Adaptive Monitoring: Application of Probing to Adapt Passive Monitoring

Abstract

Availability of good quality monitoring data is a vital need for management of today's data centers. However, effective use of monitoring tools demands an understanding of the monitoring requirements that system administrators most often lack. Instead of a well-defined process of defining a monitoring strategy, system administrators adopt a manual and intuition-based approach. In this paper, we propose to replace the ad-hoc, manual, intuition-based approach with a more systematic, automated, and analytics-based approach for system monitoring. We propose an adaptive monitoring framework where end-to-end probing-based solutions are used to adapt the at-a-point monitoring tools. We present a systematic framework to use probes for adjusting monitoring levels. We present algorithms to select and analyze probes and to dynamically adapt the monitoring policies based on probe analysis. We demonstrate the effectiveness of the proposed solution using real-world examples as well as simulations.

Framework of Security Mechanisms for Monitoring Adaptive Distributed Systems

“…Before sending the message, if the message is encrypted and sent it to destination takes more time for processing. Whereas unencrypted messages are processed faster [19]. The secrecy of message is needed to be taken care during monitoring the distributed system.…”

Adaptive monitoring: A systematic mapping

“…While evaluating the articles resulting from the search of Section 3.1.1, we have noticed that the dynamic monitor, dynamic monitors and dynamic monitoring terms, included in the search string, have been utilized by some of the studies for referring to the continuous runtime monitoring of dynamic factors (e.g., as it has been used by Bukenya et al [12] or Magalhães et al [19]) or as an adjective to describe how the adaptation process is actually conducted (e.g., as it has been used by Clark et al [20] or Jeswani et al [21]). Thus, in order to avoid noisy papers, we have decided not to consider these terms for the search string of our SMS.…”

Adaptive Loss Inference Using Unicast End-to-End Measurements

“…Although several recent methods have been proposed to address the tomographic problems [3,13,15,16,18,19], most of them are not comparable with our algorithm. For example, [13,15,16] do not select paths before they perform tomography, while selecting paths is a vital step in our algorithm; [18,19] select monitoring paths to detect or locate the failures, but our algorithm aims to determine the loss rates of links. We choose SelectPath as the baseline not only because it works on problems similar to those of our algorithm but also because it is one of the most representative path selection algorithms that has been widely approved of in the research community.…”