Incremental service level agreements violation handling with time impact analysis

Ismail, Azlan; Yan, Jun; Shen, Jun

doi:10.1016/j.jss.2013.01.052

Cited by 18 publications

(19 citation statements)

References 38 publications

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“…SLA violation [33], [34] SLA violation handling approach using incremental time impact analysis -Time inconsistency and unsatisfactory condition. -Change of service due to the service replacement.…”

Section: Fault Fault Tolerant Approach Fault Assumption/cause Remarksmentioning

confidence: 99%

“…Whenever an SLA violation occurs to a service it can impact dependent services. Ismail et al [33], [34] have proposed SLA violation handling approach using incremental time impact analysis. Their approach can efficiently recover and handle the violation in relation to the strategy of minimizing the number of service changes.…”

Section: Binding Faultmentioning

confidence: 99%

“…A customer participating in malicious or aggressive internet activities does not ensure for SLA claims and shall be in violation of the AUP (Acceptable Use Policy). There are some studies conducted on handling SLA violation in SBS as in [33].…”

Section: Sla Faultmentioning

confidence: 99%

See 2 more Smart Citations

Extended Fault Taxonomy of SOA-Based Systems

Bhandari

Gupta

2018

CIT

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Section: Fault Fault Tolerant Approach Fault Assumption/cause Remarksmentioning

confidence: 99%

Section: Binding Faultmentioning

confidence: 99%

See 1 more Smart Citation

Extended Fault Taxonomy of SOA-Based Systems

Bhandari

Gupta

2018

CIT

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“…We need to check the composite system to find the location of the faulty service and the impact region. Impact region should be expanded if the component services are associated with the faulty service as proposed by [7] and [40] .…”

Section:  Algorithm For Fault Repairing Strategies Selectormentioning

confidence: 99%

Fault Repairing Strategy Selector for ServiceOriented Architecture

Bhandari¹,

Ratneshwer²

2017

IJMECS

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Abstract-The success of a service oriented computing significantly depends on its reliability and availability. To achieve better reliability and availability, any fault of the service oriented computing has to be properly handled. Low performance on handling the service fault becomes the most proliferated challenge on fault handling approaches. In this paper, a priority based fault handling strategy selection approach for fault recovery of SOA (Service Oriented Architecture) is proposed using priority selector and fault handler. This approach starts from fault detection method and select the first priority level strategies promptly and if fault could not be handled by first highest level then fault handler selects the second level or intermediate priority level for average performance. As a final in the worst case, least level strategies are applied to resolve the faulty situation. Through experiment, the correctness of the proposed algorithm and the efficiency of the approach are proved. This approach results better performance and high rate of fault repairing.

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“…Bio-inspired algorithms offer many advantages for dealing with data-intensive service provision problems [26], [32]. Bio-inspired optimization algorithms have been proposed to solve the service provision problem, because of the simplicity of the algorithms and the rapid convergence to optimal or near-optimal solutions [15], [31], [33]. Biological entities can learn from their environment.…”

Section: Related Workmentioning

confidence: 99%

Cost and Time Aware Ant Colony Algorithm for Data Replica in Alpha Magnetic Spectrometer Experiment

Wang

Luo

Shen

et al. 2013

2013 IEEE International Congress on Big Data

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Huge collections of data have been created in recent years. Cloud computing provides a way to enable massive amounts of data to work together as data-intensive services. Considering Big Data and the cloud together, which is a practical and economical way to deal with Big Data, will accelerate the availability and acceptability of analysis of the data. Providing an efficient mechanism for optimized data-intensive services will become critical to meet the expected growth in demand. Because the competition is an extremely important factor in the marketplace, the cost model for data-intensive service provision is the key to provide a sustainable service market. As data play the dominant role in execution of data-intensive service composition, the cost and access response time of data sets influence the quality of the service that requires the data sets. In this paper, a data replica selection optimization algorithm based on an ant colony system is proposed. The performance of the data replica selection algorithm is evaluated by simulations. The background application of the work is the Alpha Magnetic Spectrometer experiment, which involves large amounts of data being transferred, organized and stored. It is critical and challenging to be cost and time aware to manage the data and services in this intensive research environment. Abstract-Huge collections of data have been created in recent years. Cloud computing provides a way to enable massive amounts of data to work together as data-intensive services. Considering Big Data and the cloud together, which is a practical and economical way to deal with Big Data, will accelerate the availability and acceptability of analysis of the data. Providing an efficient mechanism for optimized data-intensive services will become critical to meet the expected growth in demand. Because the competition is an extremely important factor in the marketplace, the cost model for data-intensive service provision is the key to provide a sustainable service market. As data play the dominant role in execution of data-intensive service composition, the cost and access response time of data sets influence the quality of the service that requires the data sets. In this paper, a data replica selection optimization algorithm based on an ant colony system is proposed. The performance of the data replica selection algorithm is evaluated by simulations. The background application of the work is the Alpha Magnetic Spectrometer experiment, which involves large amounts of data being transferred, organized and stored. It is critical and challenging to be cost and time aware to manage the data and services in this intensive research environment.

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Incremental service level agreements violation handling with time impact analysis

Cited by 18 publications

References 38 publications

Extended Fault Taxonomy of SOA-Based Systems

Extended Fault Taxonomy of SOA-Based Systems

Fault Repairing Strategy Selector for ServiceOriented Architecture

Cost and Time Aware Ant Colony Algorithm for Data Replica in Alpha Magnetic Spectrometer Experiment

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