PREDICTING INTERNET TRAFFIC BURSTS USING EXTREME VALUE THEORY

Computer networks play an important role in today’s organization and people life. These interconnected devices share a common medium and they tend to compete for it. Quality of Service (QoS) comes into play as to define what level of services users get. Accurately defining the QoS metrics is thus important. Bursts and serious deteriorations are omnipresent in Internet and considered as an important aspects of it. This thesis examines bursts and serious deteriorations in Internet traffic and applies Extreme Value Theory (EVT) to their prediction and modelling. EVT itself is a field of statistics that has been in application in fields like hydrology and finance, with only a recent introduction to the field of telecommunications. Model fitting is based on real traces from Belcore laboratory along with some simulated traces based on fractional Gaussian noise and linear fractional alpha stable motion. QoS traces from University of Napoli are also used in the prediction stage. Three methods from EVT are successfully used for the bursts prediction problem. They are Block Maxima (BM) method, Peaks Over Threshold (POT) method, and RLargest Order Statistics (RLOS) method. Bursts in internet traffic are predicted using the above three methods. A clear methodology was developed for the bursts prediction problem. New metrics for QoS are suggested based on Return Level and Return Period. Thus, robust QoS metrics can be defined. In turn, a superior QoS will be obtained that would support mission critical applications

Contributions to modelling of internet traffic by fractal renewal processes.

The principle of parsimonious modelling of Internet traffic states that a minimal number of descriptors should be used for its characterization. Until early 1990s, the conventional Markovian models for voice traffic had been considered suitable and parsimonious for data traffic as well. Later with the discovery of strong correlations and increased burstiness in Internet traffic, various self-similar count models have been proposed. But, in fact, such models are strictly mono-fractal and applicable at coarse time scales, whereas Internet traffic modelling is about modelling traffic at fine and coarse time scales; modelling traffic which can be mono and multi-fractal; modelling traffic at interarrival time and count levels; modelling traffic at access and core tiers; and modelling all the three structural components of Internet traffic, that is, packets, flows and sessions. The philosophy of this thesis can be described as: “the renewal of renewal theory in Internet traffic modelling”. Renewal theory has a great potential in modelling statistical characteristics of Internet traffic belonging to individual users, access and core networks. In this thesis, we develop an Internet traffic modelling framework based on fractal renewal processes, that is, renewal processes with underlying distribution of interarrival times being heavy-tailed. The proposed renewal framework covers packets, flows and sessions as structural components of Internet traffic and is applicable for modelling the traffic at fine and coarse time scales. The properties of superposition of renewal processes can be used to model traffic in higher tiers of the Internet hierarchy. As the framework is based on renewal processes, therefore, Internet traffic can be modelled at both interarrival times and count levels

Modeling virtualized application performance from hypervisor counters

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 61-64).Managing a virtualized datacenter has grown more challenging, as each virtual machine's service level agreement (SLA) must be satisfied, when the service levels are generally inaccessible to the hypervisor. To aid in VM consolidation and service level assurance, we develop a modeling technique that generates accurate models of service level. Using only hypervisor counters as inputs, we train models to predict application response times and predict SLA violations. To collect training data, we conduct a simulation phase which stresses the application across many workloads levels, and collects each response time. Simultaneously, hypervisor performance counters are collected. Afterwards, the data is synchronized and used as training data in ensemble-based genetic programming for symbolic regression. This modeling technique is quite efficient at dealing with high-dimensional datasets, and it also generates interpretable models. After training models for web servers and virtual desktops, we test generalization across different content. In our experiments, we found that our technique could distill small subsets of important hypervisor counters from over 700 counters. This was tested for both Apache web servers and Windows-based virtual desktop infrastructures. For the web servers, we accurately modeled the breakdown points and also the service levels. Our models could predict service levels with 90.5% accuracy on a test set. On a untrained scenario with completely different contending content, our models predict service levels with 70% accuracy, but predict SLA violation with 92.7% accuracy. For the virtual desktops, on test scenarios similar to training scenarios, model accuracy was 97.6%. Our main contribution is demonstrating that a completely data-driven approach to application performance modeling can be successful. In contrast to many other works, our models do not use workload level or response times as inputs to the models, but nevertheless predicts service level accurately. Our approach also lets the models determine which inputs are important to a particular model's performance, rather than hand choosing a few inputs to train on.by Lawrence L. Chan.M.Eng

Endless Data

Small and Medium Enterprises (SMEs), as well as micro teams, face an uphill task when delivering software to the Cloud. While rapid release methods such as Continuous Delivery can speed up the delivery cycle: software quality, application uptime and information management remain key concerns. This work looks at four aspects of software delivery: crowdsourced testing, Cloud outage modelling, collaborative chat discourse modelling, and collaborative chat discourse segmentation. For each aspect, we consider business related questions around how to improve software quality and gain more significant insights into collaborative data while respecting the rapid release paradigm

Architecting Efficient Data Centers.

Data center power consumption has become a key constraint in continuing to scale Internet services. As our society’s reliance on “the Cloud” continues to grow, companies require an ever-increasing amount of computational capacity to support their customers. Massive warehouse-scale data centers have emerged, requiring 30MW or more of total power capacity. Over the lifetime of a typical high-scale data center, power-related costs make up 50% of the total cost of ownership (TCO). Furthermore, the aggregate effect of data center power consumption across the country cannot be ignored. In total, data center energy usage has reached approximately 2% of aggregate consumption in the United States and continues to grow. This thesis addresses the need to increase computational efficiency to address this grow- ing problem. It proposes a new classes of power management techniques: coordinated full-system idle low-power modes to increase the energy proportionality of modern servers. First, we introduce the PowerNap server architecture, a coordinated full-system idle low- power mode which transitions in and out of an ultra-low power nap state to save power during brief idle periods. While effective for uniprocessor systems, PowerNap relies on full-system idleness and we show that such idleness disappears as the number of cores per processor continues to increase. We expose this problem in a case study of Google Web search in which we demonstrate that coordinated full-system active power modes are necessary to reach energy proportionality and that PowerNap is ineffective because of a lack of idleness. To recover full-system idleness, we introduce DreamWeaver, architectural support for deep sleep. DreamWeaver allows a server to exchange latency for full-system idleness, allowing PowerNap-enabled servers to be effective and provides a better latency- power savings tradeoff than existing approaches. Finally, this thesis investigates workloads which achieve efficiency through methodical cluster provisioning techniques. Using the popular memcached workload, this thesis provides examples of provisioning clusters for cost-efficiency given latency, throughput, and data set size targets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91499/1/meisner_1.pd

PREDICTING INTERNET TRAFFIC BURSTS USING EXTREME VALUE THEORY

Computer networks play an important role in today’s organization and people life. These interconnected devices share a common medium and they tend to compete for it. Quality of Service (QoS) comes into play as to define what level of services users get. Accurately defining the QoS metrics is thus important. Bursts and serious deteriorations are omnipresent in Internet and considered as an important aspects of it. This thesis examines bursts and serious deteriorations in Internet traffic and applies Extreme Value Theory (EVT) to their prediction and modelling. EVT itself is a field of statistics that has been in application in fields like hydrology and finance, with only a recent introduction to the field of telecommunications. Model fitting is based on real traces from Belcore laboratory along with some simulated traces based on fractional Gaussian noise and linear fractional alpha stable motion. QoS traces from University of Napoli are also used in the prediction stage. Three methods from EVT are successfully used for the bursts prediction problem. They are Block Maxima (BM) method, Peaks Over Threshold (POT) method, and RLargest Order Statistics (RLOS) method. Bursts in internet traffic are predicted using the above three methods. A clear methodology was developed for the bursts prediction problem. New metrics for QoS are suggested based on Return Level and Return Period. Thus, robust QoS metrics can be defined. In turn, a superior QoS will be obtained that would support mission critical applications

Packet level measurement over wireless access

PhDPerformance Measurement of the IP packet networks mainly comprise of monitoring the network performance in terms of packet losses and delays. If used appropriately, these network parameters (i.e. delay, loss and bandwidth etc) can indicate the performance status of the network and they can be used in fault and performance monitoring, network provisioning, and traffic engineering. Globally, there is a growing need for accurate network measurement to support the commercial use of IP networks. In wireless networks, transmission losses and communication delays strongly affect the performance of the network. Compared to wired networks, wireless networks experience higher levels of data dropouts, and corruption due to issues of channel fading, noise, interference and mobility. Performance monitoring is a vital element in the commercial future of broadband packet networking and the ability to guarantee quality of service in such networks is implicit in Service Level Agreements. Active measurements are performed by injecting probes, and this is widely used to determine the end to end performance. End to end delay in wired networks has been extensively investigated, and in this thesis we report on the accuracy achieved by probing for end to end delay over a wireless scenario. We have compared two probing techniques i.e. Periodic and Poisson probing, and estimated the absolute error for both. The simulations have been performed for single hop and multi- hop wireless networks. In addition to end to end latency, Active measurements have also been performed for packet loss rate. The simulation based analysis has been tried under different traffic scenarios using Poisson Traffic Models. We have sampled the user traffic using Periodic probing at different rates for single hop and multiple hop wireless scenarios. 5 Active probing becomes critical at higher values of load forcing the network to saturation much earlier. We have evaluated the impact of monitoring overheads on the user traffic, and show that even small amount of probing overhead in a wireless medium can cause large degradation in network performance. Although probing at high rate provides a good estimation of delay distribution of user traffic with large variance yet there is a critical tradeoff between the accuracy of measurement and the packet probing overhead. Our results suggest that active probing is highly affected by probe size, rate, pattern, traffic load, and nature of shared medium, available bandwidth and the burstiness of the traffic

Simulation analyses and stress testing of payment networks

This publication consists of ten separate studies on payment and settlement systems employing simulation techniques. Most of these were carried out using the payment and settlement system simulator BoF-PSS2 provided by the Bank of Finland. The preliminary versions were presented at the annual simulator seminars arranged by the Bank in 2007 and 2008. The main focus of the analyses is on continuity arrangements, operational stability, liquidity requirements, liquidity economising, gridlock resolution, transaction queuing arrangements, network features and network topologies. The studies examine systems in several countries and cover different kinds of payment systems and regimes.simulation; payment and settlement system; liquidity; gridlock; system risk; network topology

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