Cloud Virtual Network Embedding: Profit, Power and Acceptance

In this paper, we investigate maximizing the profit achieved by infrastructure providers (InPs) from embedding virtual network requests (VNRs) in IP/WDM core networks with clouds. We develop a mixed integer linear programming (MILP) model to study the impact of maximizing the profit on the power consumption and acceptance of VNRs. The results show that higher acceptance rates do not necessarily lead to higher profit due to the high cost associated with accepting some of the requests. The results also show that minimum power consumption can be achieved while maintaining the maximum profit

A deep reinforcement learning-based algorithm for reliability-aware multi-domain service deployment in smart ecosystems

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-020-05372-xThe transition towards full network virtualization will see services for smart ecosystems including smart metering, healthcare and transportation among others, being deployed as Service Function Chains (SFCs) comprised of an ordered set of virtual network functions. However, since such services are usually deployed in remote cloud networks, the SFCs may transcend multiple domains belonging to different Infrastructure Providers (InPs), possibly with differing policies regarding billing and Quality-of-service (QoS) guarantees. Therefore, efficiently allocating the exhaustible network resources to the different SFCs while meeting the stringent requirements of the services such as delay and QoS among others, remains a complex challenge, especially under limited information disclosure by the InPs. In this work, we formulate the SFC deployment problem across multiple domains focusing on delay constraints, and propose a framework for SFC orchestration which adheres to the privacy requirements of the InPs. Then, we propose a reinforcement learning (RL)-based algorithm for partitioning the SFC request across the different InPs while considering service reliability across the participating InPs. Such RL-based algorithms have the intelligence to infer undisclosed InP information from historical data obtained from past experiences. Simulation results, considering both online and offline scenarios, reveal that the proposed algorithm results in up to 10% improvement in terms of acceptance ratio and provisioning cost compared to the benchmark algorithms, with up to more than 90% saving in execution time for large networks. In addition, the paper proposes an enhancement to a state-of-the-art algorithm which results in up to 5% improvement in terms of provisioning cost.This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777067 (NECOS project) and the national project TEC2015-71329-C2-2-R (MINECO/FEDER). This work is also supported by the " Fundamental Research Funds for the Central Universities " of China University of Petroleum (East China) under Grant 18CX02139APeer ReviewedPostprint (author's final draft

A Study on Efficient Service Function Chain Placement in Network Function Virtualization Environment

電気通信大学202

Energy Efficient Core Networks with Clouds

The popularity of cloud based applications stemming from the high volume of connected mobile devices has led to a huge increase in Internet traffic. In order to enable easy access to cloud applications, infrastructure providers have invested in geographically distributed databases and servers. However, intelligent and energy efficient high capacity transport networks with near ubiquitous connectivity are needed to adequately and sustainably serve these requirements. In this thesis, network virtualisation has been identified as a potential networking paradigm that can contribute to network agility and energy efficiency improvements in core networks with clouds. The work first introduces a new virtual network embedding core network architecture with clouds and a compute and bandwidth resource provisioning mechanism aimed at reducing power consumption in core networks and data centres. Further, quality of service measures in compute and bandwidth resource provisioning such as delay and customer location have been investigated and their impact on energy efficiency established. Data centre location optimisation for energy efficiency in virtual network embedding infrastructure has been investigated by developing a MILP model that selects optimal data centre locations in the core network. The work also introduces an optical OFDM based physical layer in virtual network embedding to optimise power consumption and optical spectrum utilization. In addition, virtual network embedding schemes aimed at profit maximization for cloud infrastructure providers as well greenhouse gas emission reduction in cloud infrastructure networks have been investigated. GreenTouch, a consortium of industrial and academic experts on energy efficiency in ICTs, has adopted the work in this thesis as one of the measures of improving energy efficiency in core networks