Optical-bypass Access-aggregation Network for High-delay-sensitivity Traffics based on SDN Orchestration

AbstractRecently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

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Optimizing Routes using Genetic Algorithms based on Throughput or Delay Sensitivity of Applications using SNMP for Automatic Discovery of Network Topology

International Journal of Computer Applications ◽

10.5120/7237-7746 ◽

2012 ◽

Vol 47 (12) ◽

pp. 1-5

Author(s):

M. MohamedSurputheen ◽

G. Ravi ◽

R. Srinivasan

Keyword(s):

Genetic Algorithms ◽

Network Topology ◽

Automatic Discovery ◽

Delay Sensitivity

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A Virtual Network Mapping Method Based on Power Service Delay Sensitivity and Service Reliability

10.1109/wcsp52459.2021.9613433 ◽

2021 ◽

Author(s):

Weijun Zheng ◽

Hong Xu ◽

Zheng Wang ◽

Jinjiang Tang ◽

Jingbo Hu ◽

...

Keyword(s):

Mapping Method ◽

Virtual Network ◽

Service Reliability ◽

Service Delay ◽

Delay Sensitivity ◽

Virtual Network Mapping ◽

Network Mapping

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Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge

Applied Sciences ◽

10.3390/app10186391 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6391

Author(s):

Dien Van Nguyen ◽

Jaehyuk Choi

Keyword(s):

Data Extraction ◽

Video Data ◽

Transportation Safety ◽

High Definition ◽

Video Analytics ◽

Surveillance Camera ◽

Novel Approach ◽

Cloud Server ◽

Delay Sensitivity ◽

Automated Tools

Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, video analytics servers may not fulfill the requirements when serving a large number of cameras simultaneously. To handle these challenges, we present an edge computing-based system that minimizes the transfer of video data from the surveillance camera feeds on a cloud video analytics server. Based on a novel approach of utilizing the information from the encoded bitstream, the edge can achieve low processing complexity of object tracking in surveillance videos and filter non-motion frames from the list of data that will be forwarded to the cloud server. To demonstrate the effectiveness of our approach, we implemented a video surveillance prototype consisting of edge devices with low computational capacity and a GPU-enabled server. The evaluation results show that our method can efficiently catch the characteristics of the frame and is compatible with the edge-to-cloud platform in terms of accuracy and delay sensitivity. The average processing time of this method is approximately 39 ms/frame with high definition resolution video, which outperforms most of the state-of-the-art methods. In addition to the scenario implementation of the proposed system, the method helps the cloud server reduce 49% of the load of the GPU, 49% that of the CPU, and 55% of the network traffic while maintaining the accuracy of video analytics event detection.

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