1,013 research outputs found
Towards a novel biologically-inspired cloud elasticity framework
With the widespread use of the Internet, the popularity of web applications has
significantly increased. Such applications are subject to unpredictable workload
conditions that vary from time to time. For example, an e-commerce website may
face higher workloads than normal during festivals or promotional schemes. Such
applications are critical and performance related issues, or service disruption can
result in financial losses. Cloud computing with its attractive feature of dynamic
resource provisioning (elasticity) is a perfect match to host such applications.
The rapid growth in the usage of cloud computing model, as well as the rise in
complexity of the web applications poses new challenges regarding the effective
monitoring and management of the underlying cloud computational resources.
This thesis investigates the state-of-the-art elastic methods including the models
and techniques for the dynamic management and provisioning of cloud resources
from a service provider perspective.
An elastic controller is responsible to determine the optimal number of cloud resources,
required at a particular time to achieve the desired performance demands.
Researchers and practitioners have proposed many elastic controllers using versatile
techniques ranging from simple if-then-else based rules to sophisticated
optimisation, control theory and machine learning based methods. However,
despite an extensive range of existing elasticity research, the aim of implementing
an efficient scaling technique that satisfies the actual demands is still a challenge
to achieve. There exist many issues that have not received much attention from
a holistic point of view. Some of these issues include: 1) the lack of adaptability
and static scaling behaviour whilst considering completely fixed approaches; 2)
the burden of additional computational overhead, the inability to cope with the
sudden changes in the workload behaviour and the preference of adaptability
over reliability at runtime whilst considering the fully dynamic approaches; and 3)
the lack of considering uncertainty aspects while designing auto-scaling solutions.
This thesis seeks solutions to address these issues altogether using an integrated
approach. Moreover, this thesis aims at the provision of qualitative elasticity rules.
This thesis proposes a novel biologically-inspired switched feedback control
methodology to address the horizontal elasticity problem. The switched methodology
utilises multiple controllers simultaneously, whereas the selection of a
suitable controller is realised using an intelligent switching mechanism. Each
controller itself depicts a different elasticity policy that can be designed using the
principles of fixed gain feedback controller approach. The switching mechanism
is implemented using a fuzzy system that determines a suitable controller/-
policy at runtime based on the current behaviour of the system. Furthermore,
to improve the possibility of bumpless transitions and to avoid the oscillatory
behaviour, which is a problem commonly associated with switching based control
methodologies, this thesis proposes an alternative soft switching approach. This
soft switching approach incorporates a biologically-inspired Basal Ganglia based
computational model of action selection.
In addition, this thesis formulates the problem of designing the membership functions
of the switching mechanism as a multi-objective optimisation problem. The
key purpose behind this formulation is to obtain the near optimal (or to fine tune)
parameter settings for the membership functions of the fuzzy control system in
the absence of domain experts’ knowledge. This problem is addressed by using
two different techniques including the commonly used Genetic Algorithm and
an alternative less known economic approach called the Taguchi method. Lastly,
we identify seven different kinds of real workload patterns, each of which reflects
a different set of applications. Six real and one synthetic HTTP traces, one for
each pattern, are further identified and utilised to evaluate the performance of
the proposed methods against the state-of-the-art approaches
Towards a novel biologically-inspired cloud elasticity framework
With the widespread use of the Internet, the popularity of web applications has
significantly increased. Such applications are subject to unpredictable workload
conditions that vary from time to time. For example, an e-commerce website may
face higher workloads than normal during festivals or promotional schemes. Such
applications are critical and performance related issues, or service disruption can
result in financial losses. Cloud computing with its attractive feature of dynamic
resource provisioning (elasticity) is a perfect match to host such applications.
The rapid growth in the usage of cloud computing model, as well as the rise in
complexity of the web applications poses new challenges regarding the effective
monitoring and management of the underlying cloud computational resources.
This thesis investigates the state-of-the-art elastic methods including the models
and techniques for the dynamic management and provisioning of cloud resources
from a service provider perspective.
An elastic controller is responsible to determine the optimal number of cloud resources,
required at a particular time to achieve the desired performance demands.
Researchers and practitioners have proposed many elastic controllers using versatile
techniques ranging from simple if-then-else based rules to sophisticated
optimisation, control theory and machine learning based methods. However,
despite an extensive range of existing elasticity research, the aim of implementing
an efficient scaling technique that satisfies the actual demands is still a challenge
to achieve. There exist many issues that have not received much attention from
a holistic point of view. Some of these issues include: 1) the lack of adaptability
and static scaling behaviour whilst considering completely fixed approaches; 2)
the burden of additional computational overhead, the inability to cope with the
sudden changes in the workload behaviour and the preference of adaptability
over reliability at runtime whilst considering the fully dynamic approaches; and 3)
the lack of considering uncertainty aspects while designing auto-scaling solutions.
This thesis seeks solutions to address these issues altogether using an integrated
approach. Moreover, this thesis aims at the provision of qualitative elasticity rules.
This thesis proposes a novel biologically-inspired switched feedback control
methodology to address the horizontal elasticity problem. The switched methodology
utilises multiple controllers simultaneously, whereas the selection of a
suitable controller is realised using an intelligent switching mechanism. Each
controller itself depicts a different elasticity policy that can be designed using the
principles of fixed gain feedback controller approach. The switching mechanism
is implemented using a fuzzy system that determines a suitable controller/-
policy at runtime based on the current behaviour of the system. Furthermore,
to improve the possibility of bumpless transitions and to avoid the oscillatory
behaviour, which is a problem commonly associated with switching based control
methodologies, this thesis proposes an alternative soft switching approach. This
soft switching approach incorporates a biologically-inspired Basal Ganglia based
computational model of action selection.
In addition, this thesis formulates the problem of designing the membership functions
of the switching mechanism as a multi-objective optimisation problem. The
key purpose behind this formulation is to obtain the near optimal (or to fine tune)
parameter settings for the membership functions of the fuzzy control system in
the absence of domain experts’ knowledge. This problem is addressed by using
two different techniques including the commonly used Genetic Algorithm and
an alternative less known economic approach called the Taguchi method. Lastly,
we identify seven different kinds of real workload patterns, each of which reflects
a different set of applications. Six real and one synthetic HTTP traces, one for
each pattern, are further identified and utilised to evaluate the performance of
the proposed methods against the state-of-the-art approaches
Investigations into Elasticity in Cloud Computing
The pay-as-you-go model supported by existing cloud infrastructure providers
is appealing to most application service providers to deliver their
applications in the cloud. Within this context, elasticity of applications has
become one of the most important features in cloud computing. This elasticity
enables real-time acquisition/release of compute resources to meet application
performance demands. In this thesis we investigate the problem of delivering
cost-effective elasticity services for cloud applications.
Traditionally, the application level elasticity addresses the question of how
to scale applications up and down to meet their performance requirements, but
does not adequately address issues relating to minimising the costs of using
the service. With this current limitation in mind, we propose a scaling
approach that makes use of cost-aware criteria to detect the bottlenecks within
multi-tier cloud applications, and scale these applications only at bottleneck
tiers to reduce the costs incurred by consuming cloud infrastructure resources.
Our approach is generic for a wide class of multi-tier applications, and we
demonstrate its effectiveness by studying the behaviour of an example
electronic commerce site application.
Furthermore, we consider the characteristics of the algorithm for
implementing the business logic of cloud applications, and investigate the
elasticity at the algorithm level: when dealing with large-scale data under
resource and time constraints, the algorithm's output should be elastic with
respect to the resource consumed. We propose a novel framework to guide the
development of elastic algorithms that adapt to the available budget while
guaranteeing the quality of output result, e.g. prediction accuracy for
classification tasks, improves monotonically with the used budget.Comment: 211 pages, 27 tables, 75 figure
A Study of Very Short Intermittent DDoS Attacks on the Performance of Web Services in Clouds
Distributed Denial-of-Service (DDoS) attacks for web applications such as e-commerce are increasing in size, scale, and frequency. The emerging elastic cloud computing cannot defend against ever-evolving new types of DDoS attacks, since they exploit various newly discovered network or system vulnerabilities even in the cloud platform, bypassing not only the state-of-the-art defense mechanisms but also the elasticity mechanisms of cloud computing.
In this dissertation, we focus on a new type of low-volume DDoS attack, Very Short Intermittent DDoS Attacks, which can hurt the performance of web applications deployed in the cloud via transiently saturating the critical bottleneck resource of the target systems by means of external attack HTTP requests outside the cloud or internal resource contention inside the cloud. We have explored external attacks by modeling the n-tier web applications with queuing network theory and implementing the attacking framework based-on feedback control theory. We have explored internal attacks by investigating and exploiting resource contention and performance interference to locate a target VM (virtual machine) and degrade its performance
A survey and taxonomy of self-aware and self-adaptive cloud autoscaling systems
Autoscaling system can reconfigure cloud-based services and applications, through various configurations of cloud sofware and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior offers the foundation for achieving elasticity in modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud autoscaling system has been engineered as one of the most complex, sophisticated and intelligent artifacts created by human, aiming to achieve self-aware, self-adaptive and dependable runtime scaling. Yet, existing Self-aware and Self-adaptive Cloud Autoscaling System (SSCAS) is not mature to a state that it can be reliably exploited in the cloud. In this article, we survey the state-of-the-art research studies on SSCAS and provide a comprehensive taxonomy for this feld. We present detailed analysis of the results and provide insights on open challenges, as well as the promising directions that are worth investigated in the future work of this area of research. Our survey and taxonomy contribute to the fundamentals of engineering more intelligent autoscaling systems in the cloud
A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques
Cloud computing as a fairly new commercial paradigm, widely investigated by
different researchers, already has a great range of challenges. Pricing is a
major problem in Cloud computing marketplace; as providers are competing to
attract more customers without knowing the pricing policies of each other. To
overcome this lack of knowledge, we model their competition by an
incomplete-information game. Considering the issue, this work proposes a
pricing policy related to the regret minimization algorithm and applies it to
the considered incomplete-information game. Based on the competition based
marketplace of the Cloud, providers update the distribution of their strategies
using the experienced regret. The idea of iteratively applying the algorithm
for updating probabilities of strategies causes the regret get minimized
faster. The experimental results show much more increase in profits of the
providers in comparison with other pricing policies. Besides, the efficiency of
a variety of regret minimization techniques in a simulated marketplace of Cloud
are discussed which have not been observed in the studied literature. Moreover,
return on investment of providers in considered organizations is studied and
promising results appeared
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
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