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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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E-VM: An Elastic Virtual Machine Scheduling Algorithm to Minimize the Total Cost of Ownership in a Hybrid Cloud 19

E-VM: An Elastic Virtual Machine Scheduling Algorithm to Min...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Shetti, Milan M. Li, Bingzhe Du, David H. C. Rocket Software Waltham MA 02451 USA Oklahoma State Univ Stillwater OK 74078 USA Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665435741

A hybrid cloud that combines both public and private clouds is becoming more and more popular due to the advantages of improved security, scalability, and guaranteed SLA (Service-Level Agreement) at a lower cost than a separate private or public cloud. The existing studies rarely consider VM migrations in a hybrid cloud environment with dynamically changed VM workloads. From an enterprise's perspective, these migrations are necessary to minimize the cost of utilizing public clouds and guarantee SLAs of VMs in a hybrid cloud environment. In this paper, we propose an elastic VM allocation and migration algorithm for a hybrid cloud, called E-VM, to fully utilize the resources in a private cloud and to minimize the cost of using a public cloud while guaranteeing the SLAs of all VMs. The E-VM considers the bi-direction migration between private and public clouds. Two components, VM-predictor and VMselector, are designed and implemented in E-VM to determine if a migration has to be triggered between private and public clouds and which VMs will be migrated to the opposite cloud, respectively. Moreover, E-VM is designed based on the existing public cloud pricing models and can be easily adapted to any cloud service provider. According to simulator results based on a set of captured industrial VM traces/workloads and additional experiments directly on a real-world hybrid cloud, the proposed E-VM can significantly reduce the total cost of using the public cloud compared to the existing VM migration schemes.

关键词： Cloud computing Costs Web services Scheduling algorithms Scalability Pricing Bidirectional control

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HBMex: An Attachment for Nonbursting Accelerators to Enhance HBM Performance

HBMex: An Attachment for Nonbursting Accelerators to Enhance...

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Annual IEEE symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Canberk Sönmez Mohamed Shahawy Paolo Ienne École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (数字)9798331502812

ISBN: (纸本)9798331502829

Modern FPGAs integrate High Bandwidth Memory (HBM), offering up to 12× the DDR bandwidth distributed across multiple memory interfaces. To utilize the most of HBM's theoretical bandwidth, accelerators typically issue long bursts and exploit data locality. However, some applications like sparse matrix-vector multiplication (SpMV) and graph analytics often exhibit irregular, nonbursting memory access patterns, which hinder performance. Additionally, the HBM interconnect, essential for accessing multiple interfaces, may stall requests under certain conditions. This work introduces HBMex, a novel module designed to enhance HBM throughput for accelerators with irregular access patterns. Positioned between the accelerator and the HBM, HBMex improves parallelism by distributing memory requests across interfaces and mitigates stalls caused by the interconnect. We evaluate HBMex using memory access microbenchmarks and an SpMV accelerator, demonstrating throughput gains of up to 37% across real-world workloads compared to vendor-provided solutions. HBMex is distributed as a highly-configurable and open-source RTL generator.

关键词： Bandwidth parallel processing Benchmark testing Throughput Generators Sparse matrices Field programmable gate arrays Clocks

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Lock-free Computation of PageRank in Dynamic Graphs

Lock-free Computation of PageRank in Dynamic Graphs

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

作者： Subhajit Sahu Kishore Kothapalli Hemalatha Eedi Sathya Peri International Institute of Information Technology Hyderabad India JNTUH College of Engineering Hyderabad India Indian Institute of Technology Hyderabad India

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

PageRank is a metric that assigns importance to the vertices of a graph based on its neighbors and their scores. Recently, there has been increasing interest in computing PageRank on dynamic graphs, where the graph structure evolves due to edge insertions and deletions. However, traditional barrier-based approaches for updating PageRanks encounter significant wait times on certain graph structures, leading to high overall runtimes. Additionally, the growing trend of multicore architectures with increased core counts has raised concerns about random thread delays and failures. In this study, we propose a lock-free algorithm for updating PageRank scores on dynamic graphs. First, we introduce our Dynamic Frontier (DF) approach, which identifies and processes vertices likely to change PageRanks with minimal overhead. Subsequently, we integrate DF with our lock-free and fault-tolerant PageRank (Alg. DF LF ), incorporating a helping mechanism among threads between its two phases. Experimental results demonstrate that Alg. DF LF not only eliminates waiting times at iteration barriers but also withstands random thread delays and crashes. On average, it is 4.6× faster than lock-free Naive-dynamic PageRank (Alg. ND LF ).

关键词： Measurement Runtime Multicore processing Instruction sets Heuristic algorithms Redundancy Fault tolerant systems

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Message from the PDSEC-22 Workshop Chairs

Message from the PDSEC-22 Workshop Chairs

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

作者： Sabine Roller Peter Strazdins Raphaë l Couturier Neda Ebrahimi Pour Suzanne Michelle Shontz Thomas Rauber Gudula Runger Laurence T. Yang German Aerospace Center Dresden Germany The Australian National University Australia University Bourgogne Franche-Comte France University of Kansas USA University of Bayreuth Germany Chemnitz University of Technology Germany St. Francis Xavier University Canada

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Welcome to the 23nd IEEE international Workshop on parallel and distributed Scientific and Engineering computing (PDSEC-22), held virtually on June 3rd, 2022 in Lyon, France, in conjunction with the 36th IEEE Inter-national parallel and distributed Processing symposium (IPDPS 2022). This year, the workshop as IPDPS took place virtually due to the covid-19 pandemic.

关键词： COVID-19 distributed processing Pandemics Conferences Engineering computing

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Improved Bat Algorithms for Solving Flowtime Minimization Task Scheduling Problems in D2D-Enabled MEC

Improved Bat Algorithms for Solving Flowtime Minimization Ta...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Linhua Ma Qingran Yan Yi Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Device-to-device (D2D)-enabled mobile edge computing (MEC) is a promising communication network in which computation tasks on mobile devices (MDs) can not only be processed locally but also can be scheduled to the MEC server or nearby MDs via D2D links in order to improve computational and energy efficiency. Due to the explosive growth of information and consequent large-scale data, the conflict between reducing task response time and the limited resources of MEC server and MDs has become an essential bottleneck. In this paper, we consider a D2D-enabled MEC system and study a task scheduling problem for minimizing the flowtime of all computation tasks under the energy constraints of MDs. To solve this problem, we develop a family of metaheuristic algorithms based upon our proposed framework of improved bat algorithm (IBA), which incorporates multiple best bat individuals. Also, we introduce a new linear updating scheme to update the bat positions and facilitate the evolution process of the metaheuristics. More importantly, we provide a rigorous theoretical analysis to prove the convergence of IBA by using this new updating scheme. Furthermore, we employ an improved initial solution generation strategy for enhancing the effectiveness of IBA. Numerical experiments demonstrate that the proposed algorithms can achieve high-quality scheduling solutions, in terms of shorter flowtimes, more effectively and more efficiently over baseline algorithms.

关键词： Multi-access edge computing Processor scheduling NP-hard problem Metaheuristics Scheduling Mobile handsets Computational efficiency Servers Time factors Convergence

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Adopting parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment

Adopting Parallel Processing for Rapid Generation of Transcr...

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

作者： Mert Can Cakmak Obianuju Okeke Billy Spann Nitin Agarwal COSMOS Research Center University of Arkansas at Little Rock Little Rock USA

The growth of online media platforms, particularly multimedia-rich social networks such as YouTube, has resulted in a demand for efficient data collection and analysis techniques. One of the critical data elements for multimedia-rich social platforms is the video transcript, which is not readily available through social platforms. Traditional methods for transcript generation are time-consuming and are challenged by the vast amount of data. This study proposes a methodology that leverages parallel computing and the Python multiprocessing library to improve the speed of transcript collection from YouTube. The methodology utilizes YouTube’s Transcript API to extract YouTube-generated transcripts and OpenAI’s Whisper model to generate transcriptions on videos without native YouTube transcriptions. Additionally, the Googletrans Translation API was used to translate transcriptions from non-English videos. The results showed a significant improvement in processing time and performance, enabling researchers to conduct various studies on a larger scope of YouTube data with ease. With parallel processing, the YouTube Transcript API showed a 2100.88% performance increase, the Whisper model showed a 29.45% improvement, and the Googletrans API showed a 738.46% increase compared to the sequential processing baseline using the same process. The total time consumption was reduced by 25.54% from 105.64 hours to 78.66 hours. The methodology developed in this study is not limited to YouTube and can be applied to other social media platforms, making it a versatile solution for data collection and analysis.

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Dynamic Tasks Scheduling with Multiple Priorities on Heterogeneous computing Systems

Dynamic Tasks Scheduling with Multiple Priorities on Heterog...

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

作者： Hayfa Tayeb Bérenger Bramas Mathieu Faverge Abdou Guermouche Inria ICube Lab Bordeaux University Bordeaux France Inria ICube Lab Strasbourg University Strasbourg France Bordeaux INP CNRS Inria Bordeaux University Bordeaux France Inria LaBRI Lab Bordeaux University Bordeaux France

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The efficient utilization of heterogeneous computing systems is crucial for scientists and industrial organizations to execute computationally intensive applications. Task-based programming has emerged as an effective approach for harnessing the processing power of these systems. However, effective scheduling of task-based applications is critical for achieving high performance. Typically, these applications are represented as directed acyclic graphs (DAGs), which can be optimized through careful scheduling to minimize execution time and maximize resource utilization. In this paper, we introduce MultiPrio, a dynamic task scheduler that aims to minimize the overall completion time of parallelized task-based applications. The goal is to find a trade-off between resource affinity, task criticality, and workload balancing on the resources. To this end, we compute scores for each task and manage the available tasks in the system with a data structure based on a set of priority queues. Tasks are assigned to available resources according to these scores, which are dynamically computed by heuristics based on task affinity and criticality. We also consider workload balancing across resources and data locality awareness. To evaluate the scheduler, we study the performance of dense and sparse linear algebra task-based applications and task-based FMM application using the StarPU runtime system on heterogeneous nodes. Our scheduler shows interesting results compared to other state-of-the-art schedulers in StarPU for regular applications, and excels at optimizing irregular workloads, improving performance by up to 31 %.

关键词： Job shop scheduling Runtime Processor scheduling Organizations Programming Dynamic scheduling Data structures

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Fast And Automatic Floating Point Error Analysis With CHEF-FP

Fast And Automatic Floating Point Error Analysis With CHEF-F...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Garima Singh Baidyanath Kundu Harshitha Menon Alexander Penev David J. Lange Vassil Vassilev European Council for Nuclear Research Meyrin Switzerland Department of Physics Princeton University Princeton New Jersey USA Lawrence Livermore National Laboratory Center for Applied Scientific Computing Livermore California USA Faculty of Mathematics and Informatics University of Plovdiv Plovdiv Bulgaria

As we reach the limit of Moore’s Law, researchers are exploring different paradigms to achieve unprecedented performance. Approximate computing (AC), which relies on the ability of applications to tolerate some error in the results to trade-off accuracy for performance, has shown significant promise. Despite the success of AC in domains such as Machine Learning, its acceptance in High-Performance computing (HPC) is limited due to its stringent requirement of accuracy. We need tools and techniques to identify regions of the code that are amenable to approximations and their impact on the application output quality so as to guide developers to employ selective approximation. To this end, we propose CHEF-FP, a flexible, scalable, and easy-to-use source-code transformation tool based on Automatic Differentiation (AD) for analysing approximation errors in HPC ***-FP uses Clad, an efficient AD tool built as a plugin to the Clang compiler and based on the LLVM compiler infrastructure, as a backend and utilizes its AD abilities to evaluate approximation errors in C++ code. CHEF-FP works at the source level by injecting error estimation code into the generated adjoints. This enables the error-estimation code to undergo compiler optimizations resulting in improved analysis time and reduced memory usage. We also provide theoretical and architectural augmentations to source code transformation-based AD tools to perform FP error analysis. In this paper, we primarily focus on analyzing errors introduced by mixed-precision AC techniques, the most popular approximate technique in HPC. We also show the applicability of our tool in estimating other kinds of errors by evaluating our tool on codes that use approximate functions. Moreover, we demonstrate the speedups achieved by CHEF-FP during analysis time as compared to the existing state-of-the-art tool as a result of its ability to generate and insert approximation error estimate code directly into the derivative sourc

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C++ and Interoperability Between Libraries: The GraphBLAS C++ Specification

C++ and Interoperability Between Libraries: The GraphBLAS C+...

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

作者： Benjamin Brock Scott McMillan Aydın Buluç Timothy G. Mattson José E. Moreira Parallel Computing Lab Intel Software Engineering Institute Carnegie Mellon University Computational Research Department Lawrence Berkeley National Laboratory EECS Department University of California Berkeley IBM Thomas J. Watson Research Center

Interoperability between libraries is often hindered by incompatible data formats, which can necessitate creating new copies of data when transferring data back and forth between different libraries. This additional data movement incurs additional runtime costs, particularly for sparse applications, where the costs of data movement often dwarf compute costs. In this paper, we investigate interoperability in the context of the C++ GraphBLAS Specification, where C++ concepts allow GraphBLAS algorithms to accept any matrix type as long as it follows the matrix interface defined in the GraphBLAS matrix concept. We first develop non-owning, lazily evaluated adapted views for a number of external data structures, including two categories of graphs defined in the Northwest Graph Library (NWGraph) and traditional pointer-based CSR data structures. These adapted views fulfill the C++ GraphBLAS matrix concept, allowing them to be used inside GraphBLAS algorithms. We then evaluate the performance of these adapted views across two kernels, matrix reduction and sparse times dense matrix multiplication (SpMM), where the performance achieved using a single generic implementation with these views largely matches the performance achieved operating directly on the original data structures, with a slight performance loss in one case. We then propose a mechanism for automatically discovering the availability of these views, allowing algorithms to directly accept external data structures. We also discuss potential extensions to the C++ GraphBLAS specification that might eliminate the small performance dip observed for one of the views.

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