Edge computing

Barbalace, Antonio; Karaoui, Mohamed Lamine; Wang, Wei; Xing, Tong; Olivier, Pierre; Ravindran, Binoy

doi:10.1145/3381052.3381321

Cited by 37 publications

(2 citation statements)

References 53 publications

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“…It is now possible to migrate containers between hosts with various ISAs thanks to the integration of H-Container into Docker, which migrates natively generated containerized apps between compute nodes with CPUs of different ISAs. Integrate H-Container into Docker to enable container migration between hosts with various Instruction Set Architectures (ISAs), since Server programs originally developed for one ISA cannot do so when a client changes its physical location [28].…”

Section: Ability To Support Mobilitymentioning

confidence: 99%

A Hyperspectral Image Classifiers Within Wireless Sensor Network in Extreme Environments

Agha¹,

Abdul-Jabbar²

2023

RR. I.T.

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Progress in the field of computer networks has produced many areas of researches which paved the way for studying many new applications. One of the most popular applications that has shifted from the research level to the application level is wireless sensor networks (WSNs), based on which other advanced fields such as the Internet of Things are built. These wireless sensor networks have been applied in many applications, and has proven to be an effective tool in collecting information in different environments, but with the expansion of the range of places that humans are interested in and trying to explore, the need has become urgent to apply wireless sensor networks to environments that are difficult to deal with by humans. This imposes new obstacles on the implementation and the performance of wireless sensor networks such as space, or applications that impose immersion of sensors in water, or industrial environments with high noise or medical environments, and perhaps toxic environments or those characterized by very high or very low temperatures. All these mentioned before prompted us to open new horizons of researches for wireless sensor networks to collect data from those environments. Many studies have appeared to deal with these types of environments, and we will add to them the use of edge computing to deal with data at the place of gathering, especially in the space environment and aerial photography, where the data will be processed at the end of data collection and transmission since the transmission consumes the major part of energy, we will decrease by processing the date at the edge of the network. Classified information is sent only to the ground station to reduce bandwidth usage and perform real-time calculations.

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Section: Ability To Support Mobilitymentioning

confidence: 99%

A Hyperspectral Image Classifiers Within Wireless Sensor Network in Extreme Environments

Agha¹,

Abdul-Jabbar²

2023

RR. I.T.

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“…We intentionally control the network latency of the edge-cloud system, with Linux TC, to simulate practical scenarios. Refer to [35] and [36], the parameters are set as follows: (𝑖) For the end devices and the edge cluster, the latency is about 20 ms and the bandwidth is about 50 Mbps. (𝑖𝑖) Since the network connection within the edge cluster is through the LAN, the network conditions are better, so the latency is less than 10 ms and the bandwidth is about 1 Gbps.…”

Section: Implementation Designmentioning

confidence: 99%

Collaborative Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Network

Shen

Han

Wang

et al. 2023

IEEE/ACM Trans. Networking

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Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edgecloud networks. Besides, the hierarchical distribution of heterogeneous resources makes the modeling and scheduling of k8soriented edge-cloud network particularly challenging. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud network to improve the long-term throughput rate of request processing. First, we design a coordinated multiagent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 15.9% while reducing scheduling cost by 38.4% compared to baselines.

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SeeMe: An intelligent edge server selection method for location‐aware business task computing over IIoT

et al. 2022

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In the past few years, latency‐sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self‐driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across‐domain business task computing on distributed edge servers. With this observation, a trade‐off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low‐latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low‐latency edge server. The experiments and comparison analysis verify the availability of our method.

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Edge computing

Cited by 37 publications

References 53 publications

A Hyperspectral Image Classifiers Within Wireless Sensor Network in Extreme Environments

A Hyperspectral Image Classifiers Within Wireless Sensor Network in Extreme Environments

Collaborative Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Network

SeeMe: An intelligent edge server selection method for location‐aware business task computing over IIoT

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