Topology-Aware Correlated Network Anomaly Event Detection and Diagnosis
Abstract For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and h...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Calyam, Prasad [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
Spatial and temporal analysis filters |
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| Anmerkung: |
© Springer Science+Business Media New York 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of network and systems management - Springer US, 1993, 22(2013), 2 vom: 26. Sept., Seite 208-234 |
|---|---|
| Übergeordnetes Werk: |
volume:22 ; year:2013 ; number:2 ; day:26 ; month:09 ; pages:208-234 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10922-013-9286-0 |
|---|
| Katalog-ID: |
OLC2066987719 |
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| 520 | |a Abstract For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and historic measurements, which can be queried using web services. Analysis of these measurements using effective schemes to detect and diagnose anomaly events is vital since it allows for verifying if network behavior meets expectations. In addition, it allows for proactive notification of bottlenecks that may be affecting a large number of users. In this paper, we describe our novel topology-aware scheme that can be integrated into perfSONAR deployments for detection and diagnosis of network-wide correlated anomaly events. Our scheme involves spatial and temporal analyses on combined topology and uncorrelated anomaly events information for detection of correlated anomaly events. Subsequently, a set of ‘filters’ are applied on the detected events to prioritize them based on potential severity, and to drill-down upon the events “nature” (e.g., event burstiness) and “root-location(s)” (e.g., edge or core location affinity). To validate our scheme, we use traceroute information and one-way delay measurements collected over 3 months between the various U.S. Department of Energy national lab network locations, published via perfSONAR web services. Further, using real-world case studies, we show how our scheme can provide helpful insights for detection, visualization and diagnosis of correlated network anomaly events, and can ultimately save time, effort, and costs spent on network management. | ||
| 650 | 4 | |a Performance monitoring | |
| 650 | 4 | |a Correlated anomaly detection | |
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| 700 | 1 | |a Dhanapalan, Manojprasadh |4 aut | |
| 700 | 1 | |a Sridharan, Mukundan |4 aut | |
| 700 | 1 | |a Krishnamurthy, Ashok |4 aut | |
| 700 | 1 | |a Ramnath, Rajiv |4 aut | |
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10.1007/s10922-013-9286-0 doi (DE-627)OLC2066987719 (DE-He213)s10922-013-9286-0-p DE-627 ger DE-627 rakwb eng 004 VZ Calyam, Prasad verfasserin aut Topology-Aware Correlated Network Anomaly Event Detection and Diagnosis 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and historic measurements, which can be queried using web services. Analysis of these measurements using effective schemes to detect and diagnose anomaly events is vital since it allows for verifying if network behavior meets expectations. In addition, it allows for proactive notification of bottlenecks that may be affecting a large number of users. In this paper, we describe our novel topology-aware scheme that can be integrated into perfSONAR deployments for detection and diagnosis of network-wide correlated anomaly events. Our scheme involves spatial and temporal analyses on combined topology and uncorrelated anomaly events information for detection of correlated anomaly events. Subsequently, a set of ‘filters’ are applied on the detected events to prioritize them based on potential severity, and to drill-down upon the events “nature” (e.g., event burstiness) and “root-location(s)” (e.g., edge or core location affinity). To validate our scheme, we use traceroute information and one-way delay measurements collected over 3 months between the various U.S. Department of Energy national lab network locations, published via perfSONAR web services. Further, using real-world case studies, we show how our scheme can provide helpful insights for detection, visualization and diagnosis of correlated network anomaly events, and can ultimately save time, effort, and costs spent on network management. Performance monitoring Correlated anomaly detection Spatial and temporal analysis filters perfSONAR measurement framework Active measurements Dhanapalan, Manojprasadh aut Sridharan, Mukundan aut Krishnamurthy, Ashok aut Ramnath, Rajiv aut Enthalten in Journal of network and systems management Springer US, 1993 22(2013), 2 vom: 26. Sept., Seite 208-234 (DE-627)182373657 (DE-600)1202352-8 (DE-576)9182373655 1064-7570 nnns volume:22 year:2013 number:2 day:26 month:09 pages:208-234 https://doi.org/10.1007/s10922-013-9286-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2013 2 26 09 208-234 |
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10.1007/s10922-013-9286-0 doi (DE-627)OLC2066987719 (DE-He213)s10922-013-9286-0-p DE-627 ger DE-627 rakwb eng 004 VZ Calyam, Prasad verfasserin aut Topology-Aware Correlated Network Anomaly Event Detection and Diagnosis 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and historic measurements, which can be queried using web services. Analysis of these measurements using effective schemes to detect and diagnose anomaly events is vital since it allows for verifying if network behavior meets expectations. In addition, it allows for proactive notification of bottlenecks that may be affecting a large number of users. In this paper, we describe our novel topology-aware scheme that can be integrated into perfSONAR deployments for detection and diagnosis of network-wide correlated anomaly events. Our scheme involves spatial and temporal analyses on combined topology and uncorrelated anomaly events information for detection of correlated anomaly events. Subsequently, a set of ‘filters’ are applied on the detected events to prioritize them based on potential severity, and to drill-down upon the events “nature” (e.g., event burstiness) and “root-location(s)” (e.g., edge or core location affinity). To validate our scheme, we use traceroute information and one-way delay measurements collected over 3 months between the various U.S. Department of Energy national lab network locations, published via perfSONAR web services. Further, using real-world case studies, we show how our scheme can provide helpful insights for detection, visualization and diagnosis of correlated network anomaly events, and can ultimately save time, effort, and costs spent on network management. Performance monitoring Correlated anomaly detection Spatial and temporal analysis filters perfSONAR measurement framework Active measurements Dhanapalan, Manojprasadh aut Sridharan, Mukundan aut Krishnamurthy, Ashok aut Ramnath, Rajiv aut Enthalten in Journal of network and systems management Springer US, 1993 22(2013), 2 vom: 26. Sept., Seite 208-234 (DE-627)182373657 (DE-600)1202352-8 (DE-576)9182373655 1064-7570 nnns volume:22 year:2013 number:2 day:26 month:09 pages:208-234 https://doi.org/10.1007/s10922-013-9286-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2013 2 26 09 208-234 |
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10.1007/s10922-013-9286-0 doi (DE-627)OLC2066987719 (DE-He213)s10922-013-9286-0-p DE-627 ger DE-627 rakwb eng 004 VZ Calyam, Prasad verfasserin aut Topology-Aware Correlated Network Anomaly Event Detection and Diagnosis 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and historic measurements, which can be queried using web services. Analysis of these measurements using effective schemes to detect and diagnose anomaly events is vital since it allows for verifying if network behavior meets expectations. In addition, it allows for proactive notification of bottlenecks that may be affecting a large number of users. In this paper, we describe our novel topology-aware scheme that can be integrated into perfSONAR deployments for detection and diagnosis of network-wide correlated anomaly events. Our scheme involves spatial and temporal analyses on combined topology and uncorrelated anomaly events information for detection of correlated anomaly events. Subsequently, a set of ‘filters’ are applied on the detected events to prioritize them based on potential severity, and to drill-down upon the events “nature” (e.g., event burstiness) and “root-location(s)” (e.g., edge or core location affinity). To validate our scheme, we use traceroute information and one-way delay measurements collected over 3 months between the various U.S. Department of Energy national lab network locations, published via perfSONAR web services. Further, using real-world case studies, we show how our scheme can provide helpful insights for detection, visualization and diagnosis of correlated network anomaly events, and can ultimately save time, effort, and costs spent on network management. Performance monitoring Correlated anomaly detection Spatial and temporal analysis filters perfSONAR measurement framework Active measurements Dhanapalan, Manojprasadh aut Sridharan, Mukundan aut Krishnamurthy, Ashok aut Ramnath, Rajiv aut Enthalten in Journal of network and systems management Springer US, 1993 22(2013), 2 vom: 26. Sept., Seite 208-234 (DE-627)182373657 (DE-600)1202352-8 (DE-576)9182373655 1064-7570 nnns volume:22 year:2013 number:2 day:26 month:09 pages:208-234 https://doi.org/10.1007/s10922-013-9286-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2013 2 26 09 208-234 |
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Calyam, Prasad Dhanapalan, Manojprasadh Sridharan, Mukundan Krishnamurthy, Ashok Ramnath, Rajiv |
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Calyam, Prasad |
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10.1007/s10922-013-9286-0 |
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topology-aware correlated network anomaly event detection and diagnosis |
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Topology-Aware Correlated Network Anomaly Event Detection and Diagnosis |
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Abstract For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and historic measurements, which can be queried using web services. Analysis of these measurements using effective schemes to detect and diagnose anomaly events is vital since it allows for verifying if network behavior meets expectations. In addition, it allows for proactive notification of bottlenecks that may be affecting a large number of users. In this paper, we describe our novel topology-aware scheme that can be integrated into perfSONAR deployments for detection and diagnosis of network-wide correlated anomaly events. Our scheme involves spatial and temporal analyses on combined topology and uncorrelated anomaly events information for detection of correlated anomaly events. Subsequently, a set of ‘filters’ are applied on the detected events to prioritize them based on potential severity, and to drill-down upon the events “nature” (e.g., event burstiness) and “root-location(s)” (e.g., edge or core location affinity). To validate our scheme, we use traceroute information and one-way delay measurements collected over 3 months between the various U.S. Department of Energy national lab network locations, published via perfSONAR web services. Further, using real-world case studies, we show how our scheme can provide helpful insights for detection, visualization and diagnosis of correlated network anomaly events, and can ultimately save time, effort, and costs spent on network management. © Springer Science+Business Media New York 2013 |
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Abstract For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and historic measurements, which can be queried using web services. Analysis of these measurements using effective schemes to detect and diagnose anomaly events is vital since it allows for verifying if network behavior meets expectations. In addition, it allows for proactive notification of bottlenecks that may be affecting a large number of users. In this paper, we describe our novel topology-aware scheme that can be integrated into perfSONAR deployments for detection and diagnosis of network-wide correlated anomaly events. Our scheme involves spatial and temporal analyses on combined topology and uncorrelated anomaly events information for detection of correlated anomaly events. Subsequently, a set of ‘filters’ are applied on the detected events to prioritize them based on potential severity, and to drill-down upon the events “nature” (e.g., event burstiness) and “root-location(s)” (e.g., edge or core location affinity). To validate our scheme, we use traceroute information and one-way delay measurements collected over 3 months between the various U.S. Department of Energy national lab network locations, published via perfSONAR web services. Further, using real-world case studies, we show how our scheme can provide helpful insights for detection, visualization and diagnosis of correlated network anomaly events, and can ultimately save time, effort, and costs spent on network management. © Springer Science+Business Media New York 2013 |
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Abstract For purposes such as end-to-end monitoring, capacity planning, and performance bottleneck troubleshooting across multi-domain networks, there is an increasing trend to deploy interoperable measurement frameworks such as perfSONAR. These deployments expose vast data archives of current and historic measurements, which can be queried using web services. Analysis of these measurements using effective schemes to detect and diagnose anomaly events is vital since it allows for verifying if network behavior meets expectations. In addition, it allows for proactive notification of bottlenecks that may be affecting a large number of users. In this paper, we describe our novel topology-aware scheme that can be integrated into perfSONAR deployments for detection and diagnosis of network-wide correlated anomaly events. Our scheme involves spatial and temporal analyses on combined topology and uncorrelated anomaly events information for detection of correlated anomaly events. Subsequently, a set of ‘filters’ are applied on the detected events to prioritize them based on potential severity, and to drill-down upon the events “nature” (e.g., event burstiness) and “root-location(s)” (e.g., edge or core location affinity). To validate our scheme, we use traceroute information and one-way delay measurements collected over 3 months between the various U.S. Department of Energy national lab network locations, published via perfSONAR web services. Further, using real-world case studies, we show how our scheme can provide helpful insights for detection, visualization and diagnosis of correlated network anomaly events, and can ultimately save time, effort, and costs spent on network management. © Springer Science+Business Media New York 2013 |
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Topology-Aware Correlated Network Anomaly Event Detection and Diagnosis |
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Dhanapalan, Manojprasadh Sridharan, Mukundan Krishnamurthy, Ashok Ramnath, Rajiv |
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