A Description Logic based Grid Inferential Monitoring and Discovery Framework

Transcription

1 A Description Logic based Monitoring and Discovery Framework Edgardo Ambrosi, Marco Bianchi, Carlo Gaibisso and Giorgio Gambosi University of Florence, Florence, Italy. University of L Aquila, L Aquila, Italy. bianchi@di.univaq.it IASI Antonio Ruberti - National Research Council, Rome, Italy. gaibisso@iasi.rm.cnr.it University of Rome Tor Vergata, Rome, Italy. gambosi@mat.uniroma2.it Abstract In this paper we propose a new architecture for an inferential monitoring and discovery system for. The system supports reasoning activities on a knowledge base formalising the relevant concepts and relevant relationships to a grid computing environment. The current applied Information Index System (GIS) shows infrastructural and technological limits. First of all, LDAP, the underling technology for information data management, has not been conceived to deal with dynamic information and its data model is not expressive enough to describe a complex information system like the one needed to properly manage. Moreover GIS, does not manage effectively resources and events information. The re-actions to events are left to human responsibilities, so cannot adapt its behaviour by itself, as wished in a dynamic changing environment. The goal of this work is to define an inferential GIS that adds autonomous capabilities to the middleware. This is achieved by defining a Ontology and an agent building a KB on that Ontology. Furthermore we add capabilities to the GIS for prediction, deduction and query activities. Keywords: Description Logic; Semantic Annotation; Monitoring Agent. I. INTRODUCTION The term grid [1] was coined in the mid 90s to denote a proposal for a loosely coupled infrastructure, supporting a wide range of collaborative problem-solving and resource-brokering strategies emerging in industry, science, and engineering, such as distributed super computing or teleimmersion. The real and specific problem underlying the grid concept is to coordinate the sharing of computing power, storage, data, equipment and other resources in dynamic, multi-institutional virtual organisations, intended as a coordinated set of individuals and institutions together with some sharing rules defining what is shared, who is allowed to share and under which conditions. In the grid, entities can be categorised as information consumers and information producers. Consumers (e.g. users or resource brokers) are interested in accessing relevant and precise information about the grid in a reasonably short time. On the other side, producers (e.g. an entity on behalf of a resource) are responsible for providing actual and valid data about resources without knowledge about the actual number, type and location of potential consumers. Fig. 1. NG / VO Site Resource describes describes describes GRIS IP : The layered structure of the GIS Producers and consumers are connected by information systems. One of most representative information system for the is the Information System (GIS) [2]. GIS has three main logic components, as shown in figure 1: the Information Provider (IP), the middleware component acting as a sensor. An IP retrieves information from the grid resources at various levels (monitoring site level, execution site level, and job level) and provides it to the back-end layers of the Resource Information System; the Resource Information Service (GRIS), starting from the information collected from various IPs, implements a uniform means to query resources on a grid for their current status and configuration; the Monitoring and Discovery System (), the top component of the GIS that aggregates GRIS collections coming from various grid sites. The GIS is based on the Laboratory Universal Environment (GLUE) Schema [3], whose aim is to define an information model and mapping to some kind of representation of resources. The Globus Toolkit version 2 (GT2) [4] adopts a LDAP GLUE schema implementation to model its Information Services. In fact GRIS and are implemented using the Lightweight Directory Access Protocol (LDAP).

2 As a conseguence the current implementation of information system in computing grids include just a static tree-based data structure. This is a limitation because a tree-like data structure is not enough expressive to model the complex relationships among grid concepts. Furthermore LDAP it is not conceived to deal with dynamic data thus implying bad performance in a highly dynamic context such as grid. Finally, it is worth to note that a typical monitoring and information system should provide, at least, the following functionalities: collection, storage, dissemination, display and processing of information. Current GIS collects and stores data, by mean of the. Publishing of historical data are currently delegated to some external systems, like Ice[5] or MonaLisa [6]. As a consequence the monitoring activity only supports the discovery of resources while it is not finalised to their management. This paper proposes a new architecture for an inferential Information System (I-GIS) that supports reasoning activities on a knowledge base (KB) formalising the concepts and relationships relevant to a grid computing environment. In particular, we added inferential capabilities to the IP, GRIS, and components, according to the Description Logic [7] paradigm. These latter are able to capture and to reason on the static and dynamic aspects of a, respectively the static relationships existing between resources and components (i.e. anatomy), and their run-time behaviour (i.e. phisiology). To represents the anatomy of a, we propose a semantic annotation of the information collected by IP component. Dealing with the physiology of is a much more task to accomplish, because none of existing middleware component captures the run-time interactions between resources and components. As a consequence, we propose to decorate the resources and components source code adding semantic annotations describing actions to execute when an event, such as a function call or a sequence of function calls, occurs. In this way it is possible to generate, and consequently to catch, An I-GIS represents the first step towards the creation of an autonomous and adaptive managing of grid environment. The second section, briefly introduce the DL paradigm, the semantic annotation technique and relationship between them. In the third section a simplified grid model is described. The forth and fifth sections regard, respectively, the modelling of the inferential information system for anatomy and physiology of grid. In the sixth section the inferential monitoring and managing system is discussed. In the seventh section an inferential component, called IMA, is described. Last section reports conclusions and further works. II. BACKGROUND a) Description Logic: Information modelling is concerned with the construction of computer-based symbol structures that model some part of the real world. An information model is built up using some language, and this language influences the kinds of details that are considered. Conceptual models offer more expressive facilities for modelling applications directly and naturally [Hammer and McLeod, 1981], and for structuring information bases. These languages provide semantic terms for modelling an application, such as entity and relationship (or even activity, agent and goal), as well as means for organising information. There are several families of knowledge representation languages, including logic-based, rule-based, and class based languages. Class-based languages express knowledge in terms of objects and classes, and have inspired a huge number of formalisms in several areas of computer science, including programming languages, database models, and software specification languages. Description logics (DLs) [7] form a family of languages for modelling an application domain in terms of objects, classes and relationships between classes, and for reasoning about them. DLs permit the specification of a domain by providing the definition of classes, and by describing classes using a rich set of logical operators. They are therefore both class-based and logic-based knowledge representation languages. Notably, when using DLs, one can specify not only the necessary conditions that objects of a given class must obey, but also the sufficient conditions for an object to belong to a certain class. This feature introduces the possibility of automatically classifying objects and class descriptions. A description logic (DL) knowledge base (KB) is made up of two parts, a terminological part (the terminology or Tbox) and an assertional part (the Abox), each part consisting of a set of axioms. The Tbox asserts facts about concepts (sets of objects) and roles (binary relations), usually in the form of inclusion axioms, while the Abox asserts facts about individuals (single objects), usually in the form of instantiation axioms. The entire system is based on some logical system for deductive activities, when reasoning on Tbox and Abox. b) Semantic Annotation: Semantic annotation is a specific metadata generation and usage schema, aiming to enable new information access methods and to extend the existing ones. In a nutshell, Semantic Annotation is about assigning to the entities in the text links to their semantic descriptions. This sort of metadata provides both class and instance information about the entities. Whether these annotations should be called semantic, entity or some other way, it is all a matter of terminology. To the best of our knowledge, there neither exists a well-established term for this task, nor there is a well-established meaning for the term semantic annotation. What is more important is that the automatic semantic annotations enable many new types of applications: highlighting, indexing and retrieval, categorisation, generation of more advanced metadata, smooth traversal between unstructured text and available relevant knowledge. Semantic annotation is applicable for any sort of text web pages, regular (non-web) documents, text fields in databases, etc. Further, knowledge acquisition can be performed on the basis of the extraction of more complex dependencies analysis of relationships between entities, event and situation descriptions, etc. [8] c) Using Description Logic in Semantic Annotation technique: There are some basic prerequisites for the representa-

3 sections we are discussing about information for anatomy and functionality of a environment. We focus on the Information Data Model for both anatomy as well as physiology of, where the first kind is classified by four TBOX [9]: Component-TBOX, a grid view at components level; Node-TBOX, a grid view at site level; -TBOX, a grid view of National grid; VirtualOrganization-TBOX, a grid view of Virtual Organization level [1]; while the phisiology of the grid is modelled by: Fig. 2. tion of semantic annotations: : A simplified Reference Model an ontology (or taxonomy, at the least), defining the entity classes; it should be possible for these classes to be referred to; entity identifiers, which allow those to be distinguished and linked to their semantic descriptions; a knowledge base with entity descriptions. In this work a ontology is modelled by using DLs. Semantic Annotation process will rely on the resulting TBOXs. III. A GRID MODEL In what follows a simplified reference model of a grid architecture is briefly described. As shown in figure 2 the is considered as a collection of National. Looking inside a National, several e Sites can be organized in Virtual Organizations (VO). Each Site is a collection of middleware components, such as gatekeeper or a job execution environment, and hardware resources, such as Computing Elements (CE), Storage Elements (SE) and Data Source Engines (DSE). In what follows we will restrict our attention to CEs but the achieved results are extensible to the other components of the grid environment. Furthermore a set of Information Providers (IP) collects information about monitoring of the resource itself. IPs make available these information to the backend of GRIS that is associated to a each Site. The top component is the that provide information about national grids, virtual organisations, retrieving data from GRISs. IV. IMA AND GRID INFORMATION DATA MODEL We propose an information data model of based on an anatomic and physiology data. The IDM is specified by Description Logic that is a suitable language for conceptual modelling. Since an IDM should hold a description of relevant aspect of the environment that is describing, then this paper propose a technique and methodology for capturing both static information as well as dynamic one. In the following EventComponent-TBOX, a view at components level; EventNode-TBOX, a view at site level; Event-TBOX, a view of National grid; EventVirtualOrganization-TBOX, a view of Virtual Organization; In this paper we focus on the Component-TBOX and EventComponent-TBOX with the associated ABOX instances. A. Modelling the Information System for Anatomy It is worth to note the ABOXs generation is a critical aspect when interacting with an existing environment. Such generation of assertions about grid components concept should imply the existence of an integrated software component within the jobmanager or the local resource manager. Middleware has a lot of components highly coupled and refactoring them could be an hard activity. Avoiding changing the source code in middleware, the Semantic Annotation technique has been adopted. This technique lets to make a semantic matching between IDM instances, provided by IP in LDIF language, and Component-TBOX. The figure 3 shows the logic components involved in the semantic annotation process. The main component is the Semantic Annotator which goal is to produce IP-ABOXs. To perform this activity, the annotator binds the GluSchema Instances information, provided by IP, to a subset of concepts defined into Component-TBOX. This last one is produced at design time by TBOX Generator which supports human activity during the conceptualisation phase of GlueSchema. The conceptualisation phase is a semiautomatic activity since an expert can extend it defining new concepts and relationships. At run-time the Semantic Annotator takes in charge to mantain consistent the information collected by the IP and the Component-ABOXs. The generated Component- ABOXs and the related TBOXs are the KB used as the input of the inferential procedures applied by IMA. The definition of inferential procedure for grid is not a trivial work and is under working, and it will be presented in a next paper.

4 Fig. 3. GLUE Schema Tbox Generator Tbox Human Knowledge Contribution Semantic Annotator Abox Information Provider GLUE Schema Instances : A semi-automatic generation of grid ABOX B. Modelling the Information System for Physiology At design time, features for middleware were divided among distinct elements. These features are so encapsulated in a modular way. However, this approach fails to provide an explicit identification of certain features, such as those involving shared resources, error handling, or similar functionality affects or is affected by many different elements. These approachs are insufficient because those issues cross-cut the primary modularisation of the systems. Cross-cutting occurs when some particular concern depends on and/or must affect parts of the implementation of several of the functional modules of the system. Many cross-cuts are not weaknesses of the designs; they are a natural and unavoidable phenomena in complex systems as, and they are the basis for the concept of aspects.[10] Information models are strictly related with static functional aspects. In fact, each IDM schema is modelled referring to the architectural attributes and each IDM is an instances of anatomic aspects produced by logging/debugging activity. Usually logging activity is implemented following the explicit flow-graph corresponding to a path in the call-graph of code. But there exist a lot of implicit flows that are not identified through a path and are transversal over the call-graph. This kind of flows are usually identified by the term crosscutting concerns. For these flows the mechanism of logging/debugging cannot be preventively hard coded into middleware. As a conseguence another methology is needed for modelling, classifing, capturing and analysing at runtime the crosscutting concerns. A feasible approach is to implement aspects using metaobjects or meta-languages. Some related work such as DJ- Aspects [21] is a proposal that addresses dynamic weaving through the use of meta-programming. Other approaches include aspect-oriented frameworks like the AMF [2, 4, 5] that provides dynamic weaving in a framework. We believe that middleware can take beneficial applying the mecchanism mentioned. At now, software does not provide any support for crosscutting concerns evaluation. We propose the adoption of a suitable combination of two techniques and two paradigms to solve the issue of the crosscutting concerns. The techniques referring to are the semantic annotation and the crosscutting concerns weaving. The paradigms involved are the Description Logic and the Aspect Oriented Programming. In particular, it is possible to decorate source code with semantic annotations expressed using the Description Logic paradigm. The semantic annotation can be seen as a code sensors that will let IMA to catch events related to the modelled concerns. The captured aspects are then classified by IMA through a EventComponent-TBOX. Next, the caught collection of events are analysed through a reasoner plugged-in IMA and deductive facts will be derived using logical rules. Some consideration about the design and implementation of IMA, is that these activities involve the refactoring and coding of: the grid middleware components in order to generate events notification occurred, coding into the source code the sensors needed. the software components for the classification and mining the events. IMA is a no-intrusive solution for developing the sensors code through Aspect Oriented Paradigm, a powerful Description Logic paradigm for modelling and classification of dynamic aspect and a reasoner system for the analysis phase on aspects captured and classified with dl. As said this is technique is called the semantic annotation of source code. In particular it is possible to define a semantic matching among a function, or an aggregation of functions, with one or more chosen EventComponent-TBOX. The EventComponent- TBOX formally describes events associated to grid component function calls.[8]. This work wants to be a first step to next generation of grid, with the capacity of re-acting when some events occur, the capacity of pro-acting when after historic events analysis the reasoner system decides to apply some action. V. THE INFERENTIAL MONITORING AGENT According to the hierarchical structure presented in the paragraph 2, the IMA is distributed on three different levels. In fact the IMA is composed by three hierarchically arranged component classes, as shown in figure 4: an IP (I-IP), an GRIS (I-GRIS), and an (I-). The main goal of an I-IP is to generate GRIDComponent- ABOXs and EventComponent-ABOXs, generically called IP ABOXs in figure 4. The GRIDComponent-ABOXs are created by following the semantic annotation technique described in the previous section. In fact the IP ABOX Generator, represented by dotted polygon in figure 3, translates LDIF instances provided by the IP into GRIDComponent-ABOXs. The choice of GRIDComponent-TBOXs to be adopted during the translation activity is delegated to the the I-GRIS. The EventGRIDComponent-ABOX are created by following

5 Engine Tbox/Abox/Lbox GRIS Tbox/Abox GRIS Engine GRIS Tbox/Abox/Lbox IP Tbox/Abox IP Resource Probe Data Collector IP Tbox/Abox IP ABox Generator Fig. 4. : Overview of the IMA spanned on the grid layers Fig. 5. : Architectural Snapshot of IMA the semantic annotation at code level. To achieve it, the EventComponent-TBOXs to be adopted are selected by developers during the code decoration phase. Nevertheless, the I-GRIS can order to an I-IP to collect all assertions or to filter them using a given policy. Both the EventGRIDComponent- ABOXs erlier discussed as well as GRIDComponent-ABOXs are collected by IP Tbox-Abox. A first level of inferential activity is made at I-GRIS level. Periodically, the I-GRIS collects from I-IPs a set of ABOXs, and uses GRIS-LBOXs rules to perform reasoning activities on the entire Site. In fact, being an ABOX a particular view of a Component or a view of classified events, the entire set of IP ABOXs represents a snapshot of the current site status and can be used to generate GRIS- ABOXs. The choice of GRIS-TBOXs to be adopted during the GRIS-ABOXs generation is delegated to the top level IMA component, i.e. the I-. Periodically, the I- collects from I-GRIS a set of assertions, I-GRIS ABOXs, and uses -LBOX rules to perform reasoning activities on the entire Virtual Organization, or National. In this way I- can provide prevision and, eventually, simulation, about the future status of the. In addition, it could provide active and proactive support for humans in the management activities. VI. IMA ARCHITECTURE FRAMEWORK OVERVIEW The IMA is designed adopting the well known FIPA [11] agent programming pattern. The features of IMA can be summarized as follow: Aspect Oriented crosscutting support; Description Logic statements support; Inference Procedure capability - the reasoner pluggable into IMA, implicitly offers the support for data mining and so for monitoring activities. Coordination among IMA agents - coordination is performed by exchanging KB. Remote Action perfoming; In figure 5 an architectural snapshot of the IMA is provided. IMA has an Engine Layer that let it be equipped with a light reasoner engine plugged in. We have adopted an OWL-DL reasoner named Pellet.[12] With respect to the aim of this work, Pellet has some desiderable features as ontology analysis and repair, datatype reasoning, entailment, conjunctive ABOX query. The IMA is composed by four subsystems, that are: the KnowledgeBase, for managing the KBs. This one lets IMA works with different TBOXs, some of concerning the anatomy of and some concerning the physiology; the OntologyAlgebra, for specifying and managing LBOXs. the EngineLayer subsystem, for plugging several reasoners. This exists because there are a lot of reasoner engine useful for our agent. the Pellet, that is the core of our system. It uses the OntologyAlgebra and represent an instance of the EngineLayer. In particular Pellet has three specialized components for querying, deducting and monitoring through inferential processes. VII. CONCLUSION In this paper we have presented a new architecture for an inferential monitoring and discovery system for. We have proposed to add autonomous capabilities to the middleware by defining a Ontology and a system agent building a KB on that Ontology. Furthermore we have added inferential capabilities to the GIS for predicting, for deducting and quering activities. We have defined an effective Monitoring and Managing Agent System based for. This system is being developed using technique such as Source Code Semantic Annotating for the physiology events and the Information Data Model Semantic Annotating for the anatomy events. The Annotating process is performed by using DL

6 TBOX. The agents will be based on DL reasoning procedure for performing decisions, actions and proactivities. REFERENCES [1] I. F. et al., The anatomy of grid, International Journal of Supercomputer Application, vol. 15, no. 3, [2] K. C. et al., information service for distributed resources sharing, Proc. 10th IEEE International Symposium on High-Performance Distributed Computing, [3] S. A. et al, Glue schema, sergio. [4] I. F. et al., The globus toolkit, vol. 8, pp [5] S. F. G. T. S. Andreozzi, N. De Bortoli and C. Vistoli, ice: a monitoring service for the grid. [6] H. B. Newman, I. C. Legrand, P. Galvez, R. Voicu, and C. Cirstoiu, Monalisa : A distributed monitoring service architecture, CoRR, vol. cs.dc/ , [7] A. B. et al, Conceptual modeling with description lo. [8] A. Paar, Semantic software engineering tools. [9] M. L. Giuseppe De Giacomo, Tbox and abox reasoning in expressive description logic. [10] R. E. Filman and D. P. Friedman, Aspect-oriented programming is quantification and obliviousness, pp [11] FIPA. The foundation for intelligent physical agent. [Online]. Available: [12] B. Parsia and M. b. e. Evren Sirin MINDSWAP Research Group University of Maryland, College Park, Pellet: An owl dl reasoner.