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Ensemble Graph and Device Clustering Method based on Attention Mechanism for Decomposing Monolithic to Microservices

Ensemble Graph and Device Clustering Method based on Attenti...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Qingyong Deng Qiuming Li Qinghua Zuo Shujuan Tian Saiqin Long School of Computer Science and Engineering and the School of Software Guangxi Normal University Guilin China School of Computer Science Xiangtan University Xiangtan China College of Information Science and Technology Jinan University Guangzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Existing methods for decomposing monolithic applications into microservices in cloud environments primarily rely on the call relationships within itself. However, these methods are difficult to apply directly in resource-constrained and distributed edge network scenarios without considering the heterogeneity of the device. Therefore, this paper proposes a clustering method that ensembles graph structures and device features based on attention mechanism, which utilizes attention encoders to learn node embeddings and employs a spectral clustering algorithm to obtain decomposition results, optimizing the affinity and matching degree between microservices and devices. Experimental results demonstrate that the proposed method exhibits excellent performance in terms of functional independence, modularity, and adaptability of microservices.

关键词： Performance evaluation Industries distributed processing Attention mechanisms Clustering methods Scalability Microservice architectures Clustering algorithms Robustness Real-time systems

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An Intelligent Early Warning Support System for Financial Crisis based on Fully Homomorphic Encryption Algorithm

An Intelligent Early Warning Support System for Financial Cr...

引用

distributed systems, Computer Networks and Cybersecurity (ICDSCNC), international Conference on

作者： Fanling Kong College of Finance and Economics Guangdong University of Science &Technology Dongguan China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The financial crisis has an important impact on social and economic development, and the existing economic management methods cannot achieve massive data analysis, so on this basis This paper proposes an intelligent early warning support system for financial crisis based on fully homomorphic encryption algorithm to solve the problems of data privacy and security in the traditional early warning system. In this paper, the appropriate CKKS fully homomorphic encryption algorithm is used to implement encryption, decryption and homomorphic operations. The results show that the fully homomorphic encryption algorithm can be simplified, with a simplification rate of 10%, and polynomial adjustment can be realized, which can improve the early warning efficiency of financial crisis to 90%, which is better than the traditional method. Therefore, the financial crisis intelligent early warning system established in this paper can be used and promoted in an efficient and comprehensive manner.

关键词： Economics Data privacy Accuracy Data analysis distributed databases Alarm systems parallel processing Polynomials Homomorphic encryption Optimization

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A new Ad-Hoc parallel file system for HPC environments based on the Expand parallel file system

A new Ad-Hoc parallel file system for HPC environments based...

引用

international symposium on parallel and distributed Computing

作者： Felix Garcia-Carballeira Diego Camarmas-Alonso Alejandro Caderon-Mateos Jesus Carretero Computer Science and Engineering Departament Universidad Carlos III de Madrid Leganes Spain

An Ad-Hoc File System dynamically virtualizes storage on compute nodes into a fast storage volume to reduce congestion on parallel file systems used as backends in HPC environments and improve data locality. This paper presents Expand Ad-Hoc, a version of the Expand parallel file system, for use as an Ad-Hoc storage system for HPC environments. Such an update seeks to take better advantage of new storage technologies (on SSDs local to nodes, for example) and to adapt to new demands of current parallel applications (e.g., optimizing data access by analyzing data locality). The paper describes the new system’s features and presents a first evaluation comparing the performance of Expand with another Ad-Hoc File System (GekkoFS), and GPFS. The first results are quite satisfactory and demonstrate the good performance of Expand as an Ad-Hoc storage system.

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SVGDoc: A CRDT for Collaborative Vector Graphics Editing

SVGDoc: A CRDT for Collaborative Vector Graphics Editing

引用

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

作者： Bryn Ghiffar Jason Jeremy Wijadi Ida Bagus Kerthyayana Manuaba Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Conflict-free Replicated Data Types (CRDTs) are a class of algorithms that allow decentralized editing of documents as data structures in which concurrent operations are commutative, while there has been extensive research on generic CRDTs which allow CRDTs to be modeled according to application data. Current generic CRDTs use a CRDT map or an operation log which lack a tree structure with support for move operations to represent a vector graphics document or does not preserve updates that are concurrent to deletions. In this paper, a set of concurrent editing scenarios that are frequently made by user's of collaborative vector graphics editing are described. A CRDT for collaborative vector graphics editing which cover these scenarios named SVGDoc is then introduced. The main idea of SVGDoc is to use the UWMap (Update-Wins Map) as described by Preguica to allow updates to be preserved when concurrently made to deletions in combination with the move operation log described by Kleppmann et al to allow move operations among nodes in a tree. SVGDoc is also tested through unit testing and further validated by applying it to a minimal collaborative vector graphics editor.

关键词： Graphics Runtime Merging Memory management Collaboration parallel processing Data structures Vectors Time complexity Testing

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A Dynamic distributed Scheduler for DNN Inference on the Edge

A Dynamic Distributed Scheduler for DNN Inference on the Edg...

引用

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

作者： Fei Hu Shivakant Mishra Department of Computer Science University of Colorado Boulder CO USA

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Edge computing plays a pivotal role in IoT applications that require rapid and secure data processing. How-ever, these applications are typically resource-demanding, and the resources available at the edge are often significantly less than those available in cloud environments. Thus, optimally utilizing these constrained resources is key to fulfilling various application requirements such as low latency, privacy, and cost-effectiveness. Given the dynamic and hybrid characteristics of IoT environments, static scheduling approaches frequently fail to adequately address these complex and varying demands. This paper presents the design, implementation, and evaluation of a dynamic distributed scheduler tailored for DNN inferences on edge computing platforms, based on sophisticated sub-model profiling techniques. This scheduler's core feature is its ability to continuously monitor the state of the IoT infrastructure, dynam-ically adapting the distribution of computing tasks according to real-time environmental and network conditions. Diverging from previous research that typically segments DNN models into two parts, this study proposes a more granular approach that splits models into multiple segments, thereby maximizing the utilization of diverse edge computing resources. The implementation of this scheduler, including integration with a GPU, demonstrates its effectiveness across various DNN models, highlighting its practical utility in leveraging edge computing capabilities.

关键词： Adaptation models Processor scheduling Computational modeling Graphics processing units Prototypes Dynamic scheduling Real-time systems Internet of Things Resource management Edge computing

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TaPaS Co-AIE: An Open-Source Framework for Streaming-Based Heterogeneous Acceleration Using AMD AI Engines

TaPaS Co-AIE: An Open-Source Framework for Streaming-Based H...

引用

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

作者： Carsten Heinz Torben Kalkhof Yannick Lavan Andreas Koch Embedded Systems and Applications Group TU Darmstadt Darmstadt Germany

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

AMD AI Engines (AIEs) extend the design space and open up new options for coarse-grained processing in re-configurable accelerators. Pure FPGA designs for machine learning often struggle to compete with the high clock frequencies of GPUs for data-intensive workloads with only limited control flow. Having AIEs available on-chip with an FPGA fabric allows for low-latency co-processing and permits parts of an application to be placed on the most suitable kind of processing unit. Many data-heavy workloads, particularly in the AI domain, benefit from data streaming. With TaPaSCo-AIE, we present a framework for heterogeneous systems centered around data streams. Our framework focuses on AMD Versal devices and incorporates AI Engines and 100G network. We demonstrate the efficient use of TaPaSCo-AIE in a real-world evaluation based on a neural network, and achieve significant performance improvements over CPUs, and even exceed the performance of an A100 GPU.

关键词： Performance evaluation Neural networks Memory management Programmable logic arrays Programming parallel processing System-on-chip

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MAAD: A distributed Anomaly Detection Architecture for Microservices systems

MAAD: A Distributed Anomaly Detection Architecture for Micro...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Rongyuan Tan Zhuozhao Li Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Anomaly detection plays a critical role in microservices systems by enabling system administrators to promptly detect and respond to anomalies. However, existing anomaly detection systems often necessitate the centralization of log and trace data from diverse system components and rely on resource-intensive statistical methods or deep learning models for analysis. This approach impedes real-time anomaly detection and requires a significant demand on computing resources. In this paper, we design a multi-agent-based, distributed anomaly detection architecture called MAAD to address the limitations. MAAD consists of a collection of agents that cooperate together to identify abnormal behaviors in a distributed manner. Each agent is deployed along with a single service and applies lightweight machine learning techniques to perform local anomaly detection based on its own logs, local context, and information extracted from its parent span *** preserve the graph information in a microservices request, an agent can communicate essential features with each other, taking into account the collective patterns learned from the prior services. We evaluate the effectiveness of MAAD on two microservices datasets, TrainTicket and MicroSS, and show that MAAD achieved high precision (up to 95.8%) and recall (up to 99.6%), outperforming state-of-the-art centralized anomaly detection approaches. Compared to centralized approaches, MAAD reduces the amount of transferred data before anomaly detection by approximately 88%, facilitating real-time anomaly detection. Furthermore, the lightweight nature of MAAD allows for rapid anomaly detection with minimal impact on microservices systems. Compared to DeepLog, MAAD saves approximately 92% detection time without using GPU accelerators.

关键词： Deep learning distributed processing Statistical analysis Microservice architectures Graphics processing units Computer architecture Feature extraction

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Advanced Virtualization in Automotive Operating System: Technology Overview and Scheme Design

Advanced Virtualization in Automotive Operating System: Tech...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yi Liu Mengyun Yao Xinglong Yang Min Qin Liang Ma Haolong Xiang FAW Besturn Automotive Co. Ltd. Changchun China China Automotive Innovation Co. Ltd. Nanjing China School of Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Intelligent connected vehicles (ICV) are a key area of competition that is a new driving force for global technological change and technological innovation in vehicles, and the development of its operating system has become an increasingly important priority. As the transformation of vehicles electronic and electrical architecture accelerates, the cross-domain integration of vehicles operating systems has become a general trend. As the main technology for cross-domain integration, virtualization technology is bound to become a battleground for various vehicle manufacturers. This paper aims to take an in-depth look at virtualization of automotive operating systems. Specifically, the overall architecture of operating system virtualization is described first, and then the key functional modules are analyzed in detail, including CPU, memory, interrupts, I/O devices and GPU virtualization technology. Then, a API forwarding-based UV-GPU virtualization scheme is especially designed whose implement details and development prospect in ICV are deeply discussed.

关键词： Technological innovation distributed processing Connected vehicles Operating systems Force Graphics processing units Market research Virtualization Optimization Automotive engineering

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An Investigative Study of WebAssembly Performance in Cloud-to-Edge

An Investigative Study of WebAssembly Performance in Cloud-t...

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

作者： Sangeeta Kakati Mats Brorsson SnT University of Luxembourg Luxembourg

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Developing an application architecture that can fully utilize the resources available is essential for developers as cloud-to-edge computing keeps evolving. With its ability to operate on multiple architectures without interfering with the development process, WebAssembly has become a highly significant solution. This makes it easy for programmers to spread applications across various computer systems. To make the WebAssembly binaries work, a runtime is required. In this paper, the tests are run using the existing Wasmtime runtime and the SightGlass benchmark suite. We compare the cycle counts for the benchmarks' compilation, instantiation, and execution phases using performance counters during the evaluation stage. These tests have shown that WebAssembly is a highly effective tool that can help developers create applications that are both efficient and effective. Using WebAssembly, developers can create applications that can adapt to changing computing environments, making it possible to take full advantage of cloud-to-edge computing. Our experimental study investigated the performance of the wasmtime runtime on X86, ARM, and RISC-V architectures. Through the evaluation, we gained a valuable analysis of Wasm's performance characteristics. By examining architecture-specific outcomes, we demonstrated its capacity to fulfill the demands of a cross-architecture cloud-to-edge application framework.

关键词： Performance evaluation Runtime Program processors Focusing Data visualization Computer architecture Benchmark testing parallel processing Standards Optimization

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Performance Modeling and Workload Analysis of distributed Large Language Model Training and Inference

Performance Modeling and Workload Analysis of Distributed La...

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IEEE international Workshop/symposium on Workload Characterization

作者： Joyjit Kundu Wenzhe Guo Ali BanaGozar Udari De Alwis Sourav Sengupta Puneet Gupta Arindam Mallik IMEC Leuven Belgium UCLA California US

ISBN: (数字)9798350356038

ISBN: (纸本)9798350356045

Aligning future system design with the ever-increasing compute needs of large language models (LLMs) is undoubtedly an important problem in today’s world. Here, we propose a general performance modeling methodology and workload analysis of distributed LLM training and inference through an analytical framework that accurately considers compute, memory sub-system, network, and various parallelization strategies (model parallel, data parallel, pipeline parallel, and sequence parallel). We validate our performance predictions with published data from literature and relevant industry vendors (e.g., NVIDIA). For distributed training, we investigate the memory footprint of LLMs for different activation re-computation methods, dissect the key factors behind the massive performance gain from A100 to B200 (∼ 35x speed-up closely following NVIDIA’s scaling trend), and further run a design space exploration at different technology nodes (12 nm to 1 nm) to study the impact of logic, memory, and network scaling on the performance. For inference, we analyze the compute versus memory boundedness of different operations at a matrix-multiply level for different GPU systems and further explore the impact of DRAM memory technology scaling on inference latency. Utilizing our modeling framework, we reveal the evolution of performance bottlenecks for both LLM training and inference with technology scaling, thus, providing insights to design future systems for LLM training and inference.

关键词： Training Performance evaluation Analytical models Computational modeling Large language models systems architecture Telecommunication traffic Data models Space exploration System analysis and design

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