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
