A practical solution for functional reconfiguration of real-time service based applications through partial schedulability

REACTION 2012. 1st International workshop on Real-time and distributed computing in emerging applications. December 4th, 2012, San Juan, Puerto Rico.Timely reconfiguration in distributed real-time systems is a complex problem with many sides to it ranging from system-wide concerns down to the intrinsic non-robust nature of the specific middleware software and the used programming techniques. In an completely open distributed system, it is not possible to achieve time-deterministic functional reconfiguration; the set of possible target configurations that the system can transition to could be extremely large threatening the temporal predictability of the reconfiguration process. Therefore, a set of bounds and limitations to the structure of systems and to their open nature need to be imposed. In this paper, we present the different sides of the problem of reconfiguration. We provide a solution for timely reconfiguration based on reducing the solution space of solutions of partially closed applications; we have enhanced the logic of a middleware for distributed soft real-time applications with the proposed technique. As a result, applications require a limited number of schedulability tests to search for the valid target configuration. We present some results on the actual reduction of the configuration space achieved by our middleware.This work has been partly supported by the iLAND project (ARTEMISJU 100026) funded by the ARTEMIS JTU Call 1 and the Spanish Ministry of Industry (www.iland-artemis.org), ARTISTDesign NoE (IST-2007- 214373) of the EU 7th Framework Programme, and by the Spanish national project REM4VSS (TIN 2011-28339)

End-Point Resource Admission Control for Remote Control Multimedia Applications

One goal in certain classes of networked multimedia applications, such as full-feedback remote control, is to provide end-to-end guarantees. To achieve guarantees, all resources along the path(s) between the resource(s) and sink(s) must be controlled. Resource availability is checked by the admission service during the call establishment phase. Current admission services control only network resources such as bandwidth and network delay. To provide end-to-end guarantees, the networked applications also need operation system resources and I/O devices at the endpoints. All such resources must be included in a robust admission process. By integrating the end-point resources, we observed several dependencies which force changes in admission algorithms designed and implemented for control of a single resource. We have designed and implemented the multi-level admission service within our Omega architecture which controls the availability of end-point resources needed in remote control multimedia applications such as telerobotics

Synthesising robust schedules for minimum disruption repair using linear programming

An off-line scheduling algorithm considers resource, precedence, and synchronisation requirements of a task graph, and generates a schedule guaranteeing its timing requirements. This schedule must, however, be executed in a dynamic and unpredictable operating environment where resources may fail and tasks may execute longer than expected. To accommodate such execution uncertainties, this paper addresses the synthesis of robust task schedules using a slack-based approach and proposes a solution using integer linear programming (ILP). Earlier we formulated a time slot based ILP model whose solutions maximise the temporal flexibility of the overall task schedule. In this paper, we propose an improved, interval based model, compare it to the former, and evaluate both on a set of random scenarios using two public domain ILP solvers and a proprietary SAT/ILP mixed solver

Dynamic control of NFV forwarding graphs with end-to-end deadline constraints

There is a strong industrial drive to use cloud computing technologies and concepts for providing timing sensitive services in the networking domain since it would provide the means to share the physical resources among multiple users and thus increase the elasticity and reduce the costs. In this work, we develop a mathematical model for user-stateless virtual network functions forming a forwarding graph. The model captures uncertainties of the performance of these virtual resources as well as the time-overhead needed to instantiate them. The model is used to derive a service controller for horizontal scaling of the virtual resources as well as an admission controller that guarantees that packets exiting the forwarding graph meet their end-to-end deadline. The Automatic Service and Admission Controller (AutoSAC) developed in this work uses feedback and feedforward making it robust against uncertainties of the underlying infrastructure. Also, it has a fast reaction time to changes in the input

Feedback for increased robustness of forwarding graphs in the cloud

Cloud computing technology provides the means to share physical resources among multiple users and data center tenants by exposing them as virtual resources. There is a strong industrial drive to use similar technology and concepts to provide timing sensitive services. One such domain is a chain of connected virtual network functions. This allows the capacity of each function to be scaled up and down by adding or removing virtual resources. In this work, we develop a model of such service chain and pose the dynamic allocation of resources as an optimization problem. We design and present a set of strategies to allow virtual network nodes to be controlled in an optimal fashion subject to latency and buffer constraints. Furthermore, we derive a feedback-law for dynamically adjusting the amount of resources given to each functions in order to ensure that the system remains in the desired state even if there are modeling errors or for a stochastic input

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

사물인터넷 애플리케이션의 적응형 동적 스케줄링 기법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 하순회.IoT시스템은매우다른성능과기능을가진이기종스마트장치로구성된분산임베디드시스템이다. IoT시스템에서일반적으로리소스요구사항과실시간요구사항이서로 다른 많은 IoT 애플리케이션들이 동시에 실행된다. 또한, 전력 소비 및 장치 수명과 같은 비 기능적 특성이 중요하게 고려된다. IoT 애플리케이션은 언제든지 추가되거나 제거 될 수 있으며 런타임에 디바이스 상태가 변경 될 수 있다. 이 같이 시스템은 동적 특성을 갖기 때문에 IoT 애플리케이션을 스마트 디바이스에 매핑/스케줄링 하는 것은 매우까다로운문제이다.이문제를해결하기위해점진적매핑및글로벌재매핑의두 가지 스케줄링 기법으로 구성된 새로운 적응적 스케줄링 기법을 제안한다. 동적 환경 변화에 대한 빠른 응답을 제공하기 위해 점진적 매핑 방법을 제안하며, 정적 상태에서 비 기능적 특성에 기초하여 주어진 목적 함수를 최적화하기 위해 주기적으로 IoT 애플리케이션의 전체 태스크를 모두 다시 스케줄링 하는 유전 알고리즘 기반 글로벌 재 매핑 방법은 제안한다. 제안 된 스케줄링 방법의 두 가지 성능 지표로 애플리케이션 수용 비율 및 에너지 소비량을 사용하였으며, 성능 및 실용성은 무작위로 생성 된 시나리오를 사용한 시뮬레이션 환경을 통해 검증한다.An IoT system can be regarded as a distributed embedded system that is composed of heterogeneous smart devices with very different performance and functions. Also many IoT applications that have different resource requirements and real-time requirements will run concurrently in the IoT system. In addition, non-functional properties such as power consumption and device lifetime are considered important. Since an IoT application can be added or removed anytime and the device status may change at run-time, the system is unprecedentedly dynamic in its configuration, which brings up a challenging scheduling problem of IoT applications onto the smart devices. To tackle this problem, we propose a novel adaptive scheduling technique that consists of two scheduling techniques, incremental and global. An incremental heuristic method is proposed to provide fast responsiveness to dynamically changing configuration. During the steady-state operation, a GA-based method is applied to perform global rescheduling of IoT applications periodically to optimize a given objective function based on non-functional properties. We use the acceptance ratio of new applications and energy consumption as two performance metrics of the proposed scheduling method. The viability of the proposed approach is verified by extensive simulations with randomly generated scenarios.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Target IoT system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Motivational Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3. Schedulability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1 Transformation of a Task Graphs to Independent Tasks . . . . . . . . . . 10 3.2 Schedulability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4. Proposed Mapping Technique . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.1 Incremental Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 Global Re-mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 5. Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 5.1 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 5.2 Experiment 1 (Incremental Mapping) . . . . . . . . . . . . . . . . . . . 23 5.3 Experiment 2 (Global Re-mapping) . . . . . . . . . . . . . . . . . . . . 25 5.4 Experiment 3 (Sensitivity Analysis) . . . . . . . . . . . . . . . . . . . . 27 6. RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 요 약 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Maste

Memory-Processor Co-Scheduling in Fixed Priority Systems

ABSTRACT A major obstacle towards the adoption of multi-core platforms for real-time systems is given by the difficulties in characterizing the interference due to memory contention. The simple fact that multiple cores may simultaneously access shared memory and communication resources introduces a significant pessimism in the timing and schedulability analysis. To counter this problem, predictable execution models have been proposed splitting task executions into two consecutive phases: a memory phase in which the required instruction and data are pre-fetched to local memory (Mphase), and an execution phase in which the task is executed with no memory contention (C-phase). Decoupling memory and execution phases not only simplifies the timing analysis, but it also allows a more efficient (and predictable) pipelining of memory and execution phases through proper co-scheduling algorithms. In this paper, we take a further step towards the design of smart co-scheduling algorithms for sporadic real-time tasks complying with the M/C (memory-computation) model. We provide a theoretical framework that aims at tightly characterizing the schedulability improvement obtainable with the adopted M/C task model on a single-core systems. We identify a tight critical instant for M/C tasks scheduled with fixed priority, providing an exact response-time analysis with pseudo-polynomial complexity. We show in our experiments that a significant schedulability improvement may be obtained with respect to classic execution models, placing an important building block towards the design of more efficient partitioned multi-core systems