Jiamo Liu

Jiamo Liu

PhD student at UCSB, current research involves investigating video streaming QoE of satellite network

Publications

Characterizing Internet Access and Quality Inequities in California M-Lab Measurements

Published in ACM COMPASS, 2022

It is well documented that, in the United States (U.S.), the availability of Internet access is related to several demographic attributes. Data collected through end user network diagnostic tools, such as the one provided by the Measurement Lab (M-Lab) Speed Test, allows the extension of prior work by exploring the relationship between the quality, as opposed to only the availability, of Internet access and demographic attributes of users of the platform. In this study, we use network measurements collected from the users of Speed Test by M-Lab and demographic data to characterize the relationship between the quality-of-service (QoS) metric download speed, and various critical demographic attributes, such as income, education level, and poverty. For brevity, we limit our focus to the state of California. For users of the M-Lab Speed Test, our study has the following key takeaways: (1) geographic type (urban/rural) and income level in an area have the most significant relationship to download speed; (2) average download speed in rural areas is 2.5 times lower than urban areas; (3) the COVID-19 pandemic had a varied impact on download speeds for different demographic attributes; and (4) the U.S. Federal Communication Commission’s (FCC’s) broadband speed data significantly overrepresents the download speed for rural and low-income communities compared to what is recorded through Speed Test.

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Traffic-profile and machine learning based regional data center design and operation for 5G network

Published in Journal of Communications and Networks, 2019

Data center in the fifth generation (5G) network will serve as a facilitator to move the wireless communication industry from a proprietary hardware based approach to a more software oriented environment. Techniques such as Software defined networking (SDN) and network function virtualization (NFV) would be able to deploy network functionalities such as service and packet gateways as software. These virtual functionalities however would require computational power from data centers. Therefore, these data centers need to be properly placed and carefully designed based on the volume of traffic they are meant to serve. In this work, we first divide the city of Milan, Italy into different zones using Kmeans clustering algorithm. We then analyse the traffic profiles of these zones in the city using a network operator’s Open Big Data set. We identify the optimal placement of data centers as a facility location problem and propose the use of Weiszfeld’s algorithm to solve it. Furthermore, based on our analysis of traffic profiles in different zones, we heuristically determine the ideal dimension of the data center in each zone. Additionally, to aid operation and facilitate dynamic utilization of data center resources, we use the state of the art recurrent neural network models to predict the future traffic demands according to past demand profiles of each area.

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Traffic-aware heuristic BBU-RRH switching scheme to enhance QoS and reduce complexity

Published in PIMRC, 2018

Cloud Radio Access Network (C-RAN) is a new architecture that has been proposed to enable the current hardware to meet the ever-increasing traffic demand, as well as reducing the energy consumption of mobile base stations. This paper mainly focuses on two components of C-RAN, namely Remote Radio Heads (RRH) and Baseband Processing Unit (BBU) pool. The method of association of these two components could potentially affect the Quality of Service (QoS) and energy consumption level of the system. The connection between RRH(s) and BBU(s) is logical in C-RAN, which means that the association of RRH(s) to BBU(s) can be dynamically adjusted. Thus, a BBU-RRH switching scheme is required to manage the computational resource that the BBU pool possesses. This paper proposes a switching algorithm that works in conjunction with the knowledge of the traffic pattern of an area. This algorithm not only reduces the number of BBUs used in comparison with the traditional approach, but also decreases the number of switches required while maintaining a satisfactory level of service. In order to achieve this, the proposed algorithm reduces or limits the load of BBUs when the overall traffic of a BBU is on the rise, and vice versa. Finally, the simulation results illustrate that the proposed algorithm reduces the switching complexity and improves QoS while achieving significant reduction of BBU usage in comparison to traditional RAN.

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