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DICFaaS: parallel and Scalable Data Integrity Computation for Serverless systems

DICFaaS: Parallel and Scalable Data Integrity Computation fo...

引用

international Conference on parallel and distributed systems (ICPADS)

作者： Tianchen Xiong Bo Peng School of Software Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Serverless computing is extensively employed in distributed storage computing scenarios owing to its dynamic responsiveness and on-demand allocation capabilities. Within distributed storage computing, data integrity computation holds significant importance. Nevertheless, current serverless systems fail to consider the CPU-intensive nature of data integrity computation, leading to prolonged CPU occupancy, increased latency, and substantial performance deterioration, particularly in parallel scenarios. Therefore, there is an imperative need for a solution that can efficiently handle this kind of computation in serverless systems. In this paper, we introduce DICFaaS, a high-performance serverless system specialized for parallel and scalable data integrity computation. DICFaaS can recognize the offloading potential of servers and automatically offload CPU-intensive data integrity computation. The offloading function of DICFaaS can be easily integrated into other existing serverless systems. We evaluate the effectiveness of DICFaaS for data integrity computation compared to state-of-the-art serverless systems. For example, DICFaaS delivers up to $17 \times$ latency improvement over AWS Lambda and up to $42 \times$ latency improvement over OpenFaaS when the data size is 200M. It also saves up to $\mathbf{6 0 \%}$ CPU overhead at runtime compared to OpenFaaS.

关键词： Runtime Data integrity Heuristic algorithms Scalability Serverless computing distributed databases Quality of service parallel processing Dynamic scheduling Hardware

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Real-Time Routing in Traffic Simulations: A distributed Event Processing Approach

Real-Time Routing in Traffic Simulations: A Distributed Even...

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international symposium on parallel and distributed Computing

作者： Paul Heinrich Janek Laudan Kai Nagel Transport Systems Planning and Transport Telematics Technical University of Berlin Germany

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

This paper explores the integration of distributed event processing in the context of real-time routing within a parallel transportation simulation framework for the Multi-Agent Transport Simulation (MATSim). A novel simulation prototype, utilizing Rust and MPI, demonstrates significant re-ductions in computation time by applying domain decomposition of the network and assigning each part to a separate process. However, sharing and processing events within this setup remains challenging. We present a proof of concept for integrating distributed event processing. Our evaluation has shown that realtime routing significantly increases simulation runtime because uneven distribution of routing requests increases load imbalances and makes speedups less efficient. Sharing event data between processes benefits from smaller subdomains. However, global synchronization for sharing data leads to waiting times introduced by load imbalances.

关键词： Runtime Transportation Prototypes Information sharing Routing Real-time systems Synchronization distributed computing

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A Moving Target Defense Approach for the distributed Dynamic Network 21

A Moving Target Defense Approach for the Distributed Dynamic...

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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： Zhang, Lin Guo, Yunchuan Leng, Siyuan Li, Zifu Li, Fenghua Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed Network Address Shuffling (DNAS), is proposed to thwart attackers' network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected Neural Network (FCNN) to recognize the attacker's scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning. © 2023 IEEE.

关键词： Network security

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Application of Network Calculus Models to Heterogeneous Streaming Applications

Application of Network Calculus Models to Heterogeneous Stre...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Clayton J. Faber Roger D. Chamberlain SimpleRose Washington University in St. Louis. Dept. of Computer Science and Engineering Washington University in St. Louis

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Network calculus has seen extensive use in the performance modeling of communications systems. Here, we apply network calculus techniques to the modeling of streaming data applications running on heterogeneous computing platforms. We quantitatively compare the performance predictions from network calculus with predictions from a discrete-event simulation model and a previously presented queuing theory model.

关键词： distributed processing Communication systems Computational modeling Conferences Predictive models Calculus Data models

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An Optimization Method for National Cryptography Algorithm

An Optimization Method for National Cryptography Algorithm

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Jun Hu Jiajun Li Qiushi Xi Song He Yahui Hu Min Cheng AirForce Early Warning Academy Wuhan China

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

A Python optimization method is proposed to ad-dress the slow and inefficient encryption speed of third-party libraries (gmssl and pysmx) commonly used in Python to im-plement national security encryption algorithms. This method combines bit operations, memory construction of S-boxes, and other methods to improve the encryption speed of national secu-rity algorithms. Through laboratory comparison and verification, compared to existing third-party national security encryption algorithm libraries, this method can effectively improve the speed of implementing national security encryption algorithms in Python.

关键词： Optimization methods parallel processing Libraries Encryption National security

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Sampling Policies for Near-Optimal Device Choice in parallel Simulations on CPU/GPU Platforms 28

Sampling Policies for Near-Optimal Device Choice in Parallel...

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28th IEEE/ACM international symposium on distributed Simulation and Real Time Applications, DS-RT 2024

作者： Andelfinger, Philipp Pellegrini, Alessandro Marotta, Romolo University of Rostock Germany Tor Vergata University of Rome Italy

ISBN: (纸本)9798331527211

Heterogeneous hardware platforms comprised of CPUs, GPUs, and other accelerators offer the opportunity to choose the best-suited device for executing a given scientific simulation in order to minimize execution time and energy consumption. To this end, the recently proposed "Follow the Leader"approach dynamically selects a suitable device based on runtime performance measurements during speculative discrete-event simulations. A currently active "leader"device is periodically challenged by a "follower"device in order to negotiate the new leader. The optimality of the device choices and the associated overhead depends critically on the challenge frequency and timing. Here, we explore policies to schedule challenges with the goal of attaining Pareto-optimal combinations of execution time and energy consumption. Several heuristics are first evaluated in an abstract fashion using a "meta-simulation"by mimicking the progress and energy consumption of an idealized co-execution. In this setting, we optimize the heuristics' tuning parameters to assess their relative merits in near-optimal configurations when compared to challenge timings based on perfect knowledge. We find that under challenging stochastic workloads based on a class of mean-reverting random walks, the best heuristics can closely approximate the execution time and energy consumption achievable under an optimal device choice. Empirical support for this observation is given by measurements of a CPU/GPU co-execution of the Time Warp algorithm on physical hardware. © 2024 IEEE.

关键词： Stochastic systems

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Scalable Big Spatial Data Processing with SQL Query Compilation and distributed Morsel-driven parallelism

Scalable Big Spatial Data Processing with SQL Query Compilat...

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IEEE international Conference on Big Data

作者： Rahul Sahni Xiaozheng Zhang Sudip Chatterjee Suprio Ray University of New Brunswick Fredericton Canada

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The rapid rise in spatial data volumes from diverse sources necessitate efficient spatial data processing capability. Although most relational databases support spatial extensions of SQL query features, they offer limited scalability. Traditional relational database query processing follows a pull-based (or tuple-at-a-time) model of query processing. This is not efficient for processing large volumes of data. A number of specialized spatial data processing systems were developed that extend cluster computing frameworks, such as Spark and Hadoop. However, these systems are characterized by limited or no support for spatial SQL query execution. The few systems that support SQL querying, suffer from the overheads of the pull-based *** present a compilation-based distributed SQL query processing system. It follows a data-centric query compilation approach that takes a SQL query and generates distributed C++ (UPC++) based physical query plans. The generated code is compiled and executed on a distributed in-memory high performance framework based on the Partitioned Global Address Space (PGAS) paradigm. We also introduce morsel-driven parallelism for scalable spatial query execution in a distributed runtime. We conduct experimental evaluation of our system with two real-world datasets on a number of spatial query workloads. Experimental results demonstrate that our system performs significantly better than a leading spatial big data system Apache Sedona and distributed parallel relational database Citus.

关键词： Runtime Query processing Computational modeling Scalability Relational databases parallel processing Big Data Data processing Spatial databases Sparks

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P-RECS '22: 5th international Workshop on Practical Reproducible Evaluation of Computer systems 31

P-RECS '22: 5th International Workshop on Practical Reproduc...

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31st international symposium on High-Performance parallel and distributed Computing, HPDC 2022

作者： Liem, Radita Solorzano, Ana Veroneze Scully-Allison, Connor RWTH Aachen University Aachen Germany Northeastern University BostonMA United States University of Arizona TucsonAZ United States

ISBN: (纸本)9781450391993

Modern software engineering is getting increasingly complicated. Especially in the HPC field, we are dealing with cutting edge infrastructure and a novel problem with unprecedented scale. The ability to monitor and analyze the performance of such applications and infrastructure is imperative for the future of improvement, design, and maintenance. In the current era, the writing and maintenance of these applications have ceased to be the job solely of computer scientists and has grown to encompass a wide variety of experts in mathematics, science, and other engineering disciplines. The fact that many developers from these disciplines have not received a formal education in computer science and rely increasingly on the tools created by computer scientists to analyze and optimize their code shows that there's a need for a forum to work together. © 2022 Owner/Author.

关键词： Software engineering

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SNTA'22: The 5thWorkshop on systems and Network Telemetry and Analytics 22

SNTA'22: The 5thWorkshop on Systems and Network Telemetry an...

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31st international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Kim, Jinoh Cafaro, Massimo Chou, Jerry Sim, Alex Texas A&M Univ Commerce TX 75428 USA Univ Salento Lecce LE Italy Natl Tsing Hua Univ Hsinchu Taiwan Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781450391993

HPC and distributed systems are the driving force for the advancement of many emerging technologies, such as exascale systems, quantum machines, terabit networking, 5G/6G wireless, and cloud/edge computing. The tasks of systems and network telemetry are a key element for effective operations and management of the advancement of many emerging systems and technologies, and require more scalable telemetry and analysis techniques for comprehensive monitoring and analysis. Various input sources such as end systems, switches, firewalls, intrusion sensors and the emerging network elements speaking with different syntax and semantics make organizing and incorporating the generated data challenging for the quantitative and qualitative analysis. This workshop looks for new approaches and methods at the intersection of HPC systems and data sciences to address these difficult challenges of emerging technologies from the diverse angles of systems/network performance, availability, reliability, and security.

关键词： distributed computing methodologies parallel computing methodologies intrusion/anomaly detection and malware mitigation network security network performance evaluation

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Research and Development of Enterprise Data Warehouse Based on SAP BW Modeling

Research and Development of Enterprise Data Warehouse Based ...

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2023 IEEE international Conference on Smart Information systems and Technologies, SIST 2023

作者： Turken, Gulzat Naizabayeva, Lyazat Satymbekov, Maxatbek Abdiakhmetova, Zukhra Al-Farabi Kazakh National University Department of Computer Science Almaty Kazakhstan International Information Technology University Department of Information System Almaty Kazakhstan

ISBN: (纸本)9798350335040

The data warehouse is based on the development of relational databases, parallel processing and distributed processing technologies, as well as online analytical processing technologies. SAP BW is widely used in many enterprise data warehouses. It can issue reports of enterprises at the group level, manage the group's business operations, help find the laws and trends behind the performance of corporations and ultimately provide decision-making support for decision makers of enterprises. Therefore, how to use the SAP BW model architecture to better realize enterprise data warehouses is one of the hot topics in the data warehouse industry today. For a large corporation, its branches and subsidiaries have deployed multiple sets of enterprise data warehouses. At the same time, because there are multiple sets of data warehouse systems, when the data model needs to be modified at the group level, the data model of the branch also needs to be modified accordingly, and the flexibility of maintenance is poor. Finally, every time the corporation needs to upgrade the version of the data warehouse, it needs to maintain and upgrade the version of the data warehouse of each branch and the maintenance cost is high. Therefore, how to integrate multiple sets of data warehouses distributed in various subsidiaries into a corporation enterprise data warehouse by designing the model hierarchy, so as to ensure the reliability of data, enhance the flexibility of model modification and reduce the cost of data warehouse. This paper completes the design of an enterprise data warehouse of a large corporation, which can be used for the summary and collation of data from multiple branches such as finance and sales of a corporation, effectively improving the efficiency of data utilization and ensuring the authenticity of data. At the same time, through hierarchical design, the problems of poor flexibility in model modification and high system maintenance costs are solved. © 2023 IEEE.

关键词： Data warehouses

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