When the operators issued their whitepaper[1] challenging the industry to address network function virtualization in 2012, the expected benefits included a number of improvements in operating expenses. These included improved operational efficiency by taking advantage of the higher uniformity of the infrastructure via:
- IT orchestration mechanisms providing automated installation, scaling-up and scaling out of capacity, and re-use of Virtual Machine (VM) builds.
- More uniform staff skill base: The skills base across the industry for operating standard high volume IT servers is much larger and less fragmented than for today’s telecom-specific network equipment.
- Reduction in variety of equipment for planning & provisioning. Assuming tools are developed for automation and to deal with the increased software complexity of virtualisation.
- Mitigating failures by automated re-configuration and moving network workloads onto spare capacity using IT orchestration mechanisms, hence reducing the cost of 24/7 operations.
- More efficiency between IT and Network Operations- shared cloud infrastructure leading to shared operations
- Support in-service software upgrade (ISSU) with easy reversion by installing the new version of a Virtualised Network Appliance (VNA) as a new Virtual Machine (VM).
While there have been a number of studies addressing the potential for capex improvements (see e.g., (Naudts, et.al. 2012)(Kar et.al. 2020)), there are relatively fewer studies in the literature concerning the opex improvements (see e.g., (Hernandez-Valencia et.al. 2015)(Bouras et.al. 2015)(Karakus & Durresi 2019)). At least partly, this reflects the commercial sensitivity of expense data at network operators. Headcount is a significant cost factor in operations. Opex improvements could imply headcount reductions which would also make the topic sensitive for network operator staff.
The transformative nature of NFV, transitioning equipment spend from custom hardware to software on generic computing infrastructure, generated significant interest and rhetoric at the time (see e.g., Li & Chen 2015)), but other new technology introductions have also claimed significant opex improvements (e.g., GMPLS (Pasqualini et.al. 2005)). Telecommunications operators are large-scale businesses, so opex reductions are an ongoing area of focus. The telecom industry is characterized by significant capital investments in infrastructure leading to significant debt loads. Average industry debt ratios have been in the range 0.69 to 0.81 over the past few years (readyratios.com), implying operating expenses would include a significant component for depreciation and amortization. Examining the annual reports of tier 1 carriers shows depreciation and amortization in the range of 15-20% of operating expenses. Telecom services are mass market services, implying significant sales expenses to reach the mass market. Examining the annual reports of tier 1 carriers shows sales, general and administrative costs are on the order of 25% of operating expenses. The operations efficiency improvements expected for NFV don’t impact Depreciation or SGA expenses, hence at most they can impact the remaining 55-60% of the company’s total operating expenses.
(Bouras et. al. 2016) expected opex reduction of 63% compared to their baseline, but it is not clear how that would relate to reportable operating expenses for the company. (Hernandez-Valencia, et.al. 2015) also provided numerical percentage ranges for expected savings in a number of areas, but the relation to reportable operating expenses for the company is similarly unclear. Other studies (Karakus & Durresi 2019) (Pasqualini et al 2005) identified factors affecting operating expenses but did not have consistent terminology or scope in the operating expense factors identified. Environmental operations costs of power and real estate were identified by (Hernandez-Valencia, et.al. 2015) and (Pasqualini et al 2005), but (Karakus & Durresi 2019) refer only to energy related costs. (Hernandez-Valencia, et.al. 2015) identified service operations costs of assurance and onboarding; (Karakus & Durresi 2019) identified service provisioning; and (Pasqualini et al 2005) referred to service management processes – SLA negotiations, service provisioning, service cessation, service move/change.
The lack of consistent operating cost models may be explained by variation across operators. Service definitions and designs may be different across operators. Environmental operations expenses like real estate and power could be affected significantly by operators’ preferences for private vs public cloud infrastructures. The design of operations reflects company’s strategic choices on what to capitalize as fixed infrastructure and may be influenced by other factors (e.g. tax policies, regulatory regimes). Numerical targets for opex reductions seem difficult to generalize across organization. Even within a single organization, tracking such targets at the corporate level may be significantly impacted by other corporate activities (e.g., M&A) that impact reportable metrics. A better approach may be to focus on improvements in particular operational tasks that can be generalized across multiple operators and architectures.
References
Naudts, B., Kind, M., Westphal, F. J., Verbrugge, S., Colle, D., & Pickavet, M. (2012, October). Techno-economic analysis of software defined networking as architecture for the virtualization of a mobile network. In 2012 European workshop on software defined networking (pp. 67-72). IEEE.
Kar, B., Wu, E. H. K., & Lin, Y. D. (2020). Communication and Computing Cost Optimization of Meshed Hierarchical NFV Datacenters. IEEE Access, 8, 94795-94809.
Hernandez-Valencia, E., Izzo, S., & Polonsky, B. (2015). How will NFV/SDN transform service provider opex? IEEE Network, 29(3), 60-67.
Bouras, C., Ntarzanos, P., & Papazois, A. (2016, October). Cost modeling for SDN/NFV based mobile 5G networks. In 2016 8th international congress on ultra-modern telecommunications and control systems and workshops (ICUMT) (pp. 56-61). IEEE.
Karakus, M., & Durresi, A. (2019). An economic framework for analysis of network architectures: SDN and MPLS cases. Journal of Network and Computer Applications, 136, 132-146.
Li, Y., & Chen, M. (2015). Software-defined network function virtualization: A survey. IEEE Access, 3, 2542-2553.
Pasqualini, S., Kirstadter, A., Iselt, A., Chahine, R., Verbrugge, S., Colle, D., … & Demeester, P. (2005). Influence of GMPLS on network providers’ operational expenditures: a quantitative study. IEEE Communications Magazine, 43(7), 28-38.