Policies, grids and autonomic computing

The goals of resource management fall within the overall aims of autonomic and grid computing, namely the sharing of resources automatically, and the allocation of resources depending on both application and business needs. Resource allocation can be guided by policies which encapsulate decisions made by the management system. Policies can be used to encapsulate many different types of management decisions including possible corrective actions when a performance requirement of an application is not being satisfied and actions to take place when there is more demand then supply. System policy is derived from the interactions between Service Level Agreements (contractual agreements between businesses) and locally specified management rules. This paper explores the potential use of mathematical models (e.g., optimisation models) for relating the various types of policies. It describes the current and proposed work in applying policies to resource management in the context of autonomic and grid computing systems.

[1]  Asser N. Tantawi,et al.  Performance management for cluster-based web services , 2005, IEEE Journal on Selected Areas in Communications.

[2]  Thomas A. Corbi,et al.  The dawning of the autonomic computing era , 2003, IBM Syst. J..

[3]  Joseph L. Hellerstein,et al.  Feedback control of a Lotus Notes server: modeling and control design , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[4]  Jürgen Quittek,et al.  Using the script MIB for policy-based configuration management , 2002, NOMS 2002. IEEE/IFIP Network Operations and Management Symposium. ' Management Solutions for the New Communications World'(Cat. No.02CH37327).

[5]  Asser N. Tantawi,et al.  Performance management for cluster based Web services , 2003 .

[6]  René Wies,et al.  Using a classification of management policies for policy specification and policy transformation , 1995, Integrated Network Management.

[7]  Dirk Beyer,et al.  Policy-Based Resource Assignment in Utility Computing Environments , 2004, DSOM.

[8]  Christian Jacquenet,et al.  Distributed Dynamic Resource Management for the AF Traffic of the Differentiated Services Networks , 2005, ICCNMC.

[9]  S. Parekh,et al.  MIMO control of an Apache web server: modeling and controller design , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[10]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..

[11]  Geoffrey G. Xie,et al.  Network policy languages: a survey and a new approach , 2001, IEEE Netw..

[12]  Edmund H. Durfee,et al.  Optimal Resource Allocation and Policy Formulation in Loosely-Coupled Markov Decision Processes , 2004, ICAPS.

[13]  C. Goh Policy Management Requirements , 1998 .

[14]  Asit Dan,et al.  Connecting client objectives with resource capabilities: an essential component for grid service managent infrastructures , 2004, ICSOC '04.

[15]  David P. Anderson,et al.  SETI@home: an experiment in public-resource computing , 2002, CACM.

[16]  Alberto Gonzalez Prieto,et al.  SLS to DiffServ configuration mappings , 2001, DSOM.