Offline and online power aware resource allocation algorithms with migration and delay constraints

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In order to handle advanced mobile broadband services and Internet of Things (IoT), future Internet and 5G networks are expected to leverage the use of network virtualization, be much faster, have greater capacities, provide lower latencies, and significantly be power efficient than current mobile technologies. Therefore, this paper proposes three power aware algorithms for offline, online, and migration applications, solving the resource allocation problem within the frameworks of network function virtualization (NFV) environments in fractions of a second. The proposed algorithms target minimizing the total costs and power consumptions in the physical network through sufficiently allocating the least physical resources to host the demands of the virtual network services, and put into saving mode all other not utilized physical components. Simulations and evaluations of the offline algorithm compared to the state-of-art resulted on lower total costs by 32%. In addition to that, the online algorithm was tested through four different experiments, and the results argued that the overall power consumption of the physical network was highly dependent on the demands’ lifetimes, and the strictness of the required end-to-end delay. Regarding migrations during online, the results concluded that the proposed algorithms would be most effective when applied for maintenance and emergency conditions.Peer ReviewedPreprin

Nano-networks communication architecture: Modeling and functions

Nano-network is a communication network at the Nano-scale between Nano-devices. Nano-devices face certain challenges in functionalities, because of limitations in their processing capabilities and power management. Hence, these devices are expected to perform simple tasks, which require different and novel approaches. In order to exploit different functionalities of Nano-machines, we need to manage and control a set of Nano-devices in a full Nano-network using an appropriate architecture. This step will enable unrivaled applications in the biomedical, environmental and industrial fields. By the arrival of Internet of Things (IoT) the use of the Internet has transformed, where various types of objects, sensors and devices can interact making our future networks connect nearly everything from traditional network devices to people. In this paper, we provide an unified architectural model of Nano-network communication with a layered approach combining Software Defined Network (SDN), Network Function Virtualization (NFV) and IoT technologies and present how this combination can help in Nano-networks’ context. Consequently, we propose a set of functions and use cases that can be implemented by Nano-devices and discuss the significant challenges in implementing these functions with Nano-technology paradigm and the open research issues that need to be addressed.Peer ReviewedPostprint (published version

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

A new auto-provisioned squat-based traffic management strategy for multiclass networks

In search of being able to offer better quality of service (QoS) in multiclass networks, a strategy that aims to allow resources between different classes of service (CoS) to be shared according to the class’s needs and priority is proposed. The goal is to achieve an optimal use of the total bandwidth in a link. This paper presents shortly the proposed strategy and explains the “squatting” and “kicking” mechanisms. A model and simple results for performance to prove their utility are shown.Postprint (published version

NFV/SDN enabled architecture for efficient adaptive management of renewable and non-renewable energy

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns that drive the urgent creation of new solutions for energy management. In this regard, by leveraging the massive connectivity provided by emerging 5G communications, this paper proposes a long-term sustainable Demand-Response (DR) architecture for the efficient management of available energy consumption for Internet of Things (IoT) infrastructures. The proposal uses Network Functions Virtualization (NFV) and Software Defined Networking (SDN) technologies as enablers and promotes the primary use of energy from renewable sources. Associated with architecture, this paper presents a novel consumption model conditioned on availability and in which the consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as prioritization of the energy supply and workload scheduling using time-shifting capabilities. The complexity of the proposal is analyzed in order to present an appropriate architectural framework. The energy management solution is modeled as an Integer Linear Programming (ILP) and, to verify the improvements in energy utilization, an algorithmic solution and its evaluation are presented. Finally, open research problems and application scenarios are discussed.This work was supported in part by the Ministerio de Economía yCompetitividad of the Spanish Government under Project TEC2016-76795-C6-1-R and Project AEI/FEDER, UE, and in part by the SGRProject under Grant 2017 SGR 397 from the Generalitat de Catalunya.Peer ReviewedPostprint (published version

Online power aware coordinated virtual network embedding with 5G delay constraint

Solving virtual network embedding problem with delay constraint is a key challenge to realize network virtualization for current and future 5G core networks. It is an NP-Hard problem, composed of two sub-problems, one for virtual node embedding, and another one for virtual edges embedding, usually solved separately or with a certain level of coordination, which in general could result on rejecting some virtualization requests. Therefore, the main contributions of this paper focused on introducing an online power aware algorithm to solve the virtual network embedding problem using less resources and less power consumption, while considering end-to-end delay as a main embedding constraint. The new algorithm minimizes the overall power of the physical network through efficiently maximizing the utilization of the active infrastructure resources and putting into sleeping mode all non-active ones. Evaluations of the proposed algorithm conducted against the state of art algorithms, and simulation results showed that, when end-to-end delay was not included the proposed online algorithm managed to reduce the total power consumption of the physical network by 23% lower than the online energy aware with dynamic demands VNE algorithm, EAD-VNE. However, when end-to-end delay was included, it significantly influenced the whole embedding process and resulted on reducing the average acceptance ratios by 16% compared to the cases without delay.Peer ReviewedPostprint (published version

Evaluating impacts of traffic migration and virtual network functions consolidation on power aware resource allocation algorithms

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Power consumption minimization and speed of solving the resource allocation problem on cloud datacenters adopting network function virtualization architecture are among the hot topics for future Internet networks. Therefore, this paper proposes a new power aware resource allocation algorithm supporting physical servers’ consolidations combined with virtual networks consolidation to minimize datacenters’ total costs for offline scenario. In addition, the new algorithm is also integrated with an optional standalone traffic migration algorithm that can be triggered according to specific conditions and at anytime. Simulations and evaluations of the algorithm resulted on lower total costs by 30% compared to recent algorithms from Eramo et al. (2017), and when virtual network functions consolidations option was activated, total costs were 25% lower than when it was inactive. However, when migrations option was activated in the proposed allocation algorithm it did not provide any significant savings in the total power consumptions, mainly because of the allocation strategy used by the algorithm in the first place, which managed to help it to precisely allocate and efficiently utilize the least physical resources. Finally, the results showed that without migrations, allocation times where faster by 10 times than activating migrations, suggesting to apply the migration option for emergency or maintenance conditions, and use the algorithm without migrations for faster allocations and efficient power consumptions.Peer ReviewedPostprint (author's final draft

Optimization model for bandwidth allocation in a network virtualization environment

Bandwidth allocation is one of the main problems in network virtualization. Mechanisms to allocate bandwidth may avoid bottlenecked virtual links. This paper proposes a model based on optimization theory, to distribute the bandwidth among virtual links looking for the minimization of the spare bandwidth in the substrate network.Postprint (published version

Machine learning models for traffic classification in electromagnetic nano-networks

The number of nano-sensors connected to wireless electromagnetic nano-network generates different traffic volumes that have increased dramatically, enabling various applications of the Internet of nano-things. Nano-network traffic classification is more challenging nowadays to analyze different types of flows and study the overall performance of a nano-network that connects to the Internet through micro/nanogateways. There are traditional techniques to classify traffic, such as port-based technique and load-based technique, however the most promising technique used recently is machine learning. As machine learning models have a great impact on traffic classification and network performance evaluation in general, it is difficult to declare which is the best or the most suitable model to address the analysis of large volumes of traffic collected in operational nano-networks. In this paper, we study the classification problem of nano-network traffic captured by micro/nano-gateway, and then five supervised machine learning algorithms are used to analyze and classify the nano-network traffic from traditional traffic. Experimental analysis of the proposed models is evaluated and compared to show the most adequate classifier for nano-network traffic that gives very good accuracy and performance score to other classifiers.This work was supported in part by the ‘‘Agencia Estatal de Investigación’’ of ‘‘Ministerio de Ciencia e Innovación’’ of Spain under Project PID2019-108713RB-C51/MCIN/AEI/10.13039/501100011033, and in part by the ‘‘Agència de Gestió d’Ajuts Universitaris i de Recerca’’ (AGAUR) of the ‘‘Generalitat de Catalunya’’ under Grant 2021FI_B2 00091.Postprint (published version

A novel admission control scheme for network slicing based on squatting and kicking strategies

New services and applications impose differentquality of service (QoS) requirements on network slicing. Tomeet differentiated service requirements, current Internet servicemodel has to support emerging real-time applications from 5Gnetworks. The admission control mechanisms are expected tobe one of the key components of the future integrated serviceInternet model, for providing multi-level service guarantees withthe different classes (slices) of services. Therefore, this paperintroduces a new flexible admission control mechanism, basedon squatting and kicking techniques (SKM), which can beemployed under network slicing scenario. From the results, SKMprovides 100% total resource utilization in bandwidth contextand 100% acceptance ratio for highest priority class underdifferent input traffic volumes, which cannot be achieved byother existing schemes such as AllocTC-Sharing model due topriority constraints.Peer ReviewedPostprint (published version