Offloading mobile data via WiFi has been common practice for network operators and users alike. Operators might purchase WiFi infrastructure or lease a third-party one, to relieve their congested cellular infrastructure. User prefer to use WiFi at home, cafes, office, for bulk data transfers, video streaming, etc., as much as possible, to avoid depleting their cellular data plans, improve their rates, or sometimes to save their
battery life. As mentioned earlier, it is reported that60%percent of total mobile data traffic was offloaded onto the fixed network through Wi-Fi or femtocells in 2016 [4].
The ubiquity of WiFi access points, and the interest of network operators in their use, has motivated researchers to study a number of ways to use WiFi-based Internet access as an inexpensive complement to cellular access. One such proposal was to use WiFi access points (AP) while on the move, e.g., from a vehicle, accessing a sequence of encountered WiFi APs to download Internet content [43]. WiFi was originally de-signed for “nomadic” users, i.e., users who come into the range of the AP and stay for long periods of time, and not for moving users. A number of measurement studies were thus performed to identify the amount of data one could download from such an AP, travelling at different speeds [44, 45]. While the measured amounts suggested that reasonably sized files could already be downloaded even at high speeds, there were a number of shortcomings related to mobile WiFi access:
• The association process with a WiFi AP takes a long time (due to authentication procedures, scanning, and other suboptimalities in the protocol design). This wastes a large amount of the limited time during which a mobile node might be within communication range with that AP (and thus wastes communication capacity).
• The rate adaptation mechanism of WiFi, which reduces the PHY encoding rate (and thus the transmission rate), as a function of the SINR of the receiving node, has dire side-effects when a number of nodes on the move would try to access the same AP. In that case, there always exist at least some nodes at the edge of the AP coverage range, who receive the lowest possible transmission rate. However, it is well known that the way scheduling works in WiFi, the average performance is highly impacted by the existence of edge users [46]. Connecting to WiFi on the move greatly deteriorates the mean WiFi throughput for every node, even the ones close to the AP, as at least some of the mobile nodes are at the edge of the AP, entering its range (or leaving it).
A number of works emerged to address both these issues. Some of the ideas in-cluded streamlining the WiFi association procedure [47], maintaining a history of AP location and quality to improve the speed and efficiency of scanning [48], as well as modifications to the scheduling mechanism of WiFi, ensuring that the capacity is allo-cated to mobile nodes during the time they are close to the AP, a form ofopportunistic scheduling. While a lot of research activity was taking place in the context of protocol improvements, system design, and experimentation, there was little ongoing in terms of the theoretical understanding for WiFi offloading methods.
Chapter Contributions
This line of research made us interested in the following question:Assuming a stochas-tic traffic mix (i.e., both random traffic arrival, and random flow/session sizes), what is the expected performance of WiFi offloading, as a function of AP deployment and characteristics, mobile node behavior, and traffix characteristics?. We have attempted to answer it in the following works [49, 50]:
• F. Mehmeti, and T. Spyropoulos,“Performance Analysis of On-the-spot Mobile Data Offloading,”in Proceedings of IEEE GLOBECOM 2013.
• F. Mehmeti, and T. Spyropoulos,“Performance analysis of mobile data offload-ing in heterogeneous networks,” in IEEE Transactions on Mobile Computing, 16(2): 482-497, 2017.
Our contributions can be summarized as follows:
Contribution 5.1We proposed a queueing-theoretic model, an M/G/1 with different levels of service rate. Assuming a user generating random download requests and a common queue on top of the WiFi and cellular interfaces: when a WiFi AP is in range, these requests are always served through the WiFi interface (with one servicerate); if there is no WiFi in range, the requests are served by cellular interface (with a different service rate).
Contribution 5.2We analyzed the performance of this system for both First Come First Serve (FCFS) and Processor Sharing (PS) queueing disciplines, and derived closed form expressions for the expected amount of offloaded data, as well as the mean down-load performance. These expressions are a function of user mobility, WiFi coverage, and WiFi/Cellular capacity characteristics.
A second research direction that emerged was that of Delayed WiFi Offloading.
This was partly motivated by the study and exploitation of delay-tolerance (the appli-cation and/or the user might be tolerant to delays). As explained earlier, this delay torelance might sometimes be natural, and sometimes requires some incentives. To this end, researchers suggested that some traffic does not need to be immediately trans-mitted over the cellular interface (as required in the previous scenario), if there is no WiFi connectivity. Instead, such delay-tolerant download (or upload) requests could be queued at the WiFi interface, until a WiFi AP is encountered. If such an AP is not encountered until a maximum wait timer expires,only thenmust the request be turned over to the cellular interface [51, 52]. This more “aggressive” offload policy was shown to be able to offload even more data, at the expense of a delay increase for some traffic.
To this end, in the following works, we extended our performance analysis frame-work to investigate such delayed offloading policies as well [53, 54].
• F. Mehmeti, and T. Spyropoulos,“Is it worth to be patient? Analysis and opti-mization of delayed mobile data offloading,”in Proc. of IEEE INFOCOM 2014.
• F. Mehmeti, and T. Spyropoulos, “Performance modeling, analysis and opti-mization of delayed mobile data offloading under different service disciplines,”
in ACM/IEEE Transactions on Networking, 25(1): 550-564, 2017.
Contribution 5.3We used a queueing theoretic model with service interruptions and abandonments, to model the intermittent access to WiFi APs (the interruptions) and the possibility that a request waiting in the WiFi queue might expire and move back to the cellular interface (the abandonments).
Contribution 5.4We derived closed form expressions for the amount of offloaded data and mean per flow delay in this system, as a function of the mean delay-tolerance of the application mix considered, and other network parameters.
Contribution 5.5In a scenario where it is the user that can decide this delay tolerance, we showed our analytical expressions and convex optimization theory to show how the wait threshold can be optimized to achieve different vs-offloaded data or delay-vs-energy efficiency tradeoffs.