Showing papers by "Peter Key published in 2018"

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

Abstract: The explosive growth of the Internet has fundamentally changed the global society. The emergence of concepts like service-oriented architecture (SOA), Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Network as a Service (NaaS) and Cloud Computing in general has catalyzed the migration from the information-oriented Internet into an Internet of Services (IoS). This has opened up virtually unbounded possibilities for the creation of new and innovative services that facilitate business processes and improve the quality of life. However, this also calls for new approaches to ensuring quality and reliability of these services. The goal of this book chapter is to first analyze the state-of-the-art in the area of autonomous control for a reliable IoS and then to identify the main research challenges within it. A general background and high-level description of the current state of knowledge is presented. Then, for each of the three subareas, namely the autonomous management and real-time control, methods and tools for monitoring and service prediction, and smart pricing and competition in multi-domain systems, a brief general introduction and background are presented, and a list of key research challenges is formulated.

Optimal Pricing and Introduction Timing of Improving Technologies

Abstract: Many technologies improve over time and technology providers can provide increasingly powerful service upgrades to their customers, but at a launching cost, and the expense of the sales of existing products. We propose a model of technology introduction in the context of subscription-based services and characterize the optimal pricing and timing of technology introductions for a service provider, in the face of customers who are averse to switching to improved offerings. Overall, we show that a simple policy of Myerson (i.e., myopic) pricing and periodic introductions is approximately optimal. We first show that under a linear pricing rule, which subsumes Myerson pricing, there is no loss of optimality with a periodic schedule of introductions, and that under periodic introductions, the potential additional revenue of any pricing policy over Myerson pricing decays to zero after sufficiently many introductions. We then argue that Myerson pricing is approximately optimal under arbitrary introduction times. To do so, we first characterize prices that achieve optimal revenue in a single period, given arbitrary fixed introduction times. We then establish that Myerson pricing achieves a bounded bicriteria approximation ratio to both revenue and cost, for the infinite-horizon problem. Third, we provide analytical bounds for the approximation ratio in terms of the customer type distribution. Our bounds show that Myerson pricing is approximately optimal when switching costs for the customers who upgrade are small or large. Following our analysis, we examine our analytical bounds for Myerson pricing with simulations and show that, after sufficiently many introductions, they are tight for all values of the switching cost, for several natural distributions for the customer type. Furthermore, when we numerically compute optimal prices for fixed introduction times, rather than using our analytical bounds, we find that Myerson pricing is often several orders of magnitude closer to optimal revenue than our analytical bounds suggest. Our conclusions on the quality of Myerson pricing can robustly be carried over to realistic settings for the switching costs, the customer lifetime, and the frequency of technology introductions.

Optimal Pricing and Introduction Timing of New Virtual Machines

Abstract: As the quality of computer hardware increases over time, cloud service providers have the ability to offer more powerful virtual machines (VMs) and other resources to their customers. But providers face several trade-offs as they seek to make the best use of improved technology. On one hand, more powerful machines are more valuable to customers and command a higher price. On the other hand, there is a cost to develop and launch a new product. Further, the new product competes with existing products. Thus, the provider faces two questions. First, when should new classes of VMs be introduced? Second, how should they be priced, taking into account both the VM classes that currently exist and the ones that will be introduced in the future?This decision problem, combining scheduling and pricing new product introductions, is common in a variety of settings. One aspect that is more specific to the cloud setting is that VMs are rented rather than sold. Thus existing customers can switch to a new offering, albeit with some inconvenience. There is indeed evidence of customers' aversion to upgrades in the cloud computing services market. Based on a study of Microsoft Azure, we estimate that customers who arrive after a new VM class is launched are 50% more likely to use it than existing customers, indicating that these switching costs may be substantial.This opens up a wide range of possible policies for the cloud service provider. Our main result is that a surprisingly simple policy is close to optimal in many situations: new VM classes are introduced on a periodic schedule and each is priced as if it were the only product being offered. (Periodic introductions have been noticeable in the practice of cloud computing. For example, Amazon Elastic Compute Cloud (EC2) launched new classes of the m.xlarge series in October 2007, February 2010, October 2012, and June 2015, i.e., in intervals of 28 months, 32 months, and 32 months.) We refer to this pricing policy as Myerson pricing, as these prices can be computed as in Myerson's classic paper (1981). This policy produces a marketplace where new customers always select the newest and best offering, while existing customers may stick with older VMs due to switching costs.