Xiaoxi Zhang

Online Auctions in IaaS Clouds: Welfare and Profit Maximization with Server Costs

Auction design has recently been studied for dynamic resource bundling and virtual machine (VM) provisioning in IaaS clouds, but is mostly restricted to one-shot or offline setting. This paper targets a more realistic case of online VM auction design, where: 1) cloud users bid for resources into the future to assemble customized VMs with desired occupation durations, possibly located in different data centers; 2) the cloud provider dynamically packs multiple types of resources on heterogeneous physical machines (servers) into the requested VMs; 3) the operational costs of servers are considered in resource allocation; and 4) both social welfare and the cloud provider’s net profit are to be maximized over the system running span. We design truthful, polynomial time auctions to achieve social welfare maximization and/or the provider’s profit maximization with good competitive ratios. Our mechanisms consist of two main modules: 1) an online primal-dual optimization framework for VM allocation to maximize the social welfare with server costs, and for revealing the payments through the dual variables to guarantee truthfulness and 2) a randomized reduction algorithm to convert the social welfare maximizing auctions to ones that provide a maximal expected profit for the provider, with competitive ratios comparable to those for social welfare. We adopt a new application of Fenchel duality in our primal-dual framework, which provides richer structures for convex programs than the commonly used Lagrangian duality, and our optimization framework is general and expressive enough to handle various convex server cost functions. The efficacy of the online auctions is validated through careful theoretical analysis and trace-driven simulation studies.

On Space Information Flow: Single multicast

Departing from Network Information Flow (NIF) that studies network coding in graphs, Space Information Flow (SIF) is a new paradigm that studies network coding in a geometric space. This work focuses on the problem of min-cost multicast network coding in a 2-dimensional Euclidean space. We prove a number of properties of the optimal SIF solutions, and propose a two-phase heuristic algorithm for computing the optimal SIF. The first phase computes the optimal topology through space partitioning that translates the SIF problem into a NIF problem, which is then solved using linear optimization. The second phase computes the min-cost embedding of the SIF topology found in the first phase, by fine tuning the location of each relay node using properties that an optimal SIF must satisfy.

Online Stochastic Buy-Sell Mechanism for VNF Chains in the NFV Market

With the recent advent of network functions virtualization (NFV), enterprises and businesses are looking into network service provisioning through the service chains of virtual network functions (VNFs), instead of relying on dedicated hardware middleboxes. Accompanying this trend, an NFV market is emerging, where NFV service providers create VNF instances, assemble VNF service chains, and sell them for the use of customers, using resources (computing, bandwidth) that they own or rent from other resource suppliers. Efficient service chain provisioning and pricing mechanisms are still missing, to charge assembled service chains according to demand and the supply of resources at any time. We propose an online stochastic auction mechanism for on-demand service chain provisioning and pricing at an NFV provider. Our auction takes in buy bids for service chains from multiple customers and sell bids from various resource suppliers to supplement the NFV provider’s geo-distributed resource pool, with resource occupation/contribution durations. We extend online primal-dual optimization framework for handling both buyers and sellers, with a new competitive analysis. The online mechanism maximizes the expected social welfare of the NFV ecosystem (the NFV provider, customers and resource suppliers) with a good competitive ratio as compared with the expected offline optimal social welfare, while guaranteeing truthfulness in bidding, individual rationality for both buyers and sellers, and polynomial time for computation. We evaluate our mechanism through trace-driven simulation studies, and demonstrate a close-to-offline-optimal performance in expected social welfare under realistic settings.

Proactive VNF provisioning with multi-timescale cloud resources: Fusing online learning and online optimization

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.