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Offline and Real-Time Policy-based Management for Virtualized Services: Conflict and Redundancy Detection, and Automated Resolution 基于离线和实时策略的虚拟化服务管理:冲突和冗余检测以及自动解决方案
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-06-12 DOI: 10.1007/s10922-024-09830-y
Hanan Suwi, Nadjia Kara, Omar Abdel Wahab, Claes Edstrom, Yves Lemieux

Network Function Virtualization (NFV) is a new technology that allows service providers to improve the cost efficiency of network service provisioning. This is accomplished by decoupling the network functions from the physical environment within which they are deployed and converting them into software components that run on top of commodity hardware. Despite its importance, NFV encounters many challenges at the placement, resource management, and adaptation levels. For example, any placement strategy must take into account the minimization of several factors, including those of hardware resource utilization, network bandwidth and latency. Moreover, Virtual Network Functions (VNFs) should continuously be adjusted to keep up with the changes that occur at both the data center and user levels. Over the past few years several efforts have been made to come up with innovative placement, resource management, and readjustment policies. However, a problem arises when these policies exhibit some conflicts and/or redundancies with one another, since the policies are proposed by multiple sources (e.g., service providers, network administrators, NFV-orchestrators and customers). This constitutes a serious problem for the network service as a whole and has several negative impacts such as Service-Level Agreement (SLA) violations and performance degradation. Besides, as conflicts may occur among a set of policies, pairwise detection will not adequate. In this paper, we tackle this problem by defining a conflict and redundancy detection and an automated resolution mechanisms to identify and solve the issues within and between NFV policies. Finally, we integrate a real-time detection component into our solution to provide continuous and comprehensive conflict and redundancy resolution, as new policies are introduced. The experimental results show that the proposed policy detection and resolution tools could rapidly identify, detect and solve conflicts and redundancies among NFV policies and extremely fast than other frameworks. Furthermore, the results show that our solution is efficient even in scenarios that consist of more than 2000 policies. Moreover, our proposed detection mechanisms can detect and solve the conflicts and redundancies for various types of policies such as placement, scaling and migration.

网络功能虚拟化(NFV)是一种新技术,可使服务提供商提高网络服务供应的成本效率。实现这一目标的方法是将网络功能与其部署的物理环境分离,并将其转换为在商品硬件上运行的软件组件。尽管 NFV 非常重要,但它在部署、资源管理和适应性层面仍面临许多挑战。例如,任何部署策略都必须考虑到最大限度地减少若干因素,包括硬件资源利用率、网络带宽和延迟。此外,虚拟网络功能(VNF)应不断调整,以跟上数据中心和用户层面发生的变化。在过去几年中,人们已经做出了许多努力,提出了创新的放置、资源管理和重新调整策略。然而,由于这些策略是由多个来源(如服务提供商、网络管理员、NFV-协调器和客户)提出的,当这些策略相互之间出现一些冲突和/或冗余时,问题就出现了。这对整个网络服务来说是一个严重的问题,会产生一些负面影响,如违反服务等级协议(SLA)和性能下降。此外,由于一组策略之间可能会发生冲突,因此成对检测是不够的。在本文中,我们通过定义冲突和冗余检测以及自动解决机制来解决这一问题,从而识别并解决 NFV 策略内部和策略之间的问题。最后,我们在解决方案中集成了实时检测组件,以便在引入新策略时提供持续、全面的冲突和冗余解决方案。实验结果表明,所提出的策略检测和解决工具可以快速识别、检测和解决 NFV 策略之间的冲突和冗余问题,其速度比其他框架快得多。此外,实验结果表明,即使在包含 2000 多条策略的场景中,我们的解决方案也是高效的。此外,我们提出的检测机制可以检测和解决各种类型策略的冲突和冗余,如放置、扩展和迁移。
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引用次数: 0
Enhancing Cloud Gaming QoE Estimation by Stacking Learning 通过堆叠学习增强云游戏 QoE 估算
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-06-12 DOI: 10.1007/s10922-024-09836-6
Daniel Soares, Marcos Carvalho, Daniel F. Macedo

The Cloud Gaming sector is burgeoning with an estimated annual growth of more than 50%, poised to reach a market value of $22 billion by 2030, and notably, GeForce Now, launched in 2020, reached 20 million users by August 2022. Cloud gaming presents cost-effective advantages for users and developers by eliminating hardware investments and game purchases, reducing development costs, and optimizing distribution efforts. However, it introduces challenges for network operators and providers, demanding low latency and substantial computational power. User satisfaction in cloud gaming depends on various factors, including game content, network type, and context, all shaping Quality of Experience. This study extends prior research, merging datasets from wired and mobile cloud gaming services to create an Expanded stacking model. All data gathering involves actual users engaging in gameplay within a realistic test environment, employing protocols akin to those utilized by the Geforce Now cloud gaming platform. Results indicate significant improvements in QoE estimation across different gaming contexts, highlighting the feasibility of a versatile predictive model for cloud gaming experiences, building upon previous stacking learning approaches.

云游戏领域正在蓬勃发展,预计年增长率将超过 50%,到 2030 年,市场价值将达到 220 亿美元,特别是 2020 年推出的 GeForce Now,到 2022 年 8 月,用户已达到 2000 万。云游戏省去了硬件投资和游戏购买,降低了开发成本,优化了分发工作,为用户和开发商带来了具有成本效益的优势。然而,它也给网络运营商和提供商带来了挑战,要求低延迟和强大的计算能力。云游戏的用户满意度取决于各种因素,包括游戏内容、网络类型和环境,所有这些都会影响体验质量。本研究扩展了之前的研究,合并了有线和移动云游戏服务的数据集,创建了扩展堆叠模型。所有数据的收集都涉及到实际用户在真实测试环境中参与游戏,采用的协议与 Geforce Now 云游戏平台使用的协议类似。结果表明,在不同的游戏环境中,QoE 评估都有了明显改善,这突出表明了在以往堆叠学习方法的基础上,为云游戏体验建立多功能预测模型的可行性。
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引用次数: 0
AI-Based Intrusion Detection for a Secure Internet of Things (IoT) 基于人工智能的入侵检测,实现安全的物联网 (IoT)
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-06-09 DOI: 10.1007/s10922-024-09829-5
Reham Aljohani, Anas Bushnag, Ali Alessa

The increasing use of intelligent devices connected to the internet has contributed to the introduction of a new paradigm: the Internet of Things (IoT). The IoT is a set of devices connected via the internet that cooperate to achieve a specific goal. Smart cities, smart airports, smart transportation, smart homes, and many applications in the medical and educational fields all use the IoT. However, one major challenge is detecting malicious intrusions on IoT networks. Intrusion Detection Systems (IDSs) should detect these types of intrusions. This work proposes an effective model for detecting malicious IoT activities using machine learning techniques. The ToN-IoT dataset, which consists of seven connected devices (subdatasets), is used to construct an IoT network. The proposed model is a multilevel classification model. The first level distinguishes between attack and normal network activities. The second level is to classify the types of detected attacks. The experimental results prove the effectiveness of the proposed model in terms of time and classification performance metrics. The proposed model and seven baseline techniques in the literature are compared. The proposed model outperformed the baseline techniques in all subdatasets except for the Garage Door dataset.

与互联网相连的智能设备的使用日益增多,推动了一种新模式的出现:物联网(IoT)。物联网是一组通过互联网连接的设备,它们通过合作来实现特定的目标。智能城市、智能机场、智能交通、智能家居以及医疗和教育领域的许多应用都在使用物联网。然而,检测物联网网络上的恶意入侵是一项重大挑战。入侵检测系统(IDS)应能检测到这些类型的入侵。这项工作提出了一种利用机器学习技术检测恶意物联网活动的有效模型。ToN-IoT 数据集由七个连接设备(子数据集)组成,用于构建物联网网络。所提出的模型是一个多级分类模型。第一级区分攻击和正常网络活动。第二层是对检测到的攻击类型进行分类。实验结果证明了所提模型在时间和分类性能指标方面的有效性。实验中对所提出的模型和文献中的七种基线技术进行了比较。除了 "车库门 "数据集之外,所提出的模型在所有子数据集中的表现都优于基线技术。
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引用次数: 0
Efficient Scheduling of Charger-UAV in Wireless Rechargeable Sensor Networks: Social Group Optimization Based Approach 无线充电式传感器网络中充电器-无人机的高效调度:基于社会群体优化的方法
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-06-05 DOI: 10.1007/s10922-024-09833-9
Sk Md Abidar Rahaman, Md Azharuddin, Pratyay Kuila

Wireless power transfer (WPT) technology enables the replenishment of rechargeable battery energy by the sensor nodes (SNs) in wireless rechargeable sensor networks (WRSNs). The deployment of unmanned aerial vehicles (UAVs) as flying chargers to replenish battery energy is established as an emerging technique, especially in harsh environments. The UAV is also operated by limited battery power and, hence, is also power-constrained. Therefore, the UAV has to timely return to the depot to be fully recharged for the next cycle. The SNs should also be timely recharged before they completely deplete their energy. The design of an efficient charging schedule for the charger-UAV for WRSNs is challenging due to the above-mentioned constraints. Moreover, the problem is non-deterministic polynomial hard (NP-hard). This paper addresses the problem of scheduling the charger-UAV to replenish the energy of SNs in WRSNs. A population-based, nature-inspired algorithm, social group optimization (SGO), is employed to design an efficient charging schedule. The flying energy of the UAV is considered to ensure that the UAV will safely and timely return back to the depot. The fitness function is designed with a novel reward-based approach. The proposed work is extensively simulated, and performance comparisons are done along with statistical analysis.

无线充电传感器网络(WRSN)中的传感器节点(SN)可以利用无线功率传输(WPT)技术补充充电电池的能量。部署无人驾驶飞行器(UAV)作为飞行充电器来补充电池能量已成为一种新兴技术,尤其是在恶劣环境中。无人飞行器也是在电池电量有限的情况下运行的,因此也受到电力限制。因此,无人飞行器必须及时返回仓库,为下一个周期充满电。SN 也应在能量完全耗尽之前及时充电。由于上述限制因素,为 WRSN 的充电器-无人机设计一个高效的充电时间表具有挑战性。此外,该问题还具有非确定性多项式难(NP-hard)的特点。本文探讨了在 WRSN 中调度充电器-无人机为 SN 补充能量的问题。本文采用基于群体的自然启发算法--社会群体优化(SGO)来设计高效的充电调度。该算法考虑了无人机的飞行能量,以确保无人机能够安全及时地返回仓库。适配函数采用基于奖励的新方法设计。对提出的工作进行了广泛的模拟,并进行了性能比较和统计分析。
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引用次数: 0
EDaTAD: Energy-Aware Data Transmission Approach with Decision-Making for Fog Computing-Based IoT Applications EDaTAD:为基于雾计算的物联网应用提供具有决策功能的节能数据传输方法
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-06-03 DOI: 10.1007/s10922-024-09828-6
Ali Kadhum Idrees, Tara Ali-Yahiya, Sara Kadhum Idrees, Raphael Couturier

In the fog computing-based Internet of Things (IoT) architecture, the sensor devices represent the basic elements needed to sense the surrounding environment. They gather and send a huge amount of data to the fog gateway and then to the cloud due to their use in various real-world IoT applications. This would lead to high data traffic, increased energy consumption, and slow decisions at the fog gateway. Therefore, it is important to reduce the transmitted data to save energy and provide an accurate decision regarding the safety and health of the building’s environment. This paper suggests an energy-aware data transmission approach with decision-making (EDaTAD) for Fog Computing-based IoT applications. It works on two-level nodes in the fog computing-based TI architecture: sensor devices and fog gateways. The EDaTAD implements a Lightweight Redundant Data Removing (LiReDaR) algorithm at the sensor device level to lower the gathered data before sending it to the fog gateway. In the fog gateway, a decision-making model is proposed to provide suitable decisions to the monitoring staff in remote monitoring applications. Finally, it executes a Data Set Redundancy Elimination (DaSeRE) approach to discard the repetitive data sets before sending them to the cloud for archiving and further analysis. EDaTAD outperforms other methods in terms of transmitted data, energy consumption, and data accuracy. Furthermore, it assesses the risk efficiently and provides suitable decisions while decreasing the latency time.

在基于雾计算的物联网(IoT)架构中,传感器设备是感知周围环境所需的基本要素。由于在各种真实世界的物联网应用中使用,它们会收集大量数据并发送到雾网关,然后再发送到云端。这将导致高数据流量、能耗增加以及雾网关决策缓慢。因此,必须减少传输的数据,以节约能源并提供有关建筑环境安全和健康的准确决策。本文为基于雾计算的物联网应用提出了一种具有决策功能的能源感知数据传输方法(EDaTAD)。它适用于基于雾计算的 TI 架构中的两级节点:传感器设备和雾网关。EDaTAD 在传感器设备层实现了轻量级冗余数据移除(LiReDaR)算法,在将收集到的数据发送到雾网关之前将其降低。在雾网关中,提出了一个决策模型,为远程监控应用中的监控人员提供合适的决策。最后,它执行了一种数据集冗余消除(DaSeRE)方法,在将重复数据集发送到云端进行归档和进一步分析之前将其丢弃。EDaTAD 在传输数据、能耗和数据准确性方面都优于其他方法。此外,它还能有效评估风险并提供合适的决策,同时减少延迟时间。
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引用次数: 0
Energy-Aware Microservice-Based Application Deployment in UAV-Based Networks for Rural Scenarios 面向农村场景的无人机网络中基于能量感知的微服务应用部署
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-05-31 DOI: 10.1007/s10922-024-09825-9
Diego Ramos-Ramos, Alejandro González-Vegas, Javier Berrocal, Jaime Galán-Jiménez

Yearly, the rates of Internet penetration are on the rise, surpassing 80% in developed nations. Despite this progress, over two billion individuals in rural and low-income regions face a complete absence of Internet access. This lack of connectivity hinders the implementation of vital services like remote healthcare, emergency assistance, distance learning, and personal communications. To bridge this gap and bring essential services to rural populations, this paper leverages Unmanned Aerial Vehicles (UAVs). The proposal introduces a UAV-based network architecture and an energy-efficient algorithm to deploy Internet of Things (IoT) applications. These applications are broken down into microservices, strategically distributed among a subset of UAVs. This approach addresses the limitations associated with running an entire IoT application on a single UAV, which could lead to suboptimal outcomes due to battery and computational constraints. Simulation results conducted in a realistic scenario underscore the effectiveness of the proposed solution. The evaluation includes assessing the percentage of IoT requests successfully served to users in the designated area and reducing the energy consumption required by UAVs during the handling of such requests.

互联网普及率逐年上升,在发达国家已超过 80%。尽管取得了这一进步,但农村和低收入地区仍有 20 多亿人完全无法接入互联网。这种连接的缺乏阻碍了远程医疗、紧急援助、远程学习和个人通信等重要服务的实施。为了弥补这一差距并为农村人口提供基本服务,本文利用了无人机(UAV)。该提案介绍了一种基于无人飞行器的网络架构和一种高能效算法,用于部署物联网(IoT)应用。这些应用被分解成微服务,战略性地分布在无人机子集中。这种方法解决了在单个无人机上运行整个物联网应用的局限性,因为单个无人机可能会因电池和计算限制而导致次优结果。在现实场景中进行的模拟结果证明了所提解决方案的有效性。评估内容包括评估向指定区域内的用户成功提供物联网请求的百分比,以及降低无人机在处理此类请求时所需的能耗。
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引用次数: 0
Mobile-Aware Service Function Chain Intelligent Seamless Migration in Multi-access Edge Computing 多接入边缘计算中的移动感知服务功能链智能无缝迁移
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-05-18 DOI: 10.1007/s10922-024-09820-0
Lingyi Xu, Wenbin Liu, Zhiwei Wang, Jianxiao Luo, Jinjiang Wang, Zhi Ma

With the improvement of service delay and quality requirements for new applications such as unmanned driving, internet of vehicles, and virtual reality, the deployment of network services is gradually moving from the cloud to the edge. This transition has led to the emergence of multi-access edge computing (MEC) architectures such as distributed micro data center and fog computing. In the MEC environment, network infrastructure is distributed around users, allowing them to access the network nearby and move between different service coverage locations. However, the high mobility of users can significantly affect service orchestration and quality, and even cause service interruption. How to respond to user mobility, dynamically migrate user services, and provide users with a continuous and seamless service experience has become a huge challenge. This paper studies the dynamic migration of service function chain (SFC) caused by user mobility in MEC environments. First, we model the SFC dynamic migration problem in mobile scenarios as an integer programming problem with the goal of optimizing service delay, migration success rate, and migration time. Based on the above model, we propose a deep reinforcement learning-driven SFC adaptive dynamic migration optimization algorithm (DRL-ADMO). DRL-ADMO can perceive the underlying network resources and SFC migration requests, intelligently decide on the migration paths of multiple network functions, and adaptively allocate bandwidth, achieving parallel and seamless SFC migration. Performance evaluation results show that compared with existing algorithms, the proposed algorithm can optimize 7% service delay and 20% migration success rate at the cost of sacrificing a small amount of migration time.

随着无人驾驶、车联网和虚拟现实等新应用对服务延迟和质量要求的提高,网络服务的部署正逐渐从云端转移到边缘。这种转变导致了分布式微型数据中心和雾计算等多接入边缘计算(MEC)架构的出现。在 MEC 环境中,网络基础设施分布在用户周围,允许用户就近访问网络,并在不同的服务覆盖地点之间移动。然而,用户的高流动性会严重影响服务协调和质量,甚至导致服务中断。如何应对用户的移动性,动态迁移用户服务,为用户提供连续、无缝的服务体验,成为一个巨大的挑战。本文研究了 MEC 环境中由用户移动引起的服务功能链(SFC)的动态迁移。首先,我们将移动场景下的 SFC 动态迁移问题建模为一个整数编程问题,目标是优化服务延迟、迁移成功率和迁移时间。基于上述模型,我们提出了一种深度强化学习驱动的 SFC 自适应动态迁移优化算法(DRL-ADMO)。DRL-ADMO 可感知底层网络资源和 SFC 迁移请求,智能决定多个网络功能的迁移路径,并自适应分配带宽,实现并行、无缝的 SFC 迁移。性能评估结果表明,与现有算法相比,所提出的算法以牺牲少量迁移时间为代价,可优化 7% 的服务延迟和 20% 的迁移成功率。
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引用次数: 0
Building a Comprehensive Intent-Based Networking Framework: A Practical Approach from Design Concepts to Implementation 构建基于意图的综合网络框架:从设计概念到实施的实用方法
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-05-03 DOI: 10.1007/s10922-024-09819-7
Henry Yu, Hesam Rahimi, Christopher Janz, Dong Wang, Zhen Li, Chungang Yang, Yehua Zhao

Intent-Based Networking (IBN) is an important step towards achieving network automation. Many challenges of today’s complex network management systems can be tackled by the solutions proposed by IBN. However, although IBN has gained a lot of attention from the academic and industrial community in the second half of the last decade leading to many scientific publications and research papers, there has been little effort made on proposing a comprehensive framework for IBN, which converts system-level IBN concepts and theories into a fully featured software implementation. This paper presents such framework. Its implementation is standards-based and open-source. The framework can be used to facilitate and validate novel research ideas and test cases. The paper discusses relevant IBN design concepts and theories, how the framework’s software architecture is derived from those concepts, and the technical and implementation details on key IBN aspects and features including Intent life-cycle, Intent definition and translation, Intent orchestration, and Intent assurance using closed-loops. We also demonstrate a real intent-based use case realized by the framework in order to show and validate the proof-of-concept (PoC). The Future work of this project is also discussed.

基于意图的网络(IBN)是实现网络自动化的重要一步。IBN 提出的解决方案可以解决当今复杂网络管理系统面临的许多挑战。然而,尽管 IBN 在过去十年的后半期得到了学术界和工业界的广泛关注,发表了许多科学出版物和研究论文,但很少有人致力于提出一个全面的 IBN 框架,将系统级的 IBN 概念和理论转化为功能齐全的软件实现。本文介绍了这种框架。其实施基于标准并开源。该框架可用于促进和验证新的研究理念和测试案例。本文讨论了相关的 IBN 设计概念和理论,框架的软件架构是如何从这些概念中衍生出来的,以及 IBN 关键方面和功能的技术和实现细节,包括意图生命周期、意图定义和翻译、意图协调和使用闭环的意图保证。我们还演示了框架实现的基于意图的真实用例,以展示和验证概念验证(PoC)。我们还讨论了该项目的未来工作。
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引用次数: 0
Novel Initialization Functions for Metaheuristic-Based Online Virtual Network Embedding 基于元搜索的在线虚拟网络嵌入的新初始化函数
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-04-30 DOI: 10.1007/s10922-024-09822-y
Javier Rubio-Loyola, Christian Aguilar-Fuster

Virtual network embedding (VNE) is the process of allocating resources in a substrate (i.e. physical) network to support virtual networks optimally. The VNE problem is an NP-hard problem that has been studied for more than a decade in the continuous seek to maximize the revenue of physical infrastructures with more efficient VNE solutions. Metaheuristics have been widely used in online VNE as they incorporate mechanisms to avoid local optimum solutions, explore larger search spaces, and keep acceptable execution times. All metaheuristic optimization algorithms require initialization for which the vast majority of online VNE solutions implement random initialization. This paper proposes three novel initialization functions namely, Initialization Based on Node Selection (IFNS), Initialization Function Based on Community Detection (IFCD), and Initialization Function Based on Previous Solutions (IFPS), intending to enhance the performance of the online VNE process. Through simulation, our initialization functions have been proven to enhance the acceptance rate, revenue, and revenue-to-cost metrics of the VNE process. The enhancements achieved by our initialization functions are statistically significant and their implementation does not add computational overhead to the classic VNE approaches.

虚拟网络嵌入(VNE)是指在基质(即物理)网络中分配资源,以最佳方式支持虚拟网络的过程。虚拟网络嵌入问题是一个 NP 难度很高的问题,十多年来,人们一直在研究如何通过更高效的虚拟网络嵌入解决方案实现物理基础设施收益的最大化。元启发式已被广泛应用于在线 VNE,因为它们结合了避免局部最优解、探索更大搜索空间和保持可接受执行时间的机制。所有元启发式优化算法都需要初始化,而绝大多数在线 VNE 解决方案都采用随机初始化。本文提出了三种新颖的初始化函数,即基于节点选择的初始化(IFNS)、基于社群检测的初始化函数(IFCD)和基于先前解决方案的初始化函数(IFPS),旨在提高在线 VNE 流程的性能。通过仿真,我们的初始化功能被证明可以提高 VNE 流程的接受率、收入和收入成本比指标。我们的初始化函数所实现的提升在统计学上是显著的,而且其实施不会增加传统 VNE 方法的计算开销。
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引用次数: 0
SampleHST-X: A Point and Collective Anomaly-Aware Trace Sampling Pipeline with Approximate Half Space Trees SampleHST-X:具有近似半空间树的点和集体异常感知跟踪采样管道
IF 3.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-04-16 DOI: 10.1007/s10922-024-09818-8
Alim Ul Gias, Yicheng Gao, Matthew Sheldon, José A. Perusquía, Owen O’Brien, Giuliano Casale

The storage requirement for distributed tracing can be reduced significantly by sampling only the anomalous or interesting traces that occur rarely at runtime. In this paper, we introduce an unsupervised sampling pipeline for distributed tracing that ensures high sampling accuracy while reducing the storage requirement. The proposed method, SampleHST-X, extends our recent work SampleHST. It operates based on a budget which limits the percentage of traces to be sampled while adjusting the storage quota of normal and anomalous traces depending on the size of this budget. The sampling process relies on accurately defining clusters of normal and anomalous traces by leveraging the distribution of mass scores, which characterize the probability of observing different traces, obtained from a forest of Half Space Trees (HST). In our experiments, using traces from a cloud data center, SampleHST yields 2.3(times) to 9.5(times) better sampling performance. SampleHST-X further extends the SampleHST approach by incorporating a novel class of Half Space Trees, namely Approximate HST, that uses approximate counters to update the mass scores. These counters significantly reduces the space requirement for HST while the sampling performance remains similar. In addition to this extension, SampleHST-X includes a Family of Graph Spectral Distances (FGSD) based trace characterization component, which, in addition to point anomalies, enables it to sample traces with collective anomalies. For such traces, we observe that the SampleHST-X approach can yield 1.2(times) to 19(times) better sampling performance.

通过只对运行时很少出现的异常或有趣轨迹进行采样,可以大大降低分布式跟踪的存储需求。在本文中,我们介绍了一种用于分布式跟踪的无监督采样管道,它能在降低存储需求的同时确保高采样精度。我们提出的 SampleHST-X 方法扩展了我们最近的研究成果 SampleHST。该方法的运行基于预算,预算限制了要采样的痕迹百分比,同时根据预算的大小调整正常痕迹和异常痕迹的存储配额。采样过程依赖于利用从半空间树(HST)森林中获得的质量分数分布来准确定义正常和异常痕迹群,质量分数描述了观察到不同痕迹的概率。在我们的实验中,使用来自云数据中心的痕迹,SampleHST的采样性能提高了2.3到9.5倍。SampleHST-X 进一步扩展了 SampleHST 方法,纳入了一类新的半空间树,即近似 HST,它使用近似计数器来更新质量分数。这些计数器大大减少了 HST 所需的空间,而采样性能却保持不变。除这一扩展外,SampleHST-X 还包含基于图谱距离(FGSD)的迹线特征描述组件,除点异常外,还能对具有集体异常的迹线进行采样。对于这类踪迹,我们发现 SampleHST-X 方法的采样性能可以提高 1.2 (次)到 19 (次)。
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引用次数: 0
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