Matheuristic With Machine-Learning-Based Prediction for Software-Defined Mobile Metro-Core Networks

Alvizu, Rodolfo; Troia, Sebastian; Maier, Guido; Pattavina, Achille

doi:10.1364/jocn.9.000d19

Cited by 80 publications

(59 citation statements)

References 17 publications

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“…Using the Autoregressive Integrated Moving Average (ARIMA) model, the authors are able to increase the bandwidth utilization rate in a cloud data center. In [20], Alvizu et al propose a routing algorithm for softwaredefined mobile carrier networks. They use ANN to effectively predict traffic behavior, improve routing decisions and lower power consumption by up to 31% compared to existing standards.…”

Section: Related Workmentioning

confidence: 99%

Kalman Filtering-based Traffic Prediction for Software Defined Intra-data Center Networks

2019

KSII TIIS

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Global data center IP traffic is expected to reach 20.6 zettabytes (ZB) by the end of 2021. Intra-data center networks (Intra-DCN) will account for 71.5% of the data center traffic flow and will be the largest portion of the traffic. The understanding of traffic distribution in Intra-DCN is still sketchy. It causes significant amount of bandwidth to go unutilized, and creates avoidable choke points. Conventional transport protocols such as Optical Packet Switching (OPS) and Optical Burst Switching (OBS) allow a one-sided view of the traffic flow in the network. This therefore causes disjointed and uncoordinated decision-making at each node. For effective resource planning, there is the need to consider joining the distributed with centralized management which anticipates the system's needs and regulates the entire network. Methods derived from Kalman filters have proved effective in planning road networks. Considering the network available bandwidth as data transport highways, we propose an intelligent enhanced SDN concept applied to OBS architecture. A management plane (MP) is added to conventional control (CP) and data planes (DP). The MP assembles the traffic spatio-temporal parameters from ingress nodes, uses Kalman filtering predictionbased algorithm to estimate traffic demand. Prior to packets arrival at edges nodes, it regularly forwards updates of resources allocation to CPs. Simulations were done on a hybrid scheme (1+1) and on the centralized OBS. The results demonstrated that the proposition decreases the packet loss ratio. It also improves network latency and throughput-up to 84 and 51%, respectively, versus the traditional scheme. . His research interests include fiberoptic transmission and microwave photonics.Deming Liu received his Ph.D. degree in the department

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Section: Related Workmentioning

confidence: 99%

Kalman Filtering-based Traffic Prediction for Software Defined Intra-data Center Networks

2019

KSII TIIS

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“…and network applications, the backbone traffic is exponentially growing [1]. In parallel, backbone traffic is becoming more dynamic [1], [2], with repetitive patterns [3]. As a consequence, emerging applications, require not only high-capacity optical links, but a dynamic backbone (optical) network where, unlike the traditional quasi-static/static networks, the network connections are now provisioned over a short period of time [4].…”

Section: Introductionmentioning

confidence: 99%

“…In static optical networks, connections tend to stay in the network for a long time period; for coping with the traffic demand variations and the current operational processes that are too slow to dynamically follow those variations [3], T. Panayiotou, K. Manousakis, and G. Ellinas are with the KIOS Research and Innovation Center of Excellence, and with the Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus, email: {panayiotou.tania, manousakis.konstantinos, gellinas}@ucy.ac.cy S. P. Chatzis is with the Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, e-mail: sotirios.chatzis@cut.ac.cy network connections in these networks are commonly overprovisioned. Specifically, connections are allocated a bandwidth that is usually based on the peak-hour demand, consequently causing sub-utilization of the allocated resources outside of the peak hour.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Bandwidth Allocation Framework With QoS Considerations for EONs

Panayiotou

Manousakis

Chatzis

et al. 2019

J. Lightwave Technol.

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This work proposes a data-driven bandwidth allocation (BA) framework for periodically and dynamically reconfiguring an elastic optical network according to predictive bandwidth allocation (PBA) models. The proposed framework is scalable to the number of network connections and also adaptive to the increasing traffic of each network connection separately and to the overall network load as well. This is achieved by formulating the BA problem as a Partially Observable Markov Decision Process (POMDP), which constitutes are reinforcement learning (RL) model. Specifically, RL is performed continuously and independently (locally) for each network connection according to the most recent data that describe the traffic demand behavior of each network connection. A central controller monitors the network performance that is jointly achieved from all the PBA models and is capable of dynamically modifying the reward function of the POMDP, ensuring that the quality-of-service requirements are met. A reward function R(C) is examined with a clear impact on the network performance when C is modified. For evaluating the network performance, for each R(C), the routing and spectrum allocation (RSA) problem is solved according to an Integer Linear Programming (ILP) algorithm and an RSA heuristic alternative, with both the ILP and the heuristic RSA taking as inputs the outputs of the inferred PBA models. Results indicate that with the appropriate settings of C, bandwidth is efficiently allocated, while ensuring that the QoS requirements are met.

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“…This is especially true in current networks, where network planning functions are becoming increasingly complex in an uncertain network environment that is continuously changing, supporting heterogeneous applications and services. Existing ML applications [9,10] focus on traffic demand predictions and resource allocation optimization [11][12][13][14][15], fault detection/localization [16][17][18][19], attack detection/identification [20,21], and quality-of-transmission (QoT) estimation [22][23][24][25][26]. In most of these works, however, the diverse optical service level agreements (OSLAs) of the next generation optical networks [27] are not specifically considered.…”

mentioning

confidence: 99%

Decentralizing machine-learning-based QoT estimation for sliceable optical networks

Panayiotou

Savva

Tomkos

et al. 2020

J. Opt. Commun. Netw.

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Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G's diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work, we examine the ML-based quality-of-transmission (QoT) estimation problem under the dynamic network slicing context, where each slice has to meet a different QoT requirement. Specifically, we examine ML-based QoT frameworks with the aim of finding QoT model/s that are fine-tuned according to the diverse QoT requirements. Centralized and distributed frameworks are examined and compared according to their model accuracy, routing and spectrum allocation (RSA) accuracy, and CPU (training time) and RAM (memory) requirements. We show that the distributed QoT models outperform the centralized QoT model in accuracy and CPU usage. The RSA accuracy, i.e., measuring the accuracy of the models with regard to the QoT-aware RSA decisions, is sufficiently high for both frameworks. Regarding the RAM usage, as the distributed framework has to train in parallel several QoT models, it may require higher memory, especially as the number of diverse QoT requirements increases. This memory, however, tends to be reserved for a shorter period of time. Moreover, this work develops a dynamic multi-slice QoT-aware (RSA) framework that integrates the ML-based QoT models. The aim is to examine the network performance when the diverse QoT models are considered, as opposed to the state-of-the-art single-slice QoT-aware RSA approach where all connections/slices are provisioned according to a single QoT requirement. We show that the multi-slice QoT-aware RSA approach significantly improves network performance, a clear indicator that the commonly considered single-slice QoT-aware RSA approach may lead to connection overprovisioning.few years adopted network slicing as an emerging technology and business model [2,3].In general, the implementation of network slicing is conceived as an end-to-end feature spanning over different technology domains. While relevant work focuses mostly on the implementation of network slicing in the radio access network (RAN) segment [1,2,4,5], recent work also focuses on extending the scope of network slicing beyond the RAN to include both mobile and transport (optical) network segments [6][7][8]. In particular, recent work deals with SDN/NFV demonstrations, network orchestration implementations, and traffic demand analysis, towards the vision of end-to-end network slicing. In general, SDN/NFV, network orchestration, and analytics are considered as the key ingredients for successful implementation of end-to-end network slicing [1]. However, regarding the efficient control and management of these slices (i.e., enabling not only their creation but also their 1943-0620/20/070146-17 Journal smart deployment at the right place, at the right time, with optimized resources), little has been done so far.Without doubt, machine learning (ML) will be instrumental in the efficient control an...

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Matheuristic With Machine-Learning-Based Prediction for Software-Defined Mobile Metro-Core Networks

Cited by 80 publications

References 17 publications

Kalman Filtering-based Traffic Prediction for Software Defined Intra-data Center Networks

Kalman Filtering-based Traffic Prediction for Software Defined Intra-data Center Networks

A Data-Driven Bandwidth Allocation Framework With QoS Considerations for EONs

Decentralizing machine-learning-based QoT estimation for sliceable optical networks

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