Intelligent adaptive multi-parameter migration model for load balancing virtualized cluster of servers

Najvažnija korist virtualizacije je dobivanje okruženja s ujednačenim opterećenjem kroz prenošenje (migraciju) virtualnim strojem (VM). Djelovanje usluga u skupinama (klasterima), kao što je prosječno vrijeme reakcije - Average Response Time - reducirano je inteligentnom odlukom VM o prenošenju. Prenošenje ovisi o nizu kriterija poput korištenja resursa (uporaba CPU, korištenje RAMa, korištenje mreže, itd.) i potrebe za strojevima (fizičkim (PM) i virtualnim (VM)). To je više- kriterijski problem prenošenja koji procjenjuje, komparira i sortira niz fizičkih i virtualnih strojeva (PM i VM) na osnovu parametara istaknutih u procesu prenošenja. Ali, koji parametar (parametri) ima dominantnu ulogu nad djelovanjem klastera u određenom vremenskom odjeljku? Kako možemo odrediti težinu parametara u nadolazećim vremenskim razmacima? Postojeći algoritmi prenošenja (migration algorithms) ne uzimaju u obzir težine parametara koje se mijenjaju ovisno o vremenu. Te analize pretpostavljaju fiksnu težinu za svaki parametar kroz široki raspon vremenskih intervala. To dovodi do netočnog predviđanja o traženju rješenja za svaki server. U našem se radu predstavlja novi Inteligentni i Adaptivni Multi Parametarski (IAMP) upravljač resursima na bazi prenošenja (migracije) za virtualizirane centre podataka i klastere s novom na umjetnoj neuronskoj mreži (ANN) temeljenoj analizi težina nazvanoj Error Number of Parameter Omission (ENPO). U svakom se vremenskom razmaku težina parametara ponovo izračunava te će nevažni parametri biti oslabljeni u postupku rangiranja. Obilježili smo parametre koji utječu na performansu klastera i koristili hot migration s naglaskom na skupini servera u XEN platformi virtualizacije. Eksperimentalni rezultati temeljeni na radnim opterećenjima sastavljenim od stvarnih aplikacija pokazuju da je primjenom IAMP-a moguće poboljšati rad virtualnog klaster sustava do 23 % u usporedbi s postojećim algoritmima. Što više, on brže reagira i eliminira vruće točke zbog svog potpuno dinamičkog upravljačkog algoritma.The most important benefit of virtualization is to get a load balanced environment through Virtual Machine (VM) migration. Performance of clustered services such as Average Response Time is reduced through intelligent VM migration decision. Migration depends on a variety of criteria like resource usage (CPU usage, RAM usage, Network Usage, etc.) and demand of machines (Physical (PM) and Virtual (VM)). This is a multi-criteria migration problem that evaluates, compares and sorts a set of PMs and VMs on the basis of parameters affected on migration process. But, which parameter(s) has dominant role over cluster performance in each time window? How can we determine weight of parameters over oncoming time slots? Current migration algorithms do not consider time-dependent variable weights of parameters. These studies assume fixed weight for each parameter over a wide range of time intervals. This approach leads to imprecise prediction of recourse demand of each server. Our paper presents a new Intelligent and Adaptive Multi Parameter migration-based resource manager (IAMP) for virtualized data centres and clusters with a novel Artificial Neural Network (ANN)-based weighting analysis named Error Number of Parameter Omission (ENPO). In each time slot, weight of parameters is recalculated and non-important ones will be attenuated in ranking process. We characterized the parameters affecting cluster performance and used hot migration with emphasis on cluster of servers in XEN virtualization platform. The experimental results based on workloads composed of real applications, indicate that IAMP management framework is feasible to improve the performance of the virtualized cluster system up to 23 % compared to current algorithms. Moreover, it reacts more quickly and eliminates hot spots because of its full dynamic monitoring algorithm

Kernel-based Web switches providing content-aware routing

Locally distributed Web server systems represent a cost-effective solution to the performance problems due to high traffic volumes reaching popular Web sites. In this paper, we focus on architectures based on layer-7 Web switches because they allow a much richer set of possibilities for the Web site architecture, at the price of a scalability much lower than that provided by a layer-4 switch. In this paper, we compare the performance of three solutions for layer-7 Web switch: a two-way application-layer architecture, a two-way kernel-based architecture, and a one-way kernel-based architecture. We show quantitatively how much better the one-way architecture performs with respect to a two-way scheme, even if implemented at the kernel level. We conclude that an accurate implementation of a layer-7 Web switch may become a viable solution to the performance requirements of the majority of cluster-based information systems

An adaptive admission control and load balancing algorithm for a QoS-aware Web system

The main objective of this thesis focuses on the design of an adaptive algorithm for admission control and content-aware load balancing for Web traffic. In order to set the context of this work, several reviews are included to introduce the reader in the background concepts of Web load balancing, admission control and the Internet traffic characteristics that may affect the good performance of a Web site. The admission control and load balancing algorithm described in this thesis manages the distribution of traffic to a Web cluster based on QoS requirements. The goal of the proposed scheduling algorithm is to avoid situations in which the system provides a lower performance than desired due to servers' congestion. This is achieved through the implementation of forecasting calculations. Obviously, the increase of the computational cost of the algorithm results in some overhead. This is the reason for designing an adaptive time slot scheduling that sets the execution times of the algorithm depending on the burstiness that is arriving to the system. Therefore, the predictive scheduling algorithm proposed includes an adaptive overhead control. Once defined the scheduling of the algorithm, we design the admission control module based on throughput predictions. The results obtained by several throughput predictors are compared and one of them is selected to be included in our algorithm. The utilisation level that the Web servers will have in the near future is also forecasted and reserved for each service depending on the Service Level Agreement (SLA). Our load balancing strategy is based on a classical policy. Hence, a comparison of several classical load balancing policies is also included in order to know which of them better fits our algorithm. A simulation model has been designed to obtain the results presented in this thesis

Latency-driven replication for globally distributed systems

Steen, M.R. van [Promotor]Pierre, G.E.O. [Copromotor