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.

scholarly journals An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

2021 ◽  
Author(s):  
Jianying Miao
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Simulation Environment ◽  
Attribute Matching ◽  
Resource Allocation Strategy ◽  
And Task

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

On the Competitiveness of Online Real-Time Scheduling with Rate of Progress Guarantees

2003 ◽  
Vol 14 (03) ◽  
pp. 359-370 ◽  
Author(s):  
Michael A. Palis
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Competitive Ratio ◽  
Scheduling Algorithm ◽  
Real Time Systems ◽  
Real Time Scheduling ◽  
Qos Guarantees ◽  
Time Scheduling ◽  
Time Systems

This paper investigates the task scheduling problem in the oontext of reservation-based real-time systems that provide quality of service (QoS) guarantees. In such a system, each incoming task specifies a rate of progress requirement on the task's execution that must be met by the system in order for the computation to be deeemed usable. A new metric, called granularity, is introduced that quantifies both the maximum slowdown and the variance in execution rate that the task allows. This metric generalizes the stretch metric used in recent research on task scheduling. An online preemptive scheduling algorithm is presented that achieves a competitive ratio of g(1 - r) for every set of tasks with maximum rate r and minimum granularity g. This result generalizes a previous result based on the stretch metric that showed that a competitive ratio of (1 - r) is achievable for the case when g = 1.

scholarly journals Fog Computing: Issues, Challenges and Future Directions

2017 ◽  
Vol 7 (6) ◽  
pp. 3669 ◽  
Author(s):  
Prakash P ◽  
Darshaun K. G. ◽  
Yaazhlene. P ◽  
Medidhi Venkata Ganesh ◽  
Vasudha B
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Fog Computing ◽  
Low Latency ◽  
Use Case ◽  
Future Directions ◽  
Computing Power ◽  
Geographically Dispersed ◽  
Real Time Application ◽  
The Ideal

In Cloud Computing, all the processing of the data collected by the node is done in the central server. This involves a lot of time as data has to be transferred from the node to central server before the processing of data can be done in the server. Also it is not practical to stream terabytes of data from the node to the cloud and back. To overcome these disadvantages, an extension of cloud computing, known as fog computing, is introduced. In this, the processing of data is done completely in the node if the data does not require higher computing power and is done partially if the data requires high computing power, after which the data is transferred to the central server for the remaining computations. This greatly reduces the time involved in the process and is more efficient as the central server is not overloaded. Fog is quite useful in geographically dispersed areas where connectivity can be irregular. The ideal use case requires intelligence near the edge where ultra-low latency is critical, and is promised by fog computing. The concepts of cloud computing and fog computing will be explored and their features will be contrasted to understand which is more efficient and better suited for real-time application.

Task Scheduling and Resource Allocation of Cloud Computing Based on QoS

2014 ◽  
Vol 915-916 ◽  
pp. 1382-1385 ◽  
Author(s):  
Bai Lin Pan ◽  
Yan Ping Wang ◽  
Han Xi Li ◽  
Jie Qian
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Computing Power ◽  
Power Scheduling ◽  
Cloud Computing System ◽  
User Demand

With the enlargement of the scope of cloud computing application, the number of users and types also increases accordingly, the special demand for cloud computing resources has also improved. Cloud computing task scheduling and resource allocation are key technologies, mainly responsible for assigning user jobs to the appropriate resources to perform. But the existing scheduling algorithm is not fully consider the user demand for resources is different, and not well provided for different users to meet the requirements of its resources. As the demand for quality of service based on cloud computing and cloud computing original scheduling algorithm, the computing power scheduling algorithm is proposed based on the QoS constraints to research the cloud computing task scheduling and resource allocation problems, improving the overall efficiency of cloud computing system.

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.

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