Overlay routing is an application-level routing mechanism on overlay networks. Previous researches have revealed that the overlay routing can improve user-perceived performance. However, it may also generate traffic unintended by ISPs, incurring additional monetary cost. In addition, since ISPs and end users have their own objectives respectively regarding traffic routing, overlay routing must be operated considering both standpoints. In the present paper, we propose a method to reduce inter-ISP transit costs caused by overlay routing from the both standpoints of ISPs and end users. To determine the relationships among ASes, which are required for ISP cost-aware routing, we construct a method to estimate a transit cost of overlay-routed paths from end-to-end network performance values. Utilizing the metric, we propose a novel method that controls overlay routing from the both standpoints of ISPs and end users. Through extensive evaluations using measurement results from the actual network environments, we confirm that the advantage of the proposed method whereby we can reduce the transit cost in the overlay routing and can control the overlay routing according to the objectives of both ISPs and end users.

Content-Centric Networking (CCN) has an in-network caching mechanism, which can reduce the traffic volume along the route to the destination host. This traffic volume reduction on the transit link can decrease inter-ISP transit cost. However, the memory space for caching in CCN routers is small relative to content volume. In addition, any initial access to the content requested by a user must use the transit link, even when a nearby CCN router outside the route has the cached content. In this paper, we propose a method of cooperative cache sharing among CCN routers in multiple ISPs. It aims to attain a further reduction in the inter-ISP transit cost by improving the cache hit ratio. In the proposed method, the CCN routers share the memory space for caching of non-overlapping cache content. We evaluate the proposed method by simulation experiments using the IP-level network topology of actual ISP, and show that the inter-ISP transit traffic can be reduced by up to 28% compared with normal caching behavior of CCN.

Application-level routing that chooses an end-to-end traffic route that relays other end hosts can improve user-perceived performance metrics such as end-to-end latency and available bandwidth. However, selfish route selection performed by each end user can lead to a decrease in path performance due to overload by route overlaps, as well as an increase in the inter-ISP transit cost as a result of utilizing more transit links compared with native IP routing. In this paper, we first strictly define an optimization problem for selecting application-level traffic routes with the aim of maximizing end-to-end network performance under a transit cost constraint. We then propose an application-level traffic routing method based on distributed simulated annealing to obtain good solutions to the problem. We evaluate the performance of the proposed method by assuming that PlanetLab nodes utilize application-level traffic routing. We show that the proposed routing method can result in considerable improvement of network performance without increasing transit cost. In particular, when using end-to-end latency as a routing metric, the number of overloaded end-to-end paths can be reduced by about 65%, as compared with that when using non-coordinated methods. We also demonstrate that the proposed method can react to dynamic changes in traffic demand and select appropriate routes.

Statical causal discovery is an approach to infer the causal relationship between observed variables whose causalities are not revealed. LiNGAM (Linear Non-Gaussian Acyclic Model), an algorithm for causal discovery, can calculate the causal relationship uniquely if the independent components of variables are assumed to be non-Gaussian. However, use-cases of LiNGAM are limited because of its O(d3x) computational complexity, where dx is the number of variables. This paper shows two approaches to accelerate LiNGAM causal discovery: SIMD utilization for LiNGAM's mathematical matrixes operations and MPI parallelization. We evaluate the implementation with the supercomputer Fugaku. Using 96 nodes of Fugaku, our improved version can achieve 17,531 times faster than the original OSS implementation (completed in 17.7 hours).