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 highlevel 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.

Automated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.

Cloud elasticity augments applications to dynamically adapt to changes in demand by acquiring or releasing computational resources on the fly. In the past, we developed a framework for cloud elasticity utilizing multiple feedback controllers simultaneously. Each controller determines the scaling action with different intensity, whereby the selection of a suitable controller is realized with a fuzzy inference system. In this paper, we aim to identify the similarities between cloud elasticity and action selection mechanism in animal's brain. We treat each controller in our previous framework as an action and propose a novel bio-inspired, soft switching approach. This approach integrates a basal ganglia computational model as an action selection mechanism. Initial experimental results demonstrate that the basal ganglia based approach has higher potential to improve the overall system performance and stability.

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As cloud adoption increases, so do the number of available cloud service providers.Moving complex applications between clouds can be beneficial-or other times necessary-but achieving this so-called cloud portability is rarely straightforward. This article presents the adoption of OASIS TOSCA, a standard in the declarative description of cloud applications, to encourage and facilitate cloud portability in MiCADO, an application-level multi-cloud orchestration and auto-scaling framework. The interface to MiCADO is an Application Description Template, which draws from the TOSCA specification to describe an application in MiCADO. The generic design of these templates is presented and their applicability for achieving portability between different container and cloud environments is analysed and evaluated. A proof-of-concept where MiCADO serves as the deployment and execution engine for a Science Gateway in Sleep Healthcare is then described. In this proof-of-concept, MiCADO facilitates the deployment of a complex healthcare application, which is then moved from one cloud service provider to another with only minimal changes to the template which originally described it. This TOSCA-based approach to templates in MiCADO encourages movement between clouds by making cloud portability more approachable.

Elasticity enables cloud customers to enrich their applications to dynamically adjust the underlying cloud resources as per their needs, in order to minimize the cost of infrastructure as well as to satisfy their performance goals. Over the past few years, a plethora of techniques have been introduced in order to implement elasticity. Control theory is one such technique that offers a systematic method to design feedback controllers to implement elasticity. A number of proposals based on feedback controller concepts have been introduced in the recent past in order to guarantee the QoS needs of a system deployed over a cloud. Many of these are based on the use of a single controller approach of various types, such as adaptive and fixed. However, for systems that operate in timevarying and unpredictable operating conditions, it becomes difficult for such approaches to perform effectively at all times, in order to comply with the system stated performance goals. The systems deployed over cloud are subject to unpredictable workload conditions that vary from time to time, e.g. an e-commerce website may face higher workloads than normal during festival or promotional schemes. This paper exploits the novel use of a recently developed multi-controller based approach, where each controller is specifically designed for one operating region. Moreover, the use of fuzzy logic is exploited to enable qualitative specification for the selection of the most suitable controller in runtime, based on system current behaviour. Initial experimental evaluation in comparison with the conventional single-controller approach demonstrates that our proposed method enhances the capability of an elastic application to comply with system performance goals.

As the revolutionary change in electric power industry begins with the latest communication infrastructure, it is on the verge of a revolutionary transformation to develop a smart grid to meet the requirements of our digital society. Wide Area Power System is made up of plentiful automated transmission and distribution systems with strong communication infrastructure, all operating in a coordinated, proficient and reliable mode. This paper is fretful with the wide area power system load protection scheme and ensuing design requirement that enhances stability as well as control. It discusses the architecture that upgrades the existing scheme by controlling all the control signals traffic between generating units, server, connected loads, and protection devices using WIMAX. The main theme of the paper is on the use of information technology to obtain more flexibility and smartness in the Wide Area Power System Load Protection by designing the Communication channel using WIMAX. Faults detected in Local area networks and Information regarding the faults of Local Areas is communicated to Load Area Manager (LAM) which takes required control action to handle it. Finally the paper shows islanding operation through WAM for the areas that becomes intensive faulty. Results have been verified in MATLAB/ SIMULIMK.

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