Policy driven data administration

Batra, Vishal; Bhattacharya, Jaijit; Chauhan, Hiteshi K.C.; Gupta, Akhilesh Kumar; Mohania, Mukesh; Sharma, Uzzal

doi:10.1109/policy.2002.1011312

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…In the simplest case, we could define event-condition-action (ECA) rules that directly produce adaptation plans from specific event combinations, as already mentioned in Section 4.1. Examples of such policy languages and applications in autonomic computing include [Damianou et al 2000;Lymberopoulos et al 2003;Lobo et al 1999;Agrawal et al 2005;Batra et al 2002;Lutfiyya et al 2001;Ponnappan et al 2002].…”

Section: Planningmentioning

confidence: 99%

“…Projects are categorised by the degree of autonomicity (horizontally) and the part of the MAPE-K loop focussed on (vertically). While some of the choices are highly subjective, Batra et al 2002;Lutfiyya et al 2001;Ponnappan et al 2002;Damianou et al 2000;Manoel et al 2005;Agrawal et al 2005 Architecture models Oreizy et al 1998;1999 Garlan andSchmerl 2002b;Magee et al 1995;Georgiadis et al 2002Dashofy et al 2002bWise et al 2000 Knowledge Walsh et al 2004;Bougaev 2005;Manoel et al 2005;Shivam et al 2006;Osogami et al 2005Bhola et al 2006Dowling et al 2006;Littman et al 2003;2004 we have tried to place them in a category that highlights the major contribution of a project.…”

Section: Degrees Of Autonomicitymentioning

confidence: 99%

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A survey of autonomic computing—degrees, models, and applications

2008

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Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a "hype topic" or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed 'hype' and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering. ACM Reference Format:Huebscher, M. C. and McCann, J. A. 2008. A survey of autonomic computing-degrees, models, and applications.

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Section: Planningmentioning

confidence: 99%

Section: Degrees Of Autonomicitymentioning

confidence: 99%

A survey of autonomic computing—degrees, models, and applications

2008

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“…For work related to policy-based system management in general, the reader may refer to for example (PDDA [5], PONDER [7])…”

Section: Related Workmentioning

confidence: 99%

“…Policies are commonly used in systems to automate system behavior while reducing (preferably eliminating) the amount of human involvement/intervention needed. Recently, policy-based frameworks have been successfully implemented in various domains, such as, storage area networks [2], networked systems [1], database management [5].…”

Section: Introductionmentioning

confidence: 99%

Policy Schedule Advisor for Performance Management

Lotlikar

Vatsavai

Mohania

et al. 2005

Second International Conference on Autonomic Computing (ICAC'05)

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Policies are being increasingly used as a means of implementing autonomic computing features in IT systems. Since these policies consume system resources, the performance of a policy-based system may be hampered if resource intensive policies are scheduled at the same time when other policies and applications are being executed. However, with applications being accessed by users globally across time zones, and fast changing business needs, it is increasingly difficult to identify and maintain suitable schedules for these policies.In this paper we propose a framework to address the above aspect of the policy-based autonomic computingthe problem of determining when to schedule a given policy such that its impact on system performance is minimized, and then giving appropriate feedback. This feedback is aimed at assisting the policy maker in defining or redefining the policy schedule so that it can be executed more efficiently.In this framework, we make use of the underlying log data (i.e. resource utilization data) of a managed resource in order to determine an appropriate policy schedule. We demonstrate the efficacy of our approach using DB2 as a managed resource and policies for data management.

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“…Policies also have the benefit of being a central point of reference when multiple administrators work on a system so each understands what the other has configured the system to do [2]. In general, centralized policies provide a succinct and effective form of communication between system administrators, reducing preventable errors, which in turn lowers stress.…”

Section: Introductionmentioning

confidence: 99%

A Mass Storage System Administrator Autonomic Assistant

Halem¹,

Schauer

Second International Conference on Autonomic Computing (ICAC'05)

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The growing complexity of mass storage systems at major data centers is causing stress on system administrators to keep performance at optimal levels. As storage requirements grow, so does the number of routine tasks that the administrator must perform, as well as the time it takes for these to be executed. The solution being proposed to ease this burden is the Mass Storage System Administrator Autonomic Assistant (MSSAAA). The MSSAAA is a collection of agents that perform some of the more common tasks while the administrators handle higher-level issues. Using the principles of autonomic computing, the MSSAAA is governed by a centralized set of policies that the administrator will review on a regular basis and can adjust as necessary. The goal is to develop an autonomic assistant to substantially reduce the amount of time it takes to address specific problems in the system. Using tools such as IBM's Generic Log Adapter, Resource Model Builder, and Autonomic Management Engine, the MSSAAA has been able to (i) quickly determine when tape errors occur and correct them, (ii) monitor the network file system mounts for poor performance and report those, and (iii) correct network file system handle problems through continuous monitoring. The preliminary savings analyses show that the assistant saves the system administrator at least 185 hours per year, and over six thousand dollars in related costs. The results show how efficiently and effectively the MSSAAA handled its assigned tasks, and how it has eased the daily burden of storage system administrators. DEDICATION This thesis is dedicated to my wife Katie for her patience, understanding and constant support during the pursuit of my Master's degree. iii ACKNOWLEDGEMENTS There are many other groups of people who have provided feedback, insight and support at various levels.

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Policy driven data administration

Cited by 12 publications

References 6 publications

A survey of autonomic computing—degrees, models, and applications

A survey of autonomic computing—degrees, models, and applications

Policy Schedule Advisor for Performance Management

A Mass Storage System Administrator Autonomic Assistant

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