. The control framework has also been successfully applied to other distributed real-time applications such as end-to-end delay control in real-time image transmission. Our results show that adaptive QoS control middleware is a step towards self-managing, self-healing and self-tuning distributed computing platform

RADIO: managing the performance of large, distributed storage systems

Els sistemes informàtics d’altes prestacions continuen creixent en grandària i complexitat, i sovint han de gestionar moltes tasques diferents simultàniament. El subsistema d’entrada i sortida és freqüentment un coll d’ampolla per al rendiment general del sistema, i les interferències entre aplicacions poden conduir a la degradació desproporcionada de les prestacions, a temps d’execució impredictibles i a l’ús ineficient dels recursos. Aquesta xerrada presenta la nostra recerca en curs sobre com s’ha de gestionar i garantir l’execució de grans sistemes d’emmagatzematge distribuït. Discutirem el nostre model general per a la gestió del rendiment, supervisarem les nostres solucions per a la UCP (unitat central de processament), el disc, la xarxa, l’emmagatzematge i el servidor de memòria cau, i discutirem la nostra recerca, encaminada a aplicar aquestes solucions per al control i la gestió de sistemes distribuïts