摘要
Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.
Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.
基金
This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 91318301 and 61672276, the Key Research and Development Project of Jiangsu Province of China under Grant Nos. BE2015154 and BE2016120, the Collaborative Innovation Center of Novel Software Technology of Nanjing University, and the EU FP7 CROWN Project under Grant No. PIRSES-GA-2013-610524.
