Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds

Badshah, Afzal; Jalal, Ateeqa; Farooq, Umar; Rehman, Ghani-Ur; Band, Shahab S.; Iwendi, Celestine

doi:10.1089/big.2021.0274

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…It will increase resource utilization. The total revenue is calculated as: 64 Rev total = Rev res + Rev spot + Rev unit , (28) where Rev res is the revenue from reserved resources, Rev spot are earned from spot pricing and Rev unit is the revenue earned from unit based charges. The total profit is:…”

Section: 6mentioning

confidence: 99%

“…Customer satisfaction is a primary concern in business, showing how much services are fulfilling the customers' needs. Customers are the measures of repurchases 27,28 . Service performance directly interferes with the customers' satisfaction and providers' revenue.…”

Section: Introductionmentioning

confidence: 99%

“…Customers are the measures of repurchases. 27,28 Service performance directly interferes with the customers' satisfaction and providers' revenue. Cloud resources performance covers the number of parameters, including; running time, waiting F I G U R E 3 Customers of cloud computing time, availability, and reliability and so forth.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Badshah

Ghani

Daud

et al. 2022

Trans Emerging Tel Tech

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This study critically reviews the IaaS clouds development on revenue maximization since 2012 to answer these research queries; (i) What are the main influential factors towards revenue maximization in the cloud market? (ii) What are the main challenges and resistance towards revenue maximization in cloud computing? and (iii) What are the possible solutions and potentials to these hurdles in cloud computing? The data was analyzed and the influencing factors of revenue maximization were classified into seven distinct categories, that is, the performance of the services, service level agreement and penalties management, resources scalability, resources utilization and scheduling, customers' satisfaction, cost, and pricing management, as well as advertisement and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered. These studies are compared against each other for the seven distinct categories and solutions are proposed for the clouds' obstacles to revenue maximization. Furthermore, in the light of the findings and revenue maximization categories, the main limitations, challenges, true potential, and new directions towards revenue maximization are explored.

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Section: 6mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Badshah

Ghani

Daud

et al. 2022

Trans Emerging Tel Tech

Self Cite

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“…The proposed model evaluates cost-effectiveness by considering reliability, response time, and cost. Badshah et al [14] proposed a third-party monitoring service based on the service level agreement. This service detects violations and manages penalties.…”

Section: Related Workmentioning

confidence: 99%

Service request monitoring using Petri nets and model at runtime

Bouhamed

Kamel

D́ıaz

et al. 2022

Preprint

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The service computing paradigm has been widely adopted in various domains, such as in Web applications, Cloud computing and Internet of Things (IoT). Service providers offer services to their clients depending on their functional and non-functional requirements. Generally, the process of using a service consists of three steps discovery, selection, and invocation. This process is subject to many variables. For instance, no service with the required properties was found implying a requirement update, or just the invocation does not work. Therefore, the service monitoring is a necessary feature to be included by service providers. In this paper, we propose a model at-runtime approach to monitor this three-steps-process. We specify the proposed model using the Petri net formalism and the Flexible framework. Our approach allows service managers to monitor the three steps, change requirements when no service can satisfy the current requirements, and select other services when invocations fail. The monitoring system notifies service managers about the request state, the requirement changes required, and the invocation failures based on the Petri net marking. In addition, Python expressions can be used at runtime. Finally, a case study illustrates the approach by the implementation of a compression service.

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“…We have introduced regional computation (RC) concepts to handle big data issues in [19] [20] [21]. Edge computing (EC) is typically one hop away from the client, differentiating it from cloud computing.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Badshah,

Daud,

Khan

et al. 2024

IEEE Access

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In 2022, the digital population reached 5.4 billion, leading to network congestion, primarily attributed to the widespread use of social media, robotic programs, and prime-time television during peak hours. This congestion posed a significant challenge, as sensitive services struggled to operate effectively during peak hours, while network resources remained largely underutilized during off-peak hours. To tackle this issue, we propose the implementation of Regional Servers. These servers are designed to initially store less critical data during peak hours and subsequently transfer it to the cloud during off-peak hours, effectively optimizing network resources. The results demonstrate that the deployment of regional servers proved highly efficient in mitigating delays and reducing jitters that users typically experience during peak hours, successfully addressing the challenges posed by network congestion.

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Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds

Cited by 11 publications

References 27 publications

Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Service request monitoring using Petri nets and model at runtime

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

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