Towards a Non-intrusive Recognition of Anomalous System Behavior in Data Centers

Baldoni, Roberto; Cerocchi, Adriano; Ciccotelli, Claudio; Donno, Alessandro; Lombardi, Federico; Montanari, Luca

doi:10.1007/978-3-319-10557-4_38

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Specifically, we are using network traffic and power consumption data, taken from a datacenter enclosure, as input, for showing how the prediction accuracy of failures, occurring inside the enclosure, can be improved through the correlation of those inputs. Preliminary results have been published in [8].…”

Section: Discussionmentioning

confidence: 99%

On-line failure prediction in safety-critical systems

Baldoni

Montanari

Rizzuto

2015

Future Generation Computer Systems

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Section: Discussionmentioning

confidence: 99%

On-line failure prediction in safety-critical systems

Baldoni

Montanari

Rizzuto

2015

Future Generation Computer Systems

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“…Non-intrusive and black box monitoring, as it is used in this paper, has been introduced in Casper [13] and in NiTREC [12]. Casper uses this kind of monitoring to performs online failure prediction based on Hidden Markov Models.…”

Section: Related Workmentioning

confidence: 99%

“…These drawbacks can be mitigated by employing a nonintrusive monitoring system [12], [13], [31], which enables fault detection and identification by only resorting to coarsegrained or aggregate readings. In this paper we define as nonintrusive a monitoring system that (i) does not require the installation of software probes on the monitored machines, and (ii) does not require the administrator to log-in to monitored machines to gather needed monitoring data.…”

Section: Introductionmentioning

confidence: 99%

NIRVANA: A Non-intrusive Black-Box Monitoring Framework for Rack-Level Fault Detection

Ciccotelli

Aniello

Lombardi

et al. 2015

2015 IEEE 21st Pacific Rim International Symposium on Dependable Computing (PRDC)

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Many organizations today still manage mid or large in-house data centers that require very expensive maintenance efforts, including fault detection. Common monitoring frameworks used to quickly detect faults are complex to deploy/maintain, expensive, and intrusive as they require the installation of probes on monitored hw/sw to collect raw data. Such intrusiveness can be problematic as it imposes installation/management overhead and may interfere with security/privacy policies. In this paper we introduce NIRVANA, a novel monitoring system for fault detection that works at rack-level and is (i) non-intrusive, i.e., it does not require the installation of software probes on the hosts to be monitored and (ii) black-box, i.e., agnostic with respect to monitored applications. At the core of our solution lies the observation that aggregated features that can be monitored at rack-level in a non-intrusive and black-box way, show predictable behaviors while the system works in both fault-free and faulty states; it is therefore possible to detect and identify faults by monitoring and analyzing any perturbations to these behaviors. An extensive experimental evaluation shows that non-intrusiveness does not significantly hamper the fault detection capabilities of the monitoring system, thus validating our approach.

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“…Then, we aim to extend the work by integrating scaling and fault tolerance, thus, the scaling policy will be not only related to system overloading, but also in response to a failure. Specifically, we are striving to integrate the failure prediction and anomaly detection system we proposed in [71,72] as a further module of PASCAL interacting with the Decider module to trigger scaling decisions.…”

Section: Fault Tolerancementioning

confidence: 99%

PASCAL: An architecture for proactive auto-scaling of distributed services

Lombardi

Muti

Aniello

et al. 2019

Future Generation Computer Systems

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One of the main characteristics that today makes cloud services so popular is their ability to be elastic, i.e., they can adapt their provisioning to variable workloads, thus increasing resource utilization and reducing operating costs. At the core of any elastic service lies an automatic scaling mechanism that drives provisioning on the basis of a given strategy. In this paper we propose PASCAL, an architecture for Proactive Auto-SCALing of generic distributed services. PASCAL combines a proactive approach, to forecast incoming workloads, with a profiling system, to estimate required provision. Scale-in/out operations are decided according to an application-specific strategy, which aims at provisioning the minimum number of resources needed to sustain the foreseen workload. The main novelties introduced with PASCAL architecture are: (i) a strategy to proactively auto-scale a distributed stream processing system (namely, Apache Storm) with the aim of load balancing operators through an accurate system performance estimation model, and (ii) a strategy to proactively auto-scale a distributed datastore (namely, Apache Cassandra), focussed on how to choose when executing scaling actions on the basis of the time needed for the activation/deactivation of storage nodes so as to have the configuration ready when needed. We provide a prototype implementation of PASCAL for both use cases and, through an experimental evaluation conducted on a private cloud, we validate our approach and demonstrate the effectiveness of the proposed strategies in terms of saved resources and response time.

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Towards a Non-intrusive Recognition of Anomalous System Behavior in Data Centers

Cited by 6 publications

References 14 publications

On-line failure prediction in safety-critical systems

On-line failure prediction in safety-critical systems

NIRVANA: A Non-intrusive Black-Box Monitoring Framework for Rack-Level Fault Detection

PASCAL: An architecture for proactive auto-scaling of distributed services

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