Online VNF Scaling in Datacenters

Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network functions (VNFs) to commodity servers in place of dedicated hardware middleboxes. The VNFs are typically running on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. In this paper, we target dynamic provisioning of enterprise network services - expressed as one or multiple service chains - in cloud datacenters, and design efficient online algorithms without requiring any information on future traffic rates. The key is to decide the number of instances of each VNF type to provision at each time, taking into consideration the server resource capacities and traffic rates between adjacent VNFs in a service chain. In the case of a single service chain, we discover an elegant structure of the problem and design an efficient randomized algorithm achieving a e/(e-1) competitive ratio. For multiple concurrent service chains, an online heuristic algorithm is proposed, which is O(1)-competitive. We demonstrate the effectiveness of our algorithms using solid theoretical analysis and trace-driven simulations.Comment: 9 pages, 4 figure

Distributed VNF Scaling in Large-scale Datacenters: An ADMM-based Approach

Network Functions Virtualization (NFV) is a promising network architecture where network functions are virtualized and decoupled from proprietary hardware. In modern datacenters, user network traffic requires a set of Virtual Network Functions (VNFs) as a service chain to process traffic demands. Traffic fluctuations in Large-scale DataCenters (LDCs) could result in overload and underload phenomena in service chains. In this paper, we propose a distributed approach based on Alternating Direction Method of Multipliers (ADMM) to jointly load balance the traffic and horizontally scale up and down VNFs in LDCs with minimum deployment and forwarding costs. Initially we formulate the targeted optimization problem as a Mixed Integer Linear Programming (MILP) model, which is NP-complete. Secondly, we relax it into two Linear Programming (LP) models to cope with over and underloaded service chains. In the case of small or medium size datacenters, LP models could be run in a central fashion with a low time complexity. However, in LDCs, increasing the number of LP variables results in additional time consumption in the central algorithm. To mitigate this, our study proposes a distributed approach based on ADMM. The effectiveness of the proposed mechanism is validated in different scenarios.Comment: IEEE International Conference on Communication Technology (ICCT), Chengdu, China, 201

Monitoring and debugging using an SDK for NFV-powered telecom applications

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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

Effectiveness of segment routing technology in reducing the bandwidth and cloud resources provisioning times in network function virtualization architectures

Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one characterized by a Service Function Chain, which is a set of service functions to be executed according to a given order. It implements an algorithm for deciding where both to allocate the cloud and bandwidth resources and to route the SFCs. In a traditional orchestration algorithm, the orchestrator has a detailed knowledge of the cloud and network infrastructures and that can lead to high computational complexity of the SFC Routing and Cloud and Bandwidth resource Allocation (SRCBA) algorithm. In this paper, we propose and evaluate the effectiveness of a scalable orchestration architecture inherited by the one proposed within the European Telecommunications Standards Institute (ETSI) and based on the functional separation of an NFV orchestrator in Resource Orchestrator (RO) and Network Service Orchestrator (NSO). Each cloud domain is equipped with an RO whose task is to provide a simple and abstract representation of the cloud infrastructure. These representations are notified of the NSO that can apply a simplified and less complex SRCBA algorithm. In addition, we show how the segment routing technology can help to simplify the SFC routing by means of an effective addressing of the service functions. The scalable orchestration solution has been investigated and compared to the one of a traditional orchestrator in some network scenarios and varying the number of cloud domains. We have verified that the execution time of the SRCBA algorithm can be drastically reduced without degrading the performance in terms of cloud and bandwidth resource costs

Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G

5G networks are going to support a variety of vertical services, with a diverse set of key performance indicators (KPIs), by using enabling technologies such as software-defined networking and network function virtualization. It is the responsibility of the network operator to efficiently allocate the available resources to the service requests in such a way to honor KPI requirements, while accounting for the limited quantity of available resources and their cost. A critical challenge is that requests may be highly varying over time, requiring a solution that accounts for their dynamic generation and termination. With this motivation, we seek to make joint decisions for request admission, resource activation, VNF placement, resource allocation, and traffic routing. We do so by considering real-world aspects such as the setup times of virtual machines, with the goal of maximizing the mobile network operator profit. To this end, first, we formulate a one-shot optimization problem which can attain the optimum solution for small size problems given the complete knowledge of arrival and departure times of requests over the entire system lifespan. We then propose an efficient and practical heuristic solution that only requires this knowledge for the next time period and works for realistically-sized scenarios. Finally, we evaluate the performance of these solutions using real-world services and large-scale network topologies. {Results demonstrate that our heuristic solution performs better than a state-of-the-art online approach and close to the optimum

A Scalable Approach for Service Chain (SC) Mapping with Multiple SC Instances in a Wide-Area Network

Network Function Virtualization (NFV) aims to simplify deployment of network services by running Virtual Network Functions (VNFs) on commercial off-the-shelf servers. Service deployment involves placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a Service Chain (SC). The joint VNF placement and traffic routing is called SC mapping. In a Wide-Area Network (WAN), a situation may arise where several traffic flows, generated by many distributed node pairs, require the same SC; then, a single instance (or occurrence) of that SC might not be enough. SC mapping with multiple SC instances for the same SC turns out to be a very complex problem, since the sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions. Our study is the first to deal with the problem of SC mapping with multiple SC instances to minimize network resource consumption. We first propose an Integer Linear Program (ILP) to solve this problem. Since ILP does not scale to large networks, we develop a column-generation-based ILP (CG-ILP) model. However, we find that exact mathematical modeling of the problem results in quadratic constraints in our CG-ILP. The quadratic constraints are made linear but even the scalability of CG-ILP is limited. Hence, we also propose a two-phase column-generation-based approach to get results over large network topologies within reasonable computational times. Using such an approach, we observe that an appropriate choice of only a small set of SC instances can lead to a solution very close to the minimum bandwidth consumption. Further, this approach also helps us to analyze the effects of number of VNF replicas and number of NFV nodes on bandwidth consumption when deploying these minimum number of SC instances.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0671