Abstract: Provisioning of adequate resources to cloud workloads depends on the Quality of Service (QoS) requirements of these cloud workloads. Based on workload requirements (QoS) of cloud users, discovery and allocation of best workload-resource pair is an optimization problem. Acceptable QoS can be offered only if provisioning of resources is appropriately controlled. So, there is a need for a QoS-based resource provisioning framework for the autonomic scheduling of resources to observe the behavior of the services and adjust it dynamically in order to satisfy the QoS requirements. In this paper, framework for self-management of cloud resources for execution of clustered workloads named as SCOOTER is proposed that efficiently schedules the provisioned cloud resources and maintains the Service Level Agreement (SLA) by considering properties of self-management and the maximum possible QoS parameters are required to improve cloud based services. Finally, the performance of SCOOTER has been evaluated in a cloud environment that demonstrates the optimized QoS parameters such as execution cost, energy consumption, execution time, SLA violation rate, fault detection rate, intrusion detection rate, resource utilization, resource contention, throughput and waiting time.

Abstract: In today’s Internet, killer network services and applications, such as video and audio streaming, network storage, and online video games, are pushing the network infrastructure resources to the edge. By design and for the most part, the Internet is the best offer delivery ecosystem with little or no end-to-end quality-of-service (QoS) guarantees. Even, frameworks, such as IntServ and DiffServ that were designed and implemented to provide QoS guarantees, still fail to solve this problem at a wide scale. Software-defined networking (SDN) is a fast emerging networking paradigm that promises to provide end-to-end QoS guaranteeing by offering greater network flexibility, abstraction, control, and programmability to network resources. In this paper, we review, survey, and discuss the current state of the art on QoS provisioning in the area of SDN, with respect to applying the concept of autonomic computing (AC) to automatically support, provision, monitor, and maintain QoS requirements. This paper includes in-depth classification, taxonomy, and comparative analysis for the autonomic-based QoS provisioning in accordance with the famous influential and widely adopted the monitor-analyze-plan-execute-knowledge (MAPE-K) IBM architectural model for autonomic computing.

Abstract: In the autonomic computing context, the system is perceived as a set of autonomous elements capable of self-management, where end-users define high-level goals and the system shall adapt to achieve the desired behaviour. Runtime adaptation creates several optimization opportunities, especially if we consider approximate computing applications, where it is possible to trade off the accuracy of the result and the performance. Given that modern systems are limited by the power dissipated, autonomic computing is an appealing approach to increase the computation efficiency. In this paper, we introduce mARGOt, a dynamic autotuning framework to enhance the target application with an adaptation layer to provide self-optimization capabilities. The framework is implemented as a C++ library that works at function-level and provides to the application a mechanism to adapt in a reactive and a proactive way. Moreover, the application is capable to change dynamically its requirements and to learn online the underlying application-knowledge. We evaluated the proposed framework in three real-life scenarios, ranging from embedded to HPC applications. In the three use cases, experimental results demonstrate how, thanks to mARGOt, it is possible to increase the computation efficiency by adapting the application at runtime with a limited overhead.

Abstract: Integrating wireless body area networks (WBANs) with cloudlet introduces an edge-of-things computing environment for pervasive applications. The variation in the number of active WBANs nodes and its data transmission rate requires optimal computing resources to avoid performance degradation and data loss. We argue the research gap in terms of optimal resource provisioning that predicts and automatically adjusts the computing resources on the basis of sensory data volume and application’s type. In this paper, we propose a hybrid autonomic resource provisioning framework, which is the combination of autonomic computing, fuzzy logic control and linear regression model. The proposed framework is built over CloudSim toolkit with autonomic resource provisioning framework inspired by the cloud layer model. The effectiveness of the proposed approach is evaluated under a real workload trace. The experimental results show that the proposed approach minimizes the cost by at least 27% and SLA violations by at least 78% as compared to other approaches.

Abstract: The Internet of Things ecosystem involves configuration, control and networking of devices using the Internet Protocol. The pervasive nature of the Internet allows the deployment of a large number of these devices across multiple technological domains. However, due to a large number of such devices involved in massive deployment, manual setup, management and maintenance are infeasible. In order to overcome issues arising from manual management, intelligent and automatic procedures need to be established to manage connected devices at a large scale. Autonomic computing is one such technique that can minimize user intervention in the management of the IoT ecosystem. Autonomic computing has been proven effective for minimizing user intervention in the management of computer systems. In this paper, we describe why autonomic computing is needed and how it can be used in the context of IoT. We specifically discuss various enabling technologies that can help in attaining autonomy in IoT. In addition, we highlight the current challenges and some possible directions for future research.

Abstract: The autonomic computing layer of the smart e-Health home based on a cognitive dynamic system (CDS) can be a solution for improving health situation understanding, reducing the healthcare system costs, and improving people's quality of life. It can also be a solution for reducing the large number of sudden deaths outside of a hospital due to fatal diseases such as Arrhythmia. Towards this objective, we start from understanding the health situation, by diagnosing healthy and unhealthy persons. For this, we developed a decision-making system that is inspired by the medical doctors (MDs) decision-making processes. Our system is based on a CDS for cognitive decision-making and it can create a decision-making tree automatically. The simple, low complexity algorithmic design of the proposed system makes it suitable for real-time applications. A proof-of-concept case study of the implementation of the CDS was done on Arrhythmia disease. An accuracy of 95.4% was achieved using the proposed algorithms. Also, these algorithms can make a decision in less than 80 ms, and for one User, this includes the time for training. The proposed platform can be extended for more healthcare applications such as screening, disease class diagnosis, prevention, treatment, or monitoring healing. As a result, the proposed CDS algorithms can be an example of the first step for designing the autonomic computing layer of a smart e-Health home platform.

Abstract: A fog/cloud computing environment enables portions of a transaction to be executed at a fog server and other portions at the cloud. Fog servers act as an intermediate layer between cloud datacenters and end-user devices and provide compute, storage, and networking services between these devices and traditional clouds. An important consideration is the dynamic determination of the optimal fraction f of data processing executed at the cloud versus at fog servers. This determination requires that we consider that the processing capacity of fog servers is typically smaller than that of cloud servers. On the other hand, it may be more expensive to use cloud resources as opposed to fog servers. As f increases, more data has to be sent and received from the cloud. On the other hand, fog servers are typically resource-constrained and may not have enough capacity to handle requests from numerous sensors and other IoT devices and may become a bottleneck. The contributions of this paper are: (1) An autonomic controller, called FogQN-AC, that dynamically changes the fraction of data processing performed at the cloud. The controller seeks to optimize a utility function of the average response time and cost. This utility function uses an analytic response time and cost model previously developed by the authors. (2) An assessment of the controller against a brute-force optimal solution. (3) An experimental assessment of the controller using synthetic traces, Google traces, and a CityPulse smart city road traffic dataset. The experiments show that the controller is able to maintain a high utility in the presence of wide variations of request arrival rates.

Abstract: With the increase in the number of smartphones, the use of mobile applications is growing dramatically in today's high-tech environment. With this high user demand, the quality of mobile applicatio...

Abstract: Mobile computing has emerged as a pervasive technology that empowers its users with portable computation and context-aware communication. Smart systems and infrastructures can exploit portable and context-aware computing technologies to provide any time, any place digitized services on the go. Despite the offered benefits, such as portability, context-sensitivity, and high connectivity, mobile computing also faces some critical challenges. These challenges include resource poverty as well as data security and privacy that need to be addressed to increase the pervasiveness of mobile systems. We propose to provide a self-protection mechanism for mobile devices against the unforeseen security threats that can attack the critical resources of mobile devices. We have unified the concepts of autonomic computing and computer security to develop a framework that enables adaptive security to dynamically configure the security measures of a mobile device. We have developed a framework - an android-based prototype - that supports automation and user decision to protect the critical hardware and software resources of a device. Evaluation results demonstrate (i) framework’s accuracy for runtime detection and minimization of threats, and (ii) framework’s efficiency for device’s resource utilization.

Abstract: The workload in large-scale data repositories involves concurrent users and contains homogenous and heterogeneous data. The large volume of data, dynamic behavior and versatility of large-scale data repositories is not easy to be managed by humans. This requires computational power for managing the load of current servers. Autonomic technology can support predicting the workload type; decision support system or online transaction processing can help servers to autonomously adapt to the workloads. The intelligent system could be designed by knowing the type of workload in advance and predict the performance of workload that could autonomically adapt the changing behavior of workload. Workload management involves effectively monitoring and controlling the workflow of queries in large-scale data repositories. This work presents a taxonomy through systematic analysis of workload management in large-scale data repositories with respect to autonomic computing (AC) including database management systems and data warehouses. The state-of-the-art practices in large-scale data repositories are reviewed with respect to AC for characterization, performance prediction and adaptation of workload. Current issues are highlighted at the end with future directions.

Abstract: In complex enterprises, various events such as failure of a server, malfunction of an application etc. may happen. In many cases, these events are caused by a change or an incident in a resource which belongs to another layer of information technology architecture. This makes the detection of complex events very difficult. In this paper, a mechanism is proposed based on pheromone deposition pattern for detecting the main source of failures and deficiencies in any position in the information technology architectural layers and recognising appropriate alternative solutions. It is expected that this mechanism facilitates the coordination between the resources which are not digitally represented. For sake of evaluation, two case studies are considered which investigate the inter-layer adaptation scenarios. The results show that the adaptation process can be done in less time and with more scalability with utilisation of the proposed mechanism in comparison with classic approaches.

Abstract: As systems evolve into interconnected heterogeneous components, their security threats increase in number and complexity, and static security measures are not capable of confronting all of them. A strategy to address this issue is the use of autonomic software, which adapts the security mechanisms at runtime according to the environmental changes that impact on the required security level. An approach to achieve autonomic computing is by using the MAPE-K reference model developed by IBM, which consists of a feedback loop composed of the functions: Monitor, Analyze, Plan, and Execute. In this manuscript, adaptive security models based on MAPE-K are surveyed, their characteristics are described, and a comparison of their domains, structures, and adaptive objectives is presented.

Abstract: This book chapter reviews the history of Healthcare Information Systems (HISs), discusses recent cyber security threats affecting HISs, and then introduces the autonomic computing concept and applies the concept to design self-protecting HISs (SPHISs) that can defend themselves against cyber intrusions with little or no human intervention. To realize such SPHISs, we first study security vulnerabilities of the HIS network, communication links and protocols. Based on these vulnerabilities, the component design of a SPHIS is presented. We propose that a SPHIS should contain monitoring systems, early estimation modules, intrusion detection, network forensics analysis devices and intrusion response systems. Finally, existing self-protecting approaches for HIS, enterprise systems and industrial control systems are demonstrated in detail. This chapter provides an innovated design of an autonomic security management system that could reduce IT professional's management burdens while enhancing the security posture of their HISs.

Abstract: Every software in the universe requires maintenance and management during its life cycle. The manual management of software is costly and sometimes error-prone. The other solution is autonomic computing that induces self-management capabilities, “self-*services”, in software systems with the help of autonomic managers. The design quality of a self-management capability affects the computing infrastructure regarding processing load, the memory requirement, data channel demand and performance of perturbation restore. It is critical to assess the design quality of a self-management capability to determine its effect over the computing infrastructure when it gets invoke against some anomaly or perturbation. Moreover, there are two possible host environments for an autonomic manager to offer a self-management capability as a self-* service: the local environment and the cloud environment. A criterion is needed to decide which environment is more suitable and cost-effective to run the service. However, the literature lacks in the assessment of the design quality metrics on self-management capabilities and the suitability and cost-effectiveness of the execution environment. In this work, we have proposed a suite of design quality metrics to determine the design quality of self-management capabilities. We validate the proposed metrics with a stock trade & forecasting system that was designed as an autonomic computing system with self-management capabilities. The proposed metrics were applied to define functions that identify the suitable and cost-effective execution environment for the self-* service. The results proved that these metrics are useful in determining the design quality, suitability, and cost-effectiveness of a self-* capability for an autonomic computing system. The proposed metrics can be used to compare differently designed autonomic solutions for complexity, efficiency, performance, understandability, and maintainability.

Abstract: The IEC 61499 standard has been proposed for development of next-generation industrial automation systems to support portability, interoperability, and configurability. Compared with the traditional IEC 61131-3 standard, it provides an open reference architecture with some key features: object-oriented modeling by using function blocks as basic elements, and event-driven execution by using data/events as input/output. Recently IEC 61499 has been integrated with its enabling technologies to realize distributed intelligent automation for industrial cyber-physical systems. In this paper, two perspectives are provided through analysis of recent research: a) design paradigms for IEC 61499, including object-oriented design, component-based design, and service-oriented architecture; and b) computing paradigms for IEC 61499, including distributed intelligence, autonomic computing, and cloud computing. Future research trends are also outlined as a conclusion.

Abstract: Advancement in software technologies has helped IT developers to work efficiently and enhance quality of the products but simultaneously; it has also presented the problem of increasing complexity in terms of software architectures, programming code and software management task. These issues raise chances of vulnerabilities in the software system. Run-time solution to the faults and failures of software need to be done. One such solution for handling run-time management tasks is Autonomic Computing. Autonomic computing technique, automate the management task to prevent the occurrence of vulnerability based issues. Further, to reduce the occurrence of system failures, an idea to provide an autonomic advisor to the Software Development Life Cycle process has been proposed in this paper. The autonomic advisor gives autonomic features based required suggestions to the developers during the software development process. It will also help to perform risk analysis during development. This will lead to the development of quality and efficient software systems. This paper also provides a review of the existing work of the autonomic computing including its challenges and their effects on management process of the system.

Abstract: With the expansion of autonomous robotics and the variety of applications found nowadays (eg medical, competition, military), the biggest hurdle on the development of mobile robots lies in endowing them with the capacity of interacting with the environment and making correct decisions so that their tasks can be executed successfully Based on the wellknown IBM Autonomic Computing reference architecture (known as MAPE-K), this work defines a refined architecture following the Robotics perspective

Abstract: In cloud computing, resources could be provisioned in a dynamic way on demand for cloud services. Cloud providers seek to realize effective SLA execution mechanisms for avoiding SLA violations by provisioning the resources or applications and timely interacting to environmental changes and failures. Sufficient resource provisioning to cloud’s services relies on the requirements of the workloads to achieve a high performance for quality of service. Therefore, deciding the suitable amount of cloud’s resources for these services to achieve is one of the main works in cloud computing. During the runtime of services, the amount of cloud’s resources can be specified and provisioned based on the actual workloads changes. Determining the correct amount of cloud’s resources needed for running the services on clouds is not easy task, and it depends on the existing workloads of services. Consequently, it is required to predict the future workloads for dynamic provisioning of resources in order to meet the changes in workloads and demands of services in cloud computing environments. In this paper, we study the possibility of using a cognitive/intelligent approach for cloud resource provisioning which is a combination of the autonomic computing concept, deep learning technique and fuzzy logic control. Deep learning technique is a state-of-the-art in the machine learning field. It achieved promising results in many other fields like image classification and speech recognition. For these reasons, deep learning is proposed in this work to tackle the workload prediction in cloud computing. Additionally, we also propose to use a fuzzy logic-based method in order to make a decision in the case of uncertainty of the workload prediction. We study various exiting works on autonomic cloud resource provisioning and show that there is still an opportunity to improve the current methods. We also present the challenges that may exist on this domain.

Abstract: In this paper we introduce an adaptation engine supporting self-adaptation of running BPMN process instances. This adaptation engine implements the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) approach from autonomic computing for self-adaptation. The MAPE control loop aims at identifying the adaptation need and defining and executing the operations required to deal with these needs while the K is the knowledge needed for the MAPE control loop. More precisely, the paper presents the architecture of the adaptation engine: it details how autonomic managers responsible for self-adaptation of process instances implement the MAPE control loop.

Abstract: Autonomic Computing based development is emerging in the IT sector which performs software management intelligently. As a result, the software development team are now using their efficiency more o...

Abstract: Cloud Computing is, by nature, multi-tenant, complex, large-scale, and heterogeneous distributed systems. Thus respectively, its processes and strategies need to be automated and integrated. one of the essential processes in the Cloud computing system is negotiating the service level agreement which always has to be elastic and flexible in handling and translating the user services’ requirements. This paper aims at presenting an automated SLA negotiation framework based on autonomous and flexible agents and multi agent systems based on the autonomic computing features as suitable tools for self-detection of failures and self-monitoring for the Cloud operations and services.

Abstract: Profile measuring is a key data acquisition process in the rail manufacturing industry. In rail rolling mills, profile measurement systems inspect the shape of the rail profiles to assess their dimensional quality. This assessment can be used in order to provide feedback for shape control devices in upstream manufacturing, and also to check whether the products are compliant with rail standards and client requirements. This paper deals with designing autonomic computing capabilities, specifically self-awareness, to a rail profile measurement system based on laser range finding, and then evaluating their suitability for the following tasks: Automatically detect changes in both the working environment and the operating conditions, and warn process computers and operators of the rail rolling mill when working conditions indicate that the accuracy of the inspection system has fallen below a given threshold.

Abstract: With the expansion of autonomous robotics and its applications (eg medical, competition, military), the biggest hurdle in developing mobile robots lies in endowing them with the ability to interact with the environment and to make correct decisions so that their tasks can be executed successfully However, as the complexity of robotic systems grows, the need to organize and modularize software for their correct functioning also becomes a challenge, making the development of software for controlling robots a complex and intricate task In the robotics domain, there is a lack of reference software architectures and, although most robot architectures available in the literature facilitate the creation process with their modularity, existing solutions do not provide development guidance on reusing existing modules Based on the well- known IBM Autonomic Computing reference architecture (known as MAPE-K), this work defines a refined architecture following the Robotics perspective To explore the capabilities of the proposed refinement, we implemented the RoCS (Robotics and Cognitive Systems) framework for autonomous robots We successfully tested the framework under simulated robotics scenarios that mimic typical robotics tasks and evidence the framework reuse capability Finally, we understand the proposed framework needs further experimental evaluation, particularly, assessments on real-world scenarios

Abstract: Continuous experimentation enables companies to reduce development risks and operational costs by continuously and directly assessing user response with respect to software updates. The increasing need for data-driven rapid decisions to face unpredictable context situations demands the automation of continuous experimentation practices. Furthermore, variable conditions and constraints associated with the experimentation process, such as changes in the experimentation goals and the cost of conducting experimental trials, demand from experiments to be adaptive. This paper presents our proposal towards what we call quality-driven adaptive continuous experimentation. Our contributions are as follows. First, we present a metamodel for experimental design to enable automatic planning and execution of experiments at run-time. Second, we propose a mesh of run-time models to allow autonomic managers conduct experiments while assisting in the continuous evolution of the subject system. Finally, we propose an architecture for quality-driven adaptive experimentation. Our architecture addresses separation of concerns in the experimentation process by dedicating three feedback loops to (1) control the satisfaction of high-level experimentation goals through experimental design; (2) conduct experimental trials for infrastructure configuration variants; and (3) conduct experimental trials for architectural design variants.

Abstract: Device Management (DM) is currently industrially deployed for LAN devices, phones and workstation management. Internet of Things (IoT) devices are massive, dynamic, heterogeneous, and inter-operable. Existing solutions are not suitable for IoT management. This doctoral research in an industrial environment addresses these limitations with a novel autonomic and distributed approach for the DM.

Abstract: In recent years, energy optimization of cloud data centers got important consideration since data centers in activity frequently expend huge energy rates. In fact, the expanding processing capacity of data centers and its complexity, increases impressively their energy consumption which prompts to important losses for companies. With the end goal to reduce the energy consumption of the system, researchers and cloud specialist have proposed techniques which experience the design of locations for data centers, and others adopted methods for the optimized management of resources in data centers. While other researchers, adopted the autonomic computing paradigm for having automated and adaptive cloud data center management. Most existing energy efficiency techniques focus on how to properly solve the tradeoff between performance and energy consumption in cloud computing infrastructure. In this paper, we focus on the energy consumption of the data centers and how this can be reduced in order to make the cloud computing greener. Likewise, we highlight the importance of integrating the autonomy aspect to the solution so as to avail the most optimum level of resources management with minimized server energy consumption.