Credit-based scheme for security-aware and fairness-aware resource allocation in cloud computing

Abstract

Cloud computing systems include different types of participants with varied requirements for resources and multiple tasks; these varying requirements must be considered in the design of fairness-aware resource allocation schemes for better resources sharing. However, some participants may be malicious with a goal to damage the resource allocation fairness and increase their own utility. Hence, the resource scheduling policy must guarantee allocation fairness among the participants; further, it must ensure that fairness is not affected by the malicious usage of resources, that could cause resource exhaustion and lead to denial of service. In order to address this challenge, we propose a credit-based mechanism for resource allocation that will avoid the malicious usage of resources and, simultaneously, guarantee allocation fairness. In our scheme, a credit factor is introduced for each participant in order to evaluate the history of resource utilization and determine future resource allocation. Our model encourages a participant to release the occupied resources in timely manner after the completion of a task and imposes a punishment for malicious occupation of resources. We prove the fairness of our model and provide linear and variable gradient approaches to determine the credit factor for different scenarios. We simulate our model and perform experiments on a real cloud computing platform. The results prove the rationality, effectiveness and correctness of our approaches.