Cloud-Based High Availability Architecture Using Least Connection Load Balancer and Integrated Alert System

Since cloud computing has emerged as a commercial reality, guaranteeing high availability (HA) has become a critical requirement. Many kinds of HA mechanisms have been developed with the rapid expansion of cloud computing. However, selection and configuration are difficult in practice. In the development phase, developers must choose HA mechanisms for cloud computing based on their experience. In the use phase, users must choose and configure the HA mechanisms for their virtual machines (VMs) or services deployed on VMs. In this paper, we proposed a model-based HA configuration framework for the cloud. For the developer, we proposed an open HA mechanism library and an automatic configuration process. For users, we intend to ease the selection of HA mechanisms in the ever-changing cloud environment. We are implementing our approach in our model-based cloud platform, JadeCloud.

This paper proposes an architecture for deploying applications on a fog computing environment by adding another layer of fog nodes in a network segment that gains high software application availability. The conventional fog computing architecture would permanently shift the storage, applications, and data from cloud servers to fog nodes, thus reducing the dependency on the cloud. As a result, fog nodes are burdened with the task previously done by cloud servers and have become “mini cloud servers.” Instead of permanently shifting the tasks from cloud servers to fog nodes, the proposed architecture would only do the shifting, when necessary, like if an internet outage. Additionally, this research also introduced the middleware application that acts as a detector and replacement if service outage so that the availability of the services is not interrupted, especially during the internet outage, by adding another layer of fog node in a network segment. The computational process occurs between end-users and the fog nodes without having to rely on cloud servers. An experiment was conducted to test the performance of the proposed architecture. From the experiment, it can be concluded that the deployment of fog nodes in a segmented network is possible and able to increase the availability of data and services if an internet outage.

Cloud computing alters the way organizations manage and deploy their IT resource. It provides an organization with scalable, inexpensive, and flexible options. The complexity and dynamic nature of cloud environments pose a challenge to maintaining high availability at all times, especially when the system fails or a disaster arises. The legacy techniques of disaster recovery, fault tolerance, and high availability leave behind much to be desired. These techniques are mostly static, slow to respond, and have a dismal ability to adapt to continuously changing conditions in contemporary cloud systems. Such techniques largely depend on manual configurations and predefined policies; resulting in lots of inefficiencies and increases in the risk of service downtime. This research investigates the way Artificial Intelligence (AI) changes the paradigm on cloud resilience to promote adoption of intelligent systems for guaranteed disaster recovery, better fault-tolerant behavior, or transparent high availability. With machine learning algorithms, AI-based cloud services utilize large data volumes to reveal patterns within system logs, performance metrics, and user behavior data; thereby offering real-time anomaly detection and predictive failure analysis. For example, techniques like predictive analytics help cloud providers predict likely system outages, optimize the resources to be used, and automate failover processes (Xu et al., 2021; Lee & Kumar, 2022). AI-aided disaster recovery techniques employ complex algorithms to produce an adaptive backup mechanism, thereby minimizing loss and reducing restoration time. Fault tolerance in AI cloud systems comes from intelligent error correction, automatic isolation of faults, and self-healing features, i.e. repair of faults without the need for human supervision (Chen et al., 2020). Besides that, AI also contributes to high availability through intelligent load balancing, which ensures that at any given time, resources are optimally distributed throughout the network to sustain continuous service even during peak demand or unanticipated failures (Patel & Zhang, 2023). The approach is a comprehensive review of the various existing literature on the topic, empirical analysis of the current AI-driven cloud solutions available in the market, and case studies for comparison analysis on the different AI systems. The study scenario reveals that AI-driven solutions noticeably reduce downtimes, lead to improved recovery times, and contribute to overall system reliability as compared to traditional methods. However, other areas include model bias, data privacy, and continuous training of AI models. This study expands the trends of AI in the field of cloud computing by documenting the significance of intelligent systems in bridging traditional weaknesses of resilience strategies. It further signifies the need for AI into predictive maintenance, automated disaster response, and proactive fault management of rapidly changing dynamic cloud environments. Future studies will focus on AI integration along with edge computing and blockchain technologies for even more robust and secure services in cloud operations.

Leveraging redundant resources is a common means of addressing availability requirements, but it often implies redundant costs as well. At the same time, virtualization technologies promise cost reduction through resource consolidation. Virtualization and high-availability (HA) technologies can be combined to optimize availability while minimizing costs, but merging them properly introduces new challenges. This paper looks at how virtualization technologies and techniques can augment and amplify traditional HA approaches while avoiding potential pitfalls. Special attention is paid to applying HA configurations (such as active/active and active/passive) to virtualized environments, stretching virtual environments across physical machine boundaries, resource-sharing approaches, field experiences, and avoiding potential hazards.

The rapid development of Information Technology (IT) has made a high availability web server a necessity. CloudLinux is a CentOS-based operating system specifically for the needs of servers with good high availability. This study will implement and analyze a CloudLinux-based web server for the Embassy of the Kingdom of Morocco in Jakarta to replace the current CentOS-based web server that does not meet high availability standards. The research methodology used in this study is the PPDIOO Life-cycle from Cisco. Designing a network according to the customer's needs requires identifying several elements in it, including the goals and constraints faced by the organization. Cisco creates a network life-cycle that can help these problems into six phases: Prepare, Plan, Design, Implement, Operate, and Optimize (PPDIOO). This study aims to determine how well the uptime, response time, and load impact are on the CloudLinux-based web server. The uptime test results on the CloudLinux-based web server have a value of an average of 99.971%. Testing the response time on the CloudLinux-based web server has an average value of 684.75ms (milliseconds). The results of the load impact test on the CloudLinux-based web server using ten virtual users (10VU) has a total value of 7595 requests, load impact using fifteen virtual users (15VU) has a total value of 11315 requests, and finally, load impact using twenty-five virtual users (25VU) has a total value of 18631 requests.

Calculations of service availability of a high-availability (HA) cluster are usually based on the assumption of load- independent machine availabilities. In this paper, we study the issues and show how the service availabilities can be calculated under the assumption that machine availabilities are load dependent. We present a Markov chain analysis to derive the steady-state service availabilities of a load-dependent machine availability HA cluster. We show that with a load-dependent machine availability, the attained service availability is now policy dependent. After formulating the problem as a Markov decision process, we proceed to determine the optimal policy to achieve the maximum service availabilities by using the method of policy iteration. Two greedy assignment algorithms are studied: least load and first derivative length (FDL) based, where least load corresponds to some load balancing algorithms. We carry out the analysis and simulations on two cases of load profiles: In the first profile, a single machine has the capacity to host all services in the HA cluster; in the second profile, a single machine does not have enough capacity to host all services. We show that the service availabilities achieved under the first load profile are the same, whereas the service availabilities achieved under the second load profile are different. Since the service availabilities achieved are different in the second load profile, we proceed to investigate how the distribution of service availabilities across the services can be controlled by adjusting the rewards vector.

Meso-level Comparison of Mental Health Service Availability and Use in Chile and Spain

Performance is one of the most important business requirements for web based applications. The suitability of better platform for any web based application depends on various factors attributable to processor architecture, memory, local/ global network, input/output characteristics etc. Performance testing is an important part of any distributed or web application testing plan. Inclusion of performance estimates into planning and development cycles ensures that the application delivered to the user satisfies sustained performance even at high load, availability and scalability requirements. In this paper we have thrown light on performance comparison of web applications on single and multi core servers. The tests have been carried out on single processor single core server and also on dual processor quad core Xeon based server. The performance comparison was carried out based on response time, throughput (No. of hits v/s response time) and connection pooling v/s no connection pooling. It was found that performance of Java applications running on the multi-core servers is far better than those running on single core servers. Web applications on quad-core server give better performance, high availability and scalability. Apache JMeter was used to load test the functional behaviour and measure performance based on throughput (No of hits v/s response time) for dynamic querying of data using Java servlets on Tomcat web server. Based on the analysis of performance of web server under varying load of no. of users and threads, it was found that applications on quad core server give more throughput and less response time as compared to single core server. Connection pooling increases the performance of web applications by reusing active database connections instead of creating a new connection with every request. Performance was compared by running the tests using Java servlets for connection pooling and also by acquiring connections directly from the JDBC driver without connection pooling. Based on the test results it was found that in case of quad core server, throughput i.e the number of requests served per second is much higher and reuse of connections from pool of connections showed significant improvement in application performance. This paper describes the results of tests carried out for comparing performance (based on response time) between web applications running on single core server and on quad core server. Tests have also been carried out to analyze advantage of connection pooling over no connection pooling. The test results have been shown graphically.

As an important IT infrastructure, website is a system which requires high reliability and availability levels. Website meets the criteria as a highly available system because website must provide services to clients in real time, handle a large amount of data, and not lose data during transaction . A highly available system must meet the condition of being able to run continuously as well as guaranteeing consistency on data requests. This study designed a website with high availability. The approach was building network cluster with failover and replicated block device functions . Failover was built to provide service availability, while replicated block device provides data consistency during failure of service . With failover cluster and replicated block device approaches, a cluster which is able to handle service failures of web server and database server on the website. The result of this study was the services of the website could run well if there was any failure in node members of the cluster. The system was able to provide 99,999 (five nines) availability on database server services and 99,98 (three nines) on web server services .

Cloud computing is a distributed architecture that has shared resources, software, and information is provided to computers and other devices on a scalable platform and demand. Availability of the cloud services is one of the key security issues in it. Distributed Denial of Service (DDoS) is an attack that threats the availability of the cloud services. In this paper, effect of the DDoS attack on the cloud is investigated. Therefore, a model for attack based on the DDoS is designed, and then we simulate a cloud system on the experimental environment. Experiments show that the cloud system is vulnerable to this attack. In order to confronting this attack, several solutions are proposed in this paper. We suggest load balancing and honeypots beside the intrusion detection systems (IDS) is used to defend from the attack.

Though security measurement in cloud has become more mature and self-determining in last few years, rather than being solely dependent on vendors for trusting the cloud service, trust management is still a challenging issue for cloud researchers. Instead of having some amazing qualities like virtualization potential, high storage capacity, multi tenancy , high service availability - the only obstacle to adapt the cloud technology in a huge scale, is till now its security and trust issue. Trust being a qualitative factor can be established and determined independently and several algorithms are there to establish trust in a cloud system. This paper focuses on measuring the strength of cloud security by establishing trust, using some measurement index and with the help of some known parameters.

The explosive growth of the high-speed multimedia networks and the widespread use of web-based related applications place new demands on the network servers. The network end systems such as PC-Router, network server and host connected to high speed links must satisfy QoS requirements for multimedia traffics such as delay and loss ratio. Most works on operating systems support for high-speed network interface have focused in increasing the throughput and decreasing the interrupt handing overhead. However, the problem is that not all traffic streams are equal in terms of QoS requirements. Multimedia applications often depend more on low-latency than on high throughput. This study first compares the performance measures of hard timer and soft timer polling schemes used for high-speed network interface with high traffic load. Then it modifies the polling-based interrupt handling for high-speed network to not just eliminate the interrupt overhead but also to guarantee the QoS requirements for multimedia traffic. Meanly we propose an input network interface mechanism, which combines the advantages of using polling interrupt handling under high traffic load and using multi-priority queues scheduling algorithm to provide the QoS requirements. In addition to the throughput performance metric in which most of the literatures focus on only, other performance metrics such as CPU availability, loss ratio, packet delay are defined and studied. The performance evaluations, which are performed using a discrete event simulation, indicate that under conditions of high traffic load, the proposed system offers increased throughput and reduced latency for real time traffics.

In an era where digital transformation is reshaping government services, the need for efficient and secure national registration systems has become increasingly critical. This thesis presents the design and development of an Electronic National Registration System (ENRS) tailored for Zambia, leveraging the capabilities of cloud computing to enhance its functionality and scalability. The proposed system aims to streamline the registration process, improve data accessibility, and ensure robust security measures for national identification records. The research begins with an exploration of Zambia's current registration system and its limitations, identifying key areas for improvement (Zambia National Registration Bureau, 2023). Subsequently, a comprehensive system design is developed, incorporating cloud-based infrastructure to facilitate real-time data management, scalable storage solutions, and high availability (Smith & Jones, 2022). The design emphasizes data integrity, security, and user accessibility, addressing common challenges faced in national registration systems (Brown, 2021). Implementation details are discussed, including the selection of appropriate cloud services, system architecture, and integration strategies (Davis & Lee, 2023). The system is evaluated through a series of tests to assess its performance, reliability, and security in a Zambian context (Nguyen et al., 2024). Results indicate that the cloud-based ENRS significantly enhances the efficiency of national registration processes, offering a scalable and resilient solution that aligns with Zambia’s digital infrastructure goals (Wilson, 2023). This thesis contributes to the field of information systems by demonstrating the effectiveness of cloud computing in national registration systems and provides a model that can be adapted for similar applications in other developing countries (Taylor & Patel, 2024).

An increasing number of applications are being hosted on cloud based platforms. Cloud platforms are serving as a general computing facility and applications being hosted on these platforms range from simple multi-tier web applications to complex social networking, eCommerce and Big Data applications. High availability, performance and auto-scaling are key requirements of Cloud based applications. Cloud platforms serve these requirements using dynamic provisioning of resources in on-demand, multi-tenant fashion. A key challenge for cloud service providers is to ensure the Quality of Service (QoS), as a user / customer requires more explicit guarantees of QoS for provisioning of services. Cloud service performance problems can directly lead to extensive financial loses. Thus, control and verification of QoS become a vital concern for any production level deployment. Therefore, it is crucial to address performance as a managed objective. The success of cloud services depends critically on automated problem diagnostics and predictive analytics enabling organizations to manage their performance proactively. Moreover, effective and advance monitoring is equally important for performance management support in clouds. In this thesis, we explore the key techniques for developing monitoring and performance management systems to achieve robust cloud systems. At first, two case studies are presented as a motivation for the need of a scalable monitoring and analytics framework. It includes a case study on performance issues of a software service, which is hosted on a virtualized platform. In the second case study, cloud services are analyzed that are offered by a large IT service provider. A generalization of case studies forms the basis for the requirement specifications which are used for state-of-the-art analysis. Although, some solutions for particular challenges have already been provided, a scalable approach for performance problem diagnosis and prediction is still missing. For addressing this issue, a distributed scalable monitoring and analytics framework is presented in the first part of this thesis. We conducted a thorough analysis of technologies to be used by our framework. The framework makes use of existing monitoring and analytics technologies. However, we develop custom collectors to retrieve data non-intrusively from different layers of cloud. In addition, we develop the analytics subscriber and publisher components to retrieve service related events from different APIs and sends alerts to the SLA Management component for taking corrective measures. Further, we implemented an Open Cloud Computing Interface (OCCI) monitoring extension using OCCI Mixin mechanism. To deal with performance problem diagnosis, a novel distributed parallel approach for performance anomaly detection is presented. First all anomalous metrics are found from a distributed database of time-series for a particular window. For comparative analysis three light-weight statistical anomaly detection techniques are selected. We extend these techniques to work with MapReduce paradigm and assess and compare the methods in terms of precision, recall, execution time, speedup and scale up. Next, we correlate the anomalous metrics with the target SLO in order to locate the suspicious metrics. We implemented and evaluated our approach on a production Cloud encompassing Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) service models. Experimental results confirm that our approach is efficient and effective in capturing the metrics causing performance anomalies. Finally, we present the design and implementation of an online anomaly prediction system for cloud computing infrastructures. We further present an experimental evaluation of a set of anomaly prediction methods that aim at predicting upcoming periods of high utilization or poor performance with enough lead time to enable the appropriate scheduling, scaling, and migration of virtual resources. Using real data sets gathered from Cloud platforms of a university data center, we compare several approaches ranging from time-series (e.g. auto regression (AR)) to statistical classification methods (e.g. Bayesian classifier). We observe that linear time-series models, especially AR models, are most likely suitable to model QoS measures and forecast their future values. Moreover, linear time-series models can be integrated with Machine Learning (ML) methods to improve proactive QoS management.

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.