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When mobile apps are used extensively in our daily lives, their responsiveness has become an important factor that can negatively impact the user experience. The long response time of a mobile app can be caused by a variety of reasons, including soft hang bugs or prolonged user interface APIs (UI-APIs). While hang bugs have been researched extensively before, our investigation on UI-APIs in today’s mobile OS finds that the recursive construction of UI view hierarchy often can be time-consuming, due to the complexity of today’s UI views. To accelerate UI processing, such complex views can be pre-processed and cached before the user even visits them. However, pre-caching every view in a mobile app is infeasible due to the incurred overheads on time, energy, and cache space. In this paper, we propose MAPP, a framework for Mobile App Predictive Pre-caching. MAPP has two main modules, 1) UI view prediction based on deep learning and 2) UI-API pre-caching, which coordinate to improve the responsiveness of mobile apps. MAPP adopts a per-user and per-app prediction model that is tailored based on the analysis of collected user traces, such as location, time, or the sequence of previously visited views. A dynamic feature ranking and model selection algorithm is designed to judiciously filter out less relevant features for improving the prediction accuracy with less computation overhead. MAPP is evaluated with 61 real-world traces from 18 volunteers over 30 days to show that it can shorten the response time of mobile apps by 59.84% on average with an average cache hit rate of 92.55%. 

Autonomous mobile robots (AMRs) are capable of carrying out operations continuously for 24/7, which enables them to optimize tasks, increase throughput, and meet demanding operational requirements. To ensure seamless and uninterrupted operations, an effective coordination of task allocation and charging schedules is crucial while considering the preservation of battery sustainability. Moreover, regular preventive main- tenance plays an important role in enhancing the robustness of AMRs against hardware failures and abnormalities during task execution. However, existing works do not consider the influence of properly scheduling AMR maintenance on both task downtime and battery lifespan. In this paper, we propose MTC, a maintenance-aware task and charging scheduler designed for fleets of AMR operating continuously in highly automated envi- ronments. MTC leverages Linear Programming (LP) to first help decide the best time to schedule maintenance for a given set of AMRs. Subsequently, the Kuhn-Munkres algorithm, a variant of the Hungarian algorithm, is used to finalize task assignments and carry out the charge scheduling to minimize the combined cost of task downtime and battery degradation. Experimental results demonstrate the effectiveness of MTC, reducing the combined total cost up to 3.45 times and providing up to 68% improvement in battery capacity degradation compared to the baselines. 

Autonomous Mobile Robots (AMRs) rely on rechargeable batteries to execute several objective tasks during navigation. Previous research has focused on minimizing task downtime by coordinating task allocation and/or charge scheduling across multiple AMRs. However, they do not jointly ensure low task downtime and high-quality battery life.In this paper, we present TCM, a Task allocation and Charging Manager for AMR fleets. TCM allocates objective tasks to AMRs and schedules their charging times at the available charging stations for minimized task downtime and maximized AMR batteries’ quality of life. We formulate the TCM problem as an MINLP problem and propose a polynomial-time multi-period TCM greedy algorithm that periodically adapts its decisions for high robustness to energy modeling errors. We experimentally show that, compared to the MINLP implementation in Gurobi solver, the designed algorithm provides solutions with a performance ratio of 1.15 at a fraction of the execution time. Furthermore, compared to representative baselines that only focus on task downtime, TCM achieves similar task allocation results while providing much higher battery quality of life.