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23rd USENIX conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

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Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning

arXiv

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arXiv 2025年

作者： Chen, Xi Li, Qin Cai, Haibin Wang, Ting MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China

Federated Learning (FL) empowers multiple clients to collaboratively train machine learning models without sharing local data, making it highly applicable in heterogeneous Internet of Things (IoT) environments. However, intrinsic heterogeneity in clients’ model architectures and computing capabilities often results in model accuracy loss and the intractable straggler problem, which significantly impairs training effectiveness. To tackle these challenges, this paper proposes a novel Heterogeneity-aware Personalized Federated Learning method, named HAPFL, via multi-level Reinforcement Learning (RL) mechanisms. HAPFL optimizes the training process by incorporating three strategic components: 1) An RL-based heterogeneous model allocation mechanism. The parameter server employs a Proximal Policy Optimization (PPO)-based RL agent to adaptively allocate appropriately sized, differentiated models to clients based on their performance, effectively mitigating performance disparities. 2) An RL-based training intensity adjustment scheme. The parameter server leverages another PPO-based RL agent to dynamically fine-tune the training intensity for each client to further enhance training efficiency and reduce straggling latency. 3) A knowledge distillation-based mutual learning mechanism. Each client deploys both a heterogeneous local model and a homogeneous lightweight model named LiteModel, where these models undergo mutual learning through knowledge distillation. This uniform LiteModel plays a pivotal role in aggregating and sharing global knowledge, significantly enhancing the effectiveness of personalized local training. Experimental results across multiple benchmark datasets demonstrate that HAPFL not only achieves high accuracy but also substantially reduces the overall training time by 20.9%-40.4% and decreases straggling latency by 19.0%-48.0% compared to existing solutions. © 2025, CC BY.

关键词： Federated learning

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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New constructions of asymptotically optimal periodic and aperiodic quasi-complementary sequence sets

arXiv

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arXiv 2025年

作者： Wang, Peng Heng, Ziling Li, Chengju School of Science Chang’an University Xi’an710064 China National Mobile Communications Research Laboratory Southeast University Nanjing211111 China MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University Shanghai200062 China

Quasi-complementary sequence sets (QCSSs) play an important role in multi-carrier code division multiple access (MC-CDMA) systems as they can support more users than perfect complementary sequence sets (PCSSs). The objective of this paper is to present new constructions of asymptotically optimal periodic and aperiodic QCSSs with large set sizes. Firstly, we construct a family of asymptotically optimal periodic (p2n, pn − 1, pn − 1, pn + 1) QCSSs with small alphabet size p, which has larger set size than the known family of periodic (pn(pn−1), pn−1, pn−1, pn+1) QCSSs. Secondly, we construct five new families of asymptotically optimal aperiodic QCSSs with large set sizes and low aperiodic tolerances. Each family of these aperiodic QCSSs has set size Θ(K2) for some flock size K. compared with known asymptotically optimal aperiodic QCSSs in the literature, the proposed aperiodic QCSSs by us have better parameters or new lengths of their constituent sequences. copyright © 2025, The Authors. All rights reserved.

关键词： code division multiple access

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Archer: adaptive memory compression with page-association-rule awareness for high-speed response of mobile devices 25

Archer: adaptive memory compression with page-association-ru...

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Proceedings of the 23rd USENIX conference on File and Storage Technologies

作者： Changlong Li Zongwei Zhu Chao Wang Fangming Liu Fei Xu Edwin H.-M. Sha Xuehai Zhou School of Computer Science and Technology East China Normal University and Jianghuai Advance Technology Center Hefei China and MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application School of Software Engineering University of Science and Technology of China Hefei China and Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Huazhong University of Science and Technology and Peng Cheng Laboratory School of Computer Science and Technology East China Normal University

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate associationrule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art.

关键词：

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SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

arXiv

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arXiv 2025年

作者： Li, Nan Wang, Xiaolu Du, Xiao Cai, Puyu Wang, Ting Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education the Shangh ai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Software Engineering Institute East China Normal University Shanghai200050 China Computer Science Department New York University New YorkNY10012 United States

Federated Learning (FL) enables clients to collaboratively train machine learning models without sharing local data, preserving privacy in diverse environments. While traditional FL approaches preserve privacy, they often struggle with high computational and communication overhead. To address these issues, model pruning is introduced as a strategy to streamline computations. However, existing pruning methods, when applied solely based on local data, often produce sub-models that inadequately reflect clients’ specific tasks due to data insufficiency. To overcome these challenges, this paper introduces SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalized federated learning through client-specific clustering and Similar Client Structure Information (SCSI)-guided model pruning. SAFL employs a two-stage process: initially, it groups clients based on data similarities and uses aggregated pruning criteria to guide the pruning process, facilitating the identification of optimal sub-models. Subsequently, clients train these pruned models and engage in server-based aggregation, ensuring tailored and efficient models for each client. This method significantly reduces computational overhead while improving inference accuracy. Extensive experiments demonstrate that SAFL markedly diminishes model size and improves performance, making it highly effective in federated environments characterized by heterogeneous data. copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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connection models for the Internet-of-Things

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Frontiers of computer Science 2020年第3期14卷 65-85页

作者： Kangli HE Holger HERMANNS Hengyang WU Yixiang CHEN MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal UniversityShanghai 200062China Saarland University Saarland Informatics CampusSaarbrücken 66123Germany

The Internet-of-Things(IoT)is expected to swamp the *** order to study and understand the emergent behaviour of connected things,effective support for their modelling is *** the heart of IoT are flexible and adaptive connection patterns between things,which can naturally be modelled by channel-based coordination primitives,and characteristics of connection failure probabilities,execution and waiting times,as well as resource *** latter is especially important in light of severely limited power and computation budgets inside the *** this paper,we tackle the IoT modelling challenge,based on a conservative extension of channel-based Reo *** introduce a model called priced probabilistic timed constraint automaton,which combines models of probabilistic and timed aspects,and integrates pricing *** expressive logic called priced probabilistic timed scheduled data stream logic is presented,so as to enable the specification and verification of properties,which characterize data-flow streams and prices.A small but illustrative IoT case demonstrates the principal benefits of the proposed approach.

关键词： IoT Reo cost time probability automaton

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TSCND:Temporal Subsequence-Based convolutional Network with Difference for Time Series Forecasting

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computers, Materials & continua 2024年第3期78卷 3665-3681页

作者： Haoran Huang Weiting Chen Zheming Fan MOE Research Center of Software/Hardware Co-Design Engineering East China Normal UniversityShanghai200062China

Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.

关键词： Difference data prediction time series temporal convolutional network dilated convolution

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Recognition of facial expression based on CNN-CBP features 29

Recognition of facial expression based on CNN-CBP features

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第29届中国控制与决策会议

作者： Yize Liu Yixiang Chen School of Computer Science and Software Engineering MoE Engineering Center for Software/Hardware Co-Design Technology and ApplicationEast China Normal University

ISBN: (纸本)9781509046584

Automatical recognition of facial expression is an interesting and challenging problem,which has so many applications such as expression synthesis,human-robot interaction,metal state identification,intelligent tutoring systems,operator fatigue,music for mood,and clinical *** vital step of a successful approach is deriving features from raw facial *** existed methods of features extraction are the hand-crafted features based on geometric features or appearance features,and the auto-learned *** utilize the benefit of low computation of hand-crafted features and the high-representation of auto-learned features,we firstly proposed the combined features CNN-CBP with putting together Centralized Binary Patterns(CBP) features and convolutional Neural Network(CNN) *** then,we classified the features using Support Vector Machine(SVM).With the help of the CNN-CBP features,we achieved average recognition accuracy of 97.6%on the Extended cohn-Kanade datasets and 88.7%on the Japanese Femal Facial Expression datasets based on 10-cross validation.

关键词： Facial Expression Recognition convolutional Neural Network Centralized Binary Patterns combination of Features Support Vector Machine

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research and design of Sensor Data Management System Based on Distributed Storage

Research and Design of Sensor Data Management System Based o...

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2020 International conference on computer Science and Management technology, ICCSMT 2020

作者： Yang, Jing Chi, Xiaotong Zhu, Minghua Wang, Wenjie East China Normal University MoE Engineering Research Center for Software/Hardware Co-design Technology and Application Shanghai China

ISBN: (纸本)9781728186689

Sensor data is one of the basic source of big data in the Internet of Things(IoT). It is the data that is sensed, measured, and transmitted by sensors with real-time and dynamic characteristics. With the development of the IoT and big data technologies, new industries such as smart transportation, smart cities, and smart agriculture have gradually formed, resulting in an increasing demand for large-scale data analysis and calculations. However, the storage architecture based on relational databases has been unable to meet the current needs in terms of data storage and data read and write speed. Therefore, this paper mainly studies the sensor data management system based on distributed storage, builds a Hadoop cluster, proposes a storage structure that combines the relational database MySQL with the distributed database HBase, designs a data migration module to satisfy real-time sensor data monitoring and massive data storage requirements. In order to meet the needs of enterprises in actual production, SSM framework technology is used to build a sensor data management platform, covering the functions of system management, sensor data management, data exception management, sensor data real-time monitoring, sensor node status monitoring, data statistical analysis, etc. The platform has good security and reliability, and can effectively improve the management efficiency of the enterprise. Finally, the experiment provides that the system has obvious advantages in data upload and query ability compared with the traditional storage architecture. It provides a good data environment for data analysis and data mining in the future. © 2020 IEEE.

关键词： Sensor nodes

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