Estimating traffic parameters in Internet via active measurements for QoS and congestion control

Sharma, Vinod; Suma, M.B.

doi:10.1109/glocom.2001.966232

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…In this simulation, the period of the periodic measurement tasks is uniformly distributed in the range of [11,100] time units, and the execution times of the periodic measurement tasks are uniformly distributed in the range of [2,10] time units. The initial time of task a (τ i ) is randomly selected in the range of [1,5] time units.…”

Section: Simulation Results Of Periodic Tasks Schedulingmentioning

confidence: 99%

“…The execution time of 5 measurement tasks are uniformly distributed in the range of [2,10] time units while the execution time of the rest of 5 tasks are randomly selected in the range of [8,10] time units. The period of the tasks is uniformly distributed in the range of [11,100] time units. The initial available time of a task is randomly selected in the range of [1,5] time units.…”

Section: Simulation Results Of Periodic Tasks Schedulingmentioning

confidence: 99%

“…The execution and period times are uniformly distributed in the ranges of [2,10] and [11,100] time slots, respectively. As in the previous section, we considered the conflict probability among all measurement tasks (including both periodic and ondemand tasks) increasing from 0 to 1.0 with steps of 0.05.…”

Section: Simulation Results Of On-demand Tasks Schedulingmentioning

confidence: 99%

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Task-execution scheduling schemes for network measurement and monitoring

Qin

Rojas‐Cessa

Ansari

2010

Computer Communications

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Measurement is a required process in high performance networks for efficient quality-of-service (QoS) provisioning and service verification. Active measurement is an attractive approach because the measurement traffic injected into the network can be controlled and the measurement tasks can be distributed throughout the network. However, the execution of measurement tasks in common parts of a network may face contention for resources, such as computational power, memory, and link bandwidth. This contention could jeopardize measurement accuracy and affect network services. This contention for limited resources defines a conflict between measurement tasks. Furthermore, we consider two sets of measurement tasks, those used to monitor network state periodically, called periodic tasks, and those for casual measurements issued as needed, called on-demand measurement tasks. In this paper, we propose a novel scheduling scheme to resolve contention for resources of both periodic and on-demand measurement tasks from graph coloring perspective, called ascending-order of the sum of clique number and degree of tasks. The scheme selects tasks according to the ascending order of the sum of clique number and conflict task degree in a conflict graph and allows concurrent execution of multiple measurement tasks for high resource utilization. The scheme decreases the average waiting time of all tasks in periodic measurement tasks scheduling. For on-demand measurement tasks, the proposed scheme minimizes the waiting time of inserted on-demand tasks while keeping time space utilization high. In other words, the total time spent on finishing all the tasks is shortened. We evaluate our proposed schemes under different measurement task assignment scenarios through computer simulations, and compare the performance of this scheme with others that also allow concurrent task execution. The simulation results show that the proposed scheme produces effective contention resolution and low execution delays.

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Section: Simulation Results Of Periodic Tasks Schedulingmentioning

confidence: 99%

Section: Simulation Results Of Periodic Tasks Schedulingmentioning

confidence: 99%

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Task-execution scheduling schemes for network measurement and monitoring

Qin

Rojas‐Cessa

Ansari

2010

Computer Communications

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“…There have been many works on accuracy of active measurement e.g., [3], [4], [5], [6], [7]. These previous works focus on the packet measurement from end-to-end paths, [3], [4] and [5] in the core network, and [6] in multicast-based infrastructure, while [7] attempts to minimize probing overhead from active measurement in IP telephony by adjusting the placement of measurement devices. However, relatively few have proposed an in-depth mathematical model that could enlighten the system properties of the performance estimation in active measurement.…”

Section: Introductionmentioning

confidence: 99%

“…However, relatively few have proposed an in-depth mathematical model that could enlighten the system properties of the performance estimation in active measurement. In particular, the work in [3] has attempted to estimate the mean delay by combining the passive and active monitoring while [5] has presented a mathematical analysis to estimate mean delay in packet queue. These mathematical approaches nevertheless are not applicable in the active measurement at the time scale of call dynamics, which is of our interest.…”

Section: Introductionmentioning

confidence: 99%

Mathematical analysis for corrective performance evaluation of call based QoS in active measurement

Maihasap

Suksomboon

Aswakul

2008

2008 8th International Conference on ITS Telecommunications

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In this paper, we addressed the problem of improving call-based QoS measurement accuracy with the focus on call blocking probability as monitored via the active measurement or test drives in cellular-type networks. The system has been modelled as continuous time Markov chains with necessary assumption of Poisson traffics for both actual load and injected or intrusive load. With a product-form solution, a convenient formula has been derived to map the value of call blocking probability as seen by the intrusive traffic to the true value of call blocking probability of actual traffic in the non-intrusive system. With the derived corrective factor, QoS measurement can be accurately measured and regulated. Consequently, the cellular-type networks so widely used for wide-area, mobile communication platforms of ITS can be better engineered and hopefully become a dependable foundation for ongoing as well as future ITS developments.

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QoS routing in MPLS networks using active measurements

Mishra

Sharma

TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region

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Abstmcl-We consider the problem of selecting an appropriate ISP for a new connection request from several possible LSPs which can satisfy the QoS of the new connection. We propose an active measurement approach where a probing sUeani is sent along each LSP. Based on the end-to-end delays of the packets of the probing stream, we estimate the m w i delays and the throughput the new connection will get. Our approach allows to include the effect of the new COMClion also in estimating the mean delay and the throughput. The new CoMCction may be using TCP or UDP.fiywords: MPLS networks, QoS routing, aetive measurements.

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Estimating traffic parameters in Internet via active measurements for QoS and congestion control

Cited by 3 publications

References 9 publications

Task-execution scheduling schemes for network measurement and monitoring

Task-execution scheduling schemes for network measurement and monitoring

Mathematical analysis for corrective performance evaluation of call based QoS in active measurement

QoS routing in MPLS networks using active measurements

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