Cloudové počítanie

Abstract—Emerging as a radical paradigm in cloud computing, serverless computing allows developers to deploy and run programs free from control of underlying infrastructure. The basic ideas of serverless computing, its main traits, and its rising relevance in contemporary cloud computing are investigated in this work. We evaluate the advantages and drawbacks of serverless computing including cost effectiveness, scalability, and operational simplicity by means of an analysis of market trends, technology developments, and practical case studies. We also look at difficulties such vendor lock-in, cold-start delay, and security concerns together with possible remedies. In the end, we go over future approaches including distributed serverless infrastructures, hybrid serverless models, and workload optimizations driven by artificial intelligence. Serverless computing is a necessary step towards a totally autonomous and infrastructure-free cloud environment as cloud computing develops. Keywords—Serverless Computing, Function-as-a- Service (FaaS), Cloud Computing, Infrastructure Abstraction, AI-driven Optimization, Edge Computing, Cost Efficiency, Performance Bottlenecks

Innovation in the concept of virtualization acts as a vigorous role in cloud computing. Virtualization helps in splitting the system resources in the form of the multiple and isolated execution environments. Hypervisor-based virtualization is related to the huge requirement of resources and operative overhead due to an additional stage of abstraction. In this review, we first were given an overview of virtualization innovation and Container-based virtualization. Container-based virtualization provides performance enhancements and reduced starting time and hence it is more beneficial than Virtual Machine (VM). Container virtualization does not imitate the hardware but only isolate the processes and it is more light-weight than VM. Container-based virtualization may be successfully implemented in “Fog Computing” framework as containers are suitable to the resource-constrained environment and also enables the distribution of different type of applications on cloud and fog Nodes. This paper describes the possible application of lightweight virtualization to Fog Nodes in the form of case studies.

Cloud computing has evolved significantly and it's importance is evident in the vast amount of data processing occurring daily across various sectors. Cloud computing remains a key topic in computer science studies, particularly in big data. Trends focus on AI-driven cloud scalability, edge computing, multi-cloud adoption, serverless computing, and DevOps integration, all contributing to improved operational efficiency. Advancing cloud technology to achieve full maturity remains a priority. This study provides a brief assessment of cloud computing by reviewing related articles, highlighting its evolution and ongoing developments.

Technologies such as cloud, fog, and edge computing offer exceptional solutions for many technical problems in the Internet of Things (IoT). Cloud computing offers enormous flexibility in terms of resource availability and demand. In this paper, the role of cloud, edge, and fog computing in IoT was discussed along with the emergence of numerous IoT smart scenarios which are difficult to implement. This research is important in guiding future network paradigms that require faster processing with less delay and disruption, and the advanced technologies in them can address the challenges of IoT. It also reviews cloud, fog, and edge computing and their relevance to the IoT.

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

With the tremendous growth in migration to cloud computing for traditional applications, the demand for virtualization is the new need for further research and enhancements. The high demand for virtualization in cloud computing is been addressed with the technology enhancement and in terms of virtual machines. Majorly the hardware component management of virtual machine is considerably reaching the pick of research with the recent advancements by multiple companies and open source researches like AlphaVM, Hyper-V, Integrity Virtual Machines, JPC (Virtual Machine), PowerVM, Sun xVM, VMware Workstation and z LPARs. Another most important component of virtualization and virtual machine image storage is been the bottleneck for the further advancement. The limitations are been addressed by clustering and storage area networking, however these existing solutions are no match for the industrial grade demand for the VM image storage requirements.Hence in this work we propose a cloud based virtual machine storage framework designed for large scale deployment satisfying the other needs for storage features like replication and measurements of performance improvements. This work also demonstrates the advancements in performance for virtualization management

With the rapid development of virtualization technology, virtual machine (VM) is widely used by cloud computing - the more and more popular computing paradigm. Thus, in order to guarantee the security of cloud computing, it is necessary to securely identify the kernel of VM, the software stack running on VM and the hardware platform which VM relies on. This paper designs a system of trusted connection based on virtual machine architecture and implements a system prototype. Our system not only can securely measure and identify the kernel of VM, the software stack running on VM and the hardware platform which VM relies on, but also can realize isolations to untrusted VMs. There are three main parts in our system: trusted chain, attestation and isolation. The experiments described in this paper prove that our system ensures trusted connection of VMs and achieves isolations to untrusted VMs. The performance of our system is also analyzed and evaluated. According to the analysis results, our system is practical in terms of performance.

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

In general, operating systems (OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same configuration. Applications in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine (VM), which leads to homogeneous system calls on each VM. In addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. In this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, vSpec. There are five classes of VM: CPU-intensive, memory-intensive, I/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. vSpec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of vSpec on benchmarks and real-world applications.

Cloud computing offers cost effective services on-demand which encourage critical infrastructure providers to consider migrating to the cloud. Critical infrastructures are considered as a backbone of modern societies such as power plants and water. Information in cloud computing is likely to be shared among different entities, which could have various degrees of sensitivity. This requires robust isolation and access control mechanisms. Although various access control models and policies have been developed, they cannot fulfil requirements for a cloud based access control system. The reason is that cloud computing has a diverse sets of security requirements and unique security challenges such as multi-tenant and heterogeneity of security policies, rules and domains. This thesis provides a detailed study of cloud computing security challenges and threats, which were used to identify security requirements for various critical infrastructure providers. We found that an access control system is a crucial security requirement for the surveyed critical infrastructure providers. Furthermore, the requirement analysis was used to propose a new criteria to evaluate access control systems for cloud computing. Moreover, this work presents a new cloud based access control model to meet the identified cloud access control requirements. The model does not only ensure the secure sharing of resources among potential untrusted tenants, but also has the capacity to support different access permissions for the same cloud user. Our focused in the proposed model is the lack of data isolation in lower levels (CPU caches), which could lead to bypass access control models to gain some sensitive information by using cache side-channel attacks. Therefore, the thesis investigates various real attack scenarios and the gaps in existing mitigation approaches. It presents a new Prime and Probe cache side-channel attack, which can give detailed information about addresses accessed by a virtual machine with no need for any information about cache sets accessed by the virtual machine. The design, implementation and evaluation of a proposed solution preventing cache side-channel attacks are also presented in the thesis. It is a new lightweight solution, which introduces very low overhead (less than 15,000 CPU cycles). It can be applied in any operating system and prevents cache side-channel attacks in cloud computing. The thesis also presents a new detecting cache side-channel attacks solution. It focuses on the infrastructure used to host cloud computing tenants by counting cache misses caused by a virtual machine. The detection solutions has 0% false negative and 15% false positive.

Chapter 28 - Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction

Recently, technology of cloud computing has been rapidly developed, and it is expected to apply for various network services. Virtual technology is a part of significant technology which has been contributed for the development of cloud computing. Various virtual technologies such as virtual OS and VDE become very important for server and network management in these days. Furthermore, such a virtual technology becomes possible to provide efficient resource management or QoS with virtual network configuration. Thus, it will be important for network construction to provide hardware and software service in near future. Autonomous network configuration in virtual layer is one of such a service. Not only network configuration is constructed in physical layer, but also it should be constructed in virtual layer for providing optimal resource and QoS management. In this paper, Overlay Cloud Computing Service is proposed, and it is consisted of virtual autonomous network configuration in cloud computing service including various server and network services. In our proposal methods, first of all, there are various kinds of user policy such as eco mode, high power mode, and secure mode. Then, virtual network and server configuration is automatically held in virtual layer with the mode setting and hardware configuration. Besides, with observation values of network and server condition, virtual configuration is changed optimally. In this optimal configuration changes, Kalman filter is used for the calculation of network and server condition. Then, with the results of calculation, virtual network topology is changed. In this paper, first, introduction and cloud computing service is discussed. Then, our proposed Overlay Cloud Computing Service by autonomous network configuration is introduced. In the chapter, optimal control method by Kalman filter with observed values is also introduced. Finally, conclusion and future study is discussed.

The concept Cloud computing has not only reshaped the field of distributed systems but also fundamentally changed how business potential extends today. In recent advancements, Cloud computing applications are provided as services to end-users. The application services hosted under Cloud computing model have complex provisioning, configuration, and deployment requirements How to use Cloud computing resources efficiently and gain the maximum profits with efficient utilization of resources is one of the Cloud computing service providers' ultimate goals. Repetitive evaluation of the performance of Cloud provisioning policies, application workload models, and resources performance models in dynamic system are difficult to achieve and rather a time consuming and costly approach. To overcome this challenge, Cloud analyst simulator based on Cloud Sim has been proposed which enables the modeling and simulation in cloud's ambience. The objective of this paper is to prove that the choice of VM Scheduling Policy in Cloud computing model significantly improves the application performance under resource and service demand variations. We will discuss different Virtual Machine (VM) Scheduling Policies implemented and their performance analysis in Virtual environment of cloud computing in order to achieve better Quality of Service (QoS).

Improving consumer satisfaction in smart cities using edge computing and caching: A case study of date fruits classification

With the growth of real-time and latency-sensitive applications in the Internet of Everything (IoE), service placement cannot rely on cloud computing alone. In response to this need, several computing paradigms, such as Mobile Edge Computing (MEC), Ultra-dense Edge Computing (UDEC), and Fog Computing (FC), have emerged. These paradigms aim to bring computing resources closer to the end user, reducing delay and wasted backhaul bandwidth. One of the major challenges of these new paradigms is the limitation of edge resources and the dependencies between different service parts. Some solutions, such as microservice architecture, allow different parts of an application to be processed simultaneously. However, due to the ever-increasing number of devices and incoming tasks, the problem of service placement cannot be solved today by relying on rule-based deterministic solutions. In such a dynamic and complex environment, many factors can influence the solution. Optimization and Machine Learning (ML) are two well-known tools that have been used most for service placement. Both methods typically use a cost function. Optimization is usually a way to define the difference between the predicted and actual value, while ML aims to minimize the cost function. In simpler terms, ML aims to minimize the gap between prediction and reality based on historical data. Instead of relying on explicit rules, ML uses prediction based on historical data. Due to the NP-hard nature of the service placement problem, classical optimization methods are not sufficient. Instead, metaheuristic and heuristic methods are widely used. In addition, the ever-changing big data in IoE environments requires the use of specific ML methods. In this systematic review, we present a taxonomy of ML methods for the service placement problem. Our findings show that 96% of applications use a distributed microservice architecture. Also, 51% of the studies are based on on-demand resource estimation methods and 81% are multi-objective. This article also outlines open questions and future research trends. Our literature review shows that one of the most important trends in ML is reinforcement learning, with a 56% share of research.