Machine learning requirements for energy-efficient virtual network embedding

Network virtualization is a technology proven to be a key enabling a family of strategies in different targets, such as energy efficiency, economic revenue, network usage, adaptability or failure protection. Network virtualization allows us to adapt the needs of a network to new circumstances, resulting in greater flexibility. The allocation decisions of the demands onto the physical network resources impact the costs and the benefits. Therefore it is one of the major current problems, called virtual network embedding (VNE). Many algorithms have been proposed recently in the literature to solve the VNE problem for different targets. Due to the current successful rise of artificial intelligence, it has been widely used recently to solve technological problems. In this context, this paper investigates the requirements and analyses the use of the Q-learning algorithm for energy-efficient VNE. The results achieved validate the strategy and show clear improvements in terms of cost/revenue and energy savings, compared to traditional algorithms.This work has been supported by the Agencia Estatal de Investigación of Ministerio de Ciencia e Innovación of Spain under project PID2019-108713RB-C51 MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Service embedding in IoT networks

The Internet of Things (IoT) is the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. Application requests in service-oriented IoT architectures are translated into a business process (BP) workflow. In this paper, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and locations where sensing and/or actuation are needed. The virtual links capture the requested communication requirements between nodes. We introduce a framework, optimized using mixed integer linear programming (MILP), that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. We formulate the problem of finding the optimal set of IoT nodes and links to embed BPs into the IoT layer considering three objective functions: i) minimizing network and processing power consumption only, ii) minimizing mean traffic latency only, iii) minimizing a weighted combination of power consumption and traffic latency to study the trade-off between minimizing the power consumption and minimizing the traffic latency. We have established, as reference, a scenario where service embedding is performed to meet all the demands with no consideration to power consumption or latency. Compared to this reference scenario, our results indicate that the power savings achieved by our energy efficient embedding scenario is 42% compared with the energy-latency unaware service embedding (ELUSE) reference scenario, while our low latency embedding reduced the traffic latency by an average of 47% compared to the ELUSE scenario. Our combined energy efficient low latency service embedding approach achieved high optimality by jointly realizing 91% of the power and latency reductions obtained under the single objective of minimizing power consumption or latency

A green energy-aware hybrid virtual network embedding approach

© 2015 Published by Elsevier B.V. In the past few years, the concept of network virtualization has received significant attention from industry and research fora, as it represents a promising way to diversify existing networks and ensure the co-existence of heterogeneous network architectures on top of shared substrates. Virtual network embedding (VNE) is the process of dynamically mapping virtual resources (i.e. virtual nodes and links) onto physical substrate resources. VNE is the main resource allocation challenge in network virtualization and is considered as an NP-hard problem. Several centralized and distributed VNE approaches have been proposed, with the aim of satisfying different objectives ranging from QoS, to economical profit, and network survivability. More recently, emerging VNE approaches started investigating the optimization of new objectives such as energy-efficiency and networks\u27 security. In this work, we propose a green energy-aware hybrid VNE hybrid VN embedding approach that aims at achieving energy efficiency and resource consolidation, while minimizing CO2 emissions resulting from VNs operation. This approach consists of a hierarchical virtual networking management architecture in which control and management nodes collaborate for the splitting and embedding of sub-VNs requests to the cleanest substrate resources (i.e. the resources deployed in a sector with the smallest CO2 emission factor) available. Three different variants of our VNE algorithms, taking into consideration different resources\u27 selection criteria (i.e. energy source, request priority, and request location) are presented, and their performance is compared with two existing VNE algorithms based on centralized and distributed embedding approaches. The comparative performance analysis shows that our proposed approach enables a more efficient VN embedding in terms of: a reduced number of substrate resources needed, a faster request mapping time, as well as resource consolidation and reduced resource cost. Furthermore, it enables a reduction of the carbon footprint of the VNE operation, thus resulting in a more green and environmentally conscious approach to network virtualization

E2DNE: Energy Efficient Dynamic Network Embedding in Virtualized Wireless Sensor Networks

Efficient utilization of resources is an important challenge in traditional non-virtualized Wireless Sensor Networks (WSNs), as the applications are embedded in the sensors, precluding the sensor nodes from being re-used by other applications. Inefficient utilization of resources results in high deployment and maintenance costs. Virtualization is a promising technology that allows multiple sensing tasks from diverse applications to be executed on the same deployed WSNs concurrently. However, given that the WSN sensors are constrained with limited energy, the allocations of physical and virtual resources to the applications in an efficient manner becomes a challenge, especially for mission-critical, delay-sensitive applications. In this paper, we address the problem of virtual network embedding in virtualized WSNs, aiming at minimizing the overall energy consumption while considering the end-to-end latency and bandwidth consumption as the service level agreement (SLA) constraints. We propose our Dynamic Network Embedding (DNE) heuristic for large-scale problem instants. The results reveal that our proposed heuristic leads to close-to-optimal solutions with satisfactory execution time. Our results indicate that the proposed heuristic achieves up to an 18% optimality gap in energy consumption in the small-scale scenario, and outperforms the existing benchmark by up to 58% in the large-scale scenario

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

Online Virtual Network Provisioning in Distributed Cloud Computing Data Centers

Efficient virtualization methodologies constitute the core of cloud computing data center implementation. Clients are attracted to the cloud model by the ability to scale available resources dynamically and the flexibility in payment options. However, performance hiccups can push them to return to the buy-and-maintain model. Virtualization plays a key role in the synchronous management of the thousands of servers along with clients\u27 data residing on them. To achieve seamless virtualization, cloud providers require a system that performs the function of virtual network mapping. This includes receiving the cloud client requests and allocating computational and network resources in a way that guarantees the quality of service conditions for clients while maximizing the data center resource utilization and providers\u27 revenue. In this thesis, we introduce a comprehensive system to solve the problem of virtual network mapping for a set of connection requests sent by cloud clients. Connections are collected in time intervals called windows. Subsequently, node mapping and link mapping are performed. Different window size selection schemes are introduced and evaluated. Three schemes to prioritize connections are used and their effect is assessed. Moreover, a technique dealing with connections spanning over more than a window is introduced. Simulation results show that the dynamic window size algorithm achieves cloud service providers objectives in terms of generated revenue, served connections ratio, resource utilization and computational overhead. In addition, experimental results show that handling spanning connections independently improves the results for the performance metrics measured. Moreover, in a cloud infrastructure, handling all resources efficiently in their usage, management and energy consumption is challenging. We propose an energy efficient technique for embedding online virtual network requests in cloud data centers. The core focus of this study is to manage energy efficiently in cloud environment. A fixed windowing technique with spanning connections is used. Our algorithm, and a technique for randomly embedding nodes and links are also explained. The results clearly show that the algorithm used in this study generated better results in terms of energy consumption, served connections and revenue generation

Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding

In cloud infrastructure, accommodating multiple virtual networks on a single physical network reduces power consumed by physical resources and minimizes cost of operating cloud data centers. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be NP-hard. With considering energy efficiency, the problem becomes more complicated. In this paper, we model energy-aware virtual network embedding, devise metrics for evaluating performance of energy aware virtual network-embedding algorithms, and propose an energy aware virtual network-embedding algorithm based on multi-objective particle swarm optimization augmented with local search to speed up convergence of the proposed algorithm and improve solutions quality. Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing revenue and decreasing energy consumption.Comment: arXiv admin note: text overlap with arXiv:1504.0684

Virtual Machines Embedding for Cloud PON AWGR and Server Based Data Centres

In this study, we investigate the embedding of various cloud applications in PON AWGR and Server Based Data Centres