本論文提出一套核心實現可程序化的區塊層級存儲系統虛擬化框架，並稱之為「鯤鵬存儲系統」。此系統透過提供API允許管理者重新映射區塊層級存儲資源，並提供具通透性的底層管理機制，在系統內部的設計上則是著重在提供更具有彈性與延展性的資源池管理機制，以及無縫銜接網路傳輸和各種虛擬化功能，同時也提供線上資料遷移與資料備份之能力。
為此目的，本論文在考量相當的模組化因素後，於Linux系統核心中設計並實現了本系統雛型，而為了模組化的目的，本系統的核心模組部分被細分為許多相互獨立的實作群組，群組中各自實現其主要功能並且透過固定API之形式提供給其他群組使用，最後經由這些API的相互配合將個別實作完成的功能群組整合成本系統的最終雛型。為了衡量此系統的運作效率，本論文中經由直接對系統進行大規模資料讀寫，以及對不同資料大小進行線上遷移實驗，這兩種方法分別測試此系統在一般操作下與線上資料遷移時的延展性，實驗顯示這兩項指標都呈現線性延展的結果，這也證明本論文題出並實作的系統雛型在實務運作上是可以達到最佳效能表現的。

In this thesis we propose the KPS system, a kernel-based programmable storage system that provides a cross node block-level storage virtualization framework. KPS system allows (i) administrator to remap block level storage resource with simple interface and (ii) apply versatile manageablilty to underling storage without user interruption. The system internal design focus on supporting (i) flexible and scalable resource pooling and remapping mechanism, (ii) seamless combination of data network transmission and block level virtualize functions and (iii) online data migration and replication abilities. To this end, we design the KPS system with highly modularization consideration that divides kernel module into four major component group targets on (i) IO redirection decision, (ii) data synchronization, (iii) local disk IO operation and (iv) remote disk IO operation. Each group is further broken down into numbers of implementation modules, that concentrate on atomic and unique tasks while complying an asynchronous access model proposed and widely adapted by modern operation system, and finally collaborate with each other to achieve various storage system requirements. To demonstrate the effectiveness of our approach we evaluate the KPS system in two ways: (i) We examine the scalability of system throughput by the pure sequential raw disk IO and then run a popular file level benchmark Bonnie++ after building two filesystems on KPS system to make experiment scenario more realistic. (ii) We show the efficiency of online migration module by running all four migration location combinations with various chunks size spread widely. Finally, we find out KPS system performs linear speedup with respective to the amount of resources in used. And we make online migration to achieve constant performance behavior as well.

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