… the development and evolution of many ML … deeplearning models in the Azure cloud and on the edge in real-time [103]. To meet the computational demands required of deeplearning, …
… , the current cloudcomputing service architecture hinders the … that the cloud would have difficulty meeting since it may be … accurately model rapidly changingedge network environments …
… Asaduzzaman, “Quantum machinelearning for 6G communication networks: State-of-the-… , “Machinelearningmeetscomputation and communicationcontrol in evolvingedge and cloud: …
… cloudcomputing. The trend of deploying machinelearning (ML) at the network edge to enhance … low latency within a few milliseconds that can hardly be met by the existing cloud model. …
… ), machinelearning (ML) is envisioned to be an important technology to facilitate the evolution of … in [138] to jointly utilize communication resource in the cloud center, access points, and …
Z Lv, D Chen, R Lou, Q Wang - Future Generation Computer Systems, 2021 - Elsevier
… This article takes advantage of the flexibility of cloud-based … to each computingedge device until the stop criteria are met. … dual residuals are always changing and do not stay the same. …
S Kanungo - International Peer-Reviewed Journal, 2019 - irejournals.com
… of edge-to-cloudintelligence, which combines edge and cloudcomputing paradigms to … With today's data growth, cloudcomputing power is no longer able to meet these demands. …
… data, cloud AI is facing significant challenges to meet the delay … also drive the computing paradigm evolving in the same way … and utilize deeplearning models at each edge location to …
… Fog and edgecomputing can meet this demand by leveraging distributed resources up to … Microservices can be considered as an evolution of service-oriented architecture (SOA) to …