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 …
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 …
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 …
Every node for itself: Fully distributed service coordination
S Schneider, LD Klenner, H Karl - 2020 16th International …, 2020 - ieeexplore.ieee.org
Modern services consist of modular, interconnected components, eg, microservices forming
a service mesh. To dynamically adjust to ever-changing service demands, service …
a service mesh. To dynamically adjust to ever-changing service demands, service …
Online microservice orchestration for IoT via multiobjective deep reinforcement learning
By providing loosely coupled, lightweight, and independent services, the microservice
architecture is promising for large-scale and complex service provision requirements in the …
architecture is promising for large-scale and complex service provision requirements in the …
Deep reinforcement learning for online resource allocation in IoT networks: Technology, development, and future challenges
The growing number of complex and heterogeneous Internet of Things (IoT) applications
has imposed a high demand for scarce communications and computing resources. To meet …
has imposed a high demand for scarce communications and computing resources. To meet …
Model-based reinforcement learning framework of online network resource allocation
B Bakhshi, J Mangues-Bafalluy - ICC 2022-IEEE International …, 2022 - ieeexplore.ieee.org
Online Network Resource Allocation (ONRA) for service provisioning is a fundamental
problem in communication networks. As a sequential decision-making under uncertainty …
problem in communication networks. As a sequential decision-making under uncertainty …
Divide and conquer: Hierarchical network and service coordination
S Schneider, M Jürgens, H Karl - 2021 IFIP/IEEE International …, 2021 - ieeexplore.ieee.org
In practical, large-scale networks, services are requested by users across the globe, eg, for
video streaming. Services consist of multiple interconnected components such as …
video streaming. Services consist of multiple interconnected components such as …
DRPC: Distributed Reinforcement Learning Approach for Scalable Resource Provisioning in Container-based Clusters
Microservices have transformed monolithic applications into lightweight, self-contained, and
isolated application components, establishing themselves as a dominant paradigm for …
isolated application components, establishing themselves as a dominant paradigm for …
A hybrid learning framework for service function chaining across geo-distributed data centers
T Tang, B Wu, G Hu - IEEE Access, 2020 - ieeexplore.ieee.org
Service function chaining (SFC) focuses mainly on deploying various network functions in
geographically distributed data centers and providing interconnect routing among them …
geographically distributed data centers and providing interconnect routing among them …