Lejian Liao

Work place: Beijing Institute of technology, School of Computer science and Technology, Beijing, 10081, China

E-mail: liaolj@bit.edu.cn


Research Interests: Artificial Intelligence, Swarm Intelligence


Lejian Liao a professor in School of Computer Science and Technology, Beijing Institute of Technology. He acquired his PhD degree in 1994 and Master degree in 1988 in Chinese Academy of Sciences. His main research areas include distributed Artificial Intelligence and Web Intelligence. He published, as author or co-author, more than 80 papers in international conferences and journals, including some top conferences such as IJCAI, AAAI,and SIGIR..

Author Articles
Virtual Machine Monitor Indigenous Memory Reclamation Technique

By Muhammad Shams Ul Haq Lejian Liao Ma Lerong

DOI: https://doi.org/10.5815/ijitcs.2016.04.02, Pub. Date: 8 Apr. 2016

Sandboxing is a mechanism to monitor and control the execution of malicious or untrusted program. Memory overhead incurred by sandbox solutions is one of bottleneck for sandboxing most of applications in a system. Memory reclamation techniques proposed for traditional full virtualization do not suit sandbox environment due to lack of full scale guest operating system in sandbox. In this paper, we propose memory reclamation technique for sandboxed applications. The proposed technique indigenously works in virtual machine monitor layer without installing any driver in VMX non root mode and without new communication channel with host kernel. Proposed Page reclamation algorithm is a simple modified form of Least recently used page reclamation and Working set page reclamation algorithms. For efficiently collecting working set of application, we use a hardware virtualization extension, page Modification logging introduced by Intel. We implemented proposed technique with one of open source sandboxes to show effectiveness of proposed memory reclamation method. Experimental results show that proposed technique successfully reclaim up to 11% memory from sandboxed applications with negligible CPU overheads.

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