Name
Abstract | ||
|---|---|---|
Autonomic computing is central to the success of IT infrastructure deployment as its complexity and pervasiveness grows. This paper addresses one aspect of policy-based autonomic computing 驴 the issue of identifying dependencies between policies, knowledge of which is useful to the policy-maker while defining or updating policies. These dependencies are determined via assesment of the impact of a policy on the sensors (measurable entities at runtime). Our approach uses a simple pragmatic model over the measured runtime information from the recent past. Both static and runtime information is combined to provide effective feedback. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/POLICY.2005.6 | POLICY |
Keywords | Field | DocType |
simple pragmatic model,historical information,it infrastructure deployment,autonomic computing,effective feedback,runtime information,recent past,analyzing policy,policy-based autonomic computing,measurable entity,measured runtime information,information analysis,pervasive computing,computer science,quality of service,feedback,databases,ubiquitous computing,artificial intelligence,knowledge based systems,logic programming | Data science,Autonomic computing,Software deployment,Computer science,Knowledge-based systems,Information technology management,Ubiquitous computing,Distributed computing | Conference |
ISBN | Citations | PageRank |
0-7695-2265-3 | 0 | 0.34 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rohit Lotlikar | 1 | 246 | 15.04 |
| Sharma Chakravarthy | 2 | 0 | 0.34 |
| Ranga R. Vatsavai | 3 | 20 | 3.38 |
| Mukesh Mohania | 4 | 496 | 42.79 |