Current network switches cannot be programmed and flexibly controlled. Then, developers of a data center application system, which is composed of software and computers connected with a network, are not able to optimize behavior of network switches on which the application is running. On the other hand, Deeply Programmable Network (DPN) switches can completely analyze packet payloads and be profoundly programmed. In our previous work, we introduced an application switch based on DPN. The switch was able to be deeply programed and developers could implement a part of functions of a data center application in the switch. The switch deeply analyzed packets, which is called Deep Packet Inspection (DPI), and provided some functions of the application in the switch. However, the switch did not manage connection and not support communication with TCP. In this paper, we proposed a method for constructing an application switch supporting TCP based communication. The method analyzes IP headers, TCP headers, and payloads of packets. When the switch detects a request which the switch supports, the switch replies according to its TCP session. We then introduce our implementation and evaluate performance of our application switch. Our evaluation has demonstrated that our switch has been able to improve performance of the data center applications.

Final coalgebras as “categorical greatest fixed points” play a central role in the theory of coalgebras. Somewhat analogously, most proof methods studied therein have focused on greatest fixed-point properties like safety and bisimilarity. Here we make a step towards categorical proof methods for least fixed-point properties over dynamical systems modeled as coalgebras. Concretely, we seek a categorical axiomatization of well-known proof methods for liveness, namely ranking functions (in nondeterministic settings) and ranking supermartingales (in probabilistic ones). We find an answer in a suitable combination of coalgebraic simulation (studied previously by the authors) and corecursive algebra as a classifier for (non-)well-foundedness.

Large scale applications in data centers are composed of computers connected with a network. Traditional network switches do not perform routing based on packet contents. Thus, packets cannot be transmitted to the optimal computer for the request which is written in the packet payload. On the other hand, Deeply Programmable Network (DPN) switches can completely analyze packet payloads and perform routing based on their contents. In this paper, we focus on packet routing based on Deep Packet Inspection (DPI), i.e. analyzing packet payload, using DPN switches, and discuss performance of applications on a network using application aware DPN switches. We introduce application aware network and propose a method for improving application performance with DPN switch support. We evaluate our method with the several applications and show that our method can increase application performance.

Several load balancing techniques for IP routing scheme have appeared in the literature. However, they require optimization process to compute optimal paths to meet traffic demand so that it requires a mechanism to measure traffic demand and to share them among all routes in order to follow dynamics of traffic. It naturally results in communication overhead and losing sensitivity to follow traffic dynamics. In this paper, we investigate a load balancing mechanism from another approach, i.e., based on IP fast reroute mechanisms. The main idea is simply to forward packets into detour paths supplied by IP fast reroute mechanisms only when packets meet congestion. This strategy enables us to use vacant resources adaptively as soon as they are required to avoid and dissolve the congestion. Through traffic simulation we show that IP fast reroute based load balancing mechanisms improve the capacity of networks.

In order to find desired information on mobile phones, it is necessary to set search keywords easily and to explore a search if a user's information needs are not well defined. We propose an information navigation method to help users succeed in finding information on mobiles. Our proposal method presents to users a focus facet by analyzing context about contents, users and dialog. The focus facet is presented each time users refine a search. Users can select their desired keyword from the facet, and check the search results, and then users can define their ambiguous needs through interaction. We evaluated our method compared to a traditional search method. In our experiments, a user's load for setting some keywords of our method is lower than the traditional search method. Users can experience greater search success and satisfaction using our method than the traditional search method.