1. Power management for mobile & IoT systems
<Publications>
1. "Reward-Oriented Task Offloading under Limited Edge Server Power for Multi-Access Edge Computing", IEEE Internet-of-Things (IoT) journal 8(17), September, 2021
Abstract: In multi-access edge computing (MEC), tasks are offloaded from mobile devices to servers at the edge of the network. This speeds up task processing without incurring the latency required to reach central servers. However, the power used by edge servers is significant and needs to be cost-effective. We propose a scheme in which tasks are offloaded to servers with the aim of maximizing a reward within a limited power budget, server processing capacities, and wireless network coverage. Our algorithm determines the maximum utilization of each server while favoring the offloading of tasks with high ratios of reward to power requirement. We model the task allocation problem using a minimum-cost-maximum-flow graph, and propose two edge allocation algorithms, one of which is extended to allow task splitting, which offload tasks subject to server capacity by searching for the highest reward. In simulations, our scheme achieved between 8% and 80% higher rewards than alternative schemes, under the same power constraints.
2. "Saving Power in Video Playback on OLED Displays by Acceptable Changes to Perceived Brightness", IEEE/OSA Journal of Display Technology, vol. 12, no.5, May, 2016.
Abstract: Displays based on organic light-emitting diodes (OLEDs) are now widely used in mobile devices, in which they are major power consumers. The power drawn by an OLED display increases non-linearly with sub-pixel intensities: thus reducing brightness saves appreciable power, but can displease users. This paper examines this tradeoff, and proposes a color blending scheme in which each frame is darkened in a way that reduces power consumption significantly while limiting the visual impact. The target lightness of frames is determined, in the LAB color space, from average intensities of the original frames; and these average values are in turn obtained by adaptive sampling, so as to reduce the computational overhead. An Android smartphone uses 12% to 36% less power when running this scheme, compared to standard video playback, while a user evaluation suggested that reducing brightness to a certain extent can be largely unnoticed, or readily tolerated.
3. "Balancing Disk Energy against Reliability in Video Playback", ACM/Springer Multimedia Systems Journal, vol. 20, no. 1, February, 2014.
Abstract: Video files contain large amount of data, which can be stored cost effectively on a hard disk drive; but this consumes a significant energy when it is spinning and ready to read data. The energy used by a disk can be reduced by prefetching video frames into buffer to allow the disk to spin down. But frequent spindowns compromise disk life, so it is desirable to limit the number of times that the disk spins down. We propose a method of data prefetching that fully utilizes the available buffer, while providing continuous video playback. We analyze the effect of the amount of data comprising the frames in the buffer on disk power consumption and formulate algorithms that determine when the disk should enter standby mode, and the optimal number of disk spindowns. We implemented our scheme in the Linux 2.6 MPlayer and find that a portable 1.8-inch disk uses between 10% and 37% less energy than it does with the existing MPlayer.
4. "Aggressive Dynamic Voltage Scaling for Energy-Aware Video Playback Based on Decoding Time Estimation", ACM International Conference on Embedded Software (EMSOFT), pp. 1-10, October, 2009.
Abstract: We present the design, implementation and evaluation of a dynamic voltage scaling (DVS) scheme for portable media players. We measured decoding times on real videos and extracted a precise relationship between frame size and decoding time using logarithmic regression. Based on this model, we propose a frequency selection algorithm which accepts some deadline misses, and selects the frequencies required to achieve a specfied deadline miss ratio. We implemented this scheme in MPlayer running on the Linux 2.6 and found that its system-wide energy consumption is up to 17% less than conventional DVS schemes and up to 24% less than non-DVS schemes.
5. "Design and Implementation of Bitrate Adaptation Schemes for Power Capping in Wi-Fi Video Streaming," IEEE Access, 2020
Abstract: In dynamically adaptive streaming over HTTP (DASH), which is the de facto standard for streaming, each video is divided into segments, and each segment is further transcoded into multiple bitrate versions. This allows a client device to select the most appropriate bitrate version that matches the network bandwidth to avoid jitters or stalls. However, Wi-Fi download of a high-bitrate version may consume significant energy, especially when network conditions are good. To address this, we propose a new streaming method that limits the energy consumed by mobile devices but maintains an acceptable video quality. First, we derive a power model to analyze how bitrate selection affects power consumption in smartphones. Based on this, we propose two algorithms that determine the bitrate of each segment with the aim of maximizing overall video quality while limiting energy consumption. We use dynamic programming and heuristics to address the tradeoff between algorithm complexity and video quality. The proposed scheme was implemented on an Android-based DASH streaming platform, and various issues were resolved to cope with varying network conditions. Experimental results demonstrated that our scheme effectively optimized the video quality while limiting the energy consumption. For example: 1) our scheme uses 4% and 10% less power than DASH while maintaining an excellent video quality, and 2) the average difference between estimated and actual power consumption is 0.8%, thus keeping a precise energy bound.
<Related projects>
1. A Study on Low-Power Techniques for SVC-Based Streaming Systems (NRF, 2010.5 ~ 2012.4)
2. A Study on System-Level Power Optimization for Quality-of-Experience (QoE) Improvement for Multimedia Applications (NRF, 2012. 5 ~ 2015. 4)
3. A Study on Power Management Framework in Dynamically Adaptive Streaming Environment (NRF, 2015. 11 ~ 2018. 10)
2. System Software for Multimedia Systems
<Publications>
"Video File Allocation for Wear-Leveling in Distributed Storage Systems with Solid-State-Disks (SSDs) ", IEEE Transactions on Circuits and Systems for Video Technology, 2023.
Abstract: With the advent of new large-capacity solid-state disks (SSDs) such as quad-level-cells (QLC), SSD arrays can be effectively used in video storage systems that require large-capacity storage space. Typically, SSD manufacturers specify a drive-writes-per-day (DWPD) metric, which is the ratio of bytes written per day to the total capacity in bytes, to ensure an SSD’s specified lifetime; it is important to limit the number of write operations by considering the DWPD for each SSD. We propose a new video file allocation technique to effectively manage the heterogeneous DWPD characteristics of SSDs in distributed storage systems. To express the degree of wear-leveling for heterogeneous SSDs, we first introduce the concept of ADWD, which is the actual number of bytes written per day compared to DWPD. We then propose two algorithms for file placement and migration. The file placement algorithm places files greedily based on the bandwidth-to-space ratio (BSR) of each file and SSD to balance the bandwidth usage and storage of the SSD. The file migration algorithm moves files from overloaded to underloaded SSDs to meet bandwidth limit requirements while minimizing the overall ADWD as a result of migration, and then migrates additional popular files to improve SSD bandwidth utilization. To use these algorithms in actual distributed file systems, we implemented a suite of tools for file placement and migration in the Hadoop distributed file system (HDFS). Experimental results show that the proposed algorithm reduces the mean of ADWD by 35.44% and its standard deviation by 69.78% compared to the benchmark methods on average.
2. "Quality-Oriented Task Allocation and Scheduling in Transcoding Servers with Heterogeneous Processors", IEEE Transactions on Circuits and Systems for Video Technology, 2021.
Abstract: Dynamically adaptive streaming over HTTP requires a large-scale server to transcode various bitrate versions in which different preset parameters can be used to provide different video qualities at each resolution. When transcoding servers contain a heterogeneous mix of CPUs and GPUs, the task scheduler must choose a processor and preset parameter for each transcoding task to meet the transcoding deadlines while achieving the best possible video quality. We apply regression analysis to sample variable-bit-rate videos to provide accurate (mean absolute percentage error values from 1.3% to 13.9%) model for predicting bitrate, transcoding time and video quality at each resolution on different processors. We build this into a greedy allocation and scheduling algorithm which first satisfies deadlines with low video quality, and then redistributes the workload to improve that quality while continuing to meet the deadlines. This scheme was both simulated and implemented on a testbed server. It satisfies all deadlines while outperforming standard algorithms by between 3.12% and 15.59% in terms of popularity-weighted video quality divided by bitrate.
3. "Quality-Aware Transcoding Task Allocation under Limited Power in Live-Streaming Systems", IEEE Systems Journal, 2021
Abstract: Transcoding in video live-streaming systems requires a lot of computation, and hence a lot of power. Putting a limit on the power drawn by each of the transcoding processors in a server reduces the overall power consumption, but it also hinders the efficient allocation of transcoding tasks. We address this with a dynamic programming algorithm, together with a heuristic, which maximizes total processing capacity while limiting power consumption in a server with heterogeneous processors. A further greedy algorithm determines the bitrates at which content is transcoded for each channel, and allocates transcoding tasks to processors, while taking video quality, popularity,
and workload balance into account. The initial assumption is that all contents are transcoded to all bitrates for every channel. Then the algorithm gradually reduces the number of versions to be produced by transcoding, while minimizing the consequent reduction in popularity-weighted video quality, as well as balancing the workload across processors. Experimental results show that our scheme improves aggregate popularityweighted video quality under a power constraint by between 3.82% and 39.12%, compared to benchmark methods.
4. "QoE-Aware Video Storage Power Management Based on Hot and Cold Data Classification", ACM NOSSDAV 2018 (Best paper candidate in ACM NOSSDAV)
Abstract: Dynamically adaptive streaming over HTTP (DASH), the most common streaming technique, requires a video server to store all the transcoded versions, resulting in a lot of storage space, thereby consuming a significant disk power. A disk array can be divided into hot and cold zones to allow cold disks to be spun down, but this poses several questions such as (1) which video segments can be stored on the hot disks, (2) how to allocate video segments among the hot disks, and (3) how to handle requests to the cold disks. To address this, we propose three new algorithms; (1) a hot data classification algorithm to determine which segments should be stored on the hot disks, by taking segment popularity and quality-of-experience (QoE) into account, (2) a video segment allocation algorithm to balance workloads among the hot disks, and (3) a disk bandwidth allocation algorithm which determines the bit-rate of each segment with the aim of maximizing overall QoE. Experimental results show that our scheme can reduce the power consumption between 29% and 46% compared with the method of storing all the transcoded versions at the cost of 1.5% QoE degradation.
5. "Scheduling a Video Transcoding Server to Save Energy", ACM Transactions on Multimedia Computing Communications and Applications, vol 12. no. 2 February, 2015. (Best papers in ACM MMSys/NOSSDAV)
Abstract: Recent popular streaming services such as TV Everywhere, N-Screen and dynamic adaptive streaming over HTTP (DASH) need to deliver content to the wide range of devices, requiring video contents to be transcoded into different versions. Transcoding tasks require a lot of computation, and each task typically has its own real-time constraint. These make it difficult to manage transcoding, but the more efficient use of energy in servers is an imperative. We characterize transcoding workloads in terms of deadline and computation time, and propose a new dynamic voltage and frequency scaling (DVFS) scheme that allocates a frequency and a workload to each CPU with the aim of minimizing power consumption while meeting all transcoding deadlines. This scheme has been simulated, and also implemented in a Linux transcoding server, in which a front-end node distributes transcoding requests to heterogeneous back-end nodes. This required a new protocol for communication between nodes, a DVFS management scheme to reduce power consumption and thread management and scheduling schemes which ensure that transcoding deadlines are met. Power measurements show that this approach can reduce system-wide energy consumption by 17% to 31%, compared with the Linux Ondemand governor.
6. "Saving Disk Energy in Video Servers by Combining Caching and Prefetching", ACM Transactions on Multimedia Computing Communications and Applications, vol. 10 no. 1, January 2014. (Best papers in ACM MMSys/NOSSDAV)
Abstract: Maintenance and upgrades to the significant storage infrastructure in a video server often create a heterogenous disk array. We show how to manage the energy consumption of such an array by combining caching and prefetching techniques. We first examine how seek operations affect disk energy consumption, and then analyze the relationship between the amount of prefetched data and the number of seeks, and the effect of the size of the prefetching buffer on energy consumption. Based on this, we propose a new data prefetching scheme in which the amount of data prefetched for each video stream is dynamically adjusted to allow for the bit-rates of streams and the power characteristics of different disks. We next examine the impact of caching on disk power consumption, and propose a new caching scheme that prioritizes each stream based on the ratio of the amount of energy that can be saved to its cache requirement, so as to make effective use of limited caching space. We address the tradeoff between caching and prefetching, and propose an algorithm that dynamically divides the entire buffer space into prefetching and caching regions, with the aim of minimizing overall disk energy consumption. Experimental results show that our scheme can reduce disk energy consumption between 26% and 31%, compared to a server without prefetching and caching.
<Related projects>
1. A Study on Low-Power Storage Servers for Multimedia Applications (NRF, 2009.5 ~ 2011.4)
2. Development of Power Efficient High-Performance Multimedia Contents Service Technology using Context-Adapting Distributed Transcoding (MKE, 2012. 6 ~ 2015. 5)
3. A Study on Power Management Framework in Dynamically Adaptive Streaming Environment (NRF, 2015. 11 ~ 2018. 10)
4. Development of Energy-optimal Heterogeneous Cloud System SW for Next Generation Content Services (NRF, 2017. 11 ~ 2020. 12)