Abstract: Cloud computing enables clients to acquire cloud resources dynamically and on demand for their cloud applications and services. For cloud providers, especially, Software as a Service (SaaS) providers, the prediction of future cloud resource requirements, such as CPU usage for their cloud applications, to implement client requests is a complex task because it depends on incoming workloads. Due to workload fluctuations, it is difficult for SaaS cloud providers to predict or forecast future demand for resource usage in the next time interval and, accordingly, to allocate the required resources. Furthermore, cloud computing systems consist of many virtual machines (VMs), which increases the complexity of the prediction problem due to the correlations that exist between the large workload data in these VMs. Therefore, accurate resource usage forecasting remains a challenge, and relatively few studies have explored the prediction of CPU usage for VMs in cloud data centers. This paper proposes an autonomic and intelligent workload forecasting method for cloud resource provisioning based on the concept of autonomic computing and a deep learning approach. In particular, to predict future demand for CPU usage and determine how to respond to workload fluctuations in the next interval, we propose an efficient deep learning model based on a diffusion convolutional recurrent neural network (DCRNN). Existing deep learning models that are widely applied cannot handle accurate real-time forecasting due to the presence of inconsistent and nonlinear workloads in cloud computing systems. The goal of the proposed deep learning model is to improve forecasting accuracy and minimize the error between the predicted and the actual workloads. The effectiveness of the proposed DCRNN-based deep learning model was evaluated using experiments on a real-world dataset of PlanetLab’s CPU usage traces. The results indicate that the proposed approach outperformed other existing deep learning models, achieving a mean absolute percentage error of 0.18 and root-mean-square error of 2.40.

Abstract: Championed by IBM's vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system -- a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals despite changes in its environment. Despite a number of literature reviews on specific aspects of SASs ranging from their requirements to quality attributes, we lack a systematic understanding of the current state of the art. This paper contributes a systematic literature review into self-adaptive systems using the dblp computer science bibliography as a database. We filtered the records systematically in successive steps to arrive at 293 relevant papers. Each paper was critically analyzed and categorized into an attribute matrix. This matrix consisted of five categories, with each category having multiple attributes. The attributes of each paper, along with the summary of its contents formed the basis of the literature review that spanned 30 years (1990-2020). We characterize the maturation process of the research area from theoretical papers over practical implementations to more holistic and generic approaches, frameworks, and exemplars, applied to areas such as networking, web services, and robotics, with much of the recent work focusing on IoT and IaaS.

Abstract: Failures in computer systems can be often tracked down to software anomalies of various kinds. In many scenarios, it might be difficult, unfeasible, or unprofitable to carry out extensive debugging activity to spot the cause of anomalies and remove them. In other cases, taking corrective actions may led to undesirable service downtime. In this article, we propose an alternative approach to cope with the problem of software anomalies in cloud‐based applications, and we present the design of a distributed autonomic framework that implements our approach. It exploits the elastic capabilities of cloud infrastructures, and relies on machine learning models, proactive rejuvenation techniques, and a new load balancing approach. By putting together all these elements, we show that it is possible to improve both availability and performance of applications deployed to heterogeneous cloud regions and subject to frequent failures. Overall, our study demonstrates the viability of our approach, thus opening the way towards its adoption, and encouraging further studies and practical experiences to evaluate and improve it.

Abstract: The technological revolution during the past decades has resulted in the explosion of data leading to an emergence of cloud computing that subsequently led to fog computing. These technologies are continuously striving for increased computational capability so as to inculcate it in our daily lives and obtain ever larger infrastructures. However, inclusion of heterogeneous infrastructures in such systems poses different challenges like complexity, security, and manageability. For the same, it necessitates an autonomic, self-managing system to address the growing complexities in its realization in terms of cost and complexity. These challenges have opened avenues for Autonomic computing, an approach that aims to provide significant benefits in terms of speed and automation by managing complex and heterogeneous infrastructure. Additionally, autonomic computing overcomes the limitations of manual control by providing an economical and robust solution in minimum time. As a result, autonomic computing has observed its widespread application since its inception. The proposed chapter focuses on the various aspects of autonomic computing like self-healing, self-optimization, self-protection, and so on, and presents a simplistic architecture. The proposed architecture implements autonomic computing infrastructure to dynamically control and manage services to develop and deploy an intelligent application. Hence, the proposed framework achieves the autonomic services to maintain the autonomic requirements of a wide range of network applications and services.

Abstract: The self-* properties commonly associated with the concept of autonomic computing are capabilities desirable for avionics software platforms. They decrease the configuration effort and inherently provide new fault tolerance and resource savings possibilities. The rigid certification process and the requirements for a static and predetermined system behavior are, however, in contradiction with the adaptive and flexible nature of autonomic computing systems. We propose a partition-based architecture providing autonomic features for avionics software platforms while being compliant to regulations and accepted technologies, such as ARINC 653. The core is a platform consciousness based on a domain-specific model and a novel MAP-QE-K cycle. Moreover, we suggest a planning intelligence, a virtual qualification authority, and a minimized execution unit. For each component we define the required design assurance level and possible realization techniques. We discuss the overall feasibility and point out central challenges in the fields of runtime verification and models at runtime. These challenges need to be solved up to the realization of autonomic avionics, e.g. a virtual security assessment and a qualifiable domain-specific model database.

Abstract: With the rise of network virtualization, the workloads deployed on data center are dramatically changed to support diverse service-oriented applications, which are in general characterized by the time-bounded service response that in turn puts great burden on the data-center networks. Although there have been numerous techniques proposed to optimize the virtual network allocation in data center, the research on coordinating them in a flexible and effective way to autonomically adapt to the workloads for service time reduction is few and far between. To address these issues, in this article we propose Sova , an autonomic framework that can combine the virtual dynamic SR-IOV (DSR-IOV) and the virtual machine live migration (VLM) for virtual network allocations in data centers. DSR-IOV is a SR-IOV-based virtual network allocation technology, but its operation scope is very limited to a single physical machine, which could lead to the local hotspot issue in the course of computation and communication, likely increasing the service response time. In contrast, VLM is an often-used virtualization technique to optimize global network traffic via VM migration. Sova exploits the software-defined approach to combine these two technologies with reducing the service response time as a goal. To realize the autonomic coordination, the architecture of Sova is designed based on the MAPE-K loop in autonomic computing. With this design, Sova can adaptively optimize the network allocation between different services by coordinating DSR-IOV and VLM in autonomic way, depending on the resource usages of physical servers and the network characteristics of VMs. To this end, Sova needs to monitor the network traffic as well as the workload characteristics in the cluster, whereby the network properties are derived on the fly to direct the coordination between these two technologies. Our experiments show that Sova can exploit the advantages of both techniques to match and even beat the better performance of each individual technology by adapting to the VM workload changes.

Abstract: With the advent of cloud platform giants like AWS and Google, small- and mid-scale industries are continuously shifting their infrastructure over these platforms. Since hardware and service management of these platforms are induced with high accuracy and availability, these giants earned a huge reputation in the IT industry. As computing infrastructure is expanding exponentially, therefore, the resource management is getting complex and challenging day by day. Cloud environment is highly uncertain in terms of resource dispersion and availability. Administrators often experience challenges allocating available resources due to the factors such as sudden failure, dynamism, and heterogeneity. As a matter of fact, the existing frameworks and their resource management mechanisms have been facing multilateral issues and challenges to handle such flexible and unpredictable environments. In such a scenario, a question arises that how to handle and alleviate such issues and challenges efficiently and effectively through effective deployment and management of the computing resources over cloud? Dynamic resource management handles the issues related to deploying, configuring, provisioning, reprovisioning, and releasing related issues by understanding the dynamically scalable on-demand need of the users and their related applications. This chapter presents the broad overview of methodologies and mechanisms of autonomic cloud computing environment. Autonomic computing executes exclusively dynamic allocation resources in such a manner that it reconfigures itself with the availability of resources in real time. This chapter also provides a straightforward approach to the domain researchers in analyzing the paramount characteristics of autonomic resources management and aids research scholars in identifying and characterizing the primary techniques for the application corresponding to the environment in future research perspectives.

Abstract: Embedded sensors and smart devices have turned the environments around us into smart spaces that could automatically evolve, depending on the needs of users, and adapt to the new conditions. While smart spaces are beneficial and desired in many aspects, they could be compromised and expose privacy, security, or render the whole environment a hostile space in which regular tasks cannot be accomplished anymore. In fact, ensuring the security of smart spaces is a very challenging task due to the heterogeneity of devices, vast attack surface, and device resource limitations. The key objective of this study is to minimize the manual work in enforcing the security of smart spaces by leveraging the autonomic computing paradigm in the management of IoT environments. More specifically, we strive to build an autonomic manager that can monitor the smart space continuously, analyze the context, plan and execute countermeasures to maintain the desired level of security, and reduce liability and risks of security breaches. We follow the microservice architecture pattern and propose a generic ontology named Secure Smart Space Ontology (SSSO) for describing dynamic contextual information in security-enhanced smart spaces. Based on SSSO, we build an autonomic security manager with four layers that continuously monitors the managed spaces, analyzes contextual information and events, and automatically plans and implements adaptive security policies. As the evaluation, focusing on a current BlackBerry customer problem, we deployed the proposed autonomic security manager to maintain the security of a smart conference room with 32 devices and 66 services. The high performance of the proposed solution was also evaluated on a large-scale deployment with over 1.8 million triples.

Abstract: High Performance Computing systems are facing more and more variability in their performance, related to e.g., Input/Output (I/O) behavior and power consumption: they are less predictable, which requires more run-time management to meet the requirements. This can be addressed following feedback approach, where a management feedback loop, in response to monitored information in the systems, based on analysis of this data, decides to activate system-level or application-level adaptation mechanisms. One such regulation problem is found in the context of CiGri, a lightweight computing grid system which exploits the unused resources of a set of computing clusters. The computing power left over by the execution of premium cluster users’ HPC applications, is used to execute smaller jobs, which are injected as much as the global system allows.The feedback loop which we design has to regulate this injection of jobs in such a way as to avoid overloading of the distributed file system (or file-server), which would be detrimental to the main performance, while self-adapting to variations in load in order to make the best use of available resources. We put in place a mechanism for feedback control in system software by controlling the number of jobs to be sent to the cluster in response to system information about the current number of processed jobs and the file-server load, which has a significant impact of the performance of the priority users jobs. We perform experimental validation by comparing several control solutions.

Abstract: Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

Abstract: Autonomic computing is not a core, rather a convergence of numerous concepts and supporting technologies. It is the junction that integrates salient computing domains and subdomains to create a self-driven, self-healing, and self-manageable computing environment. The possible integration, exploration, and hybridization toward the development of the new models and applications are new knowledge contributions. Modeling is a conceptualization of autonomic computing in general and contextualization in specific applications. In the context of cloud-based autonomic computing, a model presents the fact that the operations of the autonomic computing systems are goal-oriented and driven by certain activities and follow certain policies and behavioral aspects, with the existing features. Currently, client organizations or individual clients prefer to use packaged computing products and services over the cloud or distributed systems. This chapter covers how autonomic computing models hold the promising features for simplification, and the ease of computing system management over clouds such as process management, autonomic client migration for load balancing, monitoring, energy efficiency(green), automatic updating of software tools/drivers, predictive warning before failure, error detection and correction, backups, and recovery from sudden disasters. This chapter also covers the possible applications and related issues encountered during adaption and adoption at salient scales and types of organizations. The summarized feature-based comparative analysis of the existing computing and emerging autonomic models are also incorporated. A cloud-based green broker model for cloud service selection is designed and incorporated for the autonomic brokerage of green cloud services. Furthermore, the applications of the autonomic process management architecture to salient applications such as governance, commerce, management, industrial automation, etc. are included.

Abstract: Cloud Computing, as a distributed computing paradigm, consists of the provisioning of infrastructure, platform, and software resources as services. This paradigm is being increasingly used for the deployment and execution of service-based business processes. To efficiently manage them according to the autonomic computing paradigm, service-based business processes can be associated with autonomic managers that monitor these processes, analyze monitoring data, plan configuration actions, and execute these actions on these processes. Although, during these last years, autonomic management of cloud services has received increasing attention, the optimization of autonomic managers to be assigned to cloud services remains not well explored. In fact, almost all the existing solutions on autonomic computing have been interested in modeling and implementing autonomic mechanisms without making any effort to optimize the number of used autonomic managers. Moreover, when it comes to large service-based business processes, optimization of management resources becomes a critical issue. To overcome this issue, we present in this paper a novel approach to determine how many autonomic managers to use for the management of large service-based business processes in order to minimize their cost while avoiding management bottlenecks. Experiments conducted on three different types of datasets highlight the effectiveness of our approach.

Abstract: The autonomic Internet of Things is the creation of self-management capability in the Internet of Things system by embedding some autonomic properties, with the goal of freeing humans from all detail of the operation and management of the system. At same time, this provides a system to always operate on the best performance. This paper presents a review of the recent studies related to the design of network communication protocol, which can support autonomic Internet of Things. Many of the studies come from the research and development in Wireless Sensor Network protocols, as it becomes one of the key technologies for the Internet of Things. The identified autonomic properties are self-organization, self-optimization, and self-protection. We review some protocols with the objective of energy consumption reduction and energy harvesting awareness, as it can support the self-energy-awareness property. As the result, the protocol designs are mapped according to each autonomic property supported, including protocols for MAC layer, protocols for clustering, protocols for routing, and protocols for security. This can be used to map the advances of communication protocol research for the autonomic Internet of Things and to identify the opportunities for future research.

Abstract: The combination of a few self-capabilities including self-configuration, self-healing, self-optimization, self-protection, self-awareness, and so on, is called autonomic computing. Thus, autonomous computing methodology was then used to build autonomous software systems. Autonomic computing is the ability of a computer to automatically handle itself through adaptive technologies that add computing capabilities and minimize the time taken by computer professionals to solve device problems and other maintenance, such as software updates. This approach makes support systems for different tasks self-adaptable and self-decision-making computing systems. It also helps to decrease human involvement in the process of software management. Cloud protection has become one of the most critical concerns in cloud computing with the continual growth of cloud computing. Data stored on a cloud network, for example, can be targeted, and its security is difficult to ensure. We must therefore apply weight to the question of how the data stored in the cloud should be secured. This paper presents a brief review on autonomic self-management attributes and capabilities of machine additives, describes autonomic computing architectures, autonomic adoption modes and requirements, and examines its properties over cloud computing.

Abstract: Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the “Autonomic Cycle of Data Analysis Tasks” concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.

Abstract: While walking the path through a fourth industrial revolution, the journey of transformation from the time when human intervention was needed, to the current state where the processes are completely automated and are still continuing to a new horizon. A game-changer adoption of the technology of “autonomic computing” supports industrial systems from deep inside and helps sustain with the ever-increasing complexity of systems, helps in customization to a greater extent, helps to manage the maintenance and reliability of the system that is beyond the limits of just human intervention. With this understanding, our work talks about important digital dimensions of smart manufacturing systems. It develops high-level awareness of technologies and ecosystems of smart manufacturing—Industry 4.0. Furthermore, readers would be able to identify tremendous opportunities for applications of computational intelligence referring to the capabilities and aspects of autonomic computing; those are self-healing, self-configuring, self-optimization, and self-protecting; thus pointers for innovation and applied research

Abstract: Industry 4.0 means the fourth revolution in the field of industry or fourth revolution which is mainly used for automation M to M, H to M communication, digitization, and exchange of data in different technology; this is very beneficial for cloud computing automatic technologies and IOT. Industry 4.0 is not only handling the internal operation of IOT devices, but with the help of cloud computing technology environment, a huge amount of data is stored in a centralized manner. They allow other smaller enterprises to access their centralized data or technology who would not be able to build their own data set. In the context of automatic computing in cloud resource management, the allocation of on-demand resource is one of the major utilizations in the field computing to maintain the computing cost and Qos (Quality of service). To minimize the cost of hosting node cloud resource management, Industry 4.0 is allowing users to access many services on an on-demand basis, but the only restriction is that this service is based on pay-per-use means if the user wants to use these services, but they have to pay something as these resources are not free to use. Now, this chapter focuses on some important challenges like SLA Violation to find the faulty node with the help of fault management mechanism, etc. All things are clearly explained with the help of flow diagram.

Abstract: Autonomic computing got an increasing attention during past years in the context of Cloud computing in order to optimize the Cloud systems performance and increase their cost‐effectiveness. In that direction, a broad range of various self‐adaptability strategies have been proposed to overcome uncertainties related to Cloud systems like workload variations, hardware failures and malicious attacks. A special attention in the literature has been paid to resource allocation for cost and performance optimization, and self‐healing for better faut‐tolerance. However, while a lot of progress has been done on these topics, there is still a lack of a standardized methodology, easily reproducible and allowing validation and comparison of a self‐adaptability strategies. This article proposes a methodology allowing to forecast the autonomic Cloud systems performances. The idea is to compare different self‐adaptability approaches at the design stage, and identify the most adequate and optimized configuration. This methodology is based on a new architecture description language and a stochastic Petri nets‐based modeling and simulation approach. We illustrate our methodology in this article through a running example and a set of experiments.

Abstract: Effective or optimal cloud resource management is included in the major challenges of the cloud computing environment from both the provider perspective and the user perspective. Many optimization algorithms have been introduced and redefined for the same. Every algorithm gives optimum results under certain conditions. When it comes to comparison among them, then no specific studies show that a particular algorithm behaves best in all types of problems. Research has shown that meta-heuristic algorithms are best fitted for resource management, especially for an automated environment where self-configuration and self-healing play a vital role. In this chapter, different meta-heuristic algorithms are initially classified based on different parameters and then they are described. A comparative analysis in the form of a table is also proposed which gives a clear classification among all.

Abstract: Autonomic computing is developing as a new way to deal with the plan of computing systems. It is a big challenge for the things to come in future, where the systems will oversee themselves per the goals specified by the humans. Its objective is simply the improvement of systems which include self-designing, self-healing, self-ensuring, and self-optimizing. Autonomic computing is an idea that unites numerous fields of computing to make systems that are intelligent and have the capability to adapt themselves. It has a goal to form self-managing systems, which can address the present problems of complexity and expense of ownership along with addressing the requirements of tomorrow for unavoidable and universal computation and correspondence.

Abstract: Existing approaches to cyber security in the automotive sector are not fit to deliver the resilience required for safe mass deployment of advanced driving functions and intelligent mobility services. This paper promotes an innovative approach to operational cyber resilience, the CyRes methodology, which aims to enable robust and resilient engineering practices in this sector from design to manufacture to operation. CyRes is based on three principles: engineered differences; detecting, understanding and acting on cyber events; and proactive updates. The aim of this short paper is to raise awareness of the problems and the many intellectual challenges in this particular sector. CyRes is an exciting opportunity for engineers and computer scientists to transfer widely studied, mature methods, such as those developed by the runtime verification community and the self-adaptive systems community, for cyber resilience.

Abstract: Internet of things (IoT) systems are taking an important role in daily life. Each year the number of connected devices increases considerably, and it is important to keep systems working appropriately. There are some options related to decision support systems to perform IoT systems tasks such as deployment, maintenance, and its operation on environments full of different connected devices and IoT systems interacting among them. For the decision-making process, the authors consider the complexity nature observed in IoT systems and their operational context and environments. In this sense, rather than using grain and fixed control rules/laws for the system design, the use of general principles, goals, and objectives are defined to guide the system adaptation. This has been referred to as guided self-organization (GSO) in the literature. The GSO design approach is based in evaluating the system entropy to reduce the emergence and enable self-organization. Also, in this chapter, a series of study cases from different IoT application domains are presented.

Abstract: Cloud Computing delivers on-demand metered services to its consumers which include computing power, servers, storage, networking, and intelligence over the Internet. The cloud computing technology acts as a critical enabler for Industry 4.0, as the popular cloud business model depends upon the provisioning of resources by the service provider to satisfy user demands from different geographical regions. This multitenant environment of the cloud further promotes a manufacturing ecosystem that paves the path for Industry 4.0 as it aids in the manufacturing process by providing customized products based on demand. To provision resources, in such an environment, various resource provisioning techniques are designed that abides service-level agreements (SLA). The performance of these techniques can be evaluated by different performance parameters, and achieving adequate results for these performance parameters assures the efficient management of resources. The chapter enlightens the concept of resource management in cloud computing within Industry 4.0. Different RM techniques, along with associated challenges, advantages, disadvantages, and performance parameters have been explained in detail.

Abstract: To remain competitive, companies must face the changes occurring in their environment and adapt their business processes accordingly. Those processes are implemented in business process management systems (BPMS), which mostly support manual adaptations. That means that the process users have to detect what changes in the environment require process adaptation, and what adaptation operations have to be performed. Such manual adaptations of processes are costly, time-consuming and error prone tasks. For this reason, some contributions of the literature have tried to address the issue of self-adaptations of processes. But these contributions suffer from shortcomings: isolated use of adaptation techniques, non-coverage of the process dimensions and of the adaptation types, etc.; the adaptation issue remains partially addressed. Thus we recommend in this paper a hybrid approach to ensure autonomic adaptations of running processes. According to this approach, the Plan component tries to find an appropriate model version of the concerned process. Then, if such a version does not exist, it looks to reuse an adaptation case that was applied in the past under a similar situation (context). Finally, if necessary, it applies rules, as an artificial intelligence planning technique, to define an ad hoc adaptation. Moreover, the recommended approach takes advantage of the IBM MAPE-K (Monitor, Analyze, Plan, Execute—Knowledge) control loop from autonomic computing, recognized as a prominent solution for self-adaptation at run-time. More precisely the paper addresses the resolution of adaptation needs while covering three process dimensions and all adaptation types and ensuring the separation of concerns for better portability and wide usability through the BPMN standard. It presents both the required Knowledge and the Plan component of the control loop for this resolution. It also shows the effectiveness of the approach by illustrating self-adaptation of a process from the crisis domain, and demonstrates its feasibility by reporting about its implementation and qualitative and quantitative evaluation.

Abstract: Cloud is a huge scale, heterogeneous, complex system. Due to uncertainty and dispersion of resources, cloud faces problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. They require mechanized and coordinated strong methodologies for secure, dependable, and cost-effective management of the system. Hence, there is a need of self-manageable properties like self -properties of autonomic computing. In this chapter, we have tried to identify the challenges in autonomic cloud computing with the help of a questionnaire survey; a structured questionnaire in this direction was prepared with 44 items and 200 respondents analyzed the results using IBM SPSS Tool.