Passive probes continuously collect a significant amount of traffic vol- ume, and autonomously generate statistics on a large number of metrics. A common statistical output of passive probe is represented by probability mass functions (pmf). The need for consolidation of several pmfs arises in two contexts, namely: (i) whenever a central point collects and aggregates measurement of multiple disjoint vantage points, and (ii) whenever a local measurement processed at a single vantage point needs to be distributed over multiple cores of the same physical probe, in order to cope with growing link capacity. Taking an experimental approach, we study both cases assessing the impact of different consolidation strategies, obtaining general design and tuning guidelines.
Scalable Accurate Consolidation of Passively Measured Statistical Data / Silvia, Colabrese; Dario, Rossi; Mellia, Marco. - STAMPA. - 8362:(2014), pp. 262-264. (Intervento presentato al convegno Passive and Active Measurement (PAM) tenutosi a Los Angeles, CA nel March 2014) [10.1007/978-3-319-04918-2_26].
Scalable Accurate Consolidation of Passively Measured Statistical Data
MELLIA, Marco
2014
Abstract
Passive probes continuously collect a significant amount of traffic vol- ume, and autonomously generate statistics on a large number of metrics. A common statistical output of passive probe is represented by probability mass functions (pmf). The need for consolidation of several pmfs arises in two contexts, namely: (i) whenever a central point collects and aggregates measurement of multiple disjoint vantage points, and (ii) whenever a local measurement processed at a single vantage point needs to be distributed over multiple cores of the same physical probe, in order to cope with growing link capacity. Taking an experimental approach, we study both cases assessing the impact of different consolidation strategies, obtaining general design and tuning guidelines.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2539889
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