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Task Scheduling for ICN-Based computing First Network: A Deep Reinforcement Learning Approach 8

Task Scheduling for ICN-Based Computing First Network: A Dee...

引用

8th IEEE international conference on Computer and Communications, ICCC 2022

作者： Zou, Zhuang Xie, Renchao Ren, Yuzheng Yu, F. Richard Huang, Tao Beijing University of Posts and Telecommunications State Key Lab. of Networking and Switching Technology Beijing China Carleton University Depart. of Systems and Computer Eng. OttawaON Canada

ISBN: (纸本)9781665450515

the computing first network (CFN) combines heterogeneous computing force information with network information and improves resource utilization and task execution efficiency through resource perception, service positioning, and task scheduling. However, since the heterogeneous computing force is difficult to express and perceive, and it is distributed on each node of the edge network, it is difficult to schedule tasks uniformly. therefore, this paper proposes a CFN architecture based on information-centric network (ICN), which uses the naming mechanism of ICN to characterize the computing force and tasks, and the caching mechanism and routing and forwarding mechanism to improve the computing efficiency in the network. Under this network architecture, we study the scheduling problem of multi-user tasks in a period, model it as a multi-objective optimization model, and transform it into a Markov decision process (MDP) to enable control nodes to prioritize tasks and then schedule them to computing nodes for execution, which takes into account the task queue status and node resources. To solve the proposed problem, we use the deep reinforcement learning algorithm to approximate the optimal task scheduling solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme. © 2022 IEEE.

关键词： Reinforcement learning

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BYEE: Batch Fully Private Scheme for Coded distributed Matrix Multiplication 24

BYEE: Batch Fully Private Scheme for Coded Distributed Matri...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Guojing Wang, Jin Zou, Xiaotian Li, Lingzhi Gu, Fei School of Computer Science and Technology Soochow University Suzhou215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization China

ISBN: (纸本)9798350319934

Recently, user privacy in distributed computing has received increasing attention. Matrix multiplication is one of the fundamental high-frequency operations in distributed machine learning (e.g., gradient descent, linear regression). this paper studies the batch Fully Private distributed Matrix Multiplication (FPMM) problem. In batch FPMM, the user aims to compute multiple desired matrix multiplications from two public libraries which are stored across all workers. Batch FPMM requires that colluding workers cannot obtain the indexes of all desired ma-trices in the two public libraries (i.e., privacy constraint). In this paper, we first propose the Bath fullY privatE schemE (BYEE) to encode multiple desired matrix multiplications together based on the construction of bilinear complexity. We then perform a solid theoretical analysis to prove the privacy and decodability of BYEE. Multi-aspect analysis and extensive simulation results illustrate that BYEE can significantly reduce the computation latency than state-of-the-art schemes that do not jointly encode multiple desired matrix multiplications, especially when the number of desired matrix multiplications and the number of colluding workers are large. © 2022 IEEE.

关键词： Libraries

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PCS-ADS: Privacy computing System for Agricultural Data Security 24

PCS-ADS: Privacy Computing System for Agricultural Data Secu...

引用

8th international conference on Control Engineering and Artificial Intelligence, CCEAI 2024

作者： Wei, Zizhong Li, Rui Jiang, Kai Luo, Qingcai Sun, Zongchen Han, Tao Bi, Zixiang Wang, Yang Key Laboratory of Agricultural Blockchain Application Ministry of Agriculture and Rural Affairs Beijing China Inspur Academy of Science and Technology Shandong Jinan China

ISBN: (纸本)9798400707971

With the wide application of Internet of things, big data and other technologies in smart agriculture, more and more agricultural data are collected and used. the current analysis and processing through IoT platforms and other centralised methods have more serious data security problems. Privacy computing is a technology that completes data security calculations under the premise that both parties do not disclose data, and the data of each participant participates in the calculation is in the form of cipher text to maximally reduce the risk of data leakage. Aiming at the small volume and high frequency scenario of agricultural data, this paper proposes a distributed privacy computing framework by integrating cryptography, edge computing and blockchain, and designs a privacy computing system called PCS-ADS for smart agricultural data. this paper focuses on describing the system model architecture and system function implementation, which includes key business processes, core modules, core algorithms and other parts. Finally, the security and performance evaluation of the system is carried out. the results show that, for sum and variance, the computation time for multi-party under the amount of 1w data of each party is no more than 0.1s, and for the high-frequency computation scenarios, the computation time under the amount of 100 data of each party is no more than 7s and 1.5s, respectively. Experimental results prove that PCS-ADS is effective and has a strong practicality. © 2024 ACM.

关键词： Agriculture

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Joint Latency and Energy Optimization of Online Task Offloading Algorithms for Vehicular Edge computing 24

Joint Latency and Energy Optimization of Online Task Offload...

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作者： Dong, Pingping Nie, Cheng Li, Yajing Zuo, Yuning Lu, Xiaojuan Zhang, Lianming College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9798350319934

With the advantages of low latency and easy deployment, vehicle edge computing can effectively solve the task offloading problem for vehicles as well as surrounding pedestrians. However, due to hardware device limitations, vehicle edge nodes often have limited computing power and need to draw on the free resources on other surrounding vehicles. And the mobility of the vehicle also poses a challenge for task offloading. A common solution to this type of problem is Multi-Arm Bandit (MAB) theory, but existing algorithms based on MAB are not applicable to the dynamic vehicle environment. In this paper, we design an online learning-based vehicle task offloading algorithm (OLUCB), which performs task offloading in a distributed manner without frequent acquisition of vehicle state information, and adds three online capabilities of input-awareness, occurrence-awareness and speed-awareness to adapt to dynamic environments. the algorithm learns the task offloading performance of the surrounding vehicles and converges quickly to the optimal helper vehicle (HEV) with guaranteed performance. Extensive experiments in realistic highway scenario have been conducted, and results illustrate that the OLUCB achieves low latency and low energy performance. Compared to existing task offloading algorithms based on adaptive learning, the average latency, average energy consumption, average cost and average bit cost are reduced by 3%, 51%, 43% and 42% respectively. © 2022 IEEE.

关键词： Edge computing

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Efficient. Scalable and Robust Data Shuffle Service for distributed MapReduce computing on Cloud 24

Efficient. Scalable and Robust Data Shuffle Service for Dist...

引用

作者： Gu, Rong Huang, Xu Dai, Haipeng Geng, Xiaoyu Chen, Xiaofei Huang, Yihua Xiao, Fu Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350319934

distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement. © 2022 IEEE.

关键词： Data transfer

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Proceedings - 2022 IEEE 8th international conference on Big Data Security on Cloud, IEEE international conference on High Performance and Smart computing, and IEEE international conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

Proceedings - 2022 IEEE 8th International Conference on Big ...

引用

8th IEEE international conference on Big Data Security on Cloud, 8th IEEE international conference on High Performance and Smart computing, and 8th IEEE international conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

ISBN: (纸本)9781665480697

the proceedings contain 39 papers. the topics discussed include: an insider threat detection method based on heterogeneous graph embedding;predicting relations in sg-cim model based on graph structure and semantic information;a semantic analysis-based method for smart contract vulnerability;overview of blockchain and cloud service integration;a Russian hate speech corpus for cybersecurity applications;modeling learner behavior analysis based on educational big data and dynamic Bayesian network;remote audit scheme of embedded device software based on TPM;practical verifiable computation on encrypted data;high-performance distributed feature storage and consumption services in power grid scenarios;job scheduling on edge and cloud servers;and disk failure prediction based on SW-disk feature engineering.

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Stackelberg Game Model for Optimal Dispatching of Electricity Consumption for Community Microgrid Considering Demand Response Mechanism 8

Stackelberg Game Model for Optimal Dispatching of Electricit...

引用

8th international conference on Power and Renewable Energy, ICPRE 2023

作者： Tan, Di Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

Community microgrid is a kind of new power system that acts as an intermediary between community consumers and the grid, playing an important role in realising distributed autonomy and improving the power supply level. To achieve optimal power dispatch in community microgrid, this paper establishes a Stackelberg game model with the community microgrid operator (CMO) as the leader and the consumers aggregator as the follower, and combines the internal pricing method with the demand response mechanism. the paper uses a combination of genetic algorithm and solver to solve the Stackelberg game model to maximise the interests of all parties. Simulation results show that the model can secure the interests of each game subject, balance power supply and demand, and promote peak shaving and valley filling of electrical loads. © 2023 IEEE.

关键词： Community microgrid Demand response mechanism Genetic algorithm Internal prices Stackelberg game model

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A Blockchain PoW Consensus Mechanism Based on Edge computing in the IoVs 8th

A Blockchain PoW Consensus Mechanism Based on Edge Computing...

引用

8th international Congress on Edge computing, EDGE 2024, Held as Part of the Services conference Federation, SCF 2024

作者： Xu, Liya Ge, Mingzhu Zhang, Caicai Shi, Jiaoli Dong, Xiwei Li, Hongbo Jiujiang University Jiangxi Jiujiang332005 China Taizhou Vocational College of Science and Technology Zhejiang Taizhou318020 China Zhejiang Polytechnic University of Mechanical and Electrical Engineering Zhejiang Hangzhou310000 China Daqing Normal University Heilongjiang Daqing163712 China

ISBN: (纸本)9783031770685

the explosive increase of vehicles and the emergence of novel businesses such as driverless driving have presented fresh challenges to the data security of the IoVs (Internet of Vehicles). To ensure data security, a trusted decentralized distributed storage approach for managing and storing information proves to be a feasible solution. the blockchain enables distributed nodes to jointly maintain a consistent and tamper-proof ledger, facilitating trusted storage and transmission of information among distributed nodes. However, the consensus mechanism of the blockchain demands that miners in the system possess strong computing power for mining computations. Unfortunately, the performance of nodes in the IoVs is limited and fails to meet these requirements, significantly restricting the application of the blockchain in the IoVs. this paper proposes a blockchain consensus mechanism based on edge computing, with roadside node RSU serving as the edge server to undertake the task of miners for mining, in order to address the issues of insufficient computing power and unstable topology of vehicle nodes. Simulation experiments indicate that the consensus mechanism proposed in this paper has lower total costs, including total energy consumption and delay, compared to the PoW consensus mechanism that uses vehicle nodes as miners. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Miners

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Hybrid Decision Based Multi-Agent Deep Reinforcement learning for Task Offloading in Collaborative Edge-Cloud computing 24

Hybrid Decision Based Multi-Agent Deep Reinforcement learnin...

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作者： Chen, Juan Wu, Zongling Wu, Lei Xia, Yunni Wang, Yang Xiong, Ling Shi, Canghong School of Computer and Software Engineering Xihua University Chengdu China School of Information Science and Technology Southwest Jiaotong University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350319934

To support computing-intensive and latency-sensitive Internet-of-things (IoT) applications, we establish a three-layer collaborative edge-cloud computing network, in which multiple IoT devices, edge servers and cloud servers are allocated at IoT layer, edge layer and cloud layer, ***, it is still challenging to satisfy the service requests with inevitable resource competition among task offloading. To solve the joint computation offloading and resource allocation problem, we formulate it as a multi-agent Markov decision process (MAMDP), where action space of each agent contains discrete-continuous hybrid decision. Firstly, we relax the discrete action to a continuous set by developing a probabilistic method. then, by taking advantage of a continuous action based centralized training and distributed execution strategy, a cooperative multi-agent deep reinforcement learning (MADRL) based framework with each IoT device acts as an agent is established. the objective function is to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of edge/cloud servers. the simulation results demonstrate the advantageous of the proposed MADRL over the three state-of-the-art DRL based agents in reducing the system cost. © 2022 IEEE.

关键词： Reinforcement learning

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PhD Forum Abstract: Capacity Planning for Vehicular Fog computing 8

PhD Forum Abstract: Capacity Planning for Vehicular Fog Comp...

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8th IEEE international conference on Smart computing (SMARTCOMP)

作者： Mao, Wencan Aalto Univ Dept Commun & Networking Aalto Finland

ISBN: (纸本)9781665481526

the strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. Edge/Fog computing moves computation close to the edge to shorten network latency. vehicular fog computing (VFC) proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles. Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. the complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, the trade-off between the quality of service and cost expenditure, and the mobility of client vehicles and vehicular fog nodes. To address these challenges, we present our methodology, framework, and studies on capacity planning for VFC.

关键词： Capacity planning Edge/Fog computing Vehicular fog computing

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