Deadline assignment in a distributed soft real-time system

In a distributed environment, tasks often have processing demands on multiple different sites. A distributed task is usually divided up into several subtasks, each one to be executed at some site in order. In a real-time system, an overall deadline is usually specified by an application designer indicating when a distributed task is to be finished. However, the problem of how a global deadline is automatically translated to the deadline of each individual subtask has not been well studied. This paper examines (through simulations) four strategies for subtask deadline assignment in a distributed soft real-time environment.published_or_final_versio

Convex optimization framework for intermediate deadline assignment in soft and hard real-time distributed systems

It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation

On Building Distributed Soft Real-Time Systems

When building a distributed real-time system, one can either build the whole system from scratch, or from pre-existing standard components. Although the former allows better scheduling design, it may not be economical in terms of the cost and time of development. This paper studies the performance of distributed soft real-time systems that use standard components with various scheduling algorithms and suggests ways to improve them.published_or_final_versio

Least space-time first scheduling algorithm : scheduling complex tasks with hard deadline on parallel machines

Both time constraints and logical correctness are essential to real-time systems and failure to specify and observe a time constraint may result in disaster. Two orthogonal issues arise in the design and analysis of real-time systems: one is the specification of the system, and the semantic model describing the properties of real-time programs; the other is the scheduling and allocation of resources that may be shared by real-time program modules. The problem of scheduling tasks with precedence and timing constraints onto a set of processors in a way that minimizes maximum tardiness is here considered. A new scheduling heuristic, Least Space Time First (LSTF), is proposed for this NP-Complete problem. Basic properties of LSTF are explored; for example, it is shown that (1) LSTF dominates Earliest-Deadline-First (EDF) for scheduling a set of tasks on a single processor (i.e., if a set of tasks are schedulable under EDF, they are also schedulable under LSTF); and (2) LSTF is more effective than EDF for scheduling a set of independent simple tasks on multiple processors. Within an idealized framework, theoretical bounds on maximum tardiness for scheduling algorithms in general, and tighter bounds for LSTF in particular, are proven for worst case behavior. Furthermore, simulation benchmarks are developed, comparing the performance of LSTF with other scheduling disciplines for average case behavior. Several techniques are introduced to integrate overhead (for example, scheduler and context switch) and more realistic assumptions (such as inter-processor communication cost) in various execution models. A workload generator and symbolic simulator have been implemented for comparing the performance of LSTF (and a variant -- LSTF+) with that of several standard scheduling algorithms. LSTF\u27s execution model, basic theories, and overhead considerations have been defined and developed. Based upon the evidence, it is proposed that LSTF is a good and practical scheduling algorithm for building predictable, analyzable, and reliable complex real-time systems. There remain some open issues to be explored, such as relaxing some current restrictions, discovering more properties and theorems of LSTF under different models, etc. We strongly believe that LSTF can be a practical scheduling algorithm in the near future

Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed

Configurable Component Middleware for Distributed Real-Time Systems with Aperiodic and Periodic Tasks

Many distributed real-time applications must handle mixed periodic and aperiodic tasks with diverse requirements. However, existing middleware lacks flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different applications with both periodic and aperiodic tasks. The primary contribution of this work is the design, implementation and performance evaluation of the first configurable component middleware services for admission control and load balancing of aperiodic and periodic tasks in distributed real-time systems. Empirical results demonstrate the need for and effectiveness of our configurable component middleware approach in supporting different applications with periodic and aperiodic tasks

End-to-End Scheduling Strategies for Aperiodic Tasks in Middleware

Many mission-critical distributed real-time applicationsmust handle aperiodic tasks with hard end-to-end dead-lines. Existing middleware such as RT-CORBA lacksschedulability analysis and run-time scheduling mecha-nisms that can provide real-time guarantees to aperiodictasks. This paper makes the following contributions to thestate of the art for end-to-end aperiodic scheduling in mid-dleware. First, we compare two approaches to aperiodicscheduling, the deferrable server and the aperiodic utiliza-tion bound, using representative workloads. Numerical re-sults show that the deferrable server analysis is less pes-simistic than the aperiodic utilization bounds when appliedoffline. Second, we propose a practical approach to tuningdeferrable servers for end-to-end tasks. Third, we describedeferrable server mechanisms we have developed for TAO’sfederated event channel. Finally, we present empirical re-sults from a Linux testbed that demonstrate the efficiency ofthose deferrable server mechanisms

MCFlow: Middleware for Mixed-Criticality Distributed Real-Time Systems

Traditional fixed-priority scheduling analysis for periodic/sporadic task sets is based on the assumption that all tasks are equally critical to the correct operation of the system. Therefore, every task has to be schedulable under the scheduling policy, and estimates of tasks\u27 worst case execution times must be conservative in case a task runs longer than is usual. To address the significant under-utilization of a system\u27s resources under normal operating conditions that can arise from these assumptions, several \emph{mixed-criticality scheduling} approaches have been proposed. However, to date there has been no quantitative comparison of system schedulability or run-time overhead for the different approaches. In this dissertation, we present what is to our knowledge the first side-by-side implementation and evaluation of those approaches, for periodic and sporadic mixed-criticality tasks on uniprocessor or distributed systems, under a mixed-criticality scheduling model that is common to all these approaches. To make a fair evaluation of mixed-criticality scheduling, we also address some previously open issues and propose modifications to improve schedulability and correctness of particular approaches. To facilitate the development and evaluation of mixed-criticality applications, we have designed and developed a distributed real-time middleware, called MCFlow, for mixed-criticality end-to-end tasks running on multi-core platforms. The research presented in this dissertation provides the following contributions to the state of the art in real-time middleware: (1) an efficient component model through which dependent subtask graphs can be configured flexibly for execution within a single core, across cores of a common host, or spanning multiple hosts; (2) support for optimizations to inter-component communication to reduce data copying without sacrificing the ability to execute subtasks in parallel; (3) a strict separation of timing and functional concerns so that they can be configured independently; (4) an event dispatching architecture that uses lock free algorithms where possible to reduce memory contention, CPU context switching, and priority inversion; and (5) empirical evaluations of MCFlow itself and of different mixed criticality scheduling approaches both with a single host and end-to-end across multiple hosts. The results of our evaluation show that in terms of basic distributed real-time behavior MCFlow performs comparably to the state of the art TAO real-time object request broker when only one core is used and outperforms TAO when multiple cores are involved. We also identify and categorize different use cases under which different mixed criticality scheduling approaches are preferable

Customizing Component Middleware for Distributed Real-Time Systems with Aperiodic and Periodic Tasks

Many distributed real-time applications must handle mixed aperiodic and periodic tasks with diverse requirements. However, existing middleware lacks flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different applications with both aperiodic and periodic tasks. The primary contribution of this work is the design, implementation and performance evaluation of the first configurable component middleware services for admission control and load balancing of aperiodic and periodic tasks in distributed real-time systems. Empirical results demonstrate the need for, and the effectiveness of, our configurable component middleware approach in supporting different applications with aperiodic and periodic tasks