Cloud Systems Research Articles

Quantum cloud computing: Trends and challenges

Quantum computing is a new paradigm that will revolutionize various areas of computing, especially cloud computing. Quantum computing, still in its infancy, is a costly technology that can operate in highly isolated environments because of its rapid response to environmental factors. This makes quantum computing a challenging technology for researchers to access. These problems can be solved by integrating quantum computing into an isolated remote server, such as a cloud, and making it available to users. Furthermore, experts predict that quantum computing, with its ability to swiftly resolve complex and computationally intensive operations, will offer significant benefits in systems that process large amounts of data, like cloud computing. This article presents the vision and challenges for the quantum cloud computing paradigm that will emerge with the integration of quantum and cloud computing. Next, we present the advantages of quantum computing over classical computing applications. We analyze the effects of quantum computing on cloud systems, such as cost, security, and scalability. Besides all of these advantages, we highlight research gaps in quantum cloud computing, such as qubit stability and efficient resource allocation. This article identifies the advantages and challenges of quantum cloud computing for future research, highlighting research gaps.

The Impact of Cloud Systems on Enhancing Organizational Performance through Innovative Business Models in the Digitalization Era

Abstract Information technology has massively transformed the world of business over the past fifty years - first individual functional areas within companies (“first wave”), later increasingly also cross-divisional value-added processes and trade (“second wave”), advent of the internet and the World Wide Web marked a transformative shift in how information is accessed, shared, and distributed (“third wave”), mobile computing and smartphone (“fourth wave”) and cloud computing that has emerged as a paradigm for delivering computing resources and services over the internet on-demand, providing scalability, flexibility, and cost-effectiveness (“fifth wave”). This paper investigates the transformative impact of cloud systems on increasing organizational performance through innovative business models in the age of digitalization. By employing a mixed methods approach, the study integrates qualitative and quantitative techniques to provide a comprehensive understanding of the research phenomenon.

Advancing Road Safety through Cloud Based RSU Solutions for Smart Internet of Vehicles

This research proposes a system to improve road safety by detecting and alerting drivers about hazardous road conditions in real-time. Vehicles equipped with sensors, GPS, and communication devices can autonomously detect hazardous road conditions and transmit alerts to a central cloud system through IoT MQTT protocol. Upon receiving alerts, vehicles can dynamically adjust their routes to avoid hazardous areas, reducing the risk of accidents. By utilizing IoT technology, the system ensures the reliability and authenticity of shared information, enhancing trust and overall road safety awareness. The proposed system architecture leverages cloud-based Roadside Units (RSUs) to facilitate communication among smart vehicles, providing real-time road condition information and ensuring secure and efficient data exchange. The system aims to address the challenges of existing IoV frameworks, such as data protection, key management, storage, concurrency performance, and response speed, by implementing robust security measures and efficient data management strategies.

Measurement report: Cloud and environmental properties associated with aggregated shallow marine cumulus and cumulus congestus

Abstract. Mesoscale organization of marine convective clouds into linear or clustered states is prevalent across the tropical and subtropical oceans, and its investigation served as a guiding focus for a series of process study flights conducted as part of the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) during summer 2020, 2021, and 2022. These select ACTIVATE flights involved a novel strategy for coordinating two aircraft, with respective remote sensing and in situ sampling payloads, to probe regions of organized shallow convection for several hours. The main purpose of this measurement report is to summarize the aircraft sampling approach, describe the characteristics and evolution of the cases, and provide an overview of the datasets that can serve as a starting point for more detailed modeling and analysis studies. Six flights are described, involving a total of 80 dropsonde profiles that capture the environment surrounding clustered shallow convection. The flights include detailed observations of the vertical structure of cloud systems, comprising up to 20 in situ sampling levels. Four cases involved deepening convection rooted in the marine boundary layer that developed vertically to 2–5 km with varying precipitation amounts, while two cases captured more complex and developed cumulus congestus systems extending above 5 km. In addition to the thermodynamic and dynamic characterization afforded by dropsonde and in situ measurements, the datasets include cloud and aerosol microphysics, trace gas concentrations, aerosol and droplet composition, and cloud and aerosol remote sensing from high-spectral-resolution lidar and polarimetry.

Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies

The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making.

Effects of aerosols on the lifecycle of a mesoscale cloud cluster over the Indian peninsula: A numerical study with a bin-based cloud microphysics scheme

The effects of aerosols (i.e., CCN) on the lifecycle of a well-documented mesoscale cloud cluster (CC) over the Indian peninsula are investigated in this study. The WRF model coupled with a sophisticated spectral bin microphysics scheme is employed to simulate the observed cloud system under various CCN scenarios. The CCN sensitivity experiments were carried out using three different background CCN concentrations (250, 1000, and 3000 cm−3). The prevailing environment featured a relatively dry mid-level. The introduction of more aerosols weakened the convection and moistened the upper troposphere in the initiation and mature phases. The major impacts of the enhanced aerosols include the dissipation of shallow clouds, a decrease in the number of convective cells and their overall coverage, enhanced convective organization in the early phase(s) of the lifecycle, enhancement of the cloud-free area, etc. The key microphysical changes due to enhanced aerosols are the increase in cloud liquid water, presence of numerous smaller cloud droplets, enhancement of condensation and evaporation, formation of smaller ice crystals, reduced snow mass and reduction in the aggregation process, high graupel mass and number and a reduction in graupel size, fewer raindrops with slight enhancement in raindrop size, etc. Cloud growth is significantly limited in the high aerosol scenarios due to large evaporation favored by a relatively dry environment and no invigoration effect is noted. A significant reduction in the rainfall (and associated rainfall-type) from isolated convective cores is noted due to high aerosols, especially in the initiation and mature phases. The eventual impact on the surface precipitation is a decrease in overall rainfall in the enhanced aerosol scenarios, with suppression of heavy rain. The study indicates that in a dry environment, the microphysical changes in various CCN scenarios cumulatively lead to macrophysical changes, which are found to be the primary controller of the overall surface rainfall associated with the CC.

Fractional social optimization-based migration and replica management algorithm for load balancing in distributed file system for cloud computing

ABSTRACT Effective management of data is a major issue in Distributed File System (DFS), like the cloud. This issue is handled by replicating files in an effective manner, which can minimize the time of data access and elevate the data availability. This paper devises a Fractional Social Optimization Algorithm (FSOA) for replica management along with balancing load in DFS in the cloud stage. Balancing the workload for DFS is the main objective. Here, the chunk creation is done by partitioning the file into a different number of chunks considering Deep Fuzzy Clustering (DFC) and then in the round-robin manner the Virtual machine (VM) is assigned. In that case for balancing the load considering certain objectives like resource use, energy consumption and migration cost thereby the load balancing is performed with the proposed FSOA. Here, the FSOA is formulated by uniting the Social optimization algorithm (SOA) and Fractional Calculus (FC). The replica management is done in DFS using the proposed FSOA by considering the various objectives. The FSOA has the smallest load of 0.299, smallest cost of 0.395, smallest energy consumption of 0.510, smallest overhead of 0.358, and smallest throughput of 0.537.

Containerized Microservices for Mobile Applications Deployed on Cloud Systems

This study explores the transformative role of containerized microservices in the sphere of mobile application development, especially within public cloud ecosystems. It focuses on how technologies such as Docker and Kubernetes contribute to improving deployment, scalability, and overall management of mobile applications, with an emphasis on containerizing backend services. We analyze their efficiency in streamlining deployment processes, focusing on how they improve the application’s performance and reliability. Additionally, we examine various alternative deployment strategies, such as blue-green, rolling, and canary releases, to emphasize their effectiveness in minimizing risks and facilitating smooth transitions in dynamic cloud environments. The study takes a comprehensive approach to achieve this goal, which includes a systematic review of existing literature, a thorough examination of relevant use cases, and an assessment of open-source technologies. Our findings reveal not only the practical benefits of these strategies but also their strategic application, offering important insights for software engineers and decision-makers. This study emphasizes the significance of integrating and optimizing containerized microservices in mobile app development to achieve more efficient, scalable, and manageable application lifecycles on cloud-based platforms.

DNA Cryptography for Enhanced Data Storage Security in Cloud Environments

Despite the persistent security challenges inherent in cloud systems, a distributed cloud environment necessitates an access control model that is contextually aware to effectively manage these challenges. This model should incorporate a role activation process based on the user's contextual information. Within this role activation process, the rationale behind data collection and usage is disclosed, enabling administrators to establish context-based policies. Consequently, role permissions are dynamically activated based on the association of roles with context. To mitigate complications in the role-based access control model, users are categorized into classes or groups, each with its own access control standards. Access to specific resources is determined by the user's identity upon request. Traditional access control models often fall short in cloud environments due to their inability to address all aspects of the diverse entities, resources, and users present. In the proposed access control system with perception reasoning, entities are expanded using Extensible Access Control Markup Language (XACML), while a trust module monitors user behavior dynamically, detecting and restricting malicious users attempting illegal data access. This includes assigning an identity tag to malicious users, which involves task and data classification along with database tagging.

Enhancing water management in smart agriculture: A cloud and IoT-Based smart irrigation system

It is widely acknowledged that traditional agricultural practices must effectively address the increasing global demand for food while facing water scarcity and climate change challenges. The imperative for environmentally sustainable agricultural approaches has never been more urgent. In response, IoT-based Smart Agriculture has emerged as a promising solution. Smart Agriculture can significantly bolster agricultural development by integrating renewable energy sources, particularly in arid regions with abundant sunlight. Real-time control systems utilizing big data acquisition and processing are pivotal in this advancement. This study introduces a cloud-based smart irrigation system to connect numerous small-scale smart farms and centralize pertinent data. The system optimizes irrigation water usage through comprehensive big data collection, storage, and analysis. Leveraging the insights from this data can facilitate informed decision-making regarding water management, thereby fostering conservation efforts, particularly in arid regions. Additionally, this research explores weather prediction services to enhance irrigation control, particularly during intermittent rainy periods, within a real-world testbed powered by solar energy. The testbed incorporates a sophisticated big data management system. It showcases a Smart Farm prototype leveraging the Internet of Things, embedded systems, low-cost Wireless Sensor Networks, NI CompactRIO controller, and Cloud Computing. Encouragingly, the results demonstrate tangible improvements in water conservation. Furthermore, the deployment methodology outlined in this study provides a clear roadmap that can be readily adapted for similar research endeavors.© 2023 Elsevier Inc. All rights reserved.

Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds

Abstract. We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere (i.e. between layers or outside a cloud system). Despite the fact that the strength of lidar signals from the molecular atmosphere is much lower compared to the in-cloud intervals, studies of MS effects in such regions are of interest from scientific and practical points of view. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view (RFOV). Therefore, the photons scattered within the forward peak escape the sampling volume formed by the RFOV (i.e. the escape effect). We demonstrated that the escape effect is an inherent part of MS properties within the free atmosphere beyond the cloud far edge. In the cases of the ground-based lidar, the MS contribution is lower than 5 % within the regions of the cloud-free molecular atmosphere with a distance from the cloud far edge of about 1 km or higher. In the cases of the space-borne lidar, the rate of decrease of the MS contribution is so slow that the threshold of 5 % can hardly be reached. In addition, the effect of non-uniform beam filling is extremely strong. Therefore, practitioners should employ, with proper precautions, lidar data from regions below the cloud base when treating data of a space-borne lidar. In the case of two-layered cloud, the distance of 1 km is sufficiently large so that the scattered photons emerging from the first layer do not affect signals from the second layer when we are dealing with the ground-based lidar. In contrast, signals from the near edge of the second cloud layer are severely affected by the photons emerging from the first layer in the case of a space-borne lidar. We evaluated the Eloranta model (EM) in extreme conditions and showed its good performance in the cases of ground-based and space-borne lidars. At the same time, we revealed the shortcoming that can affect practical applications of the EM. Namely, values of the key parameters – i.e. the ratios of phase functions in the backscatter direction for the nth-order-scattered photon and a singly scattered photon – depend not only on the particle phase function but also on the distance from a lidar to the cloud and the receiver field of view. Those ratios vary within a quite large range, and the MS contribution to lidar signals can be largely overestimated or underestimated if erroneous values of the ratios are assigned to the EM.

Mathematical Model and Statistical Assessment of Non Linear Scheduling Optimization in Cloud Environment using Improved Cuckoo Search Algorithm

The scheduling optimization subject is indeed one of the most effective solutions in cloud system area to ensure the high systems performance and resource usage. The novelty presented in this study is a mathematical model that draws on an Improved Cuckoo Search Algorithm as a scheduling non-linear optimizer for the cloud computing environment. The model under consideration takes into account the underlying complications as well as the varying nature of the use of cloud resources, primarily addressing the latency issue with priority given to the increase of the throughput. Improved Cuckoo Search (ICS) Algorithm, developed on 'cuckoo search' algorithm, uses adaptive step size and probabilistic switching, which, in turn, ensure that the solution space is both being properly examined and exploited. The applications’ utilization of the scheduling mathematical model is based on an elaborately designed theoretical background representing the task as a non-linear optimization problem that meets all the specific constraints and objectives important in cloud computing. Statistical reassessment involves the comparison of the proposed model with standard benchmarks. The proposed model superiority is evident in terms of convergence speed, solution quality and robustness against diverse workloads, as shown in varied tests. The findings of this research contributes to the field by deriving a specialized tool commonly used by the cloud service provider in designing resource scheduling, enhancing the service delivery and operational efficiency.

A Classification of Cloud Services and Their Security Assessment in the Healthcare Industry

The role of cloud-based services in healthcare settings is crucial in the 21st century. This paper discusses their advantages and tools in hospitals, clinics, and diagnostic centers, categorizing existing applications and services into data storage, computing power, networking, PaaS, SaaS, data analytics, business intelligence, and project management. It also provides an in-depth analysis of security and risk assessment issues in cloud-based services, supported by case studies. Additionally, it includes a comparative risk analysis diagram of secured versus non-secured cloud systems in healthcare. Finally, the paper concludes with recommendations for future work. Key Words: Healthcare facilities, diagnostic centers, cloud services, information technology systems, security, risk analysis.

Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE)

Abstract Convective clouds play an important role in the Earth’s climate system and are a known source of extreme weather. Gaps in our understanding of convective vertical motions, microphysics, and precipitation across a full range of aerosol and meteorological regimes continue to limit our ability to predict the occurrence and intensity of these cloud systems. Towards improving predictability, the National Science Foundation (NSF) sponsored a large field experiment entitled “Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE).” ESCAPE took place between 30 May - 30 Sept. 2022 in the vicinity of Houston, TX because this area frequently experiences isolated deep convection that interacts with the region's mesoscale circulations and its range of aerosol conditions. ESCAPE focused on collecting observations of isolated deep convection through innovative sampling, and on developing novel analysis techniques. This included the deployment of two research aircraft, the National Research Council of Canada Convair-580 and the Stratton Park Engineering Company Learjet, which combined conducted 24 research flights from 30 May to 17 June. On the ground, three mobile X-band radars, and one mobile Doppler lidar truck equipped with soundings, were deployed from 30 May to 28 June. From 1 August to 30 Sept. 2022, a dual-polarization C-band radar was deployed and operated using a novel, multi-sensor agile adaptive sampling strategy to track the entire lifecycle of isolated convective clouds. Analysis of the ESCAPE observations has already yielded preliminary findings on how aerosols and environmental conditions impact the convective life cycle.

Review of AI-driven Cloud Optimization

Cloud automation is the key to realization a fully-optimized performance of modern cloud platforms while cloud resources utilization. Resource allocation efficiency is valuable. We are however faced with increasing pressure for computational resources. The Long Short-Term Memory (LSTM) algorithms have found a great use case in the dynamic resource allocation problem when the problem is solved by the proactive provisioning of resources based on historical usage patterns taking advantage of recurrent neural networks. Furthermore, the concern over quality-of-service delivery (QoS) and energy efficiency is now almost as challenging for cloud providers when implementing the utilization of cloud resources, especially in a dynamic setting. Deep Reinforcement Learning (DRL) allows to pursue that end by developing agents, which might guide the work of optimizing resource allocation and reducing energy expenses at the same time. It improves the result and adaptability of the applications running on clouds. Artificial intelligence, in its diverse form, for the instance machine learning and optimization algorithms, brings in a great influence in the areas of cloud operations, resource management, and security. Furthermore, the 3rd generation of FPGAs (Reconfigurable Digital Computing-In-Memory or ReDCIM processors) and the bitwise parallelism via-memory core multipliers also improve the efficiency of computing in cloud environments. Adopting these inputting methodologies is a consequence of cloud systems achieving top performance, high scalability, and low costing. Keywords - Cloud Automation, Long Short-Term Memory (LSTM) algorithms, Deep Reinforcement Learning (DRL), Reconfigurable Digital Computing-In-Memory (ReDCIM) processors, Resource sAllocation, Energy Efficiency.

A bizarre synthesized cascaded optimized predictor (BizSCOP) model for enhancing security in cloud systems

Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

PRC6: Hybrid lightweight cipher for enhanced cloud data security in parallel environment

AbstractModern technologies of computing cloud are showing great promise, but at the same time create new security challenges that hinder full acceptance. Given that most of these services often use cloud networks as channels for communication, securing data transmission is crucial. This paper introduce a new hybrid encryption algorithm, the proposed two‐layered PRC6 cipher, tailored address security concerns in cloud computing environments with minimal resource constraints. The PRC6 cipher incorporates enhancements from Cha‐cha into an extension of the RC6 cipher. PRC6 implements double encryption. At the first level, the plain text is divided into four equal parts, each encrypted by processes derived from RC6, which include shifting, summation, modulo arithmetic, and XOR with a generated key. The second level incorporates a Quarter round function, among others, to further obscure the encoded message. PRC6 is implemented in a parallel computing model to significantly reduce overall computation time, especially important for lightweight applications. Experimental results show that the algorithm can achieve a high level of security for cloud workloads. It activates parallel mode in just seven encryption rounds, cutting calculation time to 50% in a matter of seconds. Performance evaluations against popular encryption standards also indicate that PRC6 offers promising security benefits when computational resources are limited. This hybrid approach presents a viable solution for strengthening data protection in modern cloud systems and it stand against the most popular attacks like brute force.

Secure pharmaceutical supply chain using blockchain in IoT cloud systems

Supply Chain Management (SCM) systems require time sequencing, coordination and tracking the movement of goods and processes. Internet of Things (IoT) and Blockchain technologies are useful to develop a secure automated SCM. IoT devices with in-built sensors and actuators help to keep track of the state, location, temperature or other parameters of an entity, and control the automation of routine as well as hazardous tasks. Blockchain technology supports time-stamping, authentication, process coordination, non-repudiation, commercial transactions, and also provides security for transactions and storage. The pharmaceutical SCM demands accurate, immediate and secure control system. Additionally, the supply chain process data from IoTs is stored and processed in Cloud by Analytics applications, for business planning and improvement. An efficient and secure IoT-Cloud-Blockchain based system for both SCM automation and analytics has been proposed in this work. It leverages a hierarchical IoT, Mist, Edge, Fog, Cloud computing (IMEFC) architecture to enhance Communication-Response-Compute-Security-Storage (CRCSS) in the system. Blockchain technology provides security for the SCM transactions and data. The efficiency of the Blockchain is measured in terms of upload time, download time and transaction fees for Bitcoin, Ethereum and Filecoin platforms. The Filecoin blockchain platform is quicker and cost-effective for larger file sizes, compared to Ethereum and Bitcoin, making it suitable for Pharmaceutical SCM systems.

A novel multi-step-ahead approach for cloud server aging prediction based on hybrid deep learning model

Predicting software aging in cloud servers is essential for preventing cloud system breakdown and unexpected failures. However, the aging time series data in cloud servers often exhibits non-linearity and stochastic fluctuations, posing challenges for accurate prediction. To address this issue, this study proposes a novel multi-step ahead approach for cloud server aging prediction. Firstly, the original aging time series data is denoised using discrete wavelet transformation (DWT) and then reconstructed into multi-dimensional data by phase state reconstruction (PSR) to capture potential aging patterns. Subsequently, a deep long short-term memory network (DLSTM) is developed to predict the aging trend. Additionally, a stacked auto-encoder LSTM (SAE-LSTM) is designed to optimize the initial weight matrix of DLSTM to enhance prediction abilities. Finally, multi-step prediction experiments are conducted on our time series datasets from OpenStack cloud servers and a public dataset from Alibaba Cluster Trace Program. The comparative analysis of the proposed method with five baseline methods confirms its superiority in terms of prediction accuracy. For response time and free memory test sets, the range of root mean square error (RMSE) values are produced by the proposed method distributed in [0.1688–0.1931] and [139.05–146.05] respectively, with respect to the multi-step ahead prediction horizon from 1 up to 10. These results demonstrate that the proposed method can tackle the limitations of one-step ahead prediction, effectively extracting relevant aging features, and achieving low prediction accuracy. The study provides valuable insights into revealing future aging trends in cloud servers and offers a reference for implementing preventive maintenance and prognostics in cloud servers.

An Approach to Improving Availability of Microservices for Cyber-Physical Systems

The design of modern Cyber-Physical Systems (CPS) connects physical and digital realms from cloud systems to edge devices. Microservice architecture has been widely used for IT solutions and emerges as a promising approach for supporting CPS that are more efficient, adaptable, and interconnected. However, there is an increasing need to improve the availability, reliability, and resilience of microservice systems according to the needs. This paper summarizes the challenges and drawbacks of microservice architecture used for CPS. Then, the simplified microservice model has been created, initial properties have been defined, and an improvement plan has been presented. The microservice model’s availability has been improved using a novel approach with endpoint containerization. Then, the discussion and conclusions have been offered to explore the full potential of integrating the physical and digital realms.