Self-adaptive Service Monitoring

Clark, Kassidy P.; Warnier, Martijn; Brazier, Frances M. T.

doi:10.1007/978-3-642-23857-4_15

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…Active monitoring can be combined with an alternative monitoring technique, referred to in this paper as passive monitoring [17,18]. Passive monitoring uses cryptographic primitives to generate a secure audit log of all service messages.…”

Section: Service Monitoringmentioning

confidence: 99%

Automated Non-repudiable Cloud Resource Allocation

Clark

Warnier

Brazier

2013

Communications in Computer and Information Science

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Abstract. This paper presents an Intelligent Cloud Resource Allocation Service (ICRAS) that assists consumers with the complex task of finding the optimal configuration of Cloud resources given a consumer's specific needs. The process of selecting a CSP becomes increasingly complex as the number of Cloud Service Providers (CSP) offering similar services continues to grow. Consumers can pick and choose between CSPs based on a growing number of options, including price, Quality of Service, reputation and so forth. The advent of dynamic pricing (based on real-time availability) further increases the complexity of CSP selection. ICRAS alleviates much of this burden from the consumer by automating the processes of service discovery, evaluation, negotiation and migration. Furthermore, ICRAS monitors Service Level Agreement (SLA) compliance using non-repudiable monitoring techniques.

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Section: Service Monitoringmentioning

confidence: 99%

Automated Non-repudiable Cloud Resource Allocation

Clark

Warnier

Brazier

2013

Communications in Computer and Information Science

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“…There have been also other approaches that adopt adaptive monitoring; however, the majority of these [16,17,18,19,20] are concerned with the monitoring of hardware resources such as memory, disk and CPU. Other adaptive approaches [21] mainly focus on general properties of web services such as the availability and response time.…”

Section: Chapter V Related Workmentioning

confidence: 99%

“…Other adaptive approaches [21] mainly focus on general properties of web services such as the availability and response time. There are a few studies, where domain-specific cases are considered.…”

Section: Chapter V Related Workmentioning

confidence: 99%

Adaptive Domain-Specific Service Monitoring

Ünsal¹,

Sazara²,

Aktemur

et al. 2014

Lecture Notes in Computer Science

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We propose an adaptive and domain-specific service monitoring approach to detect partner service errors in a cost-effective manner. Hereby, we not only consider generic errors such as file not found or connection timed out, but also take domain-specific errors into account. The detection of each type of error entails a different monitoring cost in terms of the consumed resources. To reduce costs, we adapt the monitoring frequency for each service and for each type of error based on the measured error rates and a cost model. We introduce an industrial case study from the broadcasting and content-delivery domain for improving the user-perceived reliability of Smart TV systems. We demonstrate the effectiveness of our approach with real data collected to be relevant for a commercial TV portal application. We present empirical results regarding the trade-off between monitoring overhead and error detection accuracy.Our results show that each service is usually subject to various types of errors with different error rates and exploiting this variation can reduce monitoring costs by up to 30% with negligible compromise on the quality of monitoring.iii

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“…Active monitoring has several drawbacks, including additional overhead costs and inherent dependence on a TTP. To address these issues, this paper presents a self-adaptive approach to SLA monitoring in the Cloud (extending earlier work [1]). Using this approach, parties are able to dynamically adapt the process by which SLAs are monitored.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive service level agreement monitoring in cloud environments1

Clark

Warnier

Brazier

2013

MGS

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Cloud Service Providers offer access to hardware and software resources across the Internet. Cloud Service Providers and their clients negotiate Service Level Agreements (SLA) that specify terms and conditions of services. To ensure that an SLA is being honored by all parties the service is monitored. This paper proposes a monitoring approach that automatically adapts the monitoring process in realtime, based on user-specified policies. The monitor can both adapt the frequency of measurements and monitoring mode: active and passive. Active mode uses an external, trusted monitoring service. Passive mode minimizes interaction with an external monitoring service, relying instead on a mutual commit protocol to ensure SLA compliance. Monitor adaptation is determined by levels of perceived risk, defined by a user's policy. The self-adaptive SLA monitoring framework is realized using agent technology and is implemented and tested in the AgentScape middleware.

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Self-adaptive Service Monitoring

Cited by 6 publications

References 15 publications

Automated Non-repudiable Cloud Resource Allocation

Automated Non-repudiable Cloud Resource Allocation

Adaptive Domain-Specific Service Monitoring

Self-adaptive service level agreement monitoring in cloud environments1

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