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.

创新点

云计算资源分配过程中, 节点能够通过非法占用资源等恶意行为, 实现自身的资源份额最大化, 破坏资源分配公平性, 甚至造成DDoS攻击, 导致平台资源枯竭。针对该问题, 本文提出一种基于信誉的机制, 在考虑资源分配公平性的同时, 能够防止资源不被节点恶意侵占, 保证了资源分配的安全性。在节点信誉度评估方面, 针对相似任务和混合任务, 分别提出了线性和可变梯度信誉评估模型, 确保了在不同任务情况下信誉值变化的合理性。仿真实验和真实平台实验验证了本文方法的合理性、有效性和正确性。