Energy and Performance of Smartphone Radio Bundling in Outdoor Environments Ana Nika*, Yibo Zhu*, Ning Ding+, Abhilash Jindal+, Y. Charlie Hu+ Xia Zhou^, Ben Y. Zhao* and Haitao Zheng* *UC Santa Barbara, ^Dartmouth College, +Purdue University anika@cs.ucsb.edu

Wireless Bandwidth Crisis Many data-hungry applications for smartphones How do we support these new applications? Today, we face an increasing problem of wireless bandwidth shortage. More and more bandwidth-hungry applications are becoming available for smartphone users, such as video streaming, video games and video conferencing. Also, new applications are coming such as mobile virtual reality and ultra high definition video. The question is, how networks can support these new applications?

Single Radio Not Enough Cellular (LTE) Optimal: 75Mbps In practice: outdoors 2%-20% movement congestion On one hand, users can use the cellular network. The latest technology is LTE that can support high speeds of 75Mbps. However, such speed is mostly theoretical and in practice users can get only a portion of the optimal speed of about 2 to 20%. This happens because of many reasons such as user movement, environmental conditions or because the network becomes congested when many users are using it at the same time.

Single Radio Not Enough Indoors 60GHz WiFi: ~150Mbps 60GHz: ~6Gbps WiFi What about outdoors? WiFi suffers from interference, poor quality 60GHz small range, easily blocked On the other hand, for indoors a good option is WiFi that can provide speeds up to 150Mbps or 60GHz with speeds of 6Gbps. But, these speeds do not hold outdoors. For example, WiFi in places such as coffee shops or hotels, suffers from interference and poor quality in network connection when many users are connected at the same network. Also, 60GHz is not a good option for outdoors because it has very small range of about 9m and can be easily blocked by obstacles.

Radio Bundling 2Mbps 1Mbps Radio Bundling: 3Mbps LTE WiFi One solution is to bring the two worlds (WiFi & LTE) together by using radio bundling. Consider the following example, where a user is outdoors, potentially outside of a coffee shop. He has an available WiFi connection of 1Mbps and an LTE connection of 2Mbps. Radio bundling means he can use both networks at the same time. So, in the best case the user can get a speed of 3Mbps. Radio bundling is doable in today’s smartphones and it is a good candidate for outdoor environments where WiFi and LTE have comparable speeds. Already, major mobile carriers, like AT&T, are building many WiFi hotspots, where they can offload traffic. But, instead of data offloading, why not use both networks at the same time? Radio Bundling: 3Mbps

Our Goals What is the maximum benefit that can be provided by bundling on today’s devices? What is the energy cost of bundling? Via real measurements using a smartphone app in 5 US cities and 63 outdoor locations. So, our goal in this work is to answer two fundamental questions. We first want to understand what is the maximum performance benefit that can be provided by bundling on today’s devices. Second, we want to understand what is the energy cost of bundling, since bundling turns on two radios at the same time that might increase energy consumption. To answer these questions, we performed real measurements using a smartphone app in 5 US cities and 63 outdoor locations.

Existing Work MPTCP: transport-layer bundling implementation non-optimal (fair to non-MPTCP users) not designed for wireless radios does not consider energy Our goal is to study optimal radio bundling understand fundamental limitations of bundling how different radio usage options compare how we can improve current implementations Current studies have analyzed MPTCP which is a transport layer bundling implementation. This provides just a single point of understanding for bundling. However, we don’t know what bundling’s full potentials are since MPTCP is not an optimal implementation. MPTCP has not been designed for wireless radios and does not consider energy consumption. The focus of our work is to understand optimal radio bundling. By shedding light on the fundamental limitations of bundling, we can understand how different radio usage options compare and how we can improve current bundling implementations. For example, if we compare the different radio options in terms of energy versus throughput, WiFi-only and LTE-only have comparable throughputs, but LTE-only will consumes much more energy, because existing studies have shown that LTE radio draws more power than the WiFi radio. But, where MPTCP lies in terms of energy throughput tradeoff. And mostly what about optimal radio bundling? Energy Bundling ? LTE-only MPTCP ? Bundling ? WiFi-only Throughput

Not available by default Methodology Android-based app turn on WiFi & LTE radios simultaneously Power model for bundling and non-bundling take into account CPU & WiFi, LTE radios Measurements at 5 cities & 63 outdoor locations file download/upload (0.5MB-5MB in size) derive throughput and energy of different radio usage options capture different RF conditions in terms of Not available by default To give answers to the questions we set about radio bundling, we followed this methodology. We first built an Android-based app where we turned WiFi and LTE radios at the same time. This is not available by default in current devices. Then, we built a power model both for bundling and non-bundling implementations. The model takes into account WiFi and LTE radios and the CPU. Finally, we performed measurements in 5 cities and 63 outdoor locations. Each measurement consisted of downloading or uploading a file of some size, where the size could be from 0.5 to 5 MBytes. In post processing, we derived the throughput and the energy consumption of different radio usage options. We captured different environmental conditions by using the ratio of WiFi throughput to LTE throughput.

Radio Usage Options Bundling Non-bundling Optimal traffic partitioning trace playback analysis MPTCP Non-bundling Single Radio: LTE-only, WiFi-only Best Radio: max (LTE-only, WiFi-only) Radio Switching: instantaneous switching between two radios In this work, we examined the following radio usage options. First, optimal bundling where we consider optimal traffic partitioning. For that, we used trace playback analysis where we stop the transmission at the optimal time. Then, we examined MPTCP, which is a non-optimal implementation of bundling. We also, examined non-bundling implementations. These are single radio, where we either use LTE-only or WiFi-only. Another non-bundling implementation is the best radio, which uses the LTE or WiFi radios based on which radio has the highest throughput. Finally, we examined radio switching, which instantaneously switches between the two radios based on which radio is better at that specific time instance.

Energy Profiling Accurate signal-strength aware power model componentized: WiFi, LTE radios & CPU takes into account energy tail < 8% error rate Power draw projection over time Details To understand the energy consumption of bundling we built an accurate power model. The model consists of three parts, the model for WiFi-radio, the model for LTE-radio and the model for CPU. Also, we take into account the energy tail of WiFi and LTE, which is the energy that smartphone consumes after it has finished the transmission. We have verified that our model achieves less that 8% error rate. You can find more details for the detailed process of energy modeling and profiling in the paper. Here, I will give you an example of how our energy profiling works. The power model is capable to provide the power draw over time for each radio and the CPU. For example consider bundling. Here, each line shows the contribution of each component in the total power draw and the highest line, the blue one, is the total power over time. You can also observe that there is little energy consumption after the transmission ends, which is the energy tail of LTE radio. Energy tail

Outline Motivation Methodology Throughput Performance Energy Consumption Conclusion So far, I have talked about our motivation and methodology. Next, I will present our results in terms of bundling throughput improvement and energy cost.

Radio Frequency Conditions  LTE 100x better than WiFi  q Before presenting the results, let me explain the metric we used to understand RF conditions. We will use the ratio of throughput of WiFi to the throughput of LTE. When this ratio is 10 to the power of -2, LTE is 100 times better than WiFi and similarly, when the ratio is 10 to the power of 2, the WiFi throughput is 100 times better than LTE. WiFi 100x better than LTE

Bundling’s Throughput Improvement Gain: 2x – 5x MPTCP:40%-85% of optimal bundling First, we show the gain of bundling over best radio and radio switching. Bundling sometimes can be similar with best radio, which can be either LTE-only or WiFi-only. This depends on the throughput of each radio. Bundling achieves the maximum gain when both radios have almost the same throughput. In this case, bundling can have 2 times higher throughput than the single radio. For, switching the gain increases up to 5 times higher, because switching suffers from a delay to switch between the two radios. We also observe that MPTCP can achieve only a portion of bundling throughput. MPTCP performance fluctuates between 40% and 85% of the optimal bundling performance. MPTCP often “sacrifices” its throughput to maintain fairness between MPTCP and non-MPTCP clients. For example if one radio path becomes congested, MPTCP immediately lowers the data transfer on this path more than that of conventional TCP designs. So, there is significant room for improvement.

Sources of Bundling Gain Radio independence LTE and WiFi radios do not interfere with each other dual-core CPU supports both radios Traffic partitioning matters!!! accurately projects radio throughput to fully utilize both radios MPTCP performs poorly. An approach with high noise performs better than MPTCP. Next, we want to understand where the bundling gain comes from. The first reason is that LTE and WiFi radios are independent. They don’t interfere with each other because their carrier frequencies are widely separated. Also, modern smartphones have dual-cores or multi-core CPUs. In this case the CPU can support both radios because it spreads the load across the two cores. The second reason is traffic partitioning. Optimal bundling accurately projects radio throughput over time to optimally utilize both radios. However, in practice this is difficult to achieve. We examined different traffic partitioning options and compare their throughput result with the ideal bundling. The first approach is even where we split the traffic equally between the two radios. We observe that it performs bad because it does not take into account the quality of each radio. Then, we examine MPTCP results and we see that it also performs slightly better, but it is still far away from the optimal implementation. Then we use the average bundling throughput and we add Gaussian noise (50% and 2%). We observe that even an approach with high noise performs better than MPTCP. Even Noisy-50% Noisy-2% MPTCP

Energy Analysis LTE is the heaviest energy drainer consumes at least 50% of the total power draw CPU consumes a significant amount of energy 60% for WiFi-only 20%-23% for Bundling, MPTCP, LTE-only Next, we use our accurate power model to perform energy analysis and understand how each component contribute in the total energy consumption. We identified that LTE radio is the heaviest energy consumer. It consumes at least 50% of the total power draw. Also, in contrast to previous studies we identified that CPU consumes a significant amount of energy, up to 60% for WiFi-only and from 20 to 23% for Bundling, MPTCP and LTE-only. Using our power model we can compare the energy consumption for different usage options. Here we compare bundling and WiFi-only when transferring the same file size under similar conditions. Bundling has higher instantaneous power draw of about 3W compared to 1W for WiFi-only, but finishes the file transfer faster. Bundling needs about 5seconds and WiFi-only needs 7 seconds.

Bundling Energy Cost Energy cost of a file transfer LTE-only WiFi-only Bundling energy cost ≤ LTE-only cost Bundling energy cost > WiFi-only cost, because of LTE radio LTE-only WiFi-only We now examine how bundling energy consumption compares to other approaches when doing a file transfer. We first compare bundling with best radio in terms of the ratio of bundling total energy during the file transfer to the energy of best radio. If the ratio is higher than 1, it means that bundling consumes more energy than best radio. When LTE throughput is better than WiFi throughput, best radio is obviously the LTE radio. In this case, we observe that bundling consumes similar or less energy than the LTE radio. This happens because with bundling the file transfer ends earlier and energy consumption is reduced. When WiFi throughput is better than LTE throughput, the better radio is the WiFi radio. We observe that bundling consumes much more energy than WiFi radio because it turn’s on the more power hungry LTE radio. We next examine the energy cost of bundling when compared with MPTCP in terms of bundling to MPTCP energy ratio. We observe that sometimes MPTCP saves energy. This is surprising because, as we showed before, MPTCP has smaller throughput than bundling, so it must run longer to finish the file transfer. We identified, that Bundling consumes more CPU energy than MPTCP because it uses two threads at the same time. MPTCP sometimes saves energy Bundling consumes more CPU energy

Energy Cost vs. Throughput Gain Potential bundling implementation Finally, we examine the energy cost of bundling versus its throughput gain. We identified that as bundling throughput gain increases, its energy cost decreases. This happens because bundling improves transfer time so the file transfer ends earlier. So, as the transfer time decreases, bundling saves energy, because it uses both radios for less time. This compensates the extra energy that comes from turning on the additional radio. An area around throughout gain of about 10 times that reduces energy consumption is the area that a bundling protocol should work. By finding an appropriate traffic partitioning and reducing CPU energy consumption we can realize such bundling implementation. Bundling improves transfer throughput & transfer ends earlier Energy saving due to reduced transmission time compensates the extra power draw of the additional radio

Conclusion Bundling is highly beneficial, achieve 2x-5x improvement over single radio MPTCP achieves only a portion of the total performance possible Bundling has higher instantaneous power draw, but can lead to lower energy cost due to faster transmission Our accurate componentized power model identifies the significant role CPUs play in energy usage There is ample room for a new bundling protocol that provides a better tradeoff between performance and energy usage So, in conclusion, we identified that radio bundling is highly beneficial and it achieves 2 to 5 times higher performance than a single radio. Although bundling can have higher instantaneous power draw, it can consumes less energy because it reduces the transmission time. The current bundling implementation, MPTCP achieves only a portion of the total performance possible. We created an accurate power models that identifies the role CPUs play in the total energy usage. We think that there is ample room for a new bundling protocol that has a better traffic partitioning and reduces CPU energy consumption.

Thank You! Questions?

Impact of File Size on Throughput Consistent across all transfer sizes Slightly higher for 512KB and 1MB transfers TCP slow start

Impact of File Size on Energy Consistent across all transfer sizes Higher for 512KB and 1MB transfers LTE energy tail