Advances in Efficient Resource Allocation for Packet-Based Real-Time Video Transmission

2005 ◽  
Vol 93 (1) ◽  
pp. 135-147 ◽  
Author(s):  
A.K. Katsaggelos ◽  
Y. Eisenberg ◽  
F. Zhai ◽  
R. Berry ◽  
T.N. Pappas
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Video Transmission ◽  
Efficient Resource

Cost efficient resource allocation for real-time tasks in embedded systems

2019 ◽  
Vol 48 ◽  
pp. 101523 ◽  
Author(s):  
Nasro Min-Allah ◽  
Muhammad Bilal Qureshi ◽  
Saleh Alrashed ◽  
Omer F. Rana
Keyword(s):  
Resource Allocation ◽  
Embedded Systems ◽  
Real Time ◽  
Efficient Resource ◽  
Cost Efficient

scholarly journals Adaptive Description of ATM Traffic Flows

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Juergen Rochol ◽  
Mouriac Halen Diemer
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Broad Band ◽  
Traffic Flows ◽  
Initial Sample ◽  
Efficient Resource

Today, the great challenge in broad band networks, such as ATM, is to support applications that need guaranteed QoS. Efficient resource allocation for connections that need guaranteed QoS is only possible if the application can provide a precise and reliable traffic descriptor that the network can understand. This work presents a methodology to obtain the parameters of the traffic descriptor of an ATM application from an initial sample of the flow. Based on real-time measurements of the flow, we suggest periodic renegotiations of the parameters, every time they vary above or below their predefined limits. We apply this method to a VBR MPEG video flow and demonstrate its performance. Observed results are discussed and some future uses of the methodology are suggested.

scholarly journals A Computational Offloading Method for Edge Server Computing and Resource Allocation Management

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Muna Al-Razgan ◽  
Taha Alfakih ◽  
Mohammad Mehedi Hassan
Keyword(s):  
Resource Allocation ◽  
Response Time ◽  
Real Time ◽  
Mobile Devices ◽  
Mapping Function ◽  
Mobile User ◽  
Computation Offloading ◽  
Efficient Resource ◽  
And Performance ◽  
Cloud Servers

The emerging technology of mobile cloud is introduced to overcome the constraints of mobile devices. We can achieve that by offloading resource intensive applications to remote cloud-based data centers. For the remote computing solution, mobile devices (MDs) experience higher response time and delay of the network, which negatively affects the real-time mobile user applications. In this study, we proposed a model to evaluate the efficiency of the close-end network computation offloading in MEC. This model helps in choosing the adjacent edge server from the surrounding edge servers. This helps to minimize the latency and increase the response time. To do so, we use a decision rule based Heuristic Virtual Value (HVV). The HVV is a mapping function based on the features of the edge server like the workload and performance. Furthermore, we propose availability of a virtual machine resource algorithm (AVM) based on the availability of VM in edge cloud servers for efficient resource allocation and task scheduling. The results of experiment simulation show that the proposed model can meet the response time requirements of different real-time services, improve the performance, and minimize the consumption of MD energy and the resource utilization.

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