Isaac Chao

Catallaxy-based Grid markets

Grid computing has recently become an important paradigm for managing computationally demanding applications, composed of a collection of services. The dynamic discovery of services, and the selection of a particular service instance providing the best value out of the discovered alternatives, poses a complex multi-attribute n:m allocation decision problem, which is often solved using a centralized resource broker. To manage complexity, this article proposes a two-layer architecture for service discovery in such Application Layer Networks (ALN). The first layer consists of a service market in which complex services are translated to a set of basic services, which are distinguished by price and availability. The second layer provides an allocation of services to appropriate resources in order to enact the specified services. This framework comprises the foundations for a later comparison of centralized and decentralized market mechanisms for allocation of services and resources in ALNs and Grids.

Tag Mechanisms Evaluated for Coordination in Open Multi-Agent Systems

Tags are arbitrary social labels carried by agents. When agents interact preferentially with those sharing the same Tag, groups are formed around similar Tags. This property can be used to achieve desired group coordination by evolving agents Tags through a group selection process. In this paper Tags performance is for the first time compared by simulation with alternative mechanisms for coordinated learning in multi-agent systems populations. We target open systems, hence we do not make costly assumptions on agent capabilities (rational or computational). It is a requirement that coordination strategies prove simple to implement and scalable. We build a simulator incorporating competition and cooperation scenarios modeled as one-shot repeated games between agents. Tags prove to be a very good coordination mechanism in both, cooperation building in competitive scenarios and agent behavior coordination in fully cooperative scenarios.

An architecture for incorporating decentralized economic models in application layer networks

Efficient resource discovery and allocation is one of the challenges of any large scale Application Layer Network (ALN) such as computational Grids, Content Distribution Networks and P2P applications. In centralized approaches, the user requests can easily be matched to the most convenient resource. These approaches, however, present scalability limits in the highly dynamic and complex ALN environments. This paper, explores an architecture for incorporating fully decentralized economic mechanisms for resource allocation. These mechanisms are implemented by a set of trading agents that operate on behalf of the clients and service providers, interacting over an overlay network and interfacing with the underlying resources of the platform. A prototype of the proposed architecture is presented and the practical implications of its implementation in a grid scenario are discussed.

Design, Implementation, and Evaluation of a Resource Management Multiagent System for a Multimedia Processing Grid

This paper proposes marketplace-based agent architecture for Grid Resource Management that works on the application level. Relaying in FIPA as agent systems standard and Globus Toolkit as “de facto” middleware solution for Grid computing, we achieve the desired flexibility and modularity in order to provide a pluggable agent layer for a broader generic Grid Computing framework. The agents in the system use a utility table built previously to the system operation as information source for improving their negotiation abilities. It allows them to, given a state of the resources on the Grid, check the predicted performance of the possible configurations and select the best-rated values for some task execution parameters. We have implemented the architecture using JADE and have performed preliminary experiments consisting on multimedia processing for the conversion between video formats.

A Group Selection Pattern Applied to Grid Resource Management

A key challenge in grid computing is the achievement of efficient and self-organized resource management. Grids are often large scale, heterogeneous, and unpredictable systems. Introducing group structures can help to distribute coordination efforts, but higher levels of adaptation and learning in the coordination protocols are still required in order to cope with system complexity. We provide a solution based on a self-organized and emergent mechanism evolving congregations of resource management agents through a group selection process which maximizes utility outcomes for system-wide performance. We provide a formalization of this process into a group selection pattern, and we propose several instantiations optimizing grid resource management scenarios such as adaptive job scheduling, market-based resource management, and policy coordination in virtual organizations (VOs). We further evaluate by simulation the performance of the mechanism in those scenarios. The results support the conclusion that group selection optimizes coordination by evolving small and dynamic groups.

A group selection pattern optimizing job scheduling in decentralized grid markets

Decentralized economic models are being considered as scalable coordination mechanism for the management of service allocations to clients. However, decentralization incorporates further dynamicity and unpredictability into the system, degrading its performance. In this paper, a solution based on a self-organized and emergent Group Selection mechanism is proposed. Dynamic congregations evolve Grid Markets participants (c and service providers) into optimized market segments, maximizing utility outcomes for system-wide performance. We provide evaluation by simulation of the Group Selection mechanism performance in a market-based resource management and job scheduling scenario for Grid computing, compared with alternative scheduling strategies such as economic in a flat population (not using groups), random and least loaded resource selection.

Integration of decentralized economic models for resource self-management in application layer networks

Resource allocation is one of the challenges for self-management of large scale distributed applications running in a dynamic and heterogeneous environment. Considering Application Layer Networks (ALN) as a general term for such applications including computational Grids, Content Distribution Networks and P2P applications, the characteristics of the ALNs and the environment preclude an efficient resource allocation by a central instance. The approach we propose integrates ideas from decentralized economic models into the architecture of a resource allocation middleware, which allows the scalability towards the participant number and the robustness in very dynamic environments. At the same time, the pursuit of the participants for their individual goals should benefit the global optimization of the application. In this work, we describe the components of this middleware architecture and introduce an ongoing prototype.

Optimising decentralised grid markets through group selection

Automatic coordination mechanisms for the grid are required due to the increasing complexity that is exhibited in large-scale distributed systems. Decentralised economic models are being considered as scalable coordination mechanisms for the management of service allocations to clients. However, decentralisation incorporates further dynamicity and unpredictability into the system. Introducing higher levels of adaptation and learning in the coordination protocols helps cope with complexity. We provide a solution based on a self-organised, emergent mechanism that evolves grid market participants through a group selection process. Dynamic congregations organise agents into optimised market segments, maximising utility and thereby improving system-wide performance. We provide a system model and evaluation by simulating the group selection mechanism. We further provide a prototype that shows the practical feasibility of the approach.

Optimizing Decentralized Grid Markets through Group Selection

Automatic coordination mechanisms for the grid are required due to the increasing complexity exhibited in large scale distributed systems. Decentralized economic models are being considered as scalable coordination mechanisms for the management of service allocations to clients. However, decentralization incorporates further dynamicity and unpredictability into the system. Introducing higher levels of adaptation and learning in the coordination protocols helps cope with complexity. We provide a solution based on a self-organized, emergent mechanism evolving grid market participants through a group selection process. Dynamic congregations organize agents into optimized market segments, maximizing utility and thereby improving system-wide performance. We provide a system model and evaluation by simulation of the group selection mechanism. We further provide a prototype showing practical feasibility of the approach.

Assessing a distributed market infrastructure for economics-based service selection

Service selection is an important issue for market-oriented Grid infrastructures. However, few results have been published on the use and evaluation of market models in deployed prototypes, making it difficult to assess their capabilities. In this paper we study the integration of an extended version of Zero Intelligence Plus (ZIP) agents in a middleware for economics-based selection of Grid services. The advantages of these agents compared to alternatives is their fairly simple messaging protocol and negotiation strategy. By deploying the middleware on several machines and running experiments we observed that services are proportionally assigned to competing traders as should be in a fair market. Furthermore, varying the environmental conditions we show that the agents are able to respond to the varying environmental constraints by adapting their market prices.