Framework for IP Performance Metrics

Paxson, Vern; Almes, Guy; Mahdavi, J.; Mathis, Matt

doi:10.17487/rfc2330

Cited by 247 publications

(188 citation statements)

References 4 publications

(8 reference statements)

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“…The article [22] describes two adaptive sampling methods to manage the processor usage on a network device. One method uses information about the current usage of the processor to adjust the sampling rate.…”

Section: Adaptive Samplingmentioning

confidence: 99%

Intelligent Network Client Profiler

Arsénio¹

2013

JACN

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Abstract-Peer2Peer traffic already accounts for a large share of the overall internet traffic. Future solutions will need to manage all the available resources in order to charge users using fair rules according to their communication profile. Obtaining information about the behavior of Internet traffic is therefore fundamental to the management, monitoring and operation activities, such as the identification of applications and protocols that customers use. However, the main obstacle to this identification is the lack of scalability for monitoring network devices. In particular, they can analyze all the network packets for this purpose. This task is extremely demanding and almost impossible to accomplish rapidly in large networks (because usually there is a number in the hundreds or thousands of customers). Furthermore, we expect such networks to become even larger, as on the internet of things all devices (sensors, appliances, etc) will be publicly connected to the internet. As such, traffic sampling strategies have been proposed to overcome this major problem of scale. This paper presents different works in the area of monitoring traffic for user profiling and security purposes. It proposes as well a solution that uses selective filtering techniques combined with an engine traffic DPI to identify applications and protocols that customers use most frequently. Thus it becomes possible to get ISPs to optimize their network in a scalable and intelligent manner, imposing security measures in order to enforce network usage according to client profiles.

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Section: Adaptive Samplingmentioning

confidence: 99%

Intelligent Network Client Profiler

Arsénio¹

2013

JACN

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“…Information collected about these packets are taken as representative of the behavior of the network. Metrics of this data are described in the framework presented in [13].…”

Section: Network Monitoring Agentmentioning

confidence: 99%

Dynamic QoS Adaptation Using COPS and Network Monitoring Feedback

Ahmed

Mehaoua

Boutaba

2002

Management of Multimedia on the Internet

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Abstract. This paper presents an approach to handle out of profile traffic using Common Open Policy Service and network monitoring feedback. The proposed approach is based on monitoring and reporting information sent by bandwidth monitors installed on each node of a Diffserv Domain. A monitor interacts with Policy Decision Point. This later, depending on the network state, pushes p olicy decision rules to the Policy Enforcement Point in order to accept, remark or drop out -of-profile traffic dynamically. This allows a dynamic reallocation and management of network resources based on current network state and applications QoS requirements. An implementation and a performance evaluation of the dynamic QoS adaptation framework using a Java COPS and a Linux-based network testbed are also presented.

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“…The essential attributes such as not treating a duplicate packet as a reordered packet and not treating the packets following a lost packet as reordered, are vital for the validity of metrics. A framework for metrics for the Internet [9] states: "The metrics must aid users and providers in understanding the performance they experience or provide." A metric for packet reordering has to go beyond reporting a mere measure that varies with reordering, to a measure that is useful to characterize reordering.…”

Section: Introductionmentioning

confidence: 99%

Metrics for packet reordering—A comparative analysis

Piratla

Jayasumana

2007

Int J Communication

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-Packet reordering is an inevitable phenomenon on the Internet. An ideal metric for packet reordering should capture reordering accurately, provide insight into nature of reordering, and help in evaluation and analysis of reordering leading to mitigation of its adverse effects. Proposed metrics for packet reordering, namely, Reorder Density, Reorder Bufferoccupancy Density, Reordering Extent, and n-Reordering, overcome to various degrees the deficiencies of percentage reordering in capturing the nature and extent of reordering. These metrics vary widely in areas such as evaluation complexity, measurement technique, usage, and in the definition used for packet reordering itself. Metrics for reordering are evaluated using a framework consisting of a set of both essential and desirable attributes of reorder metrics. The attributes include the ability to capture reordering, sensitivity to lost and duplicate packets in reorder measurements, usefulness, simplicity, and evaluation complexity. Finally, the characterization of packet reordering using these metrics is discussed, using sets of measurements carried over the Internet.

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Framework for IP Performance Metrics

Cited by 247 publications

References 4 publications

Intelligent Network Client Profiler

Intelligent Network Client Profiler

Dynamic QoS Adaptation Using COPS and Network Monitoring Feedback

Metrics for packet reordering—A comparative analysis

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