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