… In the case of reinforcement learning, we will add a new variable to represent our objective
function, eg, minimize service delay. Then we will use our network to find out the probability …

… the development and evolution of many ML … deep learning models in the Azure cloud and
on the edge in real-time [103]. To meet the computational demands required of deep learning, …

… , the current cloud computing service architecture hinders the … that the cloud would have
difficulty meeting since it may be … accurately model rapidly changing edge network environments …

… Asaduzzaman, “Quantum machine learning for 6G communication networks: State-of-the-…
, “Machine learning meets computation and communication control in evolving edge and cloud: …

… cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance
… low latency within a few milliseconds that can hardly be met by the existing cloud model. …

… ), machine learning (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 …

… This article takes advantage of the flexibility of cloud-based … to each computing edge device
until the stop criteria are met. … dual residuals are always changing and do not stay the same. …

… of edge-to-cloud intelligence, which combines edge and cloud computing paradigms to …
With today's data growth, cloud computing 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 deep learning models at each edge location to …

… Fog and edge computing can meet this demand by leveraging distributed resources up to
… Microservices can be considered as an evolution of service-oriented architecture (SOA) to …