SLA Negotiation and Renegotiation in Cloud SLA Management: Issue and Challenges

Alkhamees, Saleh

doi:10.1007/978-3-030-95987-6_11

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…It lets the customer know that the provider is (1) Sure of the service level they can provide, (2) Aware of the Quality of Service (QoS) level the customer needs, (3) Capable of expressing SLA conditions in a clear and understandable way and (4) Willing to provide details about the actual performance of the services they are offering. Prior studies have also demonstrated that legal contracts such as SLAs, which consider relational governance, lead to positive trust relationships (Alkhamees, 2022;Latif et al, 2021;Kapsoulis et al, 2021). Therefore, this study proposes the following hypothesis: H1: The SLA verification has a significant and positive influence on trust in cloud computing provider…”

Section: Hypotheses Development and Research Modelmentioning

confidence: 91%

The Role of Trust in Cloud Service Providers on the Adoption of Cloud Computing in Saudi Arabia: An Empirical Investigation

Alghaith

2023

Journal of Computer Science

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Cloud computing has emerged as one of the most significant developments in the field of Information Technology (IT) in recent years, allowing others to leverage third-party services. Therefore, it is essential to identify and address trust in cloud service providers as one of the key predictors of the acceptance of cloud computing. Several prior studies covered the technological facets of cloud-based contexts, including cloud virtualization, scalability, and security. However, it is argued that the biggest barrier to cloud computing is not technical but rather cognitive or behavioral and in particular attitudinal. Thus, this research aims to study individuals' attitudes and perceptions toward cloud computing, with a particular concentration on the perception of trust and its constructs in the cloud computing environment, namely Service Level Agreements (SLA) verification and reputation, in order to investigate the factors influencing the adoption of cloud computing in Saudi Arabia. This study presents an extended Technology Acceptance Model (TAM) to include trust as a cognition, representing a person's perception of social influence to perform or not perform a behavior under consideration. The study model also identifies factors affecting cloud computing adoption by considering reputation-based trust and SLA verification-based trust variables, which have been rarely examined before. The proposed model was able to explain 64% of the variance in behavioral intention and 78% of individuals' attitudes toward the adoption of cloud computing in Saudi Arabia. The study's findings show that the proposed model explained a significant amount of variation in cloud computing adoption. It suggests that the model expansion by incorporating trust in cloud computing service providers, reputation-based trust, and SLA verification-based trust factors were valuable explorations. Further, the results also show that "reputation-based trust" alone explains 29.46% of individuals' attitudes and 23.45% of their "behavioral intentions" toward using cloud computing. This effect on people's attitudes and intentions towards adopting cloud computing was indirect and through the trust construct.

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Section: Hypotheses Development and Research Modelmentioning

confidence: 91%

The Role of Trust in Cloud Service Providers on the Adoption of Cloud Computing in Saudi Arabia: An Empirical Investigation

Alghaith

2023

Journal of Computer Science

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“…However, with the wide-scale adoption of this paradigm, the challenge of keeping up with user demand is of high priority. When the user demands outpace the available resources, the organization loses business due to violation of the Service Level Agreement (SLA) [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments

Ahamed

Khemakhem

Eassa

et al. 2023

Sensors

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The Federated Cloud Computing (FCC) paradigm provides scalability advantages to Cloud Service Providers (CSP) in preserving their Service Level Agreement (SLA) as opposed to single Data Centers (DC). However, existing research has primarily focused on Virtual Machine (VM) placement, with less emphasis on energy efficiency and SLA adherence. In this paper, we propose a novel solution, Federated Cloud Workload Prediction with Deep Q-Learning (FEDQWP). Our solution addresses the complex VM placement problem, energy efficiency, and SLA preservation, making it comprehensive and beneficial for CSPs. By leveraging the capabilities of deep learning, our FEDQWP model extracts underlying patterns and optimizes resource allocation. Real-world workloads are extensively evaluated to demonstrate the efficacy of our approach compared to existing solutions. The results show that our DQL model outperforms other algorithms in terms of CPU utilization, migration time, finished tasks, energy consumption, and SLA violations. Specifically, our QLearning model achieves efficient CPU utilization with a median value of 29.02, completes migrations in an average of 0.31 units, finishes an average of 699 tasks, consumes the least energy with an average of 1.85 kWh, and exhibits the lowest number of SLA violations with an average of 0.03 violations proportionally. These quantitative results highlight the superiority of our proposed method in optimizing performance in FCC environments.

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SLA Negotiation and Renegotiation in Cloud SLA Management: Issue and Challenges

Cited by 2 publications

References 41 publications

The Role of Trust in Cloud Service Providers on the Adoption of Cloud Computing in Saudi Arabia: An Empirical Investigation

The Role of Trust in Cloud Service Providers on the Adoption of Cloud Computing in Saudi Arabia: An Empirical Investigation

Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments

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