Mission-critical monitoring services, such as finding criminals with a monitoring camera, require rapid detection of newly updated data, where suppressing delay is desirable. Taking this direction, this paper proposes a network design scheme to minimize this delay for monitoring services that consist of Internet-of-Things (IoT) devices located at terminal endpoints (TEs), databases (DB), and applications (APLs). The proposed scheme determines the allocation of DB and APLs and the selection of the server to which TE belongs. DB and APL are allocated on an optimal server from multiple servers in the network. We formulate the proposed network design scheme as an integer linear programming problem. The delay reduction effect of the proposed scheme is evaluated under two network topologies and a monitoring camera system network. In the two network topologies, the delays of the proposed scheme are 78 and 80 percent, compared to that of the conventional scheme. In the monitoring camera system network, the delay of the proposed scheme is 77 percent compared to that of the conventional scheme. These results indicate that the proposed scheme reduces the delay compared to the conventional scheme where APLs are located near TEs. The computation time of the proposed scheme is acceptable for the design phase before the service is launched. The proposed scheme can contribute to a network design that detects newly added objects quickly in the monitoring services.

Estimating the packet loss ratio of TCP transfers is essential for passively measuring Quality of Service (QoS) on the Internet traffic. However, only a few studies have been conducted on this issue. The Benko-Veres algorithm is one technique for estimating the packet loss ratio of two networks separated by a measurement point. However, this study shows that it leads to an estimation error of a few hundred percent in the particular environment where the packet loss probabilities between the two networks are asymmetrical. We propose a passive method for packet loss estimation that offers improved estimation accuracy by introducing classification conditions for the TCP retransmission timeout. An experiment shows that our proposed algorithm suppressed the maximum estimation error to less than 15%.