625 research outputs found
Just Queuing: Policy-Based Scheduling Mechanism for Packet Switching Networks
The pervasiveness of the Internet and its applications lead to the potential increment of the usersâ demands for more services with economical prices. The diversity of Internet traffic requires some classification and prioritisation since some traffic deserve much attention with less delay and loss compared to others. Current scheduling mechanisms are exposed to the trade-off between three major properties namely fairness, complexity and protection. Therefore, the question remains about how to improve the fairness and protection with less complex implementation. This research is designed to enhance scheduling mechanism by providing sustainability to the fairness and protection properties with simplicity in implementation; and hence higher service quality particularly for real-time applications. Extra elements are applied to the main fairness equation to improve the fairness property. This research adopts the restricted charge policy which imposes the protection of normal user. In terms of the complexity property, genetic algorithm has an advantage in holding the fitness score of the queue in separate storage space which potentially minimises the complexity of the algorithm. The integrity between conceptual, analytical and experimental approach verifies the efficiency of the proposed mechanism. The proposed mechanism is validated by using the emulation and the validation experiments involve real router flow data. The results of the evaluation showed fair bandwidth distribution similar to the popular Weighted Fair Queuing (WFQ) mechanism. Furthermore, better protection was exhibited in the results compared with the WFQ and two other scheduling mechanisms. The complexity of the proposed mechanism reached O(log(n)) which is considered as potentially low. Furthermore, this mechanism is limited to the wired networks and hence future works could improve the mechanism to be adopted in mobile ad-hoc networks or any other wireless networks. Moreover, more improvements could be applied to the proposed mechanism to enhance its deployment in the virtual circuits switching network such as the asynchronous transfer mode networks
A hybrid queueing model for fast broadband networking simulation
PhDThis research focuses on the investigation of a fast simulation method for broadband
telecommunication networks, such as ATM networks and IP networks. As a result of
this research, a hybrid simulation model is proposed, which combines the analytical
modelling and event-driven simulation modelling to speeding up the overall
simulation.
The division between foreground and background traffic and the way of dealing with
these different types of traffic to achieve improvement in simulation time is the major
contribution reported in this thesis. Background traffic is present to ensure that proper
buffering behaviour is included during the course of the simulation experiments, but
only the foreground traffic of interest is simulated, unlike traditional simulation
techniques. Foreground and background traffic are dealt with in a different way.
To avoid the need for extra events on the event list, and the processing overhead,
associated with the background traffic, the novel technique investigated in this
research is to remove the background traffic completely, adjusting the service time of
the queues for the background traffic to compensate (in most cases, the service time
for the foreground traffic will increase). By removing the background traffic from the
event-driven simulator the number of cell processing events dealt with is reduced
drastically.
Validation of this approach shows that, overall, the method works well, but the
simulation using this method does have some differences compared with experimental
results on a testbed. The reason for this is mainly because of the assumptions behind
the analytical model that make the modelling tractable.
Hence, the analytical model needs to be adjusted. This is done by having a neural
network trained to learn the relationship between the input traffic parameters and the
output difference between the proposed model and the testbed. Following this
training, simulations can be run using the output of the neural network to adjust the
analytical model for those particular traffic conditions.
The approach is applied to cell scale and burst scale queueing to simulate an ATM
switch, and it is also used to simulate an IP router. In all the applications, the method
ensures a fast simulation as well as an accurate result
Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues
In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal characteristics. By adapting the batch training of standard HMMs to incremental learning, online HMMs act as benchmarks on workloads obtained from live systems (i.e. storage systems and financial markets) and reduce time complexity of the Baum-Welch algorithm. Similarly, by extending HMM capabilities to train on multiple traces simultaneously it follows that workloads of different types are modelled in parallel by a multi-input HMM. Typically, the HMM-generated traces verify the throughput and burstiness of the real data. Applications of adaptive HMMs include predicting user behaviour in social networks and performance-energy measurements in smartphone applications. Equally important is measuring system delay through response times. For example, workloads such as Internet traffic arriving at routers are affected by queueing delays. To meet quality of service needs, queueing delays must be minimised and, hence, it is important to model and predict such queueing delays in an efficient and cost-effective manner. Therefore, we propose a class of discrete, processor-sharing queues for approximating queueing delay as response time distributions, which represent service level agreements at specific spatiotemporal levels. We adapt discrete queues to model job arrivals with distributions given by a Markov-modulated Poisson process (MMPP) and served under discriminatory processor-sharing scheduling. Further, we propose a dynamic strategy of service allocation to minimise delays in UDP traffic flows whilst maximising a utility function.Open Acces
Fairness in a data center
Existing data centers utilize several networking technologies in order to handle the performance requirements of different workloads. Maintaining diverse networking technologies increases complexity and is not cost effective. This results in the current trend to converge all traffic into a single networking fabric. Ethernet is both cost-effective and ubiquitous, and as such it has been chosen as the technology of choice for the converged fabric. However, traditional Ethernet does not satisfy the needs of all traffic workloads, for the most part, due to its lossy nature and, therefore, has to be enhanced to allow for full convergence. The resulting technology, Data Center Bridging (DCB), is a new set of standards defined by the IEEE to make Ethernet lossless even in the presence of congestion. As with any new networking technology, it is critical to analyze how the different protocols within DCB interact with each other as well as how each protocol interacts with existing technologies in other layers of the protocol stack.
This dissertation presents two novel schemes that address critical issues in DCB networks: fairness with respect to packet lengths and fairness with respect to flow control and bandwidth utilization. The Deficit Round Robin with Adaptive Weight Control (DRR-AWC) algorithm actively monitors the incoming streams and adjusts the scheduling weights of the outbound port. The algorithm was implemented on a real DCB switch and shown to increase fairness for traffic consisting of mixed-length packets. Targeted Priority-based Flow Control (TPFC) provides a hop-by-hop flow control mechanism that restricts the flow of aggressor streams while allowing victim streams to continue unimpeded. Two variants of the targeting mechanism within TPFC are presented and their performance evaluated through simulation
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