Secure Computing Resource Allocation Framework For Open Fog Computing. (30th January 2020)

• Record Type: Journal Article
• Authors: Jiang, Jiafu; Tang, Linyu; Gu, Ke; Jia, WeiJia
• Editors: Sgandurra, Daniele
• Is Part Of: Computer journal. Volume 63:Number 4(2020)
• Page Start: 567
• Page End: 592
• Publication Date: 2020-01-30
• Subjects: fog computing -- cloud computing -- computing resource allocation -- attack -- security requirement -- audit; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxz108
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Fog computing has become an emerging environment that provides data storage, computing and some other services on the edge of network. It not only can acquire data from terminal devices, but also can provide computing services to users by opening computing resources. Compared with cloud computing, fog devices can collaborate to provide users with powerful computing services through resource allocation. However, as many of fog devices are not monitored, there are some security problems. For example, since fog server processes and maintains user information, device information, task parameters and so on, fog server is easy to perform illegal resource allocation for extra benefits. In this paper, we propose a secure computing resource allocation framework for open fog computing. In our scheme, the fog server is responsible for processing computing requests and resource allocations, and the cloud audit center is responsible for auditing the behaviors of the fog servers and fog nodes. Based on the proposed security framework, our proposed scheme can resist the attack of single malicious node and the collusion attack of fog server and computing devices. Furthermore, the experiments show our proposed scheme is efficient. For example, when the number of initial idle service devices is 40, the rejection rate of allocated tasks is 10% and the total number of sub-tasks is changed from 150 to 200, the total allocation time of our scheme is only changed from 15 ms to 25 ms; additionally, when the task of 5000 order matrix multiplication is tested on 10 service devices, the total computing time of our scheme is $\sim$ 250 s, which is better than that of single computer (where single computer needs more than 1500 s). Therefore, our proposed scheme has obvious advantages when it faces some tasks that require more computational cost, such as complex scientific computing, distributed massive data query, distributed image processing and so on.