Wendy Powley

Reputation-Enhanced QoS-based Web Services Discovery

With an increasing number of Web services providing similar functionalities, quality of service (QoS) is becoming an important criterion for selection of the best available service. Currently the problem is twofold. The Universal Description, Discovery and Integration (UDDI) registries do not have the ability to publish the QoS information, and the authenticity of the advertised QoS information available elsewhere may be questionable. We propose a model of reputation-enhanced QoS- based Web services discovery that combines an augmented UDDI registry to publish the QoS information and a reputation manager to assign reputation scores to the services based on customer feedback of their performance. A discovery agent facilitates QoS-based service discovery using the reputation scores in a service matching, ranking and selection algorithm. The novelty of our model lies in its simplicity and in its coordination of the above mentioned components. We present experiments to evaluate the effectiveness of our approach using a prototype implementation of the model.

Experimental Study of a Self-Tuning Algorithm for DBMS Buffer Pools

The tasks of configuring and tuning large database management systems (DBMSs) have always been both complex and time-consuming. They require knowledge of the characteristics of the system, the data, and the workload, and of the interrelationships between them. The increasing diversity of the data and the workloads handled by today’s systems is making manual tuning by database administrators almost impossible. Self-tuning DBMSs, which dynamically reallocate resources in response to changes in their workload in order to maintain predefined levels of performance, are one approach to handling the tuning problem. In this paper, we apply self-tuning technology to managing the buffer pools, which are a key resource in a DBMS. Tuning the size of the buffer pools to a workload is crucial to achieving good performance. We describe a Buffer Pool Tuning Wizard that can be used by database administrators to determine effective buffer pool sizes. The wizard is based on a self-tuning algorithm called the Dynamic Reconfiguration algorithm (DRF), which uses the principle of goal-oriented resource management. It is an iterative algorithm that uses greedy heuristics to find a reallocation that benefits a target transaction class. We define and motivate the cost estimate equations used in the algorithm. We present the results of a set of experiments to investigate the performance of the algorithm.

Workload adaptation in autonomic DBMSs

Workload adaptation is a performance management process in which an autonomic database management system (DBMS) efficiently makes use of its resources by filtering or controlling the workload presented to it in order to meet its Service Level Objectives (SLOs). This paper presents a framework and a prototype implementation of a query scheduler that performs workload adaptation in a DBMS. The system manages multiple classes of queries to meet their performance goals by allocating DBMS resources through admission control in the presence of workload fluctuation. The resource allocation plan is derived by maximizing the objective function that encapsulates the performance goals of all classes and their importance to the business. A first-principle performance model is used to predict the performance under the new resource allocation plan. Experiments with IBM® DB2® Universal Database#8482; are conducted to show the effectiveness of the framework.

Applying Bargaining Game Theory to Web Services Negotiation

Service Level Agreements (SLAs) have obvious value for Service-Oriented Computing and have received attention from both academics and industry. However, SLAs still lack a theoretical basis and effective techniques to facilitate automatic SLA establishment. In this paper, we classify negotiations into four types, and focus on the 1-to-1 Web services negotiation between a single service provider and a single service consumer. We make three contributions. Firstly, we represent the 1-to-1 Web services negotiation as a bargaining game. Here, we are interested in a bargain that takes into account the interests of both a service provider and a service consumer, in other words, a fair solution. Secondly, we determine a Nash equilibrium that can be regarded as the fair solution to a two-player bargaining game. We also determine the fair solution to the 1-to-1 Web services negotiation. Finally, we discuss issues that may arise with the 1-to-1 Web services negotiation under credible threats, incomplete information, time constraints, and multiple attributes.

Today's DBMSs: how autonomic are they

The increasing complexity of database management systems (DBMSs) and the dearth of their experienced administrators make an urgent call for an autonomic DBMS that is capable of managing and maintaining itself. In this paper, we examine the characteristics that a DBMS should have in order to be considered autonomic and assess the position of todays commercial DBMSs such as DB2, SQL Server, and Oracle.

Autonomic management of elastic services in the cloud

Cloud computing, with its support for elastic resources that are available on an on-demand, pay-as-you-go basis, is an attractive platform for hosting Web-based services that have variable demand, yet consistent performance requirements. Effective service management is mandatory in order for services running in the cloud, which we call elastic services, to be cost-effective. In this paper we describe a management framework to facilitate elasticity of resource consumption by services in the cloud. We extend our framework for services management with the necessary concepts and properties to support elastic services. A prototype implementation is described.

Towards Autonomic Workload Management in DBMSs

Workload management is the discipline of effectively managing, controlling, and monitoring work flow across computing systems. It is an increasingly important requirement of database management systems (DBMSs) in view of the trends towards server consolidation and more diverse workloads. Workload management is necessary so the DBMS can be business-objective oriented, can provide efficient differentiated service at fine granularity, and can maintain high utilization of resources with low management costs. The authors see that workload management is shifting from offline planning to online adaptation. In this article, the authors discuss the objectives of workload management in autonomic DBMSs and provide a framework for examining how current workload management mechanisms match up with these objectives. They then use the framework to study several mechanisms from both DBMS products and research efforts. They also propose directions for future work in the area of workload management for autonomic DBMSs.

Workload class importance policy in autonomic database management systems

A key advantage of autonomic computing systems will be their ability to manage according to business policies. A key challenge to realizing this ability is the problem of automatically translating high-level business policies into low-level system tuning policies, which is the result of the different semantics used at the two levels. Economic models, which are expressed using business level concepts, have been used successfully in computer resource allocation problems. In this paper, we utilize an economic model to map business policies to resource allocation decisions in a database management system (DBMS). We focus on business policies that describe the relative importance of competing workloads on a DBMS. We present experiments with a simulation of the model that investigate a number of meanings of importance and identify how this additional information can be used to effectively allocate main memory resources in a commercial DBMS

Managing database server performance to meet QoS requirements in electronic commerce systems

Abstract.The performance of electronic commerce systems has a major impact on their acceptability to users. Different users also demand different levels of performance from the system, that is, they will have different Quality of Service (QoS) requirements. Electronic commerce systems are the integration of several different types of servers and each server must contribute to meeting the QoS demands of the users. In this paper we focus on the role, and the performance, of a database server within an electronic commerce system.We examine the characteristics of the workload placed on a database server by an electronic commerce system and suggest a range of QoS requirements for the database server based on this analysis of the workload. We argue that a database server must be able to dynamically reallocate its resources in order to meet the QoS requirements of different transactions as the workload changes. We describe Quartermaster, which is a system to support dynamic goal-oriented resource management in database management systems, and discuss how it can be used to help meet the QoS requirements of the electronic commerce database server. We provide an example of the use of Quartermaster that illustrates how the dynamic reallocation of memory resources can be used to meet the QoS requirements of a set of transactions similar to transactions found in an electronic commerce workload. We briefly describe the memory reallocation algorithms used by Quartermaster and present experiments to show the impact of the reallocations on the performance of the transactions.

Dynamic Reconfiguration Algorithm: Dynamically Tuning Multiple Buffer Pools

The tasks of configuring and tuning large database management systems (DBMSs) have always been both complex and time-consuming. They require knowledge of the characteristics of the system, the data, and the workload, and of the interrelationships between them. The buffer pools, because they exist to reduce the number of disk accesses performed by a transaction, are a key resource in a DBMS. Current DBMSs, such as DB2 Universal Database, divide the buffer area into a number of independent buffer pools and database objects (tables and indices) are assigned to a specific buffer pool. The size of each buffer pool is set by configuration parameters and page replacement is local to each buffer pool. Tuning the size of the buffer pools to a workload is crucial to achieving good performance. In this paper we describe a self-tuning algorithm, called the Dynamic Reconfiguration algorithm (DRF), for managing the buffer pools in a DBMS and we present the results of a set of experiments to investigate the performance of an implementation of the algorithm for DB2 Universal Database.