We propose a deep reinforcement learning (DRL) framework for dynamic scheduling of
delay-tolerant requests in the elastic optical networks, where the DRL agent automatically …

This paper proposes DeepRMSA, a deep reinforcement learning framework for routing,
modulation and spectrum assignment (RMSA) in elastic optical networks (EONs) …

Driven by the development of cloud computing and mobile Internet, network-based
applications have become diverse. Besides the usual immediate reservation (IR) …

For serving traffic in the inter data centers which provide services such as, duplication of
data and migration of the virtual machines, it is requisited to transfer voluminous data for …

To relieve network resource pressure and avoid network congestion, the adoption of a
sliding scheduling traffic model is crucial to achieve flexible radio access network (RAN) …

Deep reinforcement learning (DRL) has been introduced in routing, modulation and
spectrum assignment (RMSA) of the elastic optical networks. Since the DRL agent's learning …

This paper proposes joint frequency and time domain optimization of static scheduling for
advance reservation (AR) requests in elastic optical networks (EONs). We formulate the …

In this letter, we study the deep reinforcement learning (DRL)-based routing, modulation and
spectrum assignment problem in the elastic optical networks. We emphasize the importance …

The study of online routing and spectrum allocation (RSA) problem has assumed increasing
importance due to the exponential growth of dynamic traffic with uncertainty in elastic optical …

We present a new reward design for the deep reinforcement learning (DRL)-based routing,
modulation and spectrum assignment in the elastic optical networks (EONs). The …