Abstract-Network Function Virtualization (NFV) represents a new paradigm of network service provisioning. NFV providers acquire cloud resources, install virtual network functions (VNFs), assemble VNF service chains for customer usage, and dynamically scale VNF deployment against input traffic fluctuations. While existing literature on VNF scaling mostly adopts a reactive approach, we target a proactive approach that is more practical given the time overhead for VNF deployment. We aim to effectively estimate upcoming traffic rates and adjust VNF deployment a priori, for flow service quality assurance and resource cost minimization. We adapt online learning techniques for predicting future service chain workloads. We further combine the online learning method with a multi-timescale online optimization algorithm for VNF scaling, through minimization of the regret due to inaccurate demand prediction and minimization of the cost incurred by sub-optimal online decisions in a joint online optimization framework. The resulting proactive online VNF provisioning algorithm achieves a good performance guarantee, as shown by both theoretical analysis and simulation under realistic settings. Network Function Virtualization (NFV) promises to ease network service provisioning and scaling at significantly reduced costs, by implementing network functions (middleboxes) as software on standard virtualized platforms (e.g., virtual machines (VMs) in datacenters) instead of dedicated hardware [1] . In contrast with manual hardware middlebox installation, the deployment of virtualized network functions (VNFs) is more flexible, by launching a VM instance with a pre-built VNF image. Network functions are typically sequenced into service chains to provide practical network service [2], e.g., the service chain "Firewall→IDS→Proxy" is commonly deployed between external users and an enterprise's internal network for access control. Enterprises and businesses start resorting to VNFs for provisioning network services [3] . Recent initiatives suggest establishing (dedicated) NFV service providers, who own or rent cloud resources, create VNF instances, assemble VNF service chains and offer them to customers on demand, eliminating the need to build and maintain these functions at customers' premises. A European FP7 ICT project T-NOVA [4] introduces an enabling framework for NFV providers to offer VNFs to customers. Customers browse the VNF catalog at the NFV provider, and make a la carte orderings of service chains. The emerging NFV marketplace has recently witnessed its vanguard group of providers and vendors [5] [6] , racing to lead in the multi-billion dollar market envisioned [7] . At the core of NFV service provisioning is optimal resource scheduling, for efficient and dynamic provisioning of VNF instances that best serves fluctuating network traffic traversing service chains. A delay is typically incurred for VNF deployment, in copying VM images and launching VMs/VNFs. A proactive provisioning approach would thus be much appreciated, adjusting VNF deployment prior to actual traffic arrival based on effective customer demand prediction. However, most existing proposals on dynamic VNF provisioning (e.g., [8] [9]) have adopted a reactive approach, adapting VNF deployment to hitherto arrivals, jeopardizing service quality during the adjustment period. We study a new NFV brokerage service, where a NFV provider predicts upcoming traffic demand, reserves VMs at a public cloud, deploys VNFs and assembles service chains to serve customers over time. On today's public clouds [10] [11], VM instances of different resource composition can be reserved for desired durations, e.g., on-demand instances for one-hour and reserved instances for 1 or 3 years at Amazon EC2. A critical challenge arises on which types of VMs the NFV provider should purchase for VNF deployment, based on predicted fluctuation of customer traffic demand. The decisions are to be made promptly upon predicted changes, at a short decision interval on the order of seconds or minutes. VM lifetimes are typically much larger, e.g., hours or months/years in commercial cloud platforms [10] [11]. Therefore, VM purchase decisions at one time influence future resource availability and hence future VM purchase decisions. A carefully designed proactive online algorithm should (i) effectively predict upcoming traffic demand to expedite service chain provisioning, and (ii) strategically and proactively reserve VMs and deploy VNFs based on the predicted upcoming demand as well as a future perspective, to best serve customer traffic with minimal overall cost over the long run. Algorithm design for the above goals faces a number of technical challenges. Given the multiple time scales of VM duration, online VM purchase decisions are coupled with future decisions at multiple time scales. Moreover, the overall VM purchase and deployment cost is a non-convex objective function, further escalating the challenge. In online (convex) optimization problems solved by online learning techniques, decisions at each time are made based on predicted (learned) input for the current time, without direct coupling with future decisions [12] [13] . Exceptions [14][15] study online learning with decisions coupled over time in a convex function, rely-
Proactive approach: REF seek a proactive approach to provision new instances for overloaded VNFs ahead of time based on the estimated flow rates.
Proactive VNF provisioning with multi-timescale cloud resources: Fusing online learning and online optimization
IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
Computer Science