The problem of peak loads in web applications and its solutions

En aquesta tesi analitzarem els problemes que els pics de demanda causen en les aplicacions Web i quines possibles solucions podem trobar. Les sobrecàrregues s’han convertit en un problema recurrent en different àrees a mesura que Internet s’ha anat fent més accesible. Les pàgines de comerç online són un dels pitjors casos on es poden produïr sobrecàrregues. L’anàlisi que es durà a terme estarà basat en els diferents mètodes que es coneixen en l’actualitat que tracten aquesta problematica. L’objectiu principal d’aquesta tesi és recolectar i comparar aquests métodes de forma que s’en pugui fer una guia per triar quin d’ells és més adequat segons el tipus d’aplicació que tinguem. A més també podreu trobar els tests que s’han realitzat en alguns d’aquests casos

Priority scheduling service for E-commerce web servers

Service scheduling is one of the crucial issues in E-commerce environment. E-commerce web servers often get overloaded as they have to deal with a large number of customers’ requests—for example, browse, search, and pay, in order to make purchases or to get product information from E-commerce web sites. In this paper, we propose a new approach in order to effectively handle high traffic load and to improve web server’s performance. Our solution is to exploit networking techniques and to classify customers’ requests into different classes such that some requests are prioritised over others. We contend that such classification is financially beneficial to E-commerce services as in these services some requests are more valuable than others. For instance, the processing of “browse” request should get less priority than “payment” request as the latter is considered to be more valuable to the service provider. Our approach analyses the arrival process of distinct requests and employs a priority scheduling service at the network nodes that gives preferential treatment to high priority requests. The proposed approach is tested through various experiments which show significant decrease in the response time of high priority requests. This also reduces the probability of dropping high priority requests by a web server and thus enabling service providers to generate more revenue

A comparison of CMS Tier0-dataflow scenarios using the Yasper simulation tool

The CMS experiment at CERN will produce large amounts of data in short time periods. Because the data buffers at the experiment are not large enough, this data needs to be transferred to other storages. The CMS Tier0 will be an enormous job processing and storage facility at the CERN site. One part of this Tier0, called the Tier0 input buffer, has the task to readout the experiment data buffers and to supply these data to other tasks that need to be carried out with it (such as storing). It has to make sure that no data is lost. This thesis compares different scenarios to work with a set of disk servers in order to accomplish the Tier0 input buffer tasks. To increase the performance per disk server, write and read actions on the same disk server are separated. To find the optimal moments a disk server should change from accepting and writing items to supplying items to other tasks, the combination of various parameters, such as the usage of a particular queuing discipline (like FIFO, LIFO, LPTF and SPTF) and the state of the disk server has been studied. To make the actual comparisons a simulation of dataflow models of the different scenarios has been used. These simulations have been performed with the Yasper simulation tool. This tool uses Petri Net models as its input. To be more certain that the models represent the real situation, some model parts have been remodelled in a tool called GPSS. This tool is not using Petri Nets as its input model; instead it uses queuing models described in a special GPSS language. The results of the simulations show that the best queuing discipline to be used with the Tier0 input buffer is the LPTF discipline. In particular in combination with a change moment as soon as a disk server has been readout completely

Traffic Profiles and Performance Modelling of Heterogeneous Networks

This thesis considers the analysis and study of short and long-term traffic patterns of heterogeneous networks. A large number of traffic profiles from different locations and network environments have been determined. The result of the analysis of these patterns has led to a new parameter, namely the 'application signature'. It was found that these signatures manifest themselves in various granularities over time, and are usually unique to an application, permanent virtual circuit (PVC), user or service. The differentiation of the application signatures into different categories creates a foundation for short and long-term management of networks. The thesis therefore looks from the micro and macro perspective on traffic management, covering both aspects. The long-term traffic patterns have been used to develop a novel methodology for network planning and design. As the size and complexity of interconnected systems grow steadily, usually covering different time zones, geographical and political areas, a new methodology has been developed as part of this thesis. A part of the methodology is a new overbooking mechanism, which stands in contrast to existing overbooking methods created by companies like Bell Labs. The new overbooking provides companies with cheaper network design and higher average throughput. In addition, new requirements like risk factors have been incorporated into the methodology, which lay historically outside the design process. A large network service provider has implemented the overbooking mechanism into their network planning process, enabling practical evaluation. The other aspect of the thesis looks at short-term traffic patterns, to analyse how congestion can be controlled. Reoccurring short-term traffic patterns, the application signatures, have been used for this research to develop the "packet train model" further. Through this research a new congestion control mechanism was created to investigate how the application signatures and the "extended packet train model" could be used. To validate the results, a software simulation has been written that executes the proprietary congestion mechanism and the new mechanism for comparison. Application signatures for the TCP/IP protocols have been applied in the simulation and the results are displayed and discussed in the thesis. The findings show the effects that frame relay congestion control mechanisms have on TCP/IP, where the re-sending of segments, buffer allocation, delay and throughput are compared. The results prove that application signatures can be used effectively to enhance existing congestion control mechanisms.AT&T (UK) Ltd, Englan

Effective task assignment strategies for distributed systems under highly variable workloads

Heavy-tailed workload distributions are commonly experienced in many areas of distributed computing. Such workloads are highly variable, where a small number of very large tasks make up a large proportion of the workload, making the load very hard to distribute effectively. Traditional task assignment policies are ineffective under these conditions as they were formulated based on the assumption of an exponentially distributed workload. Size-based task assignment policies have been proposed to handle heavy-tailed workloads, but their applications are limited by their static nature and assumption of prior knowledge of a task's service requirement. This thesis analyses existing approaches to load distribution under heavy-tailed workloads, and presents a new generalised task assignment policy that significantly improves performance for many distributed applications, by intelligently addressing the negative effects on performance that highly variable workloads cause. Many problems associated with the modelling and optimisations of systems under highly variable workloads were then addressed by a novel technique that approximated these workloads with simpler mathematical representations, without losing any of their pertinent original properties. Finally, we obtain advance queuing metrics (such as the variance of key measurements like waiting time and slowdown that are difficult to obtain analytically) through rigorous simulation

Simulation and analysis of network traffic for efficient and reliable information transfer

With the growing commercial importance of the Internet and the development of new real-time, connection-oriented services like IP-telephony and electronic commerce resilience is becoming a key issue in the design of TP-based networks. Two emerging technologies, which can accomplish the task of efficient information transfer, are Multiprotocol Label Switching (MPLS) and Differentiated Services. A main benefit of MPLS is the ability to introduce traffic-engineering concepts due to its connection-oriented characteristic. With MPLS it is possible to assign different paths for packets through the network. Differentiated services divides traffic into different classes and treat them differently, especially when there is a shortage of network resources. In this thesis, a framework was proposed to integrate the above two technologies and its performance in providing load balancing and improving QoS was evaluated. Simulation and analysis of this framework demonstrated that the combination of MPLS and Differentiated services is a powerful tool for QoS provisioning in IP networks

Automatic Scaling in Cloud Computing

This dissertation thesis deals with automatic scaling in cloud computing, mainly focusing on the performance of interactive workloads, that is web servers and services, running in an elastic cloud environment. In the rst part of the thesis, the possibility of forecasting the daily curve of workload is evaluated using long-range seasonal techniques of statistical time series analysis. The accuracy is high enough to enable either green computing or lling the unused capacity with batch jobs, hence the need for long-range forecasts. The second part focuses on simulations of automatic scaling, which is necessary for the interactive workload to actually free up space when it is not being utilized at peak capacity. Cloud users are mostly scared of letting a machine control their servers, which is why realistic simulations are needed. We have explored two methods, event-driven simulation and queuetheoretic models. During work on the rst, we have extended the widely-used CloudSim simulation package to be able to dynamically scale the simulation setup at run time and have corrected its engine using knowledge from queueing theory. Our own simulator then relies solely on theoretical models, making it much more precise and much faster than the more general CloudSim. The tools from the two parts together constitute the theoretical foundation which, once implemented in practice, can help leverage cloud technology to actually increase the e ciency of data center hardware. In particular, the main contributions of the dissertation thesis are as follows: 1. New methodology for forecasting time series of web server load and its validation 2. Extension of the often-used simulator CloudSim for interactive load and increasing the accuracy of its output 3. Design and implementation of a fast and accurate simulator of automatic scaling using queueing theoryTato dizerta cn pr ace se zab yv a cloud computingem, konkr etn e se zam e ruje na v ykon interaktivn z at e ze, nap r klad webov ych server u a slu zeb, kter e b e z v elastick em cloudov em prost red . V prvn c asti pr ace je zhodnocena mo znost p redpov d an denn k rivky z at e ze pomoc metod statistick e anal yzy casov ych rad se sez onn m prvkem a dlouh ym dosahem. P resnost je dostate cn e vysok a, aby umo znila bu d set ren energi nebo vypl nov an nevyu zit e kapacity d avkov ymi ulohami, jejich z doba b ehu je hlavn m d uvodem pro pot rebu dlouhodob e p redpov edi. Druh a c ast se zam e ruje na simulace automatick eho sk alov an , kter e je nutn e, aby interaktivn z at e z skute cn e uvolnila prostor, pokud nen vyt e zov ana na plnou kapacitu. U zivatel e cloud u se p rev a zn e boj nechat stroj, aby ovl adal jejich servery, a pr av e proto jsou pot reba realistick e simulace. Prozkoumali jsme dv e metody, konkr etn e simulaci s prom enn ym casov ym krokem r zen ym ud alostmi a modely z teorie hromadn e obsluhy. B ehem pr ace na prvn z t echto metod jsme roz s rili siroce pou z van y simula cn bal k CloudSim o mo znost dynamicky sk alovat simulovan y syst em za b ehu a opravili jsme jeho j adro za pomoci znalost z teorie hromadn e obsluhy. N a s vlastn simul ator se pak spol eh a pouze na teoretick e modely, co z ho cin p resn ej s m a mnohem rychlej s m ne zli obecn ej s CloudSim. N astroje z obou c ast pr ace tvo r dohromady teoretick y z aklad, kter y, pokud bude implementov an v praxi, pom u ze vyu z t technologii cloudu tak, aby se skute cn e zv y sila efektivita vyu zit hardwaru datov ych center. Hlavn p r nosy t eto dizerta cn pr ace jsou n asleduj c : 1. Stanoven metodologie pro p redpov d an casov ych rad z at e ze webov ych server u a jej validace 2. Roz s ren casto citovan eho simul atoru CloudSim o mo znost simulace interaktivn z at e ze a zp resn en jeho v ysledk u 3. N avrh a implementace rychl eho a p resn eho simul atoru automatick eho sk alov an vyu z vaj c ho teorii hromadn e obsluhyKatedra kybernetik

Towards auto-scaling in the cloud: online resource allocation techniques

Cloud computing provides an easy access to computing resources. Customers can acquire and release resources any time. However, it is not trivial to determine when and how many resources to allocate. Many applications running in the cloud face workload changes that affect their resource demand. The first thought is to plan capacity either for the average load or for the peak load. In the first case there is less cost incurred, but performance will be affected if the peak load occurs. The second case leads to money wastage, since resources will remain underutilized most of the time. Therefore there is a need for a more sophisticated resource provisioning techniques that can automatically scale the application resources according to workload demand and performance constrains. Large cloud providers such as Amazon, Microsoft, RightScale provide auto-scaling services. However, without the proper configuration and testing such services can do more harm than good. In this work I investigate application specific online resource allocation techniques that allow to dynamically adapt to incoming workload, minimize the cost of virtual resources and meet user-specified performance objectives

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