基于本体的存储系统管理研究
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摘要
经过几十年的发展,存储系统经历了从简单到复杂的巨大变化,在相关的研究工作中也将其从单纯的外部设备提升到了拥有多种层次、结构,面向多种应用的主要研究方向。当前的存储系统无论在所面向的应用、承载的数据上还是在其自身的设计、实现上存在多种类型的复杂性,而且这个复杂性还在不断的增长,给存储系统的管理带来了愈发显著的挑战,缺乏管理的存储系统会造成大量的资源浪费、严重的性能损耗甚至安全性、可靠性等方面的隐患。虽然存储管理在标准化及相关技术上已取得了不少进展,但是复杂存储系统的管理仍然在很大程度上依赖于管理人员本身的知识和经验,管理的成本和效率存在显著的问题。
为了应对这个挑战,本项研究提出借鉴语义网(Semantic Web)和知识表示(Knowledge Representation)领域的相关理论与技术来改进当前的存储管理方法。首先对本体描述语言及本体构建方法进行调查分析,然后结合存储管理的实际需要,针对其管理信息的来源和构成制定拟构建本体的内容复用方案,设计用于表示存储领域有关知识的SMO (Storage Management Ontology)本体总体结构,用于涵盖对磁盘阵列和文件系统这两个具有代表性的存储系统中一般概念的描述。
在SMO的领域本体(Domain Ontology)的构建过程中存在表达能力和推理效率这两个方面的问题,模型表达能力的强弱关系到本体是否适用于实际的存储管理场景；过于庞杂的概念集合则会影响推理的效率。因此提出一种领域限定(Do main-Restricted)条件下的模型转换方法,以需要管理的目标系统为应用领域,充分复用(Reuse)存储管理规范来构建SMO领域本体。存储管理规范在工业界获得的广泛认可和经受的实践考验确保了所得本体内容的完备性；而基于描述逻辑对规范中模型内容所做的形式化分析则确保了在翻译转换其内容时,所蕴含知识概念的一致性。然后,通过对存储管理问题的初步分析评估论证了所创建的SMO领域本体的实用性。
在存储管理中使用所创建的SMO本体之前,还需要建立从一般知识到具体管理操作之间的桥梁,这里包含了本体保存和推理的框架,以及用于描述特定管理任务的本体。首先通过对本体研究领域中成熟技术的整合使用,设计和实现了基于本体的存储系统管理框架、建立了存储系统的本体知识库(Ontology Repository),使得所构建的本体可以与实际的管理环境互动以参与到具体的管理操作中。接着分别针对盘阵列管理和文件系统管理构建相应的任务本体,结合阵列技术在实际发展过程中不同层面上的推广应用,对本体中的概念作出扩展,有效地将阵列管理中的知识复用到拓展的应用场景；针对文件系统应用环境中的局部性,提出一种面向应用的文件系统管理方法(Application-Oriented Access Optimization, AO2),而这种方法依赖于对文件系统中若干信息的综合分析,难以仅通过传统的管理工具实现,因此需构建面向应用的文件系统管理任务本体(Task Ontology),并通过推理的形式给出此类管理方法的实施手段。
要将SMO应用于存储管理的实际环境中,还有一个关键的环节需要完成,即管理动作的执行。此外,还需要通过实验对管理的效果作出评价。因此针对不同操作系统平台上的几种典型文件系统分别部署了三类实验环境。在实验过程中,以面向应用的文件系统管理任务本体为基础,结合所管理系统的模型实例和有关的管理工具集,分别面向碎片整理、命名空间和文件预取这三种常见的文件系统管理任务形成管理方案、执行管理操作。测试结果表明,与传统管理工具及手段相比,基于SMO本体的存储管理方法可以使对存储系统的管理更灵活、自主、有效。
为了应对存储复杂化带来的挑战,本项研究探索了在存储管理中运用知识工程技术的方法。同时,也将语义网和知识表示技术带入了一个新的应用领域。随着相关技术的进一步发展,可以预计存储系统,乃至信息系统必然可以和智能技术实现更为深入的整合,进一步改善数据信息的使用和管理。
After several decades'evolution, storage systems have grown from early simple peripheral equipments to complex installations that contain even thousands more computers. Related research is also promoted to a comprehensive area that involes architecture, performance, security and much more. Modern storage systems reveal their complex nature in many ways including the design, implementation and more specifically, the application environment. Furthermore, these growing complexities impose an ever increasing pressure to system performance, dependability and security. Although storage management practitioners progress much in directions including standardization and technology, the management of complex storage installation still depends largely on the knowledge and experience of seasoned administrators. Problems remain big in both management cost and efficiency.
For facing this challenge, this research brings recent progressing work from Semantic Web and Knowledge Representation to storage management. The work start with a survey on ontology languages and ontology construction methods, the essential part of ontology engineering is reusing existing ontologies as far as possible. Then, based on the requirement of storage management, a reuse plan is proposed and applied to the design of storage management ontology (SMO), for covering general concepts in storage systems such as disk array and file system
There are two problems in the creation of SMO domain ontology:expressivity and tractability. Expressivity determines whether the ontology is able to depict real-world storage management scenarios, yet over-detailed concept model will affect the inference efficiency. Thus a domain-restricted model conversion is proposed, which limit the model reusing to an area defined by the targeting storage systems. The industry-approved and well-practiced storage management specification ensures the completeness of resulting ontology, and the description logic based model translation guarantees the consistency. After that, SMO domain ontology is used in a series of tentative problem analysis to verify its practicability.
Before SMO can be utilized in storage management, connection should be founded between general knowledge and particular management operations. This involves a framework that composed of ontology repository and inference engine, and task ontologies for describing specific management process. First, by adopting and integrating matured technologies from ontology research area, the framework is designed and implemented, and then S MO can be used to interact with actual management environment. Second, SMO task ontologies are created aimed at disk array management and file system management. According to the evolution and expanding application of disk array technology, the related concept in SMO can therefore be extended, and introducing the management knowledge to expanded application scenerios effectively. Considering the application locality of file systems, an application-oriented management method AO2 is proposed. AO2 demands several key information to be analyzed together, therefore cannot be realized by simply running traditional management tools. Dedicated task ontology is then constructed and used in problem inference to fulfill this mission.
There is still one key step towards applying SMO to practical storage management. This is the execution of management operation. Besides that, the outcome of such management methods should be evaluated by experiments accordingly. So 3 different experiment cases are deployed on several typical operating systems and file systems. They are application-oriented defragmentation, namespace and prefetch, each case is equipped with required toolset for carring out the management operations. The results show that, compared with traditional management tools and methods, the SMO-based management is more agile, autonomous and effective.
This research unleashes the potential of using knowledge engineering technologies in storage management, provides a method for dealing with the increasing storage complexity. On the other hand, it brings semantic web and knowledge representation technologies into a new application area. With the development in both areas, a deeper integration of storage and computing intelligence is definitely predictable, leading to a promising future of better information storage, access and management.
为了应对这个挑战,本项研究提出借鉴语义网(Semantic Web)和知识表示(Knowledge Representation)领域的相关理论与技术来改进当前的存储管理方法。首先对本体描述语言及本体构建方法进行调查分析,然后结合存储管理的实际需要,针对其管理信息的来源和构成制定拟构建本体的内容复用方案,设计用于表示存储领域有关知识的SMO (Storage Management Ontology)本体总体结构,用于涵盖对磁盘阵列和文件系统这两个具有代表性的存储系统中一般概念的描述。
在SMO的领域本体(Domain Ontology)的构建过程中存在表达能力和推理效率这两个方面的问题,模型表达能力的强弱关系到本体是否适用于实际的存储管理场景；过于庞杂的概念集合则会影响推理的效率。因此提出一种领域限定(Do main-Restricted)条件下的模型转换方法,以需要管理的目标系统为应用领域,充分复用(Reuse)存储管理规范来构建SMO领域本体。存储管理规范在工业界获得的广泛认可和经受的实践考验确保了所得本体内容的完备性；而基于描述逻辑对规范中模型内容所做的形式化分析则确保了在翻译转换其内容时,所蕴含知识概念的一致性。然后,通过对存储管理问题的初步分析评估论证了所创建的SMO领域本体的实用性。
在存储管理中使用所创建的SMO本体之前,还需要建立从一般知识到具体管理操作之间的桥梁,这里包含了本体保存和推理的框架,以及用于描述特定管理任务的本体。首先通过对本体研究领域中成熟技术的整合使用,设计和实现了基于本体的存储系统管理框架、建立了存储系统的本体知识库(Ontology Repository),使得所构建的本体可以与实际的管理环境互动以参与到具体的管理操作中。接着分别针对盘阵列管理和文件系统管理构建相应的任务本体,结合阵列技术在实际发展过程中不同层面上的推广应用,对本体中的概念作出扩展,有效地将阵列管理中的知识复用到拓展的应用场景；针对文件系统应用环境中的局部性,提出一种面向应用的文件系统管理方法(Application-Oriented Access Optimization, AO2),而这种方法依赖于对文件系统中若干信息的综合分析,难以仅通过传统的管理工具实现,因此需构建面向应用的文件系统管理任务本体(Task Ontology),并通过推理的形式给出此类管理方法的实施手段。
要将SMO应用于存储管理的实际环境中,还有一个关键的环节需要完成,即管理动作的执行。此外,还需要通过实验对管理的效果作出评价。因此针对不同操作系统平台上的几种典型文件系统分别部署了三类实验环境。在实验过程中,以面向应用的文件系统管理任务本体为基础,结合所管理系统的模型实例和有关的管理工具集,分别面向碎片整理、命名空间和文件预取这三种常见的文件系统管理任务形成管理方案、执行管理操作。测试结果表明,与传统管理工具及手段相比,基于SMO本体的存储管理方法可以使对存储系统的管理更灵活、自主、有效。
为了应对存储复杂化带来的挑战,本项研究探索了在存储管理中运用知识工程技术的方法。同时,也将语义网和知识表示技术带入了一个新的应用领域。随着相关技术的进一步发展,可以预计存储系统,乃至信息系统必然可以和智能技术实现更为深入的整合,进一步改善数据信息的使用和管理。
After several decades'evolution, storage systems have grown from early simple peripheral equipments to complex installations that contain even thousands more computers. Related research is also promoted to a comprehensive area that involes architecture, performance, security and much more. Modern storage systems reveal their complex nature in many ways including the design, implementation and more specifically, the application environment. Furthermore, these growing complexities impose an ever increasing pressure to system performance, dependability and security. Although storage management practitioners progress much in directions including standardization and technology, the management of complex storage installation still depends largely on the knowledge and experience of seasoned administrators. Problems remain big in both management cost and efficiency.
For facing this challenge, this research brings recent progressing work from Semantic Web and Knowledge Representation to storage management. The work start with a survey on ontology languages and ontology construction methods, the essential part of ontology engineering is reusing existing ontologies as far as possible. Then, based on the requirement of storage management, a reuse plan is proposed and applied to the design of storage management ontology (SMO), for covering general concepts in storage systems such as disk array and file system
There are two problems in the creation of SMO domain ontology:expressivity and tractability. Expressivity determines whether the ontology is able to depict real-world storage management scenarios, yet over-detailed concept model will affect the inference efficiency. Thus a domain-restricted model conversion is proposed, which limit the model reusing to an area defined by the targeting storage systems. The industry-approved and well-practiced storage management specification ensures the completeness of resulting ontology, and the description logic based model translation guarantees the consistency. After that, SMO domain ontology is used in a series of tentative problem analysis to verify its practicability.
Before SMO can be utilized in storage management, connection should be founded between general knowledge and particular management operations. This involves a framework that composed of ontology repository and inference engine, and task ontologies for describing specific management process. First, by adopting and integrating matured technologies from ontology research area, the framework is designed and implemented, and then S MO can be used to interact with actual management environment. Second, SMO task ontologies are created aimed at disk array management and file system management. According to the evolution and expanding application of disk array technology, the related concept in SMO can therefore be extended, and introducing the management knowledge to expanded application scenerios effectively. Considering the application locality of file systems, an application-oriented management method AO2 is proposed. AO2 demands several key information to be analyzed together, therefore cannot be realized by simply running traditional management tools. Dedicated task ontology is then constructed and used in problem inference to fulfill this mission.
There is still one key step towards applying SMO to practical storage management. This is the execution of management operation. Besides that, the outcome of such management methods should be evaluated by experiments accordingly. So 3 different experiment cases are deployed on several typical operating systems and file systems. They are application-oriented defragmentation, namespace and prefetch, each case is equipped with required toolset for carring out the management operations. The results show that, compared with traditional management tools and methods, the SMO-based management is more agile, autonomous and effective.
This research unleashes the potential of using knowledge engineering technologies in storage management, provides a method for dealing with the increasing storage complexity. On the other hand, it brings semantic web and knowledge representation technologies into a new application area. With the development in both areas, a deeper integration of storage and computing intelligence is definitely predictable, leading to a promising future of better information storage, access and management.
引文
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