Reliable Cloud Services Research Articles

A Linear Swarm – Based Intelligence for Resource Allocation and Fault Prediction in Cloud

The widespread utilization Cloud Computing (CC) services for hosting real-time applications have led to the appearance of service dependability as an essential issue in both users and Cloud Service Providers (CSP). Two diverse fault tolerant approaches are provided to improve the cloud service reliability known as proactive and reactive model. Various prevailing approaches consider the coordination problem among the Virtual Machines (VMs) executes parallel processing. Devoid of appropriate VM coordination, the parallel processing outcomes are not so appropriate. To handle this issue, a VM-based cluster allocation model is designed to diminish the total resource consumption by the data centers and network resources. Here, the VM migration process is performed for deteriorating PM to certain optimal PMs. At last, the optimal target selection is handled with an improved optimization approach known as linear Swarm-based intelligence. Here, various metrics are evaluated and compared with other approaches. The experimental outcomes illustrate the efficiency of the anticipated model.

Integrating Cloud Services with Mobile Applications for Seamless User Experience

In the modern digital era, the integration of cloud services with mobile applications has emerged as a pivotal strategy for delivering a seamless and dynamic user experience. This approach not only enhances the functionality of mobile applications but also ensures that users can access services and data in real-time, regardless of their location. As mobile devices continue to dominate the technology landscape, the demand for responsive, scalable, and user-centric applications has skyrocketed. Cloud services, with their inherent capabilities such as storage, computing power, and advanced analytics, provide the backbone for developing applications that meet these demands. The integration of cloud services with mobile applications offers numerous advantages. It allows for the offloading of heavy computational tasks to cloud servers, thereby preserving the limited processing power and battery life of mobile devices. This capability enables mobile applications to perform complex tasks such as real-time data processing, machine learning inference, and multimedia content rendering without compromising user experience. Additionally, cloud services facilitate the synchronization of data across multiple devices, ensuring that users have consistent access to their data and settings regardless of the device they are using. This is particularly important in scenarios where users switch between devices, such as moving from a smartphone to a tablet or laptop. Moreover, the use of cloud services in mobile applications enhances security and reliability. Cloud providers typically offer robust security features, including data encryption, identity and access management, and continuous monitoring, which are essential for protecting sensitive user data. By leveraging these features, mobile applications can offer a higher level of security than would be feasible with on-device storage and processing. Furthermore, the reliability of cloud services ensures that applications remain available and functional even during peak usage times or when the user is in a location with poor network connectivity. Through techniques such as caching and offline access, cloud-integrated mobile applications can continue to operate smoothly even when real-time access to cloud resources is temporarily unavailable.

RESEARCH METHODS FOR ASSESSING THE QUALITY OF CLOUD SERVICES, INCLUDING RELIABILITY, PERFORMANCE AND SECURITY METRICS

In today's world, cloud services play an important role in the success of any global business. Evaluating the quality of these services is of paramount importance for companies and individuals who use them for their operations and data management. This article is an analysis of various methods for assessing the quality of cloud services. Reliability, performance, and security metrics are discussed, and different assessment models are compared. In the introduction, the authors describe the importance of cloud services and the need to assess their quality. This article provides an overview of cloud service quality assessment methods and shows how they can be applied to different aspects of these services. In the main part of the article, the author describes different methods of assessing the quality of cloud services and compares different assessment models. The author also provides examples of how these methods are used. The methods for assessing the quality of cloud services are discussed, covering reliability, performance, and security. While these metrics provide valuable insights into the performance of cloud services, some counterarguments highlight the potential limitations of using standardized metrics for evaluation. The author also recommends choosing a cloud provider and optimizing cloud services. It is emphasized that reliability metrics, such as uptime and disaster recovery time, are important to ensure the smooth operation of cloud services. Performance metrics, such as response time and throughput, indicate how quickly and efficiently cloud services can process data and requests. Security metrics, such as data encryption and access control, are important to protect data from unauthorized access and cyber threats. The author also emphasizes the importance of considering subjective factors such as user experience and customer support when assessing the quality of cloud services. Finally, the author discusses various risk assessment models that can be used to assess the security of cloud services. The author proposes a new approach to assessing the quality of cloud services for educational institutions. This approach is based on using a combination of quantitative and qualitative indicators that consider the reliability, performance, security, and usability of cloud services. In conclusion, the author recommends choosing a cloud provider and optimizing cloud services. Assessing the quality of cloud services is a complex process that requires a comprehensive and systematic approach. You need to consider various factors such as reliability, performance, and security, as well as your organization's individual needs and requirements.

Dynamic Load Balancing in Cloud Computing: A Review and a Novel Approach

In cloud computing, load balancing is essential since it guarantees effective resource utilisation and reduces response times. It effectively distributes incoming workload across available servers. Load balancing involves dividing tasks among multiple systems or resources over the internet. By distributing traffic and workloads, load balancing ensures that no server or computer is overloaded, underutilized, or idle in a cloud computing environment. To enhance overall performance in the cloud, it optimises a number of variables, including execution time, response time, and system stability. To increase the effectiveness and reliability of cloud services, load balancing evenly distributes traffic, workloads, and computing resources across the environment. The proposed method, Enhanced Dynamic Load Balancing for Cloud Computing, adjusts load distribution and maintains a balanced system by considering variables like server capacity, workload distribution, and system load. By incorporating these factors and employing adaptive threshold adjustment, this approach optimizes resource allocation and enhances system performance. Experimental research shows that the proposed new novel approach is more effective and efficient than current load balancing techniques. In this context of cloud computing, this ground-breaking method offers a workable substitute for dynamic load balancing.

Reliability-aware failure recovery for cloud computing based automatic train supervision systems in urban rail transit using deep reinforcement learning

As urban rail transit construction advances with information technology, modernization, information, and intelligence have become the direction of development. A growing number of cloud platforms are being developed for transit in urban areas. However, the increasing scale of urban rail cloud platforms, coupled with the deployment of urban rail safety applications on the cloud platform, present a huge challenge to cloud reliability.One of the key components of urban rail transit cloud platforms is Automatic Train Supervision (ATS). The failure of the ATS cloud service would result in less punctual trains and decreased traffic efficiency, making it essential to research fault tolerance methods based on cloud computing to improve the reliability of ATS cloud services. This paper proposes a proactive, reliability-aware failure recovery method for ATS cloud services based on reinforcement learning. We formulate the problem of penalty error decision and resource-efficient optimization using the advanced actor-critic (A2C) algorithm. To maintain the freshness of the information, we use Age of Information (AoI) to train the agent, and construct the agent using Long Short-Term Memory (LSTM) to improve its sensitivity to fault events. Simulation results demonstrate that our proposed approach, LSTM-A2C, can effectively identify and correct faults in ATS cloud services, improving service reliability.

Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing

Cloud computing has become ubiquitous in the enterprise environment as its on-demand model realizes technical and economic benefits for users. Cloud users demand a level of reliability, availability, and quality of service. Improvements to reliability generally come at the cost of additional replication. Existing approaches have focused on the replication of virtual environments as a method of improving the reliability of cloud services. As cloud systems move towards microservices-based architectures, a more granular approach to replication is now possible. In this paper, we propose a cloud orchestration approach that balances the potential cost of failure with the spot market running cost, optimizing the resource usage of the cloud system. We present the results of empirical testing we carried out using a simulator to compare the outcome of our proposed approach to a control algorithm based on a static reliability requirement. Our empirical testing showed an improvement of between 37% and 72% in total cost over the control, depending on the specific characteristics of the cloud models tested. We thus propose that in clouds where the cost of failure can be reasonably approximated, our approach may be used to optimize the cloud redundancy configuration to achieve a lower total cost.

Service Cost Effective and Reliability Aware Job Scheduling Algorithm on Cloud Computing Systems

Nowadays, the cloud computing services encounter several cloud Quality of Service challenges, such as reliability, cost, and response time. The most common mechanism to improve cloud service reliability is fault-tolerant technique. However, this reliability enhancement technique inevitably results in multiple replications, which lead to high service cost. In recognition of these challenges, we build a cloud computing systems resources management architecture. Then, we analyze the cloud service execution reliability on the physical resources of a VM and used a CUDA-enabled parallel two-dimensional LSTM to predict the software faults of a cloud VM. Thirdly, we propose an effective primary/backup cloud service cost calculation approach. To overcome the cloud service response time constraint, we integrate a response time slack factor into this method. Fourthly, we formulate the cloud service reliability and cost aware job scheduling problem, which aims at minimizing the total cloud service cost and rejection rate, and improving the system reliability. Fifthly, a heuristic greedy reliability and cost aware job scheduling (RCJS) algorithm is proposed. Finally, a performance evaluation is conducted and the experimental results demonstrate that our proposed RCJS algorithm significantly outperforms optimal redundant VM placement (OPVMP), MIN-MIN algorithms in terms of average service cost and rejection rate.

Deadline and Reliability Aware Multiserver Configuration Optimization for Maximizing Profit

Maximizing profit is a key goal for cloud service providers in the modern cloud business market. Service revenue and business cost are two major factors in determining profit and highly depend on multiserver configuration. Understanding the relationship between multiserver configuration and profit is important to service providers. Although existing articles have explored this issue, few of them consider deadline miss rate and soft error reliability of cloud services in multiserver configuration for profit maximization. Since deadline misses violate cloud services’ real-time requirements and soft error prevents successful processing of cloud services, it is necessary to consider the impact of deadline miss rate and soft error reliability on service providers’ profits when configuring the multiserver. This article introduces a deadline miss rate and soft error reliability aware multiserver configuration scheme for maximizing cloud service providers’ profit. Specifically, we derive the deadline miss rate considering the heterogeneity of cloud service requests, and propose an analytical method to compute the soft error reliability of multiserver systems. Based on the new deadline miss rate and soft error reliability models, we formulate the multiserver configuration optimization problem and introduce an augmented Lagrange multiplier-based iterative method to find the optimal multiserver configuration. Extensive experiments evaluate the efficacy of the proposed multiserver configuration approach. Compared with the two state-of-the-art methods, the profit gained by our scheme can be up to 11.92% higher.

A privacy oriented authorized key agreement framework for vehicular edge computing

AbstractThe revolution of technology has presented smart vehicles. To use reliable cloud service for smart vehicles at low latency, vehicular edge computing (VEC) is introduced. VEC adopts a unique approach for providing better vehicular connection, data storage, and sharing information among IoT‐enabled vehicles. However, the establishment of secure communication and authorized access to data in these cloud‐based services are always a concern. To ensure secure and authorized communication, we construct an authenticated key agreement framework for VEC. The edge computing‐based proposed vehicular communication protocol provides secure and authorized communication for an edge‐endorsed IoT environment. The security is proved on the random oracle model. The analysis of computational efficiency is also included. Furthermore, we have conferred the comparison analysis of our designed scheme with other schemes.

MATEM

The paradigms of Cloud Computing have risen at a very rapid rate. The Cloud Computing demands that a trustworthy or reliable service should be availed by its potential user. However, it is always a challenge for any cloud user to look for service that is suitable as well as reliable in every aspect. For a trustworthy discovery and delivery of cloud services, a multi-layered model i.e., Multi Agent based Trust Evaluation Model (MATEM) has been proposed. It is a multi-agent trust model that will make use of multiple agents to perform and evaluate the credibility of trust through trust evaluation system. Also, the performance validation has proven that the final calculated values of trust will be helpful in providing reliable cloud services to its users.

Load balancing technique based on hybrid resource utilization in cloud computing

Cloud computing uses the internet to supply dynamic services including memory, data, bandwidth and applications. Work schedules have an influence on cloud service reliability and performance. A proper provisioning method is required for a systematic resource allocation, which comprises of large virtual resources. Depending on the present state of the system, load balancing solutions can be distinguished as dynamic or static. Dynamic or static load balancing solutions can be employed to increase server response time or to raise load balancing factors for quicker and more efficient resource utilization. To decrease the load across resources and maximize CPU usage, a hybrid load balancing technique is developed. In the cloud, we have a finite quantity of resources that must be efficiently managed in order to fulfill tasks. Requests are transmitted to a cloud server, which assigns work via quadratic probing. During load balancing, the load is shifted from heavy-weighted servers to lighter-weighted servers, enhancing CPU usage. The suggested methodology's performance was assessed using average mean response time, make-span, average make-span, and average resource utilization. The Load Balancing Algorithm (LBA) is created with the primary purpose of reducing job completion time and increasing the average resource utilization ratio.

Ensuring the security and balancing the load in the cloud computing by DCRI-RI hybrid method

In the cloud environment, the confidentiality of data is improved by protecting the cloud data from unauthorized user access. During data communication, the balancing of load across cloud servers helps to maintain the cloud service's reliability. Through intrusion detection, the performance of secured data communication is enhanced in a significant way. Some techniques were developed in the field of cloud computing to provide secure communication between the cloud server and cloud user. These developed techniques failed to improve the security and load balancing efficiency simultaneously during the data transmission on a cloud. There is no sufficient algorithm in the present situation to detect the intrusions and to provide improved results in the load balancing. In this research work we propose a novel "Dynamic Certificateless Random Identity (DCRI)" algorithm for ensuring cloud security and a "Rank-Indexing (RI)" algorithm based on weightage for load balancing. Our proposed DCRI-RI hybrid method will ensure a better rate of intrusion detection with higher load balancing efficiency. The DCRI algorithm is relied on a cryptographic concept based on advanced encryption and decryption signature authentication process performed on both cloud server and user end to detect and remove the unauthorized users in a significant way.

Task Failure Prediction in Cloud Data Centers Using Deep Learning

A large-scale cloud data center needs to provide high service reliability and availability with low failure occurrence probability. However, current large-scale cloud data centers still face high failure rates due to many reasons such as hardware and software failures, which often result in task and job failures. Such failures can severely reduce the reliability of cloud services and also occupy huge amount of resources to recover the service from failures. Therefore, it is important to predict task or job failures before occurrence with high accuracy to avoid unexpected wastage. Many machine learning and deep learning based methods have been proposed for the task or job failure prediction by analyzing past system message logs and identifying the relationship between the data and the failures. In order to further improve the failure prediction accuracy of the previous machine learning and deep learning based methods, in this article, we propose a failure prediction algorithm based on multi-layer Bidirectional Long Short Term Memory (Bi-LSTM) to identify task and job failures in the cloud. The goal of Bi-LSTM failure prediction algorithm is to predict whether the tasks and jobs are failed or completed. The trace-driven experiments show that our algorithm outperforms other state-of-art prediction methods with 93 percent accuracy and 87 percent for task failure and job failures respectively.

Multiple Adaptive-Resource-Allocation Real-Time Supervisor (MARS) for Elastic IIoT Hybrid Cloud Services

In this paper, a Multiple Adaptive-resource-allocation Real-time Supervisor (MARS) scheme for hybrid cloud-assisted Industrial Internet of Things (IIoT) is proposed to support reliable cloud services even under rapidly changing service demands that occur in massive IIoT networks. Virtual Machines (VMs) in both private cloud and public clouds can be elastically and accurately allocated through the proposed MARS scheme, which uses Karush-Kuhn-Tucker (KKT) optimization applied to the VM Continuous-Time Markov Chain (CTMC) scheme. Because the MARS scheme can immediately determine the optimal number of VMs based on the hybrid cloud situation, a significant improvement in the elasticity performance can be obtained. Compared to using the CTMC scheme, the results show that the MARS scheme can improve the response time up to 19.3 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 73&#x0025; (based on the activation rate) and the elasticity by 26.7&#x0025;, and reduce the cost by 1.2&#x0025;.

Failure Free Cloud Computing Architectures

Cloud computing has gained popularity over the years, some organizations are using some form of cloud computing to enhance their business operations while reducing infrastructure costs and gaining more agility by deploying applications and making changes to applications easily. Cloud computing systems just like any other computer system are prone to failure, these failures are due to the distributed and complex nature of the cloud computing platforms. Cloud computing systems need to be built for failure to ensure that they continue operating even if the cloud system has an error. The errors should be masked from the cloud users to ensure that users continue accessing the cloud services and this intern leads to cloud consumers gaining confidence in the availability and reliability of cloud services. In this paper, we propose the use of N-Modular redundancy to design and implement failure-free clouds.

Proactive Fault-Tolerance Technique to Enhance Reliability of Cloud Service in Cloud Federation Environment

Cloud federation is a new computing paradigm that has paved the way for cloud service providers (CSPs) to offer their unused resources (virtual machine) to other CSPs when their resource demands are low. Federation also allows CSPs to outsource their resource requests to other CSPs when their computing resources&#x2019; demands are high. Thus, in cloud federation environment reliability and availability of services offered by service providers increase as the CSPs are able to share their resources among themselves. Moreover, to maintain the reliability and availability of cloud services offered through federation, it is important that the computational environment of member CSPs within the federation is fault tolerant. Therefore, there is a need for fault tolerant system to guarantee cloud service reliability and availability in cloud federation environment. In this article, we propose a proactive fault tolerance system that preempts faults within the federation on the basis of CPU temperature. The fault tolerance system within the federation is modeled as a multi-objective optimization problem of maximizing profit and minimizing migration cost while redistributing resources (virtual machine) from faulty CSPs to non-faulty CSPs within the federation. To address this issue, we have also proposed an algorithm called Preference Based Fault Management (PBFM) to manage the federation in the event of faults. We perform extensive experiments to evaluate the effectiveness of our proposed mechanism and compare it with two other mechanisms MCAFM (Migration Cost Assured Fault Management) and PAFM (Profit Assured Fault Management). Results show that our proposed mechanism PBFM yields an optimized solution to the general problem of profit and migration cost trade-off in presence of faulty CSPs.

Analysis of Job Failure and Prediction Model for Cloud Computing Using Machine Learning.

Modern applications, such as smart cities, home automation, and eHealth, demand a new approach to improve cloud application dependability and availability. Due to the enormous scope and diversity of the cloud environment, most cloud services, including hardware and software, have encountered failures. In this study, we first analyze and characterize the behaviour of failed and completed jobs using publicly accessible traces. We have designed and developed a failure prediction model to determine failed jobs before they occur. The proposed model aims to enhance resource consumption and cloud application efficiency. Based on three publicly available traces: the Google cluster, Mustang, and Trinity, we evaluate the proposed model. In addition, the traces were also subjected to various machine learning models to find the most accurate one. Our results indicate a significant correlation between unsuccessful tasks and requested resources. The evaluation results also revealed that our model has high precision, recall, and F1-score. Several solutions, such as predicting job failure, developing scheduling algorithms, changing priority policies, or limiting re-submission of tasks, can improve the reliability and availability of cloud services.

Hypergeometrically Represented Responsive and Reliable Cloud Service Model for Personal and Private Clouds

Hypergeometrically Represented Responsive and Reliable Cloud Service Model for Personal and Private Clouds

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-monitoring, analysis, and reporting technology) hard drive metrics with other system metrics, such as central processing unit (CPU) utilisation. Therefore, we propose a combined system metrics approach for failure prediction based on artificial intelligence to improve reliability. We tested over 100 cloud servers’ data and four artificial intelligence algorithms: random forest, gradient boosting, long short-term memory, and gated recurrent unit, and also performed correlation analysis. Our correlation analysis sheds light on the relationships that exist between system metrics and failure, and the experimental results demonstrate the advantages of combining system metrics, outperforming the state-of-the-art.

Intelligent Fault-Tolerant Mechanism for Data Centers of Cloud Infrastructure

Fault tolerance in cloud computing is considered as one of the most vital issues to deliver reliable services. Checkpoint/restart is one of the methods used to enhance the reliability of the cloud services. However, many existing methods do not focus on virtual machine (VM) failure that occurs due to the higher response time of a node, byzantine fault, and performance fault, and existing methods also ignore the optimization during the recovery phase. This paper proposes a checkpoint/restart mechanism to enhance reliability of cloud services. Our work is threefold: (1) we design an algorithm to identify virtual machine failure due to several faults; (2) an algorithm to optimize the checkpoint interval time is designed; (3) lastly, the asynchronous checkpoint/restart with log-based recovery mechanism is used to restart the failed tasks. The valuation results obtained using a real-time dataset shows that the proposed model reduces power consumption and improves the performance with a better fault tolerance solution compared to the nonoptimization method.