On distributed virtual network embedding with guarantees

To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst-case efficiency of (1-(1/e)) relative to the optimal node embedding, or VNET embedding if virtual links are mapped to exactly one physical link. This bound is optimal, that is, no better polynomial-time approximation algorithm exists, unless P=NP. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared to existing distributed VNET embedding solutions, and we show how by appropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.CNS-0963974 - National Science Foundationhttp://www.cs.bu.edu/fac/matta/Papers/ToN-CAD.pdfAccepted manuscrip

Towards a Virtualized Next Generation Internet

A promising solution to overcome the Internet ossification is network virtualization in which Internet Service Providers (ISPs) are decoupled into two tiers: service providers (SPs), and infrastructure providers (InPs). The former maintain and customize virtual network(s) to meet the service requirement of end-users, which is mapped to the physical network infrastructure that is managed and deployed by the latter via the Virtual Network Embedding (VNE) process. VNE consists of two major components: node assignment, and link mapping, which can be shown to be NP-Complete. In the first part of the dissertation, we present a path-based ILP model for the VNE problem. Our solution employs a branch-and-bound framework to resolve the integrity constraints, while embedding the column generation process to effectively obtain the lower bound for branch pruning. Different from existing approaches, the proposed solution can either obtain an optimal solution or a near-optimal solution with guarantee on the solution quality. A common strategy in VNE algorithm design is to decompose the problem into two sequential sub-problems: node assignment (NA) and link mapping (LM). With this approach, it is inexorable to sacrifice the solution quality since the NA is not holistic and not-reversible. In the second part, we are motivated to answer the question: Is it possible to maintain the simplicity of the Divide-and-Conquer strategy while still achieving optimality? Our answer is based on a decomposition framework supported by the Primal-Dual analysis of the path-based ILP model. This dissertation also attempts to address issues in two frontiers of network virtualization: survivability, and integration of optical substrate. In the third part, we address the survivable network embedding (SNE) problem from a network flow perspective, considering both splittable and non-splittable flows. In addition, the explosive growth of the Internet traffic calls for the support of a bandwidth abundant optical substrate, despite the extra dimensions of complexity caused by the heterogeneities of optical resources, and the physical feature of optical transmission. In this fourth part, we present a holistic view of motivation, architecture, and challenges on the way towards a virtualized optical substrate that supports network virtualization

A fast robust optimization-based heuristic for the deployment of green virtual network functions

Network Function Virtualization (NFV) has attracted a lot of attention in the telecommunication field because it allows to virtualize core-business network functions on top of a NFV Infrastructure. Typically, virtual network functions (VNFs) can be represented as chains of Virtual Machines (VMs) or containers that exchange network traffic which are deployed inside datacenters on commodity hardware. In order to achieve cost efficiency, network operators aim at minimizing the power consumption of their NFV infrastructure. This can be achieved by using the minimum set of physical servers and networking equipment that are able to provide the quality of service required by the virtual functions in terms of computing, memory, disk and network related parameters. However, it is very difficult to predict precisely the resource demands required by the VNFs to execute their tasks. In this work, we apply the theory of robust optimization to deal with such parameter uncertainty. We model the problem of robust VNF placement and network embedding under resource demand uncertainty and network latency constraints using robust mixed integer optimization techniques. For online optimization, we develop fast solution heuristics. By using the virtualized Evolved Packet Core as use case, we perform a comprehensive evaluation in terms of performance, solution time and complexity and show that our heuristic can calculate robust solutions for large instances under one second.Peer ReviewedPostprint (author's final draft

Optimal and probabilistic resource and capability analysis for network slice as a service

Network Slice as a Service is one of the key concepts of the fifth generation of mobile networks (5G). 5G supports new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. In addition, safety and security critical use cases move into focus. These use cases involve diverging requirements, e.g. network reliability, latency and throughput. Network virtualization and end-to-end mobile network slicing are seen as key enablers to handle those differing requirements and providing mobile network services for the various 5G use cases and between different tenants. Network slices are isolated, virtualized, end-to-end networks optimized for specific use cases. But still they share a common physical network infrastructure. Through logical separation of the network slices on a common end-to-end mobile network infrastructure, an efficient usage of the underlying physical network infrastructure provided by multiple Mobile Service Providers (MSPs) in enabled. Due to the dynamic lifecycle of network slices there is a strong demand for efficient algorithms for the so-called Network Slice Embedding (NSE) problem. Efficient and reliable resource provisioning for Network Slicing as a Service, requires resource allocation based on a mapping of virtual network slice elements on the serving physical mobile network infrastructure. In this thesis, first of all, a formal Network Slice Instance Admission (NSIA) process is presented, based on the 3GPP standardization. This process allows to give fast feedback to a network operator or tenant on the feasibility of embedding incoming Network Slice Instance Requests (NSI-Rs). In addition, corresponding services for NSIA and feasibility checking services are defined in the context of the ETSI ZSM Reference Architecture Framework. In the main part of this work, a mathematical model for solving the NSE Problem formalized as a standardized Linear Program (LP) is presented. The presented solution provides a nearly optimal embedding. This includes the optimal subset of Network Slice Instances (NSIs) to be selected for embedding, in terms of network slice revenue and costs, and the optimal allocation of associated network slice applications, functions, services and communication links on the 5G end-to-end mobile network infrastructure. It can be used to solve the online as well as the offline NSIA problem automatically in different variants. In particular, low latency network slices require deployment of their services and applications, including Network Functions (NFs) close to the user, i.e., at the edge of the mobile network. Since the users of those services might be widely distributed and mobile, multiple instances of the same application are required to be available on numerous distributed edge clouds. A holistic approach for tackling the problem of NSE with edge computing is provided by our so-called Multiple Application Instantiation (MAI) variant of the NSE LP solution. It is capable of determining the optimal number of application instances and their optimal deployment locations on the edge clouds, even for multiple User Equipment (UE) connectivity scenarios. In addition to that multi-path, also referred to as path-splitting, scenarios with a latency sensitive objective function, which guarantees the optimal network utilization as well as minimum latency in the network slice communication, is included. Resource uncertainty, as well as reuse and overbooking of resources guaranteed by Service Level Agreements (SLAs) are discussed in this work. There is a consensus that over-provisioning of mobile communication bands is economically infeasible and certain risk of network overload is accepted for the majority of the 5G use cases. A probabilistic variant of the NSE problem with an uncertainty-aware objective function and a resource availability confidence analysis are presented. The evaluation shows the advantages and the suitability of the different variants of the NSE formalization, as well as its scalability and computational limits in a practical implementation