Optimal Unified Data Allocation and Task Scheduling for Real-Time Multi-Tasking Systems

2007 ◽  
Author(s):  
Rony Ghattas ◽  
Gregory Parsons ◽  
Alexander G. Dean
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Data Allocation ◽  
And Task

scholarly journals Energy-Aware Data Allocation and Task Scheduling on Heterogeneous Multiprocessor Systems With Time Constraints

2014 ◽  
Vol 2 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Yan Wang ◽  
Kenli Li ◽  
Hao Chen ◽  
Ligang He ◽  
Keqin Li
Keyword(s):  
Task Scheduling ◽  
Time Constraints ◽  
Multiprocessor Systems ◽  
Data Allocation ◽  
Energy Aware ◽  
Heterogeneous Multiprocessor ◽  
And Task

scholarly journals Dynamic Data Allocation and Task Scheduling on Multiprocessor Systems With NVM-Based SPM

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 1548-1559 ◽  
Author(s):  
Yan Wang ◽  
Kenli Li ◽  
Keqin Li
Keyword(s):  
Task Scheduling ◽  
Multiprocessor Systems ◽  
Data Allocation ◽  
Dynamic Data ◽  
And Task

Task Scheduling under Uncertain Timing Constraints in Real-Time Embedded Systems

2013 ◽  
pp. 211-235 ◽  
Author(s):  
Pranab K. Muhuri ◽  
K. K. Shukla
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Resource Constraints ◽  
Systems Design ◽  
Task Completion ◽  
Timing Constraints ◽  
Timing Constraint ◽  
Processing Times ◽  
And Task

In real-time embedded systems, timeliness of task completion is a very important factor. In such systems, correctness of the output depends on the timely production of results in addition to the logical outcome of computation. Thus, tasks have explicit timing constraints besides other characteristics of general systems, and task scheduling aims towards devising a feasible schedule of the tasks such that timing constraints, resource constraints, precedence constraints, etc. are complied. In real-time embedded systems, the most important timing constraint of a task is the deadline, as tasks must be completed within this time. The next important timing constraint is the processing time, because a task occupies a processor only for this duration of time. However, in the early phase of real-time embedded systems design only an approximate idea of the tasks and their characteristics are known. As a result, uncertainty or impreciseness is associated with the task deadlines and processing times; hence, it is appropriate to use fuzzy numbers to model deadlines and processing times in real-time embedded systems. The chapter introduces a new method using mixed cubic-exponential Hermite interpolation technique for intuitively defining smooth Membership Functions (MFs) for fuzzy deadlines and processing times. The effect of changes in parameterized MFs on the task schedulability and task priorities are explained. Examples are given to demonstrate the significant features and better performance of the new technique.

scholarly journals Group Mobility Model for Complex Multimission Cooperation of UAV Swarm

2022 ◽  
Vol 2022 ◽  
pp. 1-22
Author(s):  
Xiaoyan Gu ◽  
Feng He ◽  
Rongwei Wang ◽  
Liang Chen
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Network Connectivity ◽  
Mobility Model ◽  
Reference Node ◽  
Class Mobility ◽  
Time Performance ◽  
Model Based ◽  
Uav Swarm ◽  
And Task

In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.

scholarly journals Real-time edge framework (RTEF): task scheduling and realisation

2021 ◽  
Author(s):  
Volkan Gezer ◽  
Achim Wagner
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Resource Planning ◽  
Use Case ◽  
Reference Architecture ◽  
Computing Power ◽  
Research Areas ◽  
New Research ◽  
And Task

AbstractWith the big success of the Cloud Computing, or the Cloud, new research areas appeared. Edge Computing (EC) is one of the recent paradigms that is expected to overcome the Quality of Service (QoS) and latency issues caused by the best-effort behaviour of the Cloud. EC aims to bring the computation power close to the end devices as much as possible and reduce the dependency to the Cloud. Bringing computing power close to the source also enables real-time applications. In this paper, we propose a novel software reference architecture for Edge Servers, which is operating system (OS) and hardware-agnostic. Edge Servers can collaborate and execute (near) real-time tasks on time, either by downscaling or scheduling them according to their deadlines or offloading them to other Edge Servers in the network. Decision making for offloading, resource planning, and task scheduling are challenging problems in decentralized systems. The paper explains how resource planning and task scheduling can be overcome with software approach. Finally, the article realises the architecture as a framework, called Real-Time Edge Framework (RTEF) and validates its correctness with a use case.

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