Using Tuangou to reduce IP transit costs

A majority of ISPs (Internet Service Providers) support connectivity to the entire Internet by transiting their traffic via other providers. Although the transit prices per Mbps decline steadily, the overall transit costs of these ISPs remain high or even increase, due to the traffic growth. The discontent of the ISPs with the high transit costs has yielded notable innovations such as peering, content distribution networks, multicast, and peer-to-peer localization. While the above solutions tackle the problem by reducing the transit traffic, this paper explores a novel approach that reduces the transit costs without altering the traffic. In the proposed CIPT (Cooperative IP Transit), multiple ISPs cooperate to jointly purchase IP (Internet Protocol) transit in bulk. The aggregate transit costs decrease due to the economies-of-scale effect of typical subadditive pricing as well as burstable billing: not all ISPs transit their peak traffic during the same period. To distribute the aggregate savings among the CIPT partners, we propose Shapley-value sharing of the CIPT transit costs. Using public data about IP traffic of 264 ISPs and transit prices, we quantitatively evaluate CIPT and show that significant savings can be achieved, both in relative and absolute terms. We also discuss the organizational embodiment, relationship with transit providers, traffic confidentiality, and other aspects of CIPT

Temporal rate limiting: Cloud elasticity at a flat fee

Abstract-In the current usage-based pricing scheme offered by most cloud computing providers, customers are charged based on the capacity and the lease time of the resources they capture (bandwidth, number of virtual machines, IOPS rate, etc.). Taking advantage of this pricing scheme, customers can implement auto-scaling purchase policies by leasing (e.g., hourly) necessary amounts of resources to satisfy a desired QoS threshold under their current demand. Auto-scaling yields strict QoS and variable charges. Some customers, however, would be willing to settle for a more relaxed statistical QoS in exchange for a predictable flat charge. In this work we propose Temporal Rate Limiting (TRL), a purchase policy that permits a customer to allocate optimally a specified purchase budget over a predefined period of time. TRL offers the same expected QoS with auto-scaling but at a lower, flat charge. It also outperforms in terms of QoS a naive flat charge policy that splits the available budget uniformly in time. We quantify the benefits of TRL analytically and also deploy TRL on Amazon EC2 and perform a live validation in the context of a "blacklisting" application for Twitter

Quantifying the Costs of Customers for Usage-Based Pricing

This chapter focuses on the quantification of the customers' costs in communication networks, that is, how an operator should share the cost of the infrastructure among its customers. It reviews the cost of the network infrastructure and also how this cost is affected by the aggregate traffic of the customers. The chapter provides a metric, namely, discrepancy that quantifies the differences of cost-sharing policies. The first source of discrepancies in some cost allocation methods is the function that the operator uses to compute the contribution of the customers to the aggregate cost (i.e., F-discrepancy). The traffic metering method is the second source of the discrepancies (i.e., M-discrepancy). The third class of discrepancies is related to the total cost of ownership (TCO) of different devices of the network. The final type of discrepancies is caused by the different types of customer liability

Understanding individual routing behaviour

Knowing how individuals move between places is fundamental to advance our understanding of human mobility (González et al. 2008 Nature 453, 779–782. (doi:10.1038/nature06958)), improve our urban infrastructure (Prato 2009 J. Choice Model. 2, 65–100. (doi:10.1016/S1755-5345(13)70005-8)) and drive the development of transportation systems. Current route-choice models that are used in transportation planning are based on the widely accepted assumption that people follow the minimum cost path (Wardrop 1952 Proc. Inst. Civ. Eng. 1, 325–362. (doi:10.1680/ipeds.1952.11362)), despite little empirical support. Fine-grained location traces collected by smart devices give us today an unprecedented opportunity to learn how citizens organize their travel plans into a set of routes, and how similar behaviour patterns emerge among distinct individual choices. Here we study 92 419 anonymized GPS trajectories describing the movement of personal cars over an 18-month period. We group user trips by origin–destination and we find that most drivers use a small number of routes for their routine journeys, and tend to have a preferred route for frequent trips. In contrast to the cost minimization assumption, we also find that a significant fraction of drivers' routes are not optimal. We present a spatial probability distribution that bounds the route selection space within an ellipse, having the origin and the destination as focal points, characterized by high eccentricity independent of the scale. While individual routing choices are not captured by path optimization, their spatial bounds are similar, even for trips performed by distinct individuals and at various scales. These basic discoveries can inform realistic route-choice models that are not based on optimization, having an impact on several applications, such as infrastructure planning, routing recommendation systems and new mobility solutions