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Intelligent Scheduling of Serverless computing in computing Power Networks

Intelligent Scheduling of Serverless Computing in Computing ...

引用

international conference on Computer and Communications (ICCC)

作者： Bowen Liu Xiaochen Liang Guiyu Zhang Jichun Ma Minkuan Li Anqi Zhou Qinqin Tang Kailai Peng Renchao Xie China Information Technology and Telecommunication Consulting Design Institute Beijing University of Post and Telecommunications Beijing PR. China State Key Lab. of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing PR. China Purple Mountain Laboratories Nanjing China

In the rapidly evolving landscape of computing power networks, applications are becoming more complex and diverse. Consequently, this evolution has led to a growing need for sophisticated intelligent scheduling mechanisms to adeptly manage these networks. Utilizing intelligent algorithms has become pivotal in ensuring effective and reliable computational services for these intricate networks. this paper addresses the pressing issue of intelligent scheduling in serverless computing power networks. We explore a computing power network system model based on serveless computing, formulate the intelligent task scheduling problem as a multi-objective optimization problem, present a solution that designs task scheduling policies using the Deep Deterministic Policy Gradient (DDPG) algorithm from reinforcement learning. Additionally, we perform experiments to validate the practicality and effectiveness of our proposed approach.

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DTFPRS: A distributed Traffic Flow Prediction Framework Based on Road Segmentation 9

DTFPRS: A Distributed Traffic Flow Prediction Framework Base...

引用

9th IEEE international conference on Behavioural and Social computing, BESC 2022

作者： Cai, Qiqi Cao, Jian Qian, Shiyou Chen, Yirong Shanghai Jiaotong University Department of Computer Science and Engineering Shanghai China Stanford University Department of Civil and Environmental Engineering Palo Alto United States

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

Accurate traffic flow prediction is of great significance. Recently, lots of deep neural network models have been applied in predicting traffic flow, such as Attention based Spatial-Temporal Graph Neural Network(ASTGNN). However, although these models have good prediction performances, there are still some problems when they are applied to the increasingly expanding and complex traffic network, such as memory consumption and computing overheads. therefore, this paper proposes a distributed Traffic Flow Prediction Framework Based on Road Segmentation(DTFPRS). Specifically, it is consisted of Spatial Partitioning (SP) and Traffic Flow Prediction(TFP). SP segments the road into sub-roads according to the correlation of traffic flow, and TFP trains ASTGNN on each sub-road. Experiments on real-world traffic flow datasets demonstrate that DTFPRS can greatly shorten the training time and reduce the size of the model without affecting the accuracy of the prediction results which makes DTFPRS can be used to large traffic networks. © 2022 IEEE.

关键词： Deep neural networks

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Proceedings - 2021 IEEE international conference on Joint Cloud computing, JCC 2021 and 2021 9th IEEE international conference on Mobile Cloud computing, Services, and Engineering, MobileCloud 2021

Proceedings - 2021 IEEE International Conference on Joint Cl...

引用

12th IEEE international conference on Joint Cloud computing, JCC 2021 and 2021 9th IEEE international conference on Mobile Cloud computing, Services, and Engineering, MobileCloud 2021

ISBN: (纸本)9781665434799

the proceedings contain 13 papers. the topics discussed include: proteus: distributed machine learning task scheduling based on Lyapunov optimization;running scientific computing workloads in JointCloud computing environment;mathematical modeling of transaction latency on Ethereum;an empirical study of inter-cluster resource orchestration within federated cloud clusters;a cloud-edge collaborative architecture for data-driven health condition monitoring of machines;a randomized greedy method for AI applications component placement and resource selection in computing continua;a secure and efficient data sharing scheme with outsourced signcryption and decryption in mobile cloud computing;and on fair cooperation among competing fog providers.

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A Novel distributed computing Resource Operation Mechanism for Edge computing

A Novel Distributed Computing Resource Operation Mechanism f...

引用

international conference on Computer and Communications (ICCC)

作者： Hua Wei Yunxia Guan Qihui Zhao Tingting Zhang Jinglei Liu Hao Zhang Department of Network and IT Technology Research China Mobile Research Institute Beijing China

Traditional cloud computing services are generally deployed centrally, which can not meet the ultra-low delay requirements of services such as VR/AR, Internet of Vehicles, and cloud games. Edge computing can solve this problem by deploying nodes near users. Edge nodes are geographically dispersed and great in number, which usually are not provided by single edge computing service provider but by multiple. these providers are in competitive relationship. Tradition centralized cloud computing resource operation model can’t achieve unified resource orchestration and operation among edge nodes. therefore, a fair and efficient computing resources operation mechanism is necessary. In this paper, firstly, we design a new distributed computing resource operation strategy. In the state of perfect competition, each edge node decides the price of local computing resources by itself. Users can freely choose among many providers according to their own strategies, realizing fair competition among different edge computing service providers. then, to increase the market size while maximize the revenue of edge computing service providers, we define a multi-objective optimization model. Finally, we design a dynamic price adjustment mechanism to calculate reference price for edge node. the simulation results show that the computing resource operation mechanism we designed is significantly better than the traditional edge computing model.

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distributed Deep Neural Networks Training in a Multi-Worker Environment

Distributed Deep Neural Networks Training in a Multi-Worker ...

引用

international conference on Information Technology Research (ICITR)

作者： Gowsikan Nakuleswaran Jathurshan Sownthararasa Jananie Jarachanthan Department of Computer Engineering University of Jaffna

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Deep learning has become promising across numerous fields in transforming conventional paradigms into smart eras in distributed applications. Large neural networks in recent years have been popular in solving massive real-world problems. However, the challenge behind the increasing complexity of deep neural networks impacts the training time. Appropriate resource provisioning and rightsizing is the requirement in all standard platforms like the cloud to handle this performance degradation. this research explores distributed CPU clusters as a scalable and cost-effective alternative for training large neural networks. the experiments on two different multi-processing machines with workers' distributions demonstrated the change in maximum accuracies is in a range of 92.96% to 96.74%. As our approach can be adopted and experiments can be extended to serverful and serverless computing training workloads, deep learning researchers and practitioners will benefit from our solution.

关键词： Training Deep learning Degradation Accuracy Serverless computing Artificial neural networks Complexity theory Information technology Standards

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Action Recognition with PIR Sensor Array and Bidirectional Long Short-term Memory Neural Network

Action Recognition with PIR Sensor Array and Bidirectional L...

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IEEE international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Tong Liu Jianchu Liang Kai Wan Jun Liu Department of Electronics Engineering Huizhou University Huizhou China College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China Laboratory for Intelligent Networking of Wenzhou City Wenzhou China

the spatio-temporal sequence of human body movements provides important information about daily action patterns. this article presents a pyroelectric infrared (PIR) sensor array for detecting human motion features and develops a bidirectional long short-term memory (LSTM) neural network for sequence recognition. A PIR sensor array with five direction-sensitive modules is proposed to detect the changing infrared field induced by the human head, upper limb, and lower limb. the low-dimensional output of the sensor array is directly fed to a multi-layer bidirectional LSTM-based neural network for sequential dependency learning. A PIR data set of seven daily actions is generated by four subjects imitating the predefined movements. Experimental results demonstrate that the presented approach achieves high accuracy in action recognition.

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DR-DRL: distributed Redundant Task Offloading Based on Reinforcement Learning

DR-DRL: Distributed Redundant Task Offloading Based on Reinf...

引用

Information Science, Computer Technology and Transportation (ISCTT), international conference on

作者： Shizhan Lan Hongcai Lin Huaqian Liang Shan Chen Zhenyu Wang School of Software Engineering South China University of Technology Guangzhou China China Mobile Group Guangxi branch Co. Ltd Nanning China

ISBN: (数字)9798350388435

ISBN: (纸本)9798350388442

Edge computing enhances task reliability by employing redundant task executions across edge nodes. Conventional decentralized task offloading strategies, based on heuristics and game theory, either focus on optimization or are based on unrealistic assumptions. Moreover, existing Deep Reinforcement Learning (DRL) task offloading approaches underperform due to discrepancies between simulated environments and real systems, and overlook task redundancy, thus failing to meet reliability requirements. this paper proposes a DRL-based approach to offload distributed redundant tasks, namely DR-DRL (Decentralized and Redundant DRL), to solve the redundancy problem of distributed task offloading under edge computing. Experimental results show that DR-DRL has about 8% higher task success rate than other benchmark methods.

关键词： Learning systems Markov decision processes Termination of employment Transportation Spread spectrum communication Reliability theory Benchmark testing Routing Optimization Edge computing

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A Strategy for Implementing Gaussian distributed Chaotic Signals Based on the Lorenz System

A Strategy for Implementing Gaussian Distributed Chaotic Sig...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Zaixue Yang Bin Liu Yanming Chen School of Electrical Engineering Guangxi University Nanning Guangxi Zhuang Autonomous Region China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Gaussian distributed random signal has broad prospects in various applications. Random signal generators make it easy to generate Gaussian distributed random signals, greatly promoting the widespread application of Gaussian distributed random signals. Research has shown that chaotic signals not only possess randomness but also other characteristics that random signals do not have. In some application scenarios, chaotic signals are superior to random signals. However, chaotic signals are typically not in accordance with Gaussian distribution due to the invariant distribution of chaotic systems. therefore, this paper proposes a strategy for implementing Gaussian-type chaotic signals based on the Lorenz system to address the acquisition problem of Gaussian distributed chaotic signals. Firstly, the Lorenz chaotic system is selected as the intrinsic signal source, and a non-parametric estimation algorithm is used to solve its probability density function and probability distribution function. then, a transformation algorithm is proposed to transform the intrinsic signal into Gaussian distributed chaotic signals. Finally, the effectiveness of the proposed algorithm is verified by implementing Gaussian distributed chaotic signals with different parameters.

关键词： Histograms Signal processing algorithms Estimation Transforms Probability density function Gaussian distribution Signal generators Probability distribution Kernel

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Transfer mechanism of data decryption authority in JointCloud computing 12

Transfer mechanism of data decryption authority in JointClou...

引用

12th IEEE international conference on Joint Cloud computing (IEEE JCC) / 9th IEEE international conference on Mobile Cloud computing, Services, and Engineering (IEEE MobileCloud)

作者： Xiang, Liangliang Qin, Ying Shi, Peichang Li, Rui Yang, Shangzhi Parallel & Distributed Proc Lab PDL Sci & Technol Changsha Peoples R China Natl Univ Def Technol Engn Res Ctr Natl Fundamental Software Coll Comp Changsha 410073 Peoples R China Tianjin Univ Tianjin 300350 Peoples R China

ISBN: (纸本)9781665434799

Data security sharing in the Jointcloud has always been a difficult problem for researchers. Some cloud data sharing solutions have a series of problems, such as high communication overhead, difficult key management, data tampering, single cloud failure, etc. We propose a data-sharing mechanism based on blockchain and threshold proxy re-encryption to solve the problems listed above. the proxy re-encryption algorithm we adopted is improved based on the Conditional Proxy Broadcast Re-Encryption. By combining the secret sharing scheme to realize trust decentralization. the re-encryption computing is delivered to multiple cloud service providers, which reduces the centralization problem caused by single cloud processing. At the same time, blockchain is used to manage metadata information and data access control rights to solve data loss and tampering problems. Experimental results show that our mechanism achieves low re-encryption computing overhead.

关键词： data sharing JointCloud proxy reencryption blockchain

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A Comparative Study on Emotion AI using Machine Learning and Deep Learning Models

A Comparative Study on Emotion AI using Machine Learning and...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： A. Pandiaraj P. Nagaraj Bhavanam Vijaya Durga Arige Tejasri Mederametla Tarun Teja Maguluri Navi Venkata Pavan Kumar Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India

this paper discusses several Deep learning (DL) and Machine learning (ML) techniques used for sentiment analysis. We have extracted nearly ten thousand tweets from the Twitter website using API keys. Pre-processing the collected data and word embedding algorithms are used. Although there are various challenges in applying various algorithms, to obtain the most accurate results we have applied several techniques for determining people's opinions from time to time. In this study, we will compare a hybrid model, which is a combination of two algorithms, with one of the word embedding algorithms. Finally, we will develop a website to construct a model that conducts sentiment analysis and categorizes tweets as positive, negative, and neutral.

关键词： Deep learning Analytical models Sentiment analysis Machine learning algorithms Social networking (online) Communication systems Computational modeling

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