Self-learning multi-objective service coordination using deep reinforcement learning
S Schneider, R Khalili, A Manzoor… - … on Network and …, 2021 - ieeexplore.ieee.org
Modern services consist of interconnected components, eg, microservices in a service mesh
or machine learning functions in a pipeline. These services can scale and run across …
or machine learning functions in a pipeline. These services can scale and run across …
Self-driving network and service coordination using deep reinforcement learning
S Schneider, A Manzoor, H Qarawlus… - … on Network and …, 2020 - ieeexplore.ieee.org
Modern services comprise interconnected components, eg, microservices in a service mesh,
that can scale and run on multiple nodes across the network on demand. To process …
that can scale and run on multiple nodes across the network on demand. To process …
Distributed online service coordination using deep reinforcement learning
Services often consist of multiple chained components such as microservices in a service
mesh, or machine learning functions in a pipeline. Providing these services requires online …
mesh, or machine learning functions in a pipeline. Providing these services requires online …
Dynamically meeting performance objectives for multiple services on a service mesh
We present a framework that lets a service provider achieve end-to-end management
objectives under varying load. Dynamic control actions are performed by a reinforcement …
objectives under varying load. Dynamic control actions are performed by a reinforcement …
A deep reinforcement learning approach for large-scale service composition
A Moustafa, T Ito - PRIMA 2018: Principles and Practice of Multi-Agent …, 2018 - Springer
As service-oriented environments become widespread, there exists a pressing need for
service compositions to cope with the high scalability, complexity, heterogeneity and …
service compositions to cope with the high scalability, complexity, heterogeneity and …
Autonomous flow routing for near real-time quality of service assurance
S Barzegar, M Ruiz, L Velasco - IEEE Transactions on Network …, 2023 - ieeexplore.ieee.org
The deployment of beyond 5G and 6G network infrastructures will enable highly dynamic
services requiring stringent Quality of Service (QoS). Supporting such combinations in …
services requiring stringent Quality of Service (QoS). Supporting such combinations in …
Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum
Modern computing environments, thanks to the advent of enabling technologies such as
Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary …
Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary …
Queue-learning: A reinforcement learning approach for providing quality of service
End-to-end delay is a critical attribute of quality of service (QoS) in application domains such
as cloud computing and computer networks. This metric is particularly important in tandem …
as cloud computing and computer networks. This metric is particularly important in tandem …
Adaptive and large-scale service composition based on deep reinforcement learning
H Wang, M Gu, Q Yu, Y Tao, J Li, H Fei, J Yan… - Knowledge-Based …, 2019 - Elsevier
In a service-oriented system, simple services are combined to form value-added services to
meet users' complex requirements. As a result, service composition has become a common …
meet users' complex requirements. As a result, service composition has become a common …
Integrating deep reinforcement learning with pointer networks for service request scheduling in edge computing
With the increasing popularity of edge computing, service providers are more likely to deploy
services at the edge of the network to reduce the latency of service requests. However, the …
services at the edge of the network to reduce the latency of service requests. However, the …