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Discrete Geometric Coded Data Layout for Large-scale Object Storage Systems 21

Discrete Geometric Coded Data Layout for Large-scale Object ...

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： Tian, Yi Xu, Guangping Yang, Hongzhang Ni, Yue Cao, JiaXin Yang, Lei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Novel Software Technology Tianjin Key Laboratory of Intelligence Computing Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Information Technology Co. Ltd. China

ISBN: (纸本)9798350329223

Regenerating codes are new network codes proposed to reduce the data required for fault repair, which can improve the recovery efficiency of faulty nodes in data storage systems. However, unlike Reed-Solomon code, which repairs at the granularity of bytes, regenerating codes require data stored in large chunks but leads to severe read amplification, which reads out excess data when degraded read objects and increases degraded read time. That reflects a mutual constraint between improving recovery efficiency and degraded read performance, as manifested in the amplification of data reads, a vital issue considered in this *** solve this problem, we propose a new type of data layout - discrete geometric, which splits the object into a series of geometric sequences of data blocks. They are placed discretely into corresponding containers on the disks at different nodes, with containers of the same size made into a strip for encoding. The discrete characteristic ensures lower repair costs for degraded reads. The geometric characteristic ensures the repair performance of regenerating codes by large blocks, and read amplification can be mitigated through small blocks. To reduce IOPS for discrete geometric, we propose Discrete Geometric-Locally Regenerating Codes (DG-LRCs), guaranteeing lower degraded read latency while improving recovery *** results show that the degraded read time of DG-LRCs compared to regenerating codes combined with geometric partitioning is 22.56% lower at 2Gbpsand 60.56% lower at 4Gbps, and the recovery performance is 7.04 times better than that of RS code. © 2023 IEEE.

关键词： Efficiency

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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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A Firmware Vulnerability Detection Method Based on Feature Filtering 21

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

引用

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

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

引用

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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Efficient Microservice Deployment with Dependencies in Multi-Access Edge computing 29

Efficient Microservice Deployment with Dependencies in Multi...

引用

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

作者： Lu, Shuaibing Yan, Ran Wu, Jie Beijing University of Technology Faculty of Information Technology Beijing China Temple University Center for Networked Computing Philadelphia United States

ISBN: (纸本)9798350330717

In the context of mobile edge computing, efficiently deploying microservices to reduce finish time and enhance user service quality is a challenging task. However, existing research still has certain deficiencies in considering microservice deployment within edge server clusters and communication link constraints. To address this issue, we propose three microservice deployment strategies by offering flexibility and adaptability for various application scenarios. We initially consider two straightforward scenarios: one with unlimited storage resources under the bandwidth constraint, and the other with unlimited bandwidth resources under the storage constraint. For each of these two scenarios, we introduce a novel enhanced graph construction method and design two optimal solutions. For Scenario 3, which involves complex constraints on server capacity, computational capability, and communication resources, we present an optimization method based on main path partitioning and the simulated annealing algorithm. We effectively tackle challenges arising from server capacity, computational capability, and communication resource limitations. Across multiple experimental setups, our approach significantly improves microservice deployment efficiency and overall performance compared to traditional strategies. © 2023 IEEE.

关键词： dependency high-efficient microservice deployment multi-access edge computing

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Privacy-preserving Pedestrian Tracking using distributed 3D LiDARs 21

Privacy-preserving Pedestrian Tracking using Distributed 3D ...

引用

21st IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Ohno, Masakazu Ukyo, Riki Amano, Tatsuya Rizk, Hamada Yamaguchi, Hirozumi Osaka Univ Osaka Japan Tanta Univ Tanta Egypt

ISBN: (纸本)9781665453783

The growing demand for intelligent environments unleashes an extraordinary cycle of privacy-aware applications that makes individuals' life more comfortable and safe. Examples of these applications include pedestrian tracking systems in large areas. Although the ubiquity of camera-based systems, they are not a preferable solution due to the vulnerability of leaking the privacy of pedestrians. In this paper, we introduce a novel privacy-preserving system for pedestrian tracking in smart environments using multiple distributed LiDARs of non-overlapping views. The system is designed to leverage LiDAR devices to track pedestrians in partially covered areas due to practical constraints, e.g., occlusion or cost. Therefore, the system uses the point cloud captured by different LiDARs to extract discriminative features that are used to train a metric learning model for pedestrian matching purposes. To boost the system's robustness, we leverage a probabilistic approach to model and adapt the dynamic mobility patterns of individuals and thus connect their sub-trajectories. We deployed the system in a largescale testbed with 70 colorless LiDARs and conducted three different experiments. The evaluation result at the entrance hall confirms the system's ability to accurately track the pedestrians with a 0.98 F-measure even with zero-covered areas. This result highlights the promise of the proposed system as the next generation of privacy-preserving tracking means in smart environments.

关键词： Point cloud-based recognition LiDAR Privacy-preserving Re-ID Pedestrian tracking

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Towards A Massive-scale distributed Neighborhood Graph Construction

Towards A Massive-scale Distributed Neighborhood Graph Const...

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2023 international conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Iwabuchi, Keita Steil, Trevor Priest, Benjamin Pearce, Roger Sanders, Geoffrey Lawrence Livermore National Laboratory LivermoreCA United States

ISBN: (纸本)9798400707858

Graph-based approximate nearest neighbor algorithms have shown high performance and quality. However, such approaches require a large amount of memory and still take a long time to construct high-quality nearest neighbor graphs (NNGs). Using distributed memory systems is important when data is large or a shorter indexing time is desired. We develop a distributed memory version of NN-Descent, a widely known graph-based ANN algorithm, closely following algorithmic advances by PyNN-Descent authors. Our distributed NN-Descent (DNND) is built on top of MPI and leverages two existing high-performance computing libraries: YGM (an asynchronous communication library) and Metall (a persistent memory allocator). We evaluate the performance of DNND on an HPC system using billion-scale datasets, demonstrating that our approach shows high performance and strong scaling and has great potential for developing massive-scale NNG frameworks. © 2023 Owner/Author.

关键词： Nearest neighbor search

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An Efficient distributed Spatiotemporal Index for parallel Top-k Frequent Terms Query

An Efficient Distributed Spatiotemporal Index for Parallel T...

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IEEE international conference on Big Data and Smart computing (BigComp)

作者： Van Le, Hong Takasu, Atsuhiro Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781665421973

As rich user-generated geotagged data, microblogs have been exploited in several data analytic contexts, e.g., popular topic trends, popular site detection, and geo-targeted recommendation. To support such analysis, we developed an efficient multidimensional index structure and parallel processing approaches for top-k frequent spatiotemporal terms query: a common analytic query on geotagged social data. Given a spatiotemporal range, the query aggregates the frequencies of terms among the social posts and identifies the most frequent terms in that range. The present work is different from studies that extract this information from stream data because we focus on large historical datasets. The key challenge is to improve query performance with minimum storage requirements. We propose a distributed index structure that transforms spatiotemporal coordinates into unique codes to generate rowkeys in key-value stores (KVSs) and balances the data distribution across distributed systems. Then, we utilize data localization to calculate sorted term lists (STLs) in parallel. To reduce input/output between KVSs and the client, we theoretically estimate the necessary length of STLs to calculate the top-k frequent terms and send only a part of the STLs to the client. Several experiments, on both real and artificial datasets, showed our approach to have both lower space requirements and better query performance than baseline approaches.

关键词： parallel processing top-k computing spatiotemporal database key-value store geotagged social data

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