DLB: A Novel Real-time QoS Control Mechanism for Multimedia Transmission

This paper presents a new QoS guarantee scheme called R-(m,k)-firm (Relaxed-(m,k)-firm) which provides the guarantee on transmission delay of at least m out of any k consecutive packets (m´k). It has several advantages: (1) during network congestion, packets are dropped according to the (m,k) model rather than uncontrollably as the case of TD and RED, avoiding thus undesirable long consecutive packet drops; (2) it allows to admit more real-time flows than the traditional over-provisioning approach. A new mechanism, called DLB (Double Leaks Bucket) is also proposed for dropping a proportion of packets of a flow or of aggregated-flows in case of network congestion while still guaranteeing the R-(m,k)-firm constraint. The sufficient condition for this guarantee is given for configuring the DLB parameters. It is easy to implement DLB in the actual IntServ and Diffserv architectures (by simply replacing the actual leaky bucket by DLB) for providing respectively per flow and per class (m,k) guarantee, or event per flow R-(m,k)-firm guarantee in Diffserv

Cache Related Pre-emption Delays in Embedded Real-Time Systems

Real-time systems are subject to stringent deadlines which make their temporal behaviour just as important as their functional behaviour. In multi-tasking real-time systems, the execution time of each task must be determined, and then combined together with information about the scheduling policy to ensure that there are enough resources to schedule all of the tasks. This is usually achieved by performing timing analysis on the individual tasks, and then schedulability analysis on the system as a whole. In systems with cache, multiple tasks can share this common resource which can lead to cache-related pre-emption delays (CRPD) being introduced. CRPD is the additional cost incurred from resuming a pre-empted task that no longer has the instructions or data it was using in cache, because the pre-empting task(s) evicted them from cache. It is therefore important to be able to account for CRPD when performing schedulability analysis. This thesis focuses on the effects of CRPD on a single processor system, further expanding our understanding of CRPD and ability to analyse and optimise for it. We present new CRPD analysis for Earliest Deadline First (EDF) scheduling that significantly outperforms existing analysis, and then perform the first comparison between Fixed Priority (FP) and EDF accounting for CRPD. In this comparison, we explore the effects of CRPD across a wide range of system and taskset parameters. We introduce a new task layout optimisation technique that maximises system schedulability via reduced CRPD. Finally, we extend CRPD analysis to hierarchical systems, allowing the effects of cache when scheduling multiple independent applications on a single processor to be analysed

Analysis and simulation of scheduling techniques for real-time embedded multi-core architectures

In this modern era of technological progress, multi-core processors have brought significant and consequential improvements in the available processing potential to the world of real-time embedded systems. These improvements impose a rapid increment of software complexity as well as processing demand placed on the underlying hardware. As a consequence, the need for efficient yet predictable multi-core scheduling techniques is on the rise. As part of this thesis, in-depth research of currently available multi-core scheduling techniques, belonging to both partitioned and global approaches, is done in the context of real-time embedded systems. The emphasis is on the degree of their usability on hard real-time systems, focusing on the scheduling techniques offering better processor affinity and the lower number of context switching. Also, an extensive research of currently available real-time test-beds as well as real-time operating systems is performed. Finally, a subset of the analyzed multi-core scheduling techniques comprising PSN-EDF, GSN-EDF, PD$^{2}$ and PD$^{2*}$ is simulated on the real-time test-bed LITMUS$^{RT}$

Annual Report, 2013-2014

Beginning in 2004/2005- issued in online format onl

Flexible Scheduling in Middleware for Distributed rate-based real-time applications - Doctoral Dissertation, May 2002

Distributed rate-based real-time systems, such as process control and avionics mission computing systems, have traditionally been scheduled statically. Static scheduling provides assurance of schedulability prior to run-time overhead. However, static scheduling is brittle in the face of unanticipated overload, and treats invocation-to-invocation variations in resource requirements inflexibly. As a consequence, processing resources are often under-utilized in the average case, and the resulting systems are hard to adapt to meet new real-time processing requirements. Dynamic scheduling offers relief from the limitations of static scheduling. However, dynamic scheduling offers relief from the limitations of static scheduling. However, dynamic scheduling often has a high run-time cost because certain decisions are enforced on-line. Furthermore, under conditions of overload tasks can be scheduled dynamically that may never be dispatched, or that upon dispatch would miss their deadlines. We review the implications of these factors on rate-based distributed systems, and posits the necessity to combine static and dynamic approaches to exploit the strengths and compensate for the weakness of either approach in isolation. We present a general hybrid approach to real-time scheduling and dispatching in middleware, that can employ both static and dynamic components. This approach provides (1) feasibility assurance for the most critical tasks, (2) the ability to extend this assurance incrementally to operations in successively lower criticality equivalence classes, (3) the ability to trade off bounds on feasible utilization and dispatching over-head in cases where, for example, execution jitter is a factor or rates are not harmonically related, and (4) overall flexibility to make more optimal use of scarce computing resources and to enforce a wider range of application-specified execution requirements. This approach also meets additional constraints of an increasingly important class of rate-based systems, those with requirements for robust management of real-time performance in the face of rapidly and widely changing operating conditions. To support these requirements, we present a middleware framework that implements the hybrid scheduling and dispatching approach described above, and also provides support for (1) adaptive re-scheduling of operations at run-time and (2) reflective alternation among several scheduling strategies to improve real-time performance in the face of changing operating conditions. Adaptive re-scheduling must be performed whenever operating conditions exceed the ability of the scheduling and dispatching infrastructure to meet the critical real-time requirements of the system under the currently specified rates and execution times of operations. Adaptive re-scheduling relies on the ability to change the rates of execution of at least some operations, and may occur under the control of a higher-level middleware resource manager. Different rates of execution may be specified under different operating conditions, and the number of such possible combinations may be arbitrarily large. Furthermore, adaptive rescheduling may in turn require notification of rate-sensitive application components. It is therefore desirable to handle variations in operating conditions entirely within the scheduling and dispatching infrastructure when possible. A rate-based distributed real-time application, or a higher-level resource manager, could thus fall back on adaptive re-scheduling only when it cannot achieve acceptable real-time performance through self-adaptation. Reflective alternation among scheduling heuristics offers a way to tune real-time performance internally, and we offer foundational support for this approach. In particular, run-time observable information such as that provided by our metrics-feedback framework makes it possible to detect that a given current scheduling heuristic is underperforming the level of service another could provide. Furthermore we present empirical results for our framework in a realistic avionics mission computing environment. This forms the basis for guided adaption. This dissertation makes five contributions in support of flexible and adaptive scheduling and dispatching in middleware. First, we provide a middle scheduling framework that supports arbitrary and fine-grained composition of static/dynamic scheduling, to assure critical timeliness constraints while improving noncritical performance under a range of conditions. Second, we provide a flexible dispatching infrastructure framework composed of fine-grained primitives, and describe how appropriate configurations can be generated automatically based on the output of the scheduling framework. Third, we describe algorithms to reduce the overhead and duration of adaptive rescheduling, based on sorting for rate selection and priority assignment. Fourth, we provide timely and efficient performance information through an optimized metrics-feedback framework, to support higher-level reflection and adaptation decisions. Fifth, we present the results of empirical studies to quantify and evaluate the performance of alternative canonical scheduling heuristics, across a range of load and load jitter conditions. These studies were conducted within an avionics mission computing applications framework running on realistic middleware and embedded hardware. The results obtained from these studies (1) demonstrate the potential benefits of reflective alternation among distinct scheduling heuristics at run-time, and (2) suggest performance factors of interest for future work on adaptive control policies and mechanisms using this framework

Interaction-aware analysis and optimization of real-time application and operating system

Mechanical and electronic automation was a key component of the technological advances in the last two hundred years. With the use of special-purpose machines, manual labor was replaced by mechanical motion, leaving workers with the operation of these machines, before also this task was conquered by embedded control systems. With the advances of general-purpose computing, the development of these control systems shifted more and more from a problem-specific one to a one-size-fits-all mentality as the trade-off between per-instance overheads and development costs was in favor of flexible and reusable implementations. However, with a scaling factor of thousands, if not millions, of deployed devices, overheads and inefficiencies accumulate; calling for a higher degree of specialization. For the area real-time operating systems (RTOSs), which form the base layer for many of these computerized control systems, we deploy way more flexibility than what is actually required for the applications that run on top of it. Since only the solution, but not the problem, became less specific to the control problem at hand, we have the chance to cut away inefficiencies, improve on system-analyses results, and optimize the resource consumption. However, such a tailoring will only be favorable if it can be performed without much developer interaction and in an automated fashion. Here, real-time systems are a good starting point, since we already have to have a large degree of static knowledge in order to guarantee their timeliness. Until now, this static nature is not exploited to its full extent and optimization potentials are left unused. The requirements of a system, with regard to the RTOS, manifest in the interactions between the application and the kernel. Threads request resources from the RTOS, which in return determines and enforces a scheduling order that will ensure the timely completion of all necessary computations. Since the RTOS runs only in the exception, its reaction to requests from the application (or from the environment) is its defining feature. In this thesis, I will grasp these interactions, and thereby the required RTOS semantic, in a control-flow-sensitive fashion. Extracted automatically, this knowledge about the reciprocal influence allows me to fit the implementation of a system closer to its actual requirements. The result is a system that is not only in its usage a special-purpose system, but also in its implementation and in its provided guarantees. In the development of my approach, it became clear that the focus on these interactions is not only highly fruitful for the optimization of a system, but also for its end-to-end analysis. Therefore, this thesis does not only provide methods to reduce the kernel-execution overhead and a system's memory consumption, but it also includes methods to calculate tighter response-time bounds and to give guarantees about the correct behavior of the kernel. All these contributions are enabled by my proposed interaction-aware methodology that takes the whole system, RTOS and application, into account. With this thesis, I show that a control-flow-sensitive whole-system view on the interactions is feasible and highly rewarding. With this approach, we can overcome many inefficiencies that arise from analyses that have an isolating focus on individual system components. Furthermore, the interaction-aware methods keep close to the actual implementation, and therefore are able to consider the behavioral patterns of the finally deployed real-time computing system

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts