近幾年，虛擬化技術成為了目前資訊產業熱門的主題。對於企業或者個人使用者而言，虛擬化技術所帶來的快速佈署的能力，可以有效地降低硬體建置的成本。虛擬化平台本身所具備的高可靠度和調整性，也增進了上層應用服務的質與量。在經濟效益方面，虛擬機器彼此之間能共享實體資源，依照不同虛擬機器的需求去調配，增進整體系統上資源的使用率。除此之外，虛擬機器平台上提供的管控功能，亦能讓管理者更加有效掌控整體資源的使用狀況。

然而，針對追求最大資源可用度進行運作的虛擬化平台而言，運作中的虛擬化系統勢必需要一個有效的管理機制去控管實體資源。為了使虛擬層管理者能夠根據虛擬機器的實際運作狀況作更有彈性的調控，本論文提出了一個實作於虛擬層與應用層的中介軟體模組，設計適應性資源管理機制去動態調配與控管虛擬機器所應分配到的運算資源。盡可能在不超過最大分配限制的前提下去滿足虛擬機器中虛擬處理器的需求，並增進實體處理器中的運算資源分配。除此之外本論文亦實作一個調配管理者模組，能夠取得跨層的軟硬體監控資訊，並根據虛擬機器的監控資訊來持續提供建議的參數設定，使虛擬機器分配到較合理的資源。此一依據預設政策調整虛擬機器之方式，能增進虛擬機器的使用效能以及整體資源的分配效益，實現具適應性的虛擬機器資源管理機制於虛擬機器平台中。

Since virtualization technology has been more and more prevalent in recent years, the reliable service and quick development help either the enterprise or the personal users reduce the cost of hardware deployment to the least. Virtualized architecture with high-availability and scalability also improve the quality and quantity of application for servicing wild area users. For the reason of financial issue, aggregating free resource on virtualized infrastructure for efficiently using can reduce payment of hardware deployment. And the features of virtualized environment appeals supervisors to manage and monitor whole system easily.

To keep maximum available resource for using, the virtualization system needs a useful way for control mechanism to handle resource management on resource of real infrastructure. For addressing situation regarding the ability of hypervisor, this thesis proposed a middle-ware development for doing enhanced control – Adaptive Resource Control Module on virtualized infrastructure. And try dispatching reasonable computing resource to fulfill requirements of virtual machine. Also a supervisor is designed for analyzing current resource utilization to decide priority and property values for every single VM with keeping collect monitory information from operating system. For more, according to existing information, control module will try finding adaptable concepts to coordinate current virtual machines by policies, being able to make resource usage of VM platform more adaptive on virtualized environment.

[1] A. Kivity, Y. Kamay, D. Laor, U. Lublin and A. Liguori, "kvm: the Linux Virtual Machine Monitor," Proceedings of the Linux Symposium, Volume One, 2007.
[2] Amazon Elastic Compute Cloud - http://aws.amazon.com/ec2
[3] C. Baun, M. Kunze, J. Nimis and S. Tai, “Cloud Computing: Web-Based Dynamic IT Services, ” 2011
[4] Citrix, “Workload Balancing Administration Tool,” June 28, 2010. - http://support.citrix.com/article/CTX125365
[5] CPUBurn-in - http://www.cpuburnin.com
[6] Curt Schimmel, "UNIX systems for modern architectures: symmetric multiprocessing and caching for kernel programmers," 1994.
[7] Eucalyptus - http://www.eucalyptus.com
[8] Extensible Markup Language (XML) - http://www.w3.org/XML
[9] Fabrice Bellard, "QEMU, a Fast and Portable Dynamic Translator," 2005 USENIX Annual Technical Conference, April 10–15, 2005.
[10] Features of XenServer on Different Editions - http://www.citrix.com/English/ps2/products/subfeature.asp?contentID=2300456
[11] H. Chen, H. Jin, K. Hu, "Affinity-aware Proportional Share Scheduling for Virtual Machine System," 2010 Ninth International Conference on Grid and Cloud Computing.
[12] GNU - http://www.gnu.org
[13] IBM, "Virtualization in education," October 2007. (Retrieved 6, July 2010.)
[14] IEEE Standard for Local and metropolitan area networks Media Access Control (MAC) Bridges - http://standards.ieee.org/getieee802/download/802.1D-2004.pdf
[15] J. Heo, X. Zhu, P. Padala and Z. Wang, "Memory Overbooking and Dynamic Control of Xen Virtual Machines in Consolidated Environments," IFIP/IEEE Symposium on Integrated Management, Jun 2009.
[16] J. Hu, J. Gu, G. Sun, T.Zhao, "A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment," 3rd International Symposium on Parallel Architectures, Algorithms and Programming, 18-20 Dec. 2010.
[17] J. Knowles, Dynamic Memory Control - http://wiki.xensource.com/xenwiki/Dynamic_Memory_Control
[18] L. Cherkasova, D. Gupta and A. Vahdat, "Comparison of the Three CPU Schedulers in Xen," Performance Evaluation Review. Vol 35, Number 2. Sept 3, 2007.
[19] L. Youseff, R. Wolski, B. Gorda and C. Krintz, "Paravirtualization for HPC Systems," Lecture Notes in Computer Science, 2006, Volume 4331/2006, pp. 474-486.
[20] M. Shreedhar and G. Varghese, "Efficient fair queuing using deficit round-robin," Networking, IEEE/ACM Transactions on June, 1996.
[21] OpenNebula - http://opennebula.org
[22] OpenNebula Scheduler 3.0 – http://opennebula.org/documentation:archives:rel3.0:schg
[23] OpenStack - http://openstack.org
[24] P. Padala, X. Zhu and et al., "Adaptive Control of Virtualized Resources in Utility Computing Environments," EuroSys 2007.
[25] P. Padala, X. Zhu and et al., "Automated Control of Multiple Virtualized Resources," EuroSys 2009.
[26] RFC 2132, Section 9.13 - http://tools.ietf.org/html/rfc2132#section-9.13
[27] R. Rosen, "Introduction to the Xen Virtual Machine," Linux Journal, Sep. 1, 2005.
[28] S. B. Nigmandjanovich and C. Ahn, "Policy-based dynamic resource allocation for virtual machines on Xen-enabled virtualization environment," International Conference on Advanced Computer Control (ICACC), 2010.
[29] SuperPI - http://www.superpi.net
[30] The Architecture of VMware ESXi - http://www.vmware.com/files/pdf/vmware_esxi_architecture_wp.pdf
[31] VMWare - http://www.vmware.com
[32] W. Voorsluys, J. Broberg, S. Venugopal and R. Buyya1, "Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation," International Conference on Cloud Computing 2009.
[33] XAPI - http://wiki.xensource.com/xenwiki/XAPI_Developer_Guide
[34] Y. Xu, Y. Sekiya, "Scheme of Resource Optimization using VM Migration for Federated Cloud," 32nd Asia-Pacific Advanced Network Meeting, Aug 22-26, 2011.