PALOMA Latency Minimization in Mobile Apps
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Solution Overview
Problem
Mobile applications experience significant latency due to network bottlenecks, particularly in wireless networks with high latency and low bandwidth, which negatively impacts user experience and has economic consequences, and existing prefetching techniques are limited by reliance on server hints, developer annotations, and historical data, making them inefficient and scalable.
Innovation Solution
The Program Analysis for Latency Optimization of Mobile Apps (PALOMA) employs a client-centric, automated approach using string and callback analysis to identify and prefetch HTTP requests, allowing immediate responses by overlapping speculative executions with on-demand requests, thereby reducing user-perceived latency to near zero.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional prefetching techniques using server hints, developer annotations, and historical data are used, then some latency reduction may be achieved, but the system remains complex and scalability is limited
Solution Approach 1:
The system performs self-service by automatically analyzing its own code to identify HTTP requests and generate prefetching strategies without requiring external server hints or developer annotations. The code analyzer examines the mobile application's source code, identifies HTTP request patterns, and creates prefetching rules autonomously, eliminating the need for complex external coordination systems.
Solution Approach 2:
The system performs preliminary action by prefetching HTTP requests before they are actually needed. The method identifies potential HTTP requests through static analysis, determines optimal prefetching points in the code, and executes these requests in advance, so that when the user actually triggers the request, the data is already available locally, reducing perceived latency to near zero.
2Loss of time
If HTTP requests are prefetched in advance, then user-perceived latency is reduced to near zero, but network bandwidth and device resources are consumed
Solution Approach 1:
The system applies partial action by selectively prefetching only specific HTTP requests rather than all requests. The code analyzer identifies which requests are candidates for prefetching based on their patterns and importance, and the system prefetches only those that will provide the most benefit, avoiding unnecessary consumption of network bandwidth and device resources for requests that wouldn't otherwise be made.
Solution Approach 2:
The system changes parameters by dynamically adjusting prefetching behavior based on runtime conditions. The method monitors network status, device resources, and user behavior patterns, and modifies prefetching parameters accordingly - such as adjusting the aggressiveness of prefetching, selecting different prefetching points, or changing which requests to prefetch - thereby optimizing the balance between latency reduction and resource consumption.
3Measurement precision
If static analysis is used to identify prefetching candidates, then prefetching accuracy is improved, but analysis time and processing overhead increase
Solution Approach 1:
The system applies segmentation by dividing the code analysis into distinct phases and components. The code analyzer is broken down into modules that perform different functions: identifying HTTP requests, analyzing control flow, determining prefetching points, and generating prefetching rules. This modular approach allows each segment to be optimized independently and enables parallel processing, reducing overall analysis time while maintaining high accuracy.
Data Source
AI summary
Systems and methods for reducing latency in use of mobile applications include creating a list of potential internet requests from a mobile application based on an analysis of the mobile application. The systems and methods include creating a trigger map that maps each of a plurality of trigger points of the mobile application with a corresponding target internet request to be prefetched from the list of potential internet requests. The systems and methods include creating a URL map that maps each of a plurality of the potential internet requests with corresponding URL values. The systems and methods include identifying that a current app function matches a trigger point of the plurality of trigger points. The systems and methods include performing the potential internet request in response to identifying that the current app function matches the trigger point prior to the target request being received in order to reduce latency.