Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey

Duan, Sijing; Wang, Dan; Ren, Ju; Lyu, Feng; Zhang, Ye; Wu, Huaqing

doi:10.1109/comst.2022.3218527

Cited by 116 publications

(19 citation statements)

References 227 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computation can be performed near data sources on edge devices. Edge computing has become more important nowadays, as pointed out in a few studies, e.g., [28,80,109,11,29]. The MEC framework primarily relies on edge devices, which have limited resources.…”

Section: B Edge-cloud Computingmentioning

confidence: 99%

An Overview on Generative AI at Scale With Edge–Cloud Computing

Wang,

Xue,

Wei

et al. 2023

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what humans create. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of user requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.

show abstract

Section: B Edge-cloud Computingmentioning

confidence: 99%

An Overview on Generative AI at Scale With Edge–Cloud Computing

Wang,

Xue,

Wei

et al. 2023

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…Given the output uniqueness of the linear classifier in (5), there is a one-to-one mapping between µ ℓ and Pµ ℓ , leading to Pr µ ℓ | f = Pr Pµ ℓ | f . Then, using the Bayes' theorem, the probability Pr Pµ ℓ | f can be expressed as Pr Pµ…”

Section: B Proof Of Propositionmentioning

confidence: 99%

“…The natural fusion of the two distinctive 6G functions, termed Integrated Sensing and Edge AI (ISEA), shall provide a powerful platform for automating a broad range of IoT applications including auto-pilot, robotic control, digital twins, augmented reality, and localization and tracking [1], [4]. Unleashing the full potential of ISEA calls for a new goaloriented design approach that integrates sensing, AI, and communication to optimize the end-to-end (E2E) performance [5], [6]. In this work, we contribute to the theoretic characterization of the E2E performance of ISEA, thereby laying a foundation for goal-oriented designs.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks

Chen

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This article proposes a composite method applied in the Wuyuan City, Jiangxi Province, in China. This method can detect water gauge areas and number areas from complex and changeable scenes, accurately detect the water level line from various water gauges, and finally, obtain the accurate water level value. Firstly, FCOS is improved by fusing a contextual adjustment module to meet the requirements of edge computing and ensure considerable detection accuracy. Secondly, to deal with scenes with indistinct water level features, we also apply the contextual adjustment module for Deeplabv3+ to segment the water gauge area above the water surface. Then, the area can be used to obtain the position of the water level line. Finally, the results of the previous two steps are combined to calculate the water level value. Detailed experiments prove that this method solves the problem of water level recognition in complex hydrological scenes. Furthermore, the recognition error of the water level by this method is less than 1 cm, proving it is capable of being applied in real river scenes.

show abstract

“…To handle the cloud deficiencies, edge computing has emerged as a promising candidate with the precise motivation to bring the cloud facilities (such as computing, storage, and networking resources) closer to the consumer [ 6 , 7 ]. Edge computing provides large computations at the network edge in the closed vicinity of the user with reduced bandwidth consumption and fewer delays, minimizing the chances of congestion in the network [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

MIA-NDN: Microservice-Centric Interest Aggregation in Named Data Networking

Imran

din

Rehman

et al. 2023

Sensors

View full text Add to dashboard Cite

The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may result in false aggregation. Moreover, the microservice-centric requested computations may fail to accomplish in the default 4s PIT timer due to the input size. To address these challenges, this paper presents MIA-NDN: microservice-centric interest aggregation in named data networking. We designed microservice-centric interest-naming to enable name-based communication. MIA-NDN develops a robust interest aggregation mechanism that not only performs the interest aggregation based on the interest name but also considers the input parameter counts and their corresponding values in the interest aggregation process to avoid false packet aggregations. A dynamic PIT timer mechanism based on input size was devised that avoids the PIT entry losses if the execution time exceeds the default PIT timer value to avoid computation losses and uphold the application quality of service (QoS). Extensive software-based simulations confirm that the MIA-NDN outperforms the benchmark scheme in terms of microservice-centric interest aggregation, microservice satisfaction rate, and communication overhead.

show abstract

Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey

Cited by 116 publications

References 227 publications

An Overview on Generative AI at Scale With Edge–Cloud Computing

An Overview on Generative AI at Scale With Edge–Cloud Computing

An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks

MIA-NDN: Microservice-Centric Interest Aggregation in Named Data Networking

Contact Info

Product

Resources

About