Characterizing, modeling, and improving the QoE of mobile devices with low battery level

Yan, Kaige; Zhang, Xingyao; Fu, Xin

doi:10.1145/2830772.2830786

Cited by 19 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• A different class of optimizations is that where the human user takes a central role. These user centric techniques have been advocated especially for mobile embedded computing platforms [106], [107], [108], [109], [110], [111]. These techniques model and then predict the user behavior to identify optimization opportunities for reducing energy consumption without degrading user-perceived performance.…”

Section: Section 5 Discussionmentioning

confidence: 99%

“…There is however another user-related variable−that of psychology of users−which can be exploited towards further energy savings. For example, the study in [111] exploits psychological changes during the low battery phase of mobile devices of different users to design a quality of experience (QoE) aware frequency governor. The role of the governor is to dynamically change the processor frequency in order to operate at the best QoE at for different users in different environments during low battery phases.…”

Section: Section 5 Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

Ababei

Moghaddam

2019

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

Section: Section 5 Discussionmentioning

confidence: 99%

Section: Section 5 Discussionmentioning

confidence: 99%

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

Ababei

Moghaddam

2019

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

“…Several user studies have leveraged dynamic voltage and frequency scaling (DVFS) to help balance the competing demands of energy efficiency and user satisfaction [16,22,23,24]. Individualized quality of service (QoS) metrics have also been proposed as a means towards achieving this balance [27,28,29]. Other work identifies the components and design configurations that yield higher user satisfaction [8,26].…”

Section: Related Workmentioning

confidence: 99%

How Much is Performance Worth to Users? A Quantitative Approach

Hastings¹,

Chilton²,

Sethumadhavan³

2022

Preprint

View full text Add to dashboard Cite

Architects and systems designers artfully balance multiple competing design constraints during the design process but are unable to translate between system metrics and end user experience. This work presents three methodologies to fill in this gap. The first is an incentive-compatible methodology that determines a "ground truth" measurement of users' value of speed in terms of US dollars, and find that users would accept a performance losses of 10%, 20%, and 30% to their personal computer in exchange for $2.27, $4.07, and $4.43 per day, respectively. However, while highly accurate the methodology is a painstaking process and does not scale with large numbers of participants. To allow for scalability, we introduce a second methodology-a lab-based simulation experiment-which finds that users would accept a permanent performance loss of 10%, 20%, and 30% to their

show abstract

“…Many past works investigate how to reduce data movement cost using a range of different compute-centric (e.g., prefetchers [26, 35, 42, 59, 67, 68, 72, 83, 101, 103, 104, 120, 144, 162, 182, 188, 192, 193, 225, 232-234, 313, 379], speculative execution [144,161,[290][291][292]296], value-prediction [55, 56, 107, 109, 119, 122, 139, 144, 253, 254, 302, 313, 351, 353, 400-402, 413, 433], data compression [8, 19, 73, 99, 108, 128, 157, 158, 183, 324-329, 410, 429, 445], approximate computing [226,272,310,433]) and memory-centric techniques [5,13,30,34,49,50,90,126,186,218,284,285,299,335,354,392,395,397,398,408,409,434,439,442]. These works evaluate the impact of data movement in different systems, including mobile systems [49,304,318,369,427], data centers [27,114...…”

Section: Related Workmentioning

confidence: 99%

DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

F.¹,

Gómez-Luna²,

Orosa³

et al. 2021

Preprint

View full text Add to dashboard Cite

Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data movement, spanning from traditional mechanisms (e.g., deep multi-level cache hierarchies, aggressive hardware prefetchers) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Prior NDP works investigate the root causes of data movement bottlenecks using different profiling methodologies and tools. However, there is still a lack of understanding about the key metrics that can identify different data movement bottlenecks and their relation to traditional and emerging data movement mitigation mechanisms. Our goal is to methodically identify potential sources of data movement over a broad set of applications and to comprehensively compare traditional compute-centric data movement mitigation techniques (e.g., caching and prefetching) to more memory-centric techniques (e.g., NDP), thereby developing a rigorous understanding of the best techniques to mitigate each source of data movement.With this goal in mind, we perform the first large-scale characterization of a wide variety of applications, across a wide range of application domains, to identify fundamental program properties that lead to data movement to/from main memory. We develop the first systematic methodology to classify applications based on the sources contributing to data movement bottlenecks. From our large-scale characterization of 77K functions across 345 applications, we select 144 functions to form the first open-source benchmark suite (DAMOV) for main memory data movement studies. We select a diverse range of functions that (1) represent different types of data movement bottlenecks, and (2) come from a wide range of application domains. Using NDP as a case study, we identify new insights about the different data movement bottlenecks and use these insights to determine the most suitable data movement mitigation mechanism for a particular application. We open-source DAMOV and the complete source code for our new characterization methodology at https://github.com/CMU-SAFARI/DAMOV. CCS Concepts: • Hardware → Dynamic memory; • Computing methodologies → Model development and analysis; • Computer systems organization → Architectures.

show abstract

Characterizing, modeling, and improving the QoE of mobile devices with low battery level

Cited by 19 publications

References 21 publications

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

How Much is Performance Worth to Users? A Quantitative Approach

DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

Contact Info

Product

Resources

About