<p>Next-generation (Next-Gen) mobile applications, Extended Reality (XR), which encompasses Virtual/Augmented/Mixed Reality (VR/AR/MR), promise to revolutionize how people interact with technology and the world, ushering in a new era of immersive experiences. However, the hardware capacity of mobile devices will not grow proportionally with the escalating resource demands of the mobile apps due to their battery constraint. To bridge the gap, edge computing has emerged as a promising approach. It is further boosted by emerging 5G cellular networks, which promise low latency and high bandwidth. However, realizing the full potential of edge computing faces several fundamental challenges.</p>
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<p>In this thesis, we first discuss a set of fundamental design challenges in supporting Next-Gen mobile applications via edge computing. These challenges extend across the three key system components involved — mobile clients, edge servers, and cellular networks. We then present how we address several of these challenges, including (1) how to coordinate mobile clients and edge servers to achieve stringent QoE requirements for Next-Gen apps; (2) how to optimize energy consumption of running Next-Gen apps on mobile devices to ensure long-lasting user experience; and (3) how to model and generate control-plane traffic of cellular networks to enable innovation on mobile network architectural design to support Next-Gen apps not only over 4G but also over 5G and beyond.</p>
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<p>First, we present how to optimize the latency in edge-assisted XR system via the mobile-client and edge-server co-design. Specifically, we exploit key insights about frame similarity in VR to build the first multiplayer edge-assisted VR design, Coterie. We demonstrate that compared with the prior work on single-player VR, Coterie reduces the per-player network load by 10.6X−25.7X, and can easily support 4 players for high-quality VR apps on Pixel 2 over 802.11ac running at 60 FPS and under 16ms responsiveness without exhausting the finite wireless bandwidth.</p>
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<p>Second, we focus on the energy perspective of running Next-Gen apps on mobile devices. We study a major limitation of a classic and de facto app energy management technique, reactive energy-aware app adaptation, which was first proposed two decades ago. We propose, design, and validate a new solution, the first proactive energy-aware app adaptation, that effectively tackles the limitation and achieves higher app QoE while meeting a given energy drain target. Compared with traditional approaches, our proactive solution improves the QoE by 44.8% (Pixel 2) and 19.2% (Moto Z3) under low power budget.</p>
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<p>Finally, we delve into the third system component, cellular networks. To facilitate innovation in mobile network architecture to better support Next-Gen apps, we characterize and model the control-plane traffic of cellular networks, which has been mostly overlooked by prior work. To model the control-plane traffic, we first prove that traditional probability distributions that have been widely used for modeling Internet traffic (e.g., Poisson, Pareto, and Weibull) cannot model the control-plane traffic due to the much higher burstiness and longer tails in the cumulative distributions of the control-plane traffic. We then propose a two-level state-machine-based traffic model based on the Semi-Markov model. We finally validate that the synthesized traces by using our model achieve small differences compared with the real traces, i.e., within 1.7%, 4.9% and 0.8%, for phones, connected cars, and tablets, respectively. We also show that our model can be easily adjusted from LTE to 5G, enabling further research on control-plane design and optimization for 4G/5G and beyond.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/23647608 |
| Date | 10 July 2023 |
| Creators | Jiayi Meng (16512234) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/System_Support_for_Next-Gen_Mobile_Applications/23647608 |
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