Model-based high availability configuration framework for cloud

Ensuring optimal service continuity remains a critical challenge in cloud computing, especially when dealing with high traffic loads and system failure potential that can cause losses. To address this, this research presents the implementation of a high availability (HA) cloud system using the Least Connection load balancing algorithm implemented with Nginx, integrated with early anomaly detection and alert mechanisms. The HA architecture is implemented across two geographically distributed cloud service providers, Alibaba Cloud and Google Cloud, to analyze latency and performance differences under high load conditions. The system's resilience and scalability were evaluated through load testing using K6, simulating workloads ranging from 100 to 1000 Virtual Users (VUs) for single server configurations and 200 to 2000 VUs for HA configurations. The experiment results showed a significant improvement in service availability, reaching 100% uptime with the HA configuration compared to a peak of 98.79% in the single server environment. The Least Connection strategy effectively balanced traffic by monitoring active connections, resulting in a 29.73% increase in processed requests and a 42% reduction in system load at 1000 VUs. Additionally, the alert system successfully sent real-time Telegram notifications for delays or failures, enabling proactive mitigation. These results confirm that combining dynamic load balancing with proactive alerts can significantly improve service reliability, resource efficiency, and resilience to failures in distributed cloud infrastructure providing a viable model for robust and scalable cloud service architectures.

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.

Cloud is a popular and attractive paradigm for providing online computing services to the end users. Recently many of the users move their business applications to the cloud and become tenants for the cloud service providers. Some tenants expect their applications that are provided as services to be highly available (HA) at any time. Managing HA of applications services in the cloud is a big challenge due to the dynamic nature and the huge number of the provision services in the cloud. Limited number of solutions address the HA of services in the cloud platforms that use containers instead of Virtual Machines (VMs). In addition, HA measurements are still missing by the proposed solutions in the literature. Therefore, in this article we propose a framework to incorporate the HA feature for the applications that are deployed in cloud platforms that use the containers. The framework depends on the novel idea of integrating HA middlewares OpenSAF and Pacemaker with the containers to manage HA of the applications services. As a proof of concept, we build a prototype for our framework using our private cloud Container-based platform. For the evaluation purposes, we compare the same framework using our cloud VM-based platform. The measurements show the ability of the proposed framework to manage HA of different services using the containers with faster service recovery time and shorter service outage time than using the VMs.

Load balancing is the core of virtual resource management and scheduling in cloud computing. For network applications, the cost of user would be greatly saved if load balancer could dynamically adjust cluster resources in accordance with the current applied load. The current load balancing products of cloud, such as Amazon's ELB, can be used to manage virtual machines in the cloud. The main drawbacks are still only supporting template-based deployment of new virtual machines, not supporting the trend prediction, failing to gain resources dynamically, and not sufficiently providing the elastic management of resources. Since the virtual machine for load balancing management in cloud computing can be dynamically applied and released, an algorithm of prediction-based elastic load balancing resource management (TeraScaler ELB) is presented to overcome the drawbacks. Experiments have shown that the required number of virtual machines change in compliance with the change of network load, thus TeraScaler ELB is able to dynamically adjust the processing capacity of back-end server cluster with the applied load. Besides it could make full use of the 'use on demand' feature of cloud computing, TeraScaler ELB leads to a better application of prediction based load balancing in cloud computing. It concludes that compared with the traditional elastic resource management algorithm, TeraScaler ELB is more reasonable for providing scalability and high availability.

Purpose – The physical server transition to virtualized infrastructure server have encountered crucial problems such as server consolidation, virtual machine (VM) performance, workload density, total cost of ownership (TCO), and return on investments (ROIs). In order to solve the problems mentioned above, the purpose of this paper is to perform the analysis of virtualized cloud server together with shared storage as well as the estimation of consolidation ratio and TCO/ROI in server virtualization. Design/methodology/approach – This paper introduces five distinct virtualized cloud computing servers (VCCSs), and provides the appropriate assessment to five well-known hypervisors built in VCCSs. The methodology the authors proposed in this paper will gives people an insight into the problem of physical server transition to virtualized infrastructure server. Findings – As a matter of fact, VM performance seems almost to achieve the same level, but the estimation of VM density and TCO/ROI are totally different among hypervisors. As a result, the authors have the recommendation to choose the hypervisor ESX server if you need a scheme with higher ROI and lower TCO. Alternatively, Proxmox VE would be the second choice if you like to save the initial investment at first and own a pretty well management interface at console. Research limitations/implications – In the performance analysis, instead of ESX 5.0, the authors adopted ESXi 5.0 that is free software, its function is limited, and does not have the full functionality of ESX server, such as: distributed resource scheduling, high availability, consolidated backup, fault tolerance, and disaster recovery. Moreover, this paper do not discuss the security problem on VCCS which is related to access control and cryptograph in VMs to be explored in the further work. Practical implications – In the process of virtualizing the network, ESX/ESXi has restrictions on the brand of the physical network card, only certain network cards can be detected by the VM. For instance: Intel and Broadcom network cards. The newer versions of ESXi 5.0.0 and above now support parts of Realtek series (Realtek 8186, Realtek 8169, and Realtek 8111E). Originality/value – How to precisely assess the hypervisor for server/desktop virtualization is also of hard question needed to deal with it crisply before deploying new IT with VCCS on site. The authors have utilized the VMware calculator and developed an approach to server/desktop consolidation, virtualization performance, VM density, TCO, and ROIs. As a result, in this paper the authors conducted a comprehensive approach to analyze five well-known hypervisors and will give the recommendation for IT manager to choose a right solution for server virtualization.

In recent years, the technology of Cloud Computing has been developed rapidly. It makes IT infrastructure available as a service through the network. Using Cloud Computing, users can find a new effective solution for building systems and applications according to their demands. How to make the existing services reliably run on the cloud platform, and satisfy the high scalability and availability requirements becomes a hot topic. In this paper, we propose a Cloud Computing Service Platform based on OpenStack which supports service management, auto-scaling, security control and high availability, etc. To achieve the effective management of resources and ensure the quality of service, we implement the resource monitoring subsystem. It can monitor performance metrics CPU Utilization, Memory Usage and Network I/O of physical and virtual resource like. The persistent data can be used by the resource management subsystem and alarm subsystem.

Nowadays, cloud computing is a growing field in research and the marketplace, including virtualization, internet service, computer software, and web services. A cloud comprises multiple elements like a consumer's data center and servers. It provides high availability, efficiency, scalability, mobility, fault tolerance, decreased overhead of users, reduced expenses of ownership, on-demand services, etc. A Variety Of users need various Quality of Service (QoS) demands. Therefore, the cloud supplier needs to arrange the tasks to get maximum advantages regarding their services, and the consumer's quality of service demands are satisfied. Currently, the need for cloud is increasing day by day, people moved towards cloud simultaneously, so its scale is up that's why the service providers are required to deal with enormous requests. The biggest challenge is the availability of services and maintaining the performance equivalent or more effective whenever workload occurs. Multiple requests are processed simultaneously; that's why the workload increased. The load balancer uses to resolve that issue. Our research shows how to balance the load in a cloud environment using fuzzy logic. The processor speed, storage capacity, and assigned load of Virtual Machine (VM) utilize to balance the load in cloud computing through fuzzy logic to achieve better processing time and storage utilization.

Cloud computing and big data: Technologies and applications

The article examines the importance of container migration within the context of container placement tasks in cloud data centers. Cloud computing offers organizations significant benefits such as flexibility, scalability, and reduced costs for equipment, energy, and maintenance. Cloud providers face the challenge of not only efficiently placing containers initially but also dynamically reallocating them, along with virtual machines, in response to changing demands and workloads. This necessitates the development and application of migration algorithms. The study analyzes how migration is a critical component for ensuring optimal resource use, maintaining high service availability, and reducing service level agreement (SLA) violations. The research aims to enhance the productivity of cloud data centers in the task of container placement by utilizing container mi-gration algorithms. This objective is driven by the need for cloud providers to use their resources effectively to provide higher quality service and maintain competitive pricing. The use of migrations prevents overloading or underloading of servers and virtual machines, which is especially impor-tant in the highly dynamic environment of cloud computing. The research developed a hybrid mi-gration algorithm that includes the migration of both containers and virtual machines. Mathemati-cal aspects of the container placement task are described, and the migration algorithm is detailed. Experiments showed that implementing this algorithm reduces operational costs by 24% compared to traditional methods that do not use migration, and by 9% compared to less flexible migration strategies. There was also a demonstrated reduction in SLA violation times by 40%, indicating im-provements in service quality and reliability of cloud services. The conclusions of this study confirm that container and virtual machine migration is necessary for effective management of dynamic re-sources in cloud data centers in the context of container placement tasks.

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

Trusted detection of ransomware in a private cloud using machine learning methods leveraging meta-features from volatile memory

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.

Information and communication technology has been growing rapidly for the last few decades. In today's age of information and globalization massive computing power is desired to generate business insights and competitive advantage [1]. Cloud Computing is an emerging technology and has attracted a lot of attention in both commercial and academic spheres. It is growing very fast and provides an alternative to conventional computing. There are cloud service providers who provide services in a flexible manner. The main goal of cloud service provider is to establish an efficient load balancing algorithm which ensures a fair distribution of loads among virtual machines and better resource utilization. In this paper we present a heuristic based load balanced scheduling model for efficient execution of tasks. The proposed model balances the loads coming from several users among datacenters and hence it offers better resource utilization and high availability in the form of improved response time and turnaround time. The proposed algorithm is implemented using CloudSim simulator and the result shows that the proposed algorithm outperforms to existing algorithms on similar objectives.

High availability is the most important and challenging problem for cloud providers. However, virtual machine monitor (VMM), a crucial component of the cloud infrastructure, has to be frequently updated and restarted to add security patches and new features, undermining high availability. There are two existing live update methods to improve the cloud availability: kernel live patching and Virtual Machine (VM) live migration. However, they both have serious drawbacks that impair their usefulness in the large cloud infrastructure: kernel live patching cannot handle complex changes (e.g., changes to persistent data structures); and VM live migration may incur unacceptably long delays when migrating millions of VMs in the whole cloud, for example, to deploy urgent security patches. In this paper, we propose a new method, VMM live upgrade, that can promptly upgrade the whole VMM (KVM & QEMU) without interrupting customer VMs. Timely upgrade of the VMM is essential to the cloud because it is both the main attack surface of malicious VMs and the component to integrate new features. We have built a VMM live upgrade system called Orthus. Orthus features three key techniques: dual KVM, VM grafting, and device handover. Together, they enable the cloud provider to load an upgraded KVM instance while the original one is running and cut-and-paste'' the VM to this new instance. In addition, Orthus can seamlessly hand over passthrough devices to the new KVM instance without losing any ongoing (DMA) operations. Our evaluation shows that Orthus can reduce the total migration time and downtime by more than $99%$ and $90%$, respectively. We have deployed Orthus in one of the largest cloud infrastructures for a long time. It has become the most effective and indispensable tool in our daily maintenance of hundreds of thousands of servers and millions of VMs.

The financial services sector demands high availability and security due to its critical role in global economies and the sensitive nature of its data. Active/active data center strategies have emerged as a robust solution, enabling continuous operations even during outages or maintenance by ensuring that all data centers are fully operational and synchronized. This paper explores the benefits and challenges of implementing active/active data centers in financial services, focusing on the intricate balance between achieving high availability and maintaining rigorous security standards. Active/active configurations provide unparalleled redundancy and uptime, crucial for minimizing service disruptions that could lead to significant financial losses or reputational damage. By distributing workloads across multiple active data centers, financial institutions can enhance resilience against localized failures and ensure seamless failover capabilities. However, this high availability comes with complex challenges, particularly in ensuring that security protocols are uniformly applied across all active sites. The risk of data breaches and cyberattacks increases as more endpoints are exposed, making it essential to implement advanced security measures, such as end-to-end encryption, real-time threat detection, and stringent access controls. The review also discusses the importance of regulatory compliance, as financial institutions must navigate a landscape of diverse and often stringent regulations across different jurisdictions. Achieving a balance between high availability and security requires not only technological innovation but also strategic planning and ongoing risk assessment. Emerging trends in cloud computing and software-defined networking (SDN) are also considered, highlighting how these technologies can support the dynamic and secure operation of active/active data centers. Ultimately, this review underscores the need for financial institutions to adopt a holistic approach that integrates high availability with robust security frameworks, ensuring that their data centers can meet the demands of a rapidly evolving digital environment while safeguarding against potential threats. Keywords: Security, Financial Services, Data Center, Balancing High Availability, Active.