Simulation Study of Low Latency Network Architecture Using Mobile Edge Computing

• Published in: IEICE Transactions on Information and Systems Vol. E100.D; no. 5; pp. 963 - 972
• Main Authors: INTHARAWIJITR, Krittin, IIDA, Katsuyoshi, KOGA, Hiroyuki
• Format: Journal Article
• Language: English
• Published: Tokyo The Institute of Electronics, Information and Communication Engineers 01.01.2017; Japan Science and Technology Agency
• Subjects: and performance evaluation; Augmented reality; Cellular communication; Cloud computing; Communication; Computer simulation; Data centers; Edge computing; low latency; Microprocessors; Mobile computing; mobile edge computing; network architecture; Network latency; processor sharing; Quality of service architectures; Virtual reality; Wireless networks
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.2016NTP0003

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.