Run-time correlation engine for system monitoring and testing

Holub, Viliam; Parsons, Trevor; O’Sullivan, Patrick; Murphy, John

doi:10.1145/1555228.1555237

Cited by 14 publications

(13 citation statements)

References 10 publications

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“…Instead of exact performance model we use an estimated model calculated based on monitored data from the Data Correlation Engine represented in the Figure 6 [20]. Result of this linked data operation is named event data model, which is a standardized model and can be used indistinctly in service management or network infrastructure management domains.…”

Section: Matchmaking and Iot Cloud Service Controlmentioning

confidence: 99%

Resource Optimisation in IoT Cloud Systems by Using Matchmaking and Self-management Principles

Serrano

Phuoc

Zaremba

et al. 2013

The Future Internet

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Abstract.IoT Cloud systems provide scalable capacity and dynamic behaviour control of virtual infrastructures for running applications, services and processes. Key aspects in this type of complex systems are the resource optimisation and the performance of dynamic management based on distributed user data metrics and/or IoT application data demands and/or resource utilisation metrics. In this paper we particularly focus on Cloud management perspective -integrating IoT Cloud service data management -based on annotated data of monitored Cloud performance and user profiles (matchmaking) and enabling management systems to use shared infrastructures and resources to enable efficient deployment of IoT services and applications. We illustrate a Cloud service management approach based on matchmaking operations and self-management principles which enable improved distribution and management of IoT services across different Cloud vendors and use the results from the analysis as mechanism to control applications and services deployment in Cloud systems. For our IoT Cloud data management solution we utilize performance metrics expressed with linked data in order to integrate monitored performance data and end user profile information (via linked data relations).

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Section: Matchmaking and Iot Cloud Service Controlmentioning

confidence: 99%

Resource Optimisation in IoT Cloud Systems by Using Matchmaking and Self-management Principles

Serrano

Phuoc

Zaremba

et al. 2013

The Future Internet

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“…The Run-time Correlation Engine (RTCE) [6][7] is a framework that collects, correlates, and presents events produced by complex distributed software systems. Its main goal is to significantly speedup log analysis and assist in monitoring live applications.…”

Section: Run-time Correlation Engine -Rtcementioning

confidence: 99%

A semantic monitoring and management framework for end-to-end services

Keeney¹,

Conlan²,

Holub³

et al. 2011

12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops

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Abstract-Modern distributed applications and communication services have become increasingly complex, composed of diverse heterogeneous sub-systems, and it is progressively more unrealistic that the users of these systems will be able to manage them in a holistic end-to-end manner. In particular, it is increasingly difficult to understand how such systems operate, the meaning of errors, and how they can be manipulated in a managed way, cognisant of the end-to-end nature of these systems. This paper describes an approach to semantically enrich monitoring information, events and faults, and management actions in such a way that they can be presented to a manager in manner that can be understood and leveraged. This work is based on one of the key scenarios FAME research project.

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“…Ongoing work is focusing on extending the current systems to better support asynchronous event-based data in addition to query or stream based data. A particular focus is the incorporation of more semantic queries, temporal and causal relations, and the use of symptom databases to perform real-correlation of events and faults at runtime [12]. Another body of work is building up a repository of composable customizable visualization widgets to consume the information from SARA and Insight and to support the manager in monitoring and managing their networks and services.…”

Section: Current Status and Future Workmentioning

confidence: 99%

Towards non-expert users monitoring networks and services through semantically enhanced visualizations

Conlan

Keeney

Hampson

et al. 2010

2010 International Conference on Network and Service Management

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Abstract-Managing networks and services in large virtual enterprises often requires significant expertise to decipher and reconcile information coming from several heterogeneous sources. This presents a significant challenge as the person with the network management expertise is not necessarily the person who has the task or vision of how to maximize the value of these services. Conversely it would be valuable for dedicated network managers to have insight into the meaning of the services running over the network. This paper presents an approach to empowering users to monitor and eventually manage network devices and services that they are not necessarily expert in. This paper details this challenge, presents two enabling technological frameworks and offers two compelling case studies currently being addressed in the Federated, Autonomic Management of End-to-End Communications Services (FAME) project.

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Run-time correlation engine for system monitoring and testing

Cited by 14 publications

References 10 publications

Resource Optimisation in IoT Cloud Systems by Using Matchmaking and Self-management Principles

Resource Optimisation in IoT Cloud Systems by Using Matchmaking and Self-management Principles

A semantic monitoring and management framework for end-to-end services

Towards non-expert users monitoring networks and services through semantically enhanced visualizations

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