Tools for Distributed Systems Monitoring

Kufel, L.

doi:10.1515/fcds-2016-0014

Cited by 12 publications

(6 citation statements)

References 10 publications

(12 reference statements)

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“…We have already mentioned in the Introduction the tools currently used for the security of cloud-based systems (Kufel, 2016) and highlighted their limitations concerning the auditing requirements of blockchains and distributed ledgers. Several contributions (Cucurull and Puiggali, 2016;Ahmad et al, 2018;Li et al, 2020;Ali et al, 2022) should be mentioned that are orthogonal to ours, in that rather than the analytics on logs generated in distributed environments such as blockchains and distributed ledgers, they are aimed at using blockchains for secure log management.…”

Section: Related Workmentioning

confidence: 99%

“…Yet, there is a lack of a standardized tool that can be integrated with most frameworks and is endowed with real-time monitoring capabilities. Existing tools designed for cloud and decentralized systems (Kufel, 2016) are not easy to integrate with development frameworks for blockchain applications and are not up to the challenges regarding complete log auditing of blockchain systems (Cangemi and Brennan, 2019). We started from these premises to define a general methodology and architecture for constructing a log auditing system suitable for blockchain and distributed ledger requirements.…”

Section: Introductionmentioning

confidence: 99%

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Listening to what the system tells us: Innovative auditing for distributed systems

Pilla

Pareschi

Salzano

et al. 2023

Front. Comput. Sci.

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IntroductionIn recent years, software ecosystems have become more complex with the proliferation of distributed systems such as blockchains and distributed ledgers. Effective management of these systems requires constant monitoring to identify any potential malfunctions, anomalies, vulnerabilities, or attacks. Traditional log auditing methods can effectively monitor the health of conventional systems. Yet, they run short of handling the higher levels of complexity of distributed systems. This study aims to propose an innovative architecture for system auditing that can effectively manage the complexity of distributed systems using advanced data analytics, natural language processing, and artificial intelligence.MethodsTo develop this architecture, we considered the unique characteristics of distributed systems and the various signals that may arise within them. We also felt the need for flexibility to capture these signals effectively. The resulting architecture utilizes advanced data analytics, natural language processing, and artificial intelligence to analyze and interpret the various signals emitted by the system.ResultsWe have implemented this architecture in the DELTA (Distributed Elastic Log Text Analyzer) auditing tool and applied it to the Hyperledger Fabric platform, a widely used implementation of private blockchains.DiscussionThe proposed architecture for system auditing can effectively handle the complexity of distributed systems, and the DELTA tool provides a practical implementation of this approach. Further research could explore this approach's potential applications and effectiveness in other distributed systems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Listening to what the system tells us: Innovative auditing for distributed systems

Pilla

Pareschi

Salzano

et al. 2023

Front. Comput. Sci.

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“…At the same time, the overhead generated by resource control and management techniques should also remain low. This requirement determines the selection of observation techniques [40], [41], [42]. -The possibility to specify the application's demand for resources (particularly application level bandwidth guarantees [43]) -the specification should describe the configuration of computational resources influencing the effectiveness of processing operations.…”

Section: Requirements Of Policy-driven Infra-structurementioning

confidence: 99%

Experimental Evaluation of Rule-Based Autonomic Computing Management Framework for Cloud-Native Applications

Kosińska

Zieliński

2023

IEEE Trans. Serv. Comput.

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The policy-based management paradigm in a flexible manner governs the system behavior. For Cloud-native applications, additionally, it simplifies the compliance with CI/CD objectives. Hence, the velocity of changes in requirements made at runtime does not influence the system implementation. Continuously the adjustments are integrated into the system on the fly. This paper evaluates the rule-based approach to representing policies in the context of Cloud-native applications. Deploying applications in orchestrated environments is one of the main principles of Cloud-native. Our approach represents the extension of the management characteristics that are available in current implementations of the orchestrators. The presented study also shows a general methodology for experimental evaluation of complex Cloud-native environments. We propose two categories of experiments. Both evaluate the rule-based approach. The first category evaluates the impacts of dynamic adjustment of resources in the context of the Cloud-native execution environment. The second category assesses the influence of the rule engine approach on the autonomic management process. Given the wide range of available experiments, we additionally assume that evaluation is performed from the point of view of the execution environment's resources. This approach tightly embraces the capabilities of the proposed solution realized by the AMoCNA system and demonstrates its usability.

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“…There are many open-source monitoring tools available to build monitoring systems, and their pros and cons have been evaluated [8,9]. The most commonly used tools in large scientific facilities are Zabbix, Ganglia, MRTG, Nagios, Cacti, OpenNMS, and Prometheus [10,11].…”

Section: Comparison and Decision-making On Monitoring Toolsmentioning

confidence: 99%

Control infrastructure monitoring system at the NSRL facility cluster

Qin

Li²,

Liu³

2022

J. Inst.

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The National Synchrotron Radiation Laboratory (NSRL) facility cluster is a collection of user facilities developed by NSRL, including the Hefei Light Source-II (HLS-II), Tunable Infrared Laser for Fundamental of Energy Chemistry (FELiChEM), and THz near-field high-flux material physical property test system (NFTHZ). User facilities generally have high operational availability requirements. The NSRL facility cluster relies on the control infrastructure to provide computing, network, and storage resources, as well as various services that must be available 24 hours a day, 7 days a week. The monitoring system is responsible for tracking the operational status of the control infrastructure, gathering information on faults, performance degradation and cybersecurity issues, and distributing alarm messages in time. It facilitates the operator troubleshooting problems efficiently to improve the availability of the user facilities. The monitoring system is developed by integrating several free and open source software tools. Zabbix is selected as the monitoring tool and collects metrics data from the control infrastructure. Three upper-layer applications are developed for data visualization. The dashboard shows the operational status of the network and various devices. The alarm system collects and distributes alarm messages via web-based GUI and WeChat. The reporting system periodically generates metrics and alarm reports. The monitoring system has been deployed since March 2022. The results indicate that the monitoring system can effectively identify hidden hazards in the control infrastructure.

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Tools for Distributed Systems Monitoring

Cited by 12 publications

References 10 publications

Listening to what the system tells us: Innovative auditing for distributed systems

Listening to what the system tells us: Innovative auditing for distributed systems

Experimental Evaluation of Rule-Based Autonomic Computing Management Framework for Cloud-Native Applications

Control infrastructure monitoring system at the NSRL facility cluster

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