Nick Godwin

An automated negotiation mechanism based on co-evolution and game theory

The problems associated with current automated negotiation approaches are of little feasibility in practical industry applications. This paper describes a new method that combines a game theory approach and a co-evolutionary approach to support an effective negotiation model for agents to resolve conflict. Under this proposed method, the agents without knowing the other agents strategies and payoffs, produce an optimised resolution that complies Nash equilibrium and Pareto efficiency concepts. We use a finitely repeated prisoners dilemma game to demonstrate the proposed method.

Architecture of an agent-based negotiation mechanism

One of the central issues in facilitating mobile teamwork is the creation and establishment of teams from autonomous agents. It is widely accepted that team building assumes an expression of and agreement on, common interests. The paper describes an approach to the provision of mechanisms to facilitate the creation of teams and to help resolve conflict through automated negotiation. The negotiation mechanism is implemented by a combination of a game theory approach and a co-evolutionary approach. This scheme involves a process that iterates over the generation of a set of strategies by the co-evolutionary approach, the encoding of these strategies into a payoff matrix, and the reasoning on the matrix by the game theory approach in order to find an optimised point. The process terminates when the game theory approach finds an optimised point that satisfies both agents. The main advantage of this system is that agents, without knowing each others strategies, agree on an optimised solution that conforms to Nash equilibrium and Pareto efficiency.

Exception diagnosis in open multi-agent systems

Open multi-agent systems (MAS) are decentralized and highly distributed systems that consist of a large number of loosely coupled autonomous agents. Diagnosing exceptions in such systems is a complex task due to the distributed nature of their data and their control. This complexity is exacerbated in open environments where independently developed autonomous agents interact with each other in order to achieve their goals. Inevitably, exceptions will occur in such MAS and these exceptions can arise at one of three levels, namely environmental, knowledge and social levels. In this paper we propose a novel exception diagnosis system that is able to analyse and detect exceptions effectively. The proposed architecture consists of specialised exception diagnosis agents called sentinel agents. The sentinel agents are equipped with knowledge of observable abnormal situations, and their underlying causes.

Exception diagnosis in agent-based grid computing

Diagnosing exceptions in multi-agent systems (MAS) is a complex task due to the distributed nature of the data and control in such systems. This complexity is exacerbated in open environments where independently developed autonomous agents interact with each other in order to achieve their goals. Inevitably, exceptions would occur in such MAS and these exceptions can arise at one of three levels, namely environmental, knowledge and social levels. In this paper we propose a novel exception diagnosis system that is able to analyse and detect exceptions effectively. The proposed architecture consists of specialised exception diagnosis agents called sentinel agents. The sentinel agents are equipped with knowledge of observable abnormal situations, their underlying causes, and resolution strategies associated with these causes. The sentinel agent applies a heuristic classification approach to collect related data from affected agents in order to uncover the underlying causes of the observed symptoms. We illustrate and evaluate our proposed architecture using an agent-based grid computing case study.

Performance models and dynamic characteristics analysis for HDFS write and read operations: A systematic view

Abstract Hadoop has emerged as a successful framework for large-scale data-intensive computing applications. However, there is no research on performance models for the Hadoop Distributed File System (HDFS). Due to the complexity of HDFS and the difficulty of modeling the multiple impact factors for HDFS performance, to establish HDFS performance models based directly on these impact factors is very complicated. In this paper, the relationship between file size and HDFS Write/Read (denoted as W/R for short) throughput, i.e., the average flow rate of a HDFS W/R operation, is studied to build HDFS performance models from a systematic view. Based on the measured data of specially designed experiments (in which HDFS W/R operations can be viewed as single-input single-output systems), a system identification-based approach is applied to construct performance models for HDFS W/R operations under different conditions. Furthermore, dynamic characteristics metrics for HDFS performance are defined, and based on the identified performance models and these metrics, the dynamic characteristics of HDFS W/R operations, such as steady state and overshoot, are studied, and the relationships between impact factors and dynamic characteristics are analyzed. These analysis results can provide effective guidance and implications for the design and configuration of HDFS and Hadoop-based applications.

A hybrid model for cloud providers and consumers to agree on QoS of cloud services

This paper describes a new agenda based approach which facilitates multi-issue negotiation process between service consumer and service provider over the quality of service (QoS) requirements in cloud services. We assume that the order of agenda (issues) has impact on the negotiation outcome, but a group of service consumers and providers may have conflicting preferences over the importance of issues. In a multiple-issue negotiation process it is difficult to reach an optimal outcome when their preferences and the relative importance of their associated issues are not known to each other. The proposed agenda based preference ordering approach helps consumers and providers to reach a consensus over the issues and to construct a common preference sequence to improve the efficiency of the issue-by-issue negotiation outcome. Consequently, a co-evolutionary negotiation model based on the result of preference ordering approach is introduced for the agents to negotiate and reach an agreement, if there is any. The contribution of the research is a new mechanism that is able to formulate issue sequence along with a co-evolutionary negotiation approach that can effectively facilitate negotiation over QoS issues in cloud computing. Finally, a case study is provided to illustrate the proposed approach. This research has proposed a new negotiation mechanism to deal with the Cloud Computing Market.The negotiation mechanism can efficiently and effectively identify a possible agreement in a large and complex search space.A case study is adopted to evaluate and demonstrate the proposed mechanism.

Using Automated Negotiation for Grid Services

The popularity of grid services has widened their application to numerous domains and increased the utilization of computational resources. In order to create more incentives for the resources owners to lease their resources and prevent users from wasting the resources, the introduction of a market-oriented grid is inevitable. However, the issues for the negotiation between service provider and consumer over the supply and demand of resources can be complex, with highly interdependent issues. In this research, a simulated automated negotiation mechanism including a co-evolutionary mechanism and a modified game theory approach is proposed, to assist them in reaching an agreement over the conflicting issues. In the proposed architecture, the co-evolution process is able to reduce the multiple dimensional search space into a two-dimension search space and identify the appropriate negotiation strategies for the negotiating agents to form a payoff matrix which can be used for the game theory related stage of their interaction. The multiple stage negotiation process is introduced to improve the negotiation result. In this paper, an application which requires a large amount of computational resources to process the data generated from mobile devises is used to demonstrate that the proposed system is able to resolve the conflicts and obtain a valid solution.

Core-based Agent for Service-Oriented Market

The advantages of service-oriented architecture for information systems development have increased its popularity and improved service availability over the Internet. In order to maintain its sustainability and prevent users from exploiting free services, a service-oriented market mechanism should be introduced to regulate the market. However, this issue has not been well addressed in the community. In this work, we propose core-based agents that can collect information from service-oriented markets to convince service providers and consumers to form a coalition based on the core concept solution from game theory. With the introduction of the agent to the service-oriented market, a number of multiple core markets can be merged into a virtual market with a unique core which satisfies the requirements of stability and efficiency. An example of a travel agent web service, which is a composite web service, will be used to illustrate the roles of the agents in this paper.

A mobile agent framework for telecardiology

It is generally recognised that telecommunications and the internet in particular are changing the way healthcare is delivered in cardiology. Current. implementations of telecardiology are often characterised by a centralised approach. Within this set-up a central system is connected to various remote sites. The central system has to keep track of the activities and of the state of these sites through constant communication with them. This scheme requires large volumes of data, particularly in the case of electrocardiograms (ECGs), to be generated in the remote sites and then to be transmitted to a central control system. This often leads to bottlenecks in communication that may adversely affect the quality of care. In this paper we propose a decentralised approach based on a combination of mobile agents (MA) and an Object Request Broker (ORB) mechanism. Its main aim is to support interoperability and to optimise the monitoring processes by reducing unnecessary communication. MAs possess a degree of autonomy that enables them to filter data on the remote site, and thus ease the load on the central monitoring system. they have the added advantage that they can be customised to meet individual needs. The ORB mechanism is incorporated in order to increase the reliability of MAs and to facilitate the integration of various ECG analysis software systems available on the market. It is expected that the proposed system will provide a framework for improved monitoring of patients and will lead therefore to better healthcare in cardiology.

OLAP for Multidimensional Semantic Web Databases

Semantic Web (SW) and web data have become increasingly important sources to support Business Intelligence (BI), but it is difficult to manage due to its scalability in their volumes, inconsistency in semantics and complexity in representations. On-Line Analytical Processing (OLAP) is an important tool in analysing large and complex BI data, but it lacks the capability of processing disperse SW data due to the nature of its design. A new concept with a richer vocabulary than the existing ones for OLAP is needed to model distributed multidimensional semantic web databases. In this paper we proposed a new OLAP framework with multiple layers including additional vocabulary, extended OLAP operators, and SPARSQL to model heterogeneous semantic web data, unify multidimensional structures, and provide new enabling functions for interoperability. We present the framework with examples to demonstrate its capability to unify RDF Data Cube (QB) [2] and QB4OLAP [1] with additional vocabulary elements to handle both informational and topological data [3] in Graph OLAP. It is also able to compose multiple databases (e.g. energy consumptions and property market values etc.) to generate observations through semantic pipe-like operators.