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

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

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 Reviewe

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 Reviewe