Yongchun Zheng, Changlong Li, Yi Xiong, Weihong Liu, Cheng Ji, Zongwei Zhu, Lichen Yu
{"title":"iAware: Interaction Aware Task Scheduling for Reducing Resource Contention in Mobile Systems","authors":"Yongchun Zheng, Changlong Li, Yi Xiong, Weihong Liu, Cheng Ji, Zongwei Zhu, Lichen Yu","doi":"10.1145/3609391","DOIUrl":null,"url":null,"abstract":"To ensure the user experience of mobile systems, the foreground application can be differentiated to minimize the impact of background applications. However, this article observes that system services in the kernel and framework layer, instead of background applications, are now the major resource competitors. Specifically, these service tasks tend to be quiet when people rarely interact with the foreground application and active when interactions become frequent, and this high overlap of busy times leads to contention for resources. This article proposes iAware, an interaction-aware task scheduling framework in mobile systems. The key insight is to make use of the previously ignored idle period and schedule service tasks to run at that period. iAware quantify the interaction characteristic based on the screen touch event, and successfully stagger the periods of frequent user interactions. With iAware, service tasks tend to run when few interactions occur, for example, when the device’s screen is turned off, instead of when the user is frequently interacting with it. iAware is implemented on real smartphones. Experimental results show that the user experience is significantly improved with iAware. Compared to the state-of-the-art, the application launching speed and frame rate are enhanced by 38.89% and 7.97% separately, with no more than 1% additional battery consumption.","PeriodicalId":50914,"journal":{"name":"ACM Transactions on Embedded Computing Systems","volume":"25 1","pages":"0"},"PeriodicalIF":2.8000,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Embedded Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3609391","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
To ensure the user experience of mobile systems, the foreground application can be differentiated to minimize the impact of background applications. However, this article observes that system services in the kernel and framework layer, instead of background applications, are now the major resource competitors. Specifically, these service tasks tend to be quiet when people rarely interact with the foreground application and active when interactions become frequent, and this high overlap of busy times leads to contention for resources. This article proposes iAware, an interaction-aware task scheduling framework in mobile systems. The key insight is to make use of the previously ignored idle period and schedule service tasks to run at that period. iAware quantify the interaction characteristic based on the screen touch event, and successfully stagger the periods of frequent user interactions. With iAware, service tasks tend to run when few interactions occur, for example, when the device’s screen is turned off, instead of when the user is frequently interacting with it. iAware is implemented on real smartphones. Experimental results show that the user experience is significantly improved with iAware. Compared to the state-of-the-art, the application launching speed and frame rate are enhanced by 38.89% and 7.97% separately, with no more than 1% additional battery consumption.
期刊介绍:
The design of embedded computing systems, both the software and hardware, increasingly relies on sophisticated algorithms, analytical models, and methodologies. ACM Transactions on Embedded Computing Systems (TECS) aims to present the leading work relating to the analysis, design, behavior, and experience with embedded computing systems.