A Dynamic Data-Driven Simulation Approach for Preventing Service Level Agreement Violations in Cloud Federation

The new possibility of accessing an infinite pool of computational resources at a drastically reduced price has made cloud computing popular. With the increase in its adoption and unpredictability of workload, cloud providers are faced with the problem of meeting their service level agreement (SLA) claims as demonstrated by large vendors such as Amazon and Google. Therefore, users of cloud resources are embracing the more promising cloud federation model to ensure service guarantees. Here, users have the option of selecting between multiple cloud providers and subsequently switching to a more reliable one in the event of a provider’s inability to meet its SLA. In this paper, we propose a novel dynamic data-driven architecture capable of realising resource provision in a cloud federation with minimal SLA violations. We exemplify the approach with the aid of case studies to demonstrate its feasibility. Keywords

A Data-Driven Framework for Dynamic Trust Management

AbstractReputation and trust-based models have been used extensively in different application domains. These include large online communities such as eBay, Amazon, YouTube and ad-hoc and wireless sensor networks. Recently, the use of the models has gained popularity due to their effectiveness in providing trusted systems or networks. Thesemodels focus on online and historical data to determine the reputation of domain members. In this paper, we propose a novel approach for obtaining trust values by focusing not only on online and historical data but also possible future scenarios to anticipate events in the next time intervals. The data-driven framework is able to dynamically obtain and inject data to predict the future trust value of every identity in the system. The advantage of this proactive approach compared to other approaches is that informed decisions about the domain can be made before a compromise occurs

A Data-Driven Framework for Dynamic Trust Management

Reputation and trust-based models have been used extensively in different application domains. These include large online communities such as eBay, Amazon, YouTube and ad-hoc and wireless sensor networks. Recently, the use of the models has gained popularity due to their effectiveness in providing trusted systems or networks. These models focus on online and historical data to determine the reputation of domain members. In this paper, we propose a novel approach for obtaining trust values by focusing not only on online and historical data but also possible future scenarios to anticipate events in the next time intervals. The data-driven framework is able to dynamically obtain and inject data to predict the future trust value of every identity in the system. The advantage of this proactive approach compared to other approaches is that informed decisions about the domain can be made before a compromise occurs