An architecture for incorporating decentralized economic models in application layer networks

نویسندگان

  • Oscar Ardaiz-Villanueva
  • Pablo Chacin
  • Isaac Chao
  • Felix Freitag
  • Leandro Navarro-Moldes
چکیده

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

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

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 efficien...

متن کامل

Prediction of the deformation modulus of rock masses using Artificial Neural Networks and Regression methods

Static deformation modulus is recognized as one of the most important parameters governing the behavior of rock masses. Predictive models for the mechanical properties of rock masses have been used in rock engineering because direct measurement of the properties is difficult due to time and cost constraints. In this method the deformation modulus is estimated indirectly from classification syst...

متن کامل

Flood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique

Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that th...

متن کامل

Tool Support for Incorporating Trust Models into Decentralized Applications

The role of decentralized trust and reputation management in the establishment of trust relationships between peers in decentralized applications has been well-recognized. Several reputation-based trust models exist in the literature. PACE is an architectural style for decentralized trust management. PACE provides specific principles that guide the incorporation of trust and reputation models w...

متن کامل

Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data

This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values.  Seismic surveying was performed next on these models. F...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Multiagent and Grid Systems

دوره 1  شماره 

صفحات  -

تاریخ انتشار 2005