Q-GSM: A QoS Oriented Grid Service Management Framework Hanhua Chen, Hai Jin, Feng Mao, Hao Wu, Yijiao Yu Cluster and Grid Computing Lab Huazhong University of Science and Technology, Wuhan, 430074, China Email:
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Abstract. Effective and efficient Quality of Service (QoS) management is critical for a service grid to meet the requirements of both grid users and service providers. We introduce a scalable framework of service management in grid environment to guarantee the QoS of grid services. Several characters of our framework are 1) grid level resource reservation mechanism to use existing heterogeneous resource reservation systems at different level from operating system level to local and wide-area level, 2) mapping service levels to sufficient quantitative reserved resources through resources negotiating with reservation interface, and 3) contract-like service level agreement management of grid services. The simulation result proves the availability of our framework.
1.
Introduction
Service oriented grid architecture presents a vision of an integrated approach to supporting both e-science and e-business [1]. It focuses on developing standard, open, general-purpose protocols and interfaces to access wide-area services and delivering nontrivial qualities of service to grid users. The most striking technical contributions of Web Services to the Grid are in the area of extensibility and interoperability. Open Grid Service Architecture (OGSA) builds on the Web services technology mechanisms to uniformly expose Grid Services semantics, such as service invocation, lifetime management, resource status interface, and security interfaces that ensure fundamental interoperability among all grid services. Although technical specifications such as Open Grid Service Infrastructure (OGSI) [2] and Web Service Resource Framework (WSRF) [3] specify the basic functionalities of OGSA in detail, lesser attention is paid to QoS management of Grid Services. QoS management has been a major area of study in real-time computing, computer networking, and system middleware. For service grid, QoS guarantee and enhancement have recently started to receive some attention. It is a key step for the grid to evolve to an e-business ‘market’. We note that QoS problem results from resource sharing. For example, sometimes resources have sufficient capacity for one application, but the actual capacity available to that application may fluctuate because the resource is being shared with other applications. Unless the resource has provisioning as a fundamental capability, predictable quality of service cannot be delivered to a grid consumer. We aim at using
existing reservation mechanisms to map service level capacity, for example response time of a service, into sufficient quantitative reserved resources. We proposed a framework called Q-GSM to integrate service management and resource management to guarantee service level of grid applications. In a highly competitive service grid environment, quality of service is one of the substantial aspects for differentiating between similar service providers. It is important for the grid to support dynamic service allocation in accordance with QoS capacity. Service capacity predictability is required to permit proper service scheduling. At the same time grid service customers must be given assurance of QoS. The terms such as response time, availability, throughput, security, and performance, need to be agreed upon before accessing grid services and to be monitored during accessing. As grid services in different field may have different characteristics, a flexible and precise formalization helps us to create generic SLA management for managing a range of different SLAs. SLAs for Web Services and grid services have recently been widely studied. Web Service Level Agreement (WSLA) is adapted to Web Service QoS management. In [5], OGSI-Agreement interfaces are proposed as OGSI portTypes in a document-extensible style, to support richly expressive extensions. The QoS obligation terms described in a SLA should be negotiated before use and monitored during the service lifetime. Actions should be taken upon violation occurs. Accordingly, we design an SLA Management (SLM) framework that manages the lifecycle of the service level agreement for a grid service. The rest of this paper is organized as follows. Section 2 presents our QoS oriented grid service management framework, Q-GSM. Section 3 describes the SLA management in detail. Section 4 shows the simulation result of the framework. In section 5, we review related work on QoS management of grid services. The paper is concluded in Section 6. And our future work is also described in this section.
2
Q-GSM Architecture
In previous work [6] we proposed the model of Service Virtualization in grid environment. The main idea of Service Virtualization is encapsulating diverse implementations, which may have different QoS capacities, behind a common service interface that standardizes the business function. Based on the virtual service model we designed Q-GSM to provide a more available framework to guarantee QoS of grid services. Several main characteristics of our framework are 1) grid level virtual service for resource reservation that provides an abstract interface to use existing heterogeneous resource reservation mechanisms at different level from operating system level to local and wide-area level, 2) mapping service levels to sufficient quantitative reserved resources through resources negotiating with reservation interface, and 3) contract like service level agreement management of grid services. Fig.1 shows the architecture of the framework of G-QSM. Components of our framework can be classed into three classes: client components, grid middleware components and grid server components.
Service Invoking
Client
QoS feedback
UDDI Registery
QoS Meta Data Repository Service Broker
SLA Manager
Middleware
QoS Admission Control Grid Service Instance
Resource Reservation Virtual Service
Server Reservation-enable OS Kernel/ Resource Manager Middleware
Fig. 1. The architecture of Q-GSM
2.1
Grid Server
Grid server is the host environment of grid services. It consists of an admission controller, reservation enabled resource management service, and a virtual resource reservation service. They work together to provide sufficient resources to guarantee the QoS of grid services. 2.1.1 Resource manager and virtual resource reservation service As discussed in section 1, QoS guarantee is fairly difficult unless the ability to provision resources is supported. In Q-GSM, the resource management service can be any type of the existing reservation enabled resource management system, which has the ability to assign different amount of system resources to different service instance. The virtual resource reservation service serves as an abstract interface to different resource reservation system. It is responsible for function semantics abstraction and protocol transmission. Resource reservation in a grid wide area environment is a great challenge because grid services are located on heterogonous grid resource environments from a workstation, to a shared cluster and even a managed grid resource pool. Effective resource managing and sharing is the base of the service grid. However, to date, two main problems remain unsolved.
The first is how to describe existing resources of so many different kinds and characters, and the number of resources types is increasing. Major resource description languages are in wide spread use in current grids environments such as the Globus Toolkit Resource Specification Language (RSL) and condor classified ads (ClassAds) [7]. In both cases, the set of attributes of every resource type, as well as their names and meaning, is determined by convention, and not by any agreed-upon semantics. This prevents these resource description languages to be general-purpose and scalable resource models. Within the commercial sector, significant progress has been made in developing resource models for enterprise system management. The Simple Network Management Protocol’s management (SNMP) [8] and more recently, the Common Information Model (CIM) [9] are two examples. Typically, CIM is based on an object-oriented meta-model, which can be described in XML to provide an extensible framework describing new resource types. At present, both SNMP and CIM cannot encompass all resources present in a typical Grid. However, given CIM’s broad industry backing and its flexible object model, it promises well to become the resource description language in a web-service-based grid [10]. The second problem is that it is too hard to develop a general mechanism to reserve heterogeneous resources in the grid. Different reservation systems are implemented at different system levels. Many commercial and open source operating systems, such as Solaris [11], IRIX [12] and FreeBSD [13], already support resource reservation. Local resource management systems have offered increasing availability of local resource reservation. Such systems include Oceano [14], UDC [15], Sharc [16] and so on. Typically, Sharc is a shared cluster system that supports reservation of CPU and network interface bandwidth for distributed applications and provides performance isolation to applications. At wide-area level, Globus Architecture for Reservation and Allocation (GARA) [3] provides a flexible architecture that makes it possible for the application to make advance reservation for networks, CPUs, disks, and graphic pipelines. Our Q-GSM uses existing reservation mechanisms to map service level capacity into sufficient quantitative reserved resources. Our resource reservation virtual service abstracts the semantics of the reservation functions. Organizations such as Distributed Management Task Force (DMTF) can standardize the ontology of the resources and the reservation operation semantics. The service virtualization model simplifies the implementation work. 2.1.2 Admission controller Another important component of the grid server is the admission controller. It is responsible to create grid service instance. During the creation of the grid service instances, the service instance interacts with the resource reservation virtual service to bind sufficient resource to the service instance. The server admits a request only when it can reserve a sufficient amount of resources o achieve the desired quality. 2.2 Service Middleware The main responsibility of the service middleware is service virtualization and service QoS management. Service middleware consists of a service broker, SLA manager and the QoS metadata repository.
2.2.1 Service broker We deploy the service broker of every common service on the grid as a virtual service and the services located on local resources are called physical services. A broker acts as an intermediary between a client and a set of physical services by providing a single point of submission for request. To avoid bring a single point-of-failure or bottleneck to the grid, multiple brokers can be deployed in every distributed domains. The requests with QoS requirements from the client to the service broker are scheduled to physical services according to QoS capacity of physical services in the QoS metadata repository. 2.2.2 QoS metadata repository The QoS metadata repository helps to predictable quality of service delivered to a grid consumer. It is responsible for querying the QoS capacity of the every physical service registered to the virtual service and identifying those services whose QoS capacities match those desired by the service consumer. The selection is driven by high-level application criteria, such as time to completion, reliability, or cost. The distributed nature of the Grid environment makes precise determination of the service state impossible. The QoS metadata repository does not imply any commitment, so we separation of discovery from allocation operation. QoS metadata repository only gives a prediction of the QoS capacity of physical services. To actually allocate the request, the SLA manager will interact with every candidate server side to make sure that the service provider will guarantee the QoS of the request and negotiate a contract like Service Level Agreement (SLA). The QoS metadata is assumed to be specified and be updated by the service provider according to the resource status. The feedbacks of the client are also used to adjust the QoS metadata. 2.2.3 Service level agreement manager SLA manager is responsible for negotiating SLAs between service client and server. It is also responsible for SLA management during the service lifetime. QoS objective terms specified in SLAs are monitored during the service lifetime and actions are taken upon violation occurs. Section 3 introduces the SLA manager in detail. 2.3
Client
The client of the virtual service is for the end user to send their service request with QoS requirements to the service broker. Generally the end users care about he overall capacity of a service, for example the response time, throughput of a remote service. Client submits such requirements to the service broker, and it is up for the service broker to return the response. When the client receives the response of the service, it sends the feedback information of the QoS to the SLA manager. SLA manager determines whether the QoS requirement of the user has been met or not, produces the statistical QoS information about the service, and stores it in the QoS metadata repository as the historical QoS information. Such feedback will help to enhance the precise of the predication.
2.4
Sequence Diagram among the Components of the Framework
Fig 2 shows a sequence diagram among service client, service middleware component and server side component. Client
SB
SLAM
QoSR
AC
GS
RRVS
SR QR QMQ QMQ-R NSLA
SC RR RR-R SC-R
NSLA-R QR-R SI SI-R SR-R QF QU
SB: service broker SLAM: service level agreement manager QoSR: QoS repository AC: admission controller GS: grid service RRVS: resouce reservation virtual service SR: service request. Requesting service with QoS attribute SR-R: service request reply QR: QoS requirments submit QR-R: reply a physical service handle QMQ: querying the QoS metadata repository QMQ-R: reply possible physical service candidates NSLA: negotiating service level agreement NSLA-R: reply to NSLA SC: creating a grid service instance SC-R: reply to the service creation request RR: reserving resources RR-R: reply to RR SI: service invocation SI-R: service response QF: QoS information feedback QU: QoS information update
Fig. 2. Sequence diagram among the components of the framework
3.
SLA Management of Grid Services
Best effort service is no longer sufficient in a service grid environment and users must give some form of commitment and assurances on top of the allocated services. The assurance terms, such as time-restrict, performance, availability, security, fail-over policies, need to be agreed upon before used and manifested in form of a SLA. The service management in Q-GSM is SLA-driven. The SLAM dynamically determines whether enough spare capacity is available to accommodate additional SLAs. This section focuses on SLA management of our framework. We designed a XML scheme as a language to form service agreements and designed a SLA management framework. 3.1
Specifying Service Level Agreements for QoS Gurantee
In Q-GSM, we propose a flexible and simple XML scheme for grid SLA formalizations. The XML scheme consists of following components (Fig.3): Parties - describes the parties involved in the SLA and their respective roles. It often includes the customer and the service provider. Lifetime - defines the lifetime of the SLA of a grid service instance. It indicates the time of which a given SLA is valid.
Obligation - specifies the obligation terms a service (instance) promises. The Obligation is defined in service operation gratuity. The CostExpression defines the actual content of the guarantee of the operation. The logic Relation contains the usual operators and, or, not etc, which connect CostExpressions. The Cost element is a simple 3-tuple (Parameter, Value, Predication), for example, (responsetime, 300ms,
