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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34268 条记录，以下是931-940 订阅

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A Firmware Vulnerability Detection Method Based on Feature Filtering 21

A Firmware Vulnerability Detection Method Based on Feature F...

引用

21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xia, Bing Tang, Chongjun Liu, Wenbo Chu, Shihao Dong, Yu ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Dept Zhengzhou China ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Dept Zhengzhou China ZhongYuan University of Technology Dept Zhengzhou China

ISBN: (纸本)9798350329223

Open source code reuse and code cross-platform deployment accelerate the spread of software vulnerabilities, and pose challenges for accurate detection of cross-platform vulnerabilities. The binary vulnerability similarity detection method based on machine learning lacks a fine-grained vulnerability similarity feature filtering mechanism, and cannot solve cross-function vulnerabilities triggered by data dependencies, resulting in a high vulnerability false positive rate in existing solutions, which limits practical applications. Software code vulnerabilities are usually caused by function key statements. Therefore, based on binary function decompilation, we identify the key statement that causes the vulnerability, analyze the cross-function data dependency of the key statements, extract the code similarity features and code vulnerability features of the key statements, and propose a feature filtering-based Binary vulnerability detection method SimFilter. SimFilter calculates the similarity of key statements by comparing the characteristics of function key statements, and infers the similarity of binary function vulnerabilities based on the similarity of key statements. The experimental results show that the vulnerability semantic features extracted by SimFilter are highly interpretable, and the accuracy rate is increased by 10.8% compared with other models. At the same time, we analyzed and evaluated the impact of different similarity features on the accuracy of SimFilter, and gave the best empirical value of the SimFilter model in firmware vulnerability detection. © 2023 IEEE.

关键词： Firmware

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Research on communication mechanism optimization based on distributed graph computing environment 23

Research on communication mechanism optimization based on di...

引用

Proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Jianchao Lin Chunna Cao Chunhui Ren State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd China

ISBN: (纸本)9798400716669

In recent years, with the rapid development of the Internet of Things, the Internet, and social networks, the storage of data in the network is growing at an explosive rate and is becoming more and more closely related to the real natural world. Driven by large-scale data mining and machine learning applications, distributed graph computing models that use graph data structures to describe data and relationships between data have been increasingly widely used. Therefore, this paper studies the optimization of communication mechanisms based on a distributed graph computing environment. Firstly, a BSP model based on a pure message-passing communication mechanism of a distributed graph computing system is established. Secondly, the optimization model is evaluated from two aspects: data communication and convergence condition judgment. Finally, large-scale data sets are used to test and evaluate performance optimization. The results show that this method can greatly improve the efficiency of graph parallel computing.

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On Scaling Neuronal Network Simulations Using distributed computing

On Scaling Neuronal Network Simulations Using Distributed Co...

引用

2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Omelyusik, Vladimir Shehzad, Khawar Banks, Tyler Rao, Praveen Nair, Satish S. University of Missouri United States

ISBN: (纸本)9798350355543

We investigated the computational capabilities of FABRIC, a nationwide research infrastructure with nearly 40 sites, for scaling neuroscience simulations. From the hardware standpoint, single-site characterization showed that FABRIC is a promising alternative to conventional neuroscience setups, particularly due to the availability of powerful graphics processing units (GPUs). While multi-site simulations are affected by network latency, it becomes less critical for larger networks. From the software perspective, we found that in the popular CoreNEURON library, cell distribution strategy (for parallel execution) does not affect the simulation time for biologically realistic networks, while other cases can be addressed with a minimum k-cut graph partitioning algorithm. Overall, scalability experiments revealed that FABRIC can be used to simulate networks of up to twenty-five thousand cells, with the limiting issue being GPU memory. © 2024 IEEE.

关键词： FABRIC GPUs networks neuroscience scalability simulation

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Online Function Caching in Serverless Edge computing 29

Online Function Caching in Serverless Edge Computing

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Xuan Gu, Hongjun Li, Guopeng He, Xin Tan, Haisheng University of Science and Technology of China School of Computer Science and Technology Hefei China Anhui Normal University School of Computer and Information Wuhu China

ISBN: (纸本)9798350330717

Serverless edge computing has emerged as a new paradigm for running short-lived computations on edge devices. Considering the challenges posed by multiple edge servers and non-negligible cold start latency in serverless edge computing, we investigate the problem of function caching on multiple edge servers with relaying and bypassing. Our objective is to minimize the total latency of serving all function requests, which may either be processed by an idle container on the local server, initiate a new container on the local server, relayed to other edge servers, or bypassed to the cloud server. We propose FunCa, a greedy-based algorithm, and FunCa+, an extension version that supports bypassing. Large-scale simulation experiments using Azure trace and Alibaba trace demonstrate that compared to Camul, the state-of-the-art algorithm for handling requests on multiple edge servers, FunCa can reduce latency by 52.2% and 73.27% in the two traces, respectively. © 2023 IEEE.

关键词： Edge computing Serverless Compuing

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Research on Data Storage Security in Cloud computing Environment 4

Research on Data Storage Security in Cloud Computing Environ...

引用

4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Guo, Chen Su, Mohan Cui, Fang China Mobile Group Device Co. Ltd China

ISBN: (纸本)9798350337181

This paper conducts in-depth research and discussion on data storage security in cloud computing environment. This study collected 1000 dummy data from different organizations as the basic data set for the study. This data covers various types of sensitive information, including personally identifiable information, financial data, and medical records. We generate these data using appropriate data generation methods to ensure realistic characteristics and distributions. Based on existing backup technologies and algorithms, we evaluate the impact of different backup strategies on data security. Experimental results show that our data backup and recovery strategy and the application of encryption technology effectively improve the security of data in cloud storage. By selecting backup strategies and recovery mechanisms reasonably, we can ensure data availability and integrity. At the same time, employing a proper encryption scheme can protect data from the risk of unauthorized access and disclosure. © 2023 IEEE.

关键词： Cloud storage

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Exploring Adaptive Data Acquisition Technology for Chip-based Communication Transmission Link 3

Exploring Adaptive Data Acquisition Technology for Chip-base...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Tian, Lin Zhu, Yifeng Zheng, Wulue Li, Renxin China Southern Power Grid Ehv Transmission Company Guangdong Guangzhou China

ISBN: (纸本)9798350318609

In recent years, with the rapid development of applications such as mobile internet and the Internet of Things, the amount and speed of data transmission have been continuously increasing, which has also brought about a higher demand for signal quality. Due to the complex working environment in which the communication system operates, there are many uncertain factors and interference sources. Therefore, how to adapt and optimize the transmission link in a dynamic and ever-changing working environment, thereby improving transmission quality and data transmission rate, is a key issue that urgently needs to be solved in the field of communication. This paper proposes a chip based adaptive data acquisition technology scheme to address the issues of traditional data acquisition methods being unable to adapt to different communication links for data acquisition speed and system real-time performance. By analyzing the communication quality of different communication transmission links and ensuring the data acquisition rate, the sampling rate is adaptively adjusted according to different communication transmission links. The experimental results show that this scheme can adaptively adjust the acquisition rate according to the amount of data while meeting the real-time requirements of the system. This method can effectively prevent data distortion and has higher energy efficiency. © 2024 IEEE.

关键词： Data acquisition

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A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images 18

A Lightweight Depthwise Separable Convolution Neural Network...

引用

18th Annual international conference on distributed computing in Sensor Systems (DCOSS)

作者： Subramanian, Malliga Sathishkumar, V. E. Ramya, C. Kogilavani, S., V Deepti, R. Kongu Engn Coll Dept Comp Sci & Engn Erode Tamil Nadu India Hanyang Univ Dept Ind Engn 222 Wangsimini Ro Seoul 04763 South Korea

ISBN: (纸本)9781665495127

Because Covid-19 spreads swiftly in the community, an automatic detection system is required to prevent Covid-19 from spreading among humans as a rapid diagnostic tool. In this paper, we propose to employ Convolution Neural Networks to detect coronavirus-infected patients using Computed Tomography and X-ray images. In addition, we look into the transfer learning of a deep CNN model, DenseNet201 for detecting infection from CT and X-ray scans. grid Search optimization is utilized to select ideal values for hyperparameters, while image augmentation is employed to increase the model's capacity to generalize. We further modify DenseNet architecture to incorporate a depthwise separable convolution for detecting coronavirus-infected patients utilizing CT and Xray images. Interestingly, all of the proposed models scored greater than 94% accuracy, which is equivalent to or higher than the accuracy of earlier deep learning models. Further, we demonstrate that depthwise separable convolution reduces the training time and computation complexity.

关键词： Covid-19 CT Images Convolution Neural Networks DenseNet201 Transfer learning grid Search Optimization Depthwise separable convolution

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Resource Management Technology in Smart grids enabled by Regional Energy Internet 9

Resource Management Technology in Smart Grids enabled by Reg...

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9th international conference on Computer and Communications, ICCC 2023

作者： Jiang, Jing Xin, Peizhe Xiao, Zhihong Ji, Yuejie State Grid Economic and Technological Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350317251

There are numerous distributed new energy and diversified loads in the regional energy Internet. New business demands such as virtual power plant group dispatching and control, demand side response, and energy big data centers are constantly emerging, putting higher requirements on the reliability, security, and processing capacity of communication networks. As the core of the regional energy Internet, the power grid's advanced communication and computing resources management is an effective way to meet the needs of the new business. In order to meet the differentiated requirements of services and security isolation, the network slicing technology is applied to the power grid. In this paper, we proposed a computing and bandwidth resource management scheme in smart grids enabled by regional energy Internet to reduce energy consumption and network delay. We modeled the joint allocation of bandwidth and computing resources of network slicing as a convex optimization problem, aiming to minimize the weighted sum of system delay consumption and computational energy consumption. The Lagrange multiplier iterative optimization method is used to solve the optimal allocation scheme of computing resources and bandwidth resources. The simulation results show that the proposed solution can meet the differentiated delay requirements of the service and effectively reduce the delay consumption and energy consumption of the system. © 2023 IEEE.

关键词： Resource allocation

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Real-Time Information Collection and Scheduling Control in the Distribution Station Area with distributed New Energy 5

Real-Time Information Collection and Scheduling Control in t...

引用

5th international conference on Smart Power and Internet Energy Systems, SPIES 2023

作者： Guanghui, Hua Yuchen, Hao Jianjun, Zhu Haixuan, Liu Chun, Wang Xiaohui, Xu China Electric Power Research Institute Jiangsu Province Nanjing China State Grid Jiangsu Electric Power Co. Ltd. Jiangsu Province Nanjing China

ISBN: (纸本)9798350371741

The low-voltage distribution network is facing a situation of blowout, small capacity and decentralized distributed new energy access, with the emergence of large-scale power reverse transmission and frequent voltage fluctuations at station area level. However, power grid companies find it difficult to timely perceive real-time information of electricity generation and consumption within the station area, witch increasing the difficulty of operation control and management of the distribution network, and bringing adverse effects to safe, stable and economic power supply. Following the idea of edge computing, a real-time information acquisition and dispatching control method suitable for distribution station area with distributed new energy is proposed to realize the information interconnection and integration of local equipment at a lower cost, so that various adjustable resources can participate in the multi-level source network load interaction operation, and improve the comprehensive benefits of the distribution system in the station area. © 2023 IEEE.

关键词： Electric power distribution

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Scalable Skill-oriented Task Allocation in Crowdsourcing within a Serverless Ecosystem 23

Scalable Skill-oriented Task Allocation in Crowdsourcing wit...

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24th international conference on distributed computing and Networking (ICDCN)

作者： Sethi, Biswajeet Samanta, Riya Ghosh, Soumya K. Das, Sajal K. Indian Inst Technol Kharagpur Kharagpur W Bengal India Missouri Univ Sci & Technol Rolla MO 65409 USA

ISBN: (纸本)9781450397964

Allocating the most competent crowdworkers to each upcoming task is a fundamental challenge in crowdsourcing. The mechanism becomes complicated when the arriving tasks require a high level of expertise within a constrained budget. The validation of skill matching between tasks and crowdworkers adds a new dimension to the traditional problem of task allocation. In addition, in real-world scenarios, the influx of both tasks and workers is dynamic, making it nearly impossible to predict the precise amount of computational resources required for the crowdsourcing platform to operate efficiently. Serverless computing is a new pay-per-use, autoscalable, Function-as-a-Service based model, that ensures parallel execution of lightweight event-driven functions. The developer with serverless can solely concentrate on writing application logic with zero effort on resource provision, server management, environmental configuration, and availability. Today, collaboration has become the new competition. In light of these considerations, we propose a novel framework to facilitate task allocation strategies for crowdsourcing applications, deployed within a serverless platform in order to improve performance. The results obtained are compared to the baseline, Online-Greedy, and simulations are run in both serverless and local environments.

关键词： serverless computing crowdsourcing skill-oriented task allocation

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