467 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
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
A control theoretical view of cloud elasticity: taxonomy, survey and challenges
The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational cost of the system. However, the efficient management of hired computational resources is a challenging task. Over the last decade, researchers and practitioners made use of various techniques to propose new methods to address cloud elasticity. Amongst many such techniques, control theory emerges as one of the popular methods to implement elasticity. A plethora of research has been undertaken on cloud elasticity including several review papers that summarise various aspects of elasticity. However, the scope of the existing review articles is broad and focused mostly on the high-level view of the overall research works rather than on the specific details of a particular implementation technique. While considering the importance, suitability and abundance of control theoretical approaches, this paper is a step forward towards a stand-alone review of control theoretic aspects of cloud elasticity. This paper provides a detailed taxonomy comprising of relevant attributes defining the following two perspectives, i.e., control-theory as an implementation technique as well as cloud elasticity as a target application domain. We carry out an exhaustive review of the literature by classifying the existing elasticity solutions using the attributes of control theoretic perspective. The summarized results are further presented by clustering them with respect to the type of control solutions, thus helping in comparison of the related control solutions. In last, a discussion summarizing the pros and cons of each type of control solutions are presented. This discussion is followed by the detail description of various open research challenges in the field
Design and evaluation of a biologically-inspired cloud elasticity framework
The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions 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. The existing methods suffer from issues like: (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. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches
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