Attaining extremely low latency service in 5G cellular networks is an important challenge in the communication research field. A higher QoS in the next-generation network could enable several unprecedented services, such as Tactile Internet, Augmented Reality, and Virtual Reality. However, these services will all need support from powerful computational resources provided through cloud computing. Unfortunately, the geolocation of cloud data centers could be insufficient to satisfy the latency aimed for in 5G networks. The physical distance between servers and users will sometimes be too great to enable quick reaction within the service time boundary. The problem of long latency resulting from long communication distances can be solved by Mobile Edge Computing (MEC), though, which places many servers along the edges of networks. MEC can provide shorter communication latency, but total latency consists of both the transmission and the processing times. Always selecting the closest edge server will lead to a longer computing latency in many cases, especially when there is a mass of users around particular edge servers. Therefore, the research studies the effects of both latencies. The communication latency is represented by hop count, and the computation latency is modeled by processor sharing (PS). An optimization model and selection policies are also proposed. Quantitative evaluations using simulations show that selecting a server according to the lowest total latency leads to the best performance, and permitting an over-latency barrier would further improve results.

A transmission control protocol (TCP) using an additive increase multiplicative decrease (AIMD) algorithm for congestion control plays a leading role in advanced Internet services. However, the AIMD method shows only low link utilization in lossy networks with long delay such as satellite networks. This is because the cwnd dynamics of TCP are reduced by long propagation delay, and TCP uses an inadequate congestion control algorithm, which does not distinguish packet loss from wireless errors from that due to congestion of the wireless networks. To overcome these problems, we propose an exponential recovery (ER) TCP that uses the exponential recovery function for rapidly occupying available bandwidth during a congestion avoidance period, and an adaptive congestion window decrease scheme using timestamp base available bandwidth estimation (TABE) to cope with wireless channel errors. We simulate the proposed ER-TCP under various test scenarios using the ns-2 network simulator to verify its performance enhancement. Simulation results show that the proposal is a more suitable TCP than the several TCP variants under long delay and heavy loss probability environments of satellite networks.

This paper describes an application of a speech translation system to another task/domain in the real-world by using developmental data collected from real-world interactions. The total cost for this task-alteration was calculated to be 9 Person-Month. The newly applied system was also evaluated by using speech data collected from real-world interactions. For real-world speech having a machine-friendly speaking style, the newly applied system could recognize typical sentences with a word accuracy of 90% or better. We also found that, concerning the overall speech translation performance, the system could translate about 80% of the input Japanese speech into acceptable English sentences.