Security Requirements for Multi-operator Virtualized Network and Service Orchestration for 5G

IEEE/ACM Trans. Networking

et al. 2019

104

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, and efficiently. To this end, we formulate a queuingbased model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out accounting for multiple scenarios representative of real-world services, shows that MaxZ performs substantially better than state-ofthe-art alternatives and consistently close to the optimum. INTRODUCTION5G networks are envisioned to provide the computational, memory, and storage resources needed to run multiple third parties (referred to as vertical industries or verticals) with diverse communication and computation needs. Verticals provide network operators with the specification of the services they want to provide, e.g., the virtual network functions (VNFs) they want to use to process their data and the associated quality of service.Mobile network operators are in charge of mapping the requirements of the verticals into infrastructure management decisions. This task is part of the network orchestration, and includes making decisions concerning (i) the placement of the VNFs needed by the verticals across the infrastructure; (ii) the assignment of CPU, memory and storage resources to the VNFs; (iii) the routing of data across network nodes.These decisions interact with each other in ways that are complex and often counterintuitive. In this paper, we focus on the allocation of computational and network resources, and make such decisions jointly, accounting for (i) the requirements of each VNF and vertical; (ii) the capabilities of the network operator's infrastructure; (iii) the capacity • S. Agarwal is with IIT Guwahati, India. F. Malandrino and C.-F. Chiasserini are with Politecnico di Torino, Italy and CNR-IEIIT, Italy. S. De is with IIT Delhi, India. • A preliminary version [1] of this work was presented at the IEEE INFOCOM 2018 conference.

Section: Related Workmentioning

confidence: 99%

Section: Steps 1-2: Convex Formulationmentioning

confidence: 99%

VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

Agarwal

IEEE/ACM Trans. Networking

et al. 2019

104

“…5G networks based on network slicing have attracted substantial attention, with several works focusing on 5G architecture [3], [31], associated decision-making issues [32], [33], and security [34], [35].…”

Section: Related Workmentioning

confidence: 99%

Reducing Service Deployment Cost Through VNF Sharing

IEEE/ACM Trans. Networking

Einziger

et al. 2019

Thanks to its computational and forwarding capabilities, the mobile network infrastructure can support several third-party ("vertical") services, each composed of a graph of virtual (network) functions (VNFs). Importantly, one or more VNFs are often common to multiple services, thus the services deployment cost could be reduced by letting the services share the same VNF instance instead of devoting a separate instance to each service. By doing that, however, it is critical that the target KPI (key performance indicators) of all services are met. To this end, we study the VNF sharing problem and make decisions on (i) when sharing VNFs among multiple services is possible, (ii) how to adapt the virtual machines running the shared VNFs to the combined load of the assigned services, and (iii) how to prioritize the services traffic within shared VNFs. All decisions aim to minimize the cost for the mobile operator, subject to requirements on end-to-end service performance, e.g., total delay. Notably, we show that the aforementioned priorities should be managed dynamically and vary across VNFs. We then propose the FlexShare algorithm to provide near-optimal VNF-sharing and priority assignment decisions in polynomial time. We prove that FlexShare is within a constant factor from the optimum and, using real-world VNF graphs, we show that it consistently outperforms baseline solutions.

“…5G networks based on network slicing have attracted substantial attention, with several works focusing on 5G architecture [2], [23], associated decision-making issues [24], and security [25].…”

Section: Related Workmentioning

confidence: 99%

Getting the Most Out of Your VNFs: Flexible Assignment of Service Priorities in 5G

2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

2019

Through their computational and forwarding capabilities, 5G networks can support multiple vertical services. Such services may include several common virtual (network) functions (VNFs), which could be shared to increase resource efficiency. In this paper, we focus on the seldom studied VNFsharing problem, and decide (i) whether sharing a VNF instance is possible/beneficial or not, (ii) how to scale virtual machines hosting the VNFs to share, and (iii) the priorities of the different services sharing the same VNF. These decisions are made with the aim to minimize the mobile operator's costs while meeting the verticals' performance requirements. Importantly, we show that the aforementioned priorities should not be determined a priori on a per-service basis, rather they should change across VNFs since such additional flexibility allows for more efficient solutions. We then present an effective methodology called FlexShare, enabling near-optimal VNF-sharing decisions in polynomial time. Our performance evaluation, using real-world VNF graphs, confirms the effectiveness of our approach, which consistently outperforms baseline solutions using per-service priorities.