Distributed Cloud Environment Research Articles

Adaptive load balancing in distributed cloud environment: Hybrid Kookaburra-Osprey optimization algorithm

A distributed cloud environment is characterized by the dispersion of computing resources, services, and applications across multiple locations or data centres. This distribution enhances scalability, redundancy, and resource utilization efficiency. To optimize performance and prevent any single node from becoming a bottleneck, it is imperative to implement effective load-balancing strategies, particularly as user demands vary and certain nodes experience increased processing requirements. This research introduces an Adaptive Load Balancing (ALB) approach aimed at maximizing the efficiency and reliability of distributed cloud environments. The approach employs a three-step process: Chunk Creation, Task Allocation, and Load Balancing. In the Chunk Creation step, a novel Improved Fuzzy C-means clustering (IFCMC) clustering method categorizes similar tasks into clusters for assignment to Physical Machines (PMs). Subsequently, a hybrid optimization algorithm called the Kookaburra-Osprey Updated Optimization Algorithm (KOU), incorporating the Kookaburra Optimization Algorithm (KOA) and Osprey Optimization Algorithm (OOA), allocates tasks assigned to PMs to Virtual Machines (VMs) in the Task Allocation step, considering various constraints. The Load Balancing step ensures even distribution of tasks among VMs, considering migration cost and efficiency. This systematic approach, by efficiently distributing tasks across VMs within the distributed cloud environment, contributes to enhanced efficiency and scalability. Further, the contribution of the ALB approach in enhancing the efficiency and scalability of distributed cloud environments is evaluated through analyses. The KBA is 1189.279, BES is 629.240, ACO is 1017.889, Osprey is 1147.300, SMO is 1215.148, APDPSO is 1191.014, and DGWO is 1095.405, respectively. The resource utilization attained by the KOU method is 1224.433 at task 1000.

Efficient Privacy-Preserving Scheme for Distributed Machine Learning Against Collusion Attacks (EPPS-DMLCA)

We propose EPPS-DMLCA, a novel method integrating homomorphic encryption and DP to protect data from attacks. EPPS-DMLCA improves techniques to protect data from attacks in several ways. One, it protects data while being more computationally efficient compared to extant methods. Two, it is more robust to collusion attacks than current methods are. We evaluated EPPS-DXMCA using MNIST. We tested our method on Amazon’s cloud using 40 cloud servers. We found that our method performed as well as a method that did not protect privacy. Additionally, our method uses a firm privacy budget, which provides robust privacy protection. Last, we found that because of the low computational overhead, our method is more resistant to collusion attacks than state-of-the-art methods are. Our results show that EPPS-DMLCA is an important contribution to the literature. We propose EPPS-DMLCA, which integrates homomorphic encryption and DP to protect data from attacks. We used MNIST in a distributed cloud environment with 40 cloud servers. Our results show that EPPS-DMLCA is up to 3 times more efficient than FHE methods. Moreover, the model performs 97.8%, the same as unprotected methods. Last, DP’s noise was well-tuned, as evidenced by the model and the sufficient parameters to provide robust protection from privacy. Moreover, EPPS-DMLCA is more resistant to collusion than the state-of-the-art. Machine learning models are essential for creating information from big data in a constantly changing world. However, access to such big data raises privacy issues. Fortunately, various ways exist to protect data from attacks. One such method is DP, which adds noise to the data. The goal of DP is to generate robust protection from privacy. DP is a valuable tool, but the method has one drawback. The method uses many noise probes to be effective. This means that FHE methods do not perform as well as unprotected methods. Because of this drawback, I propose EPPS-DMLCA to users.

Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

: The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included.

Container Scheduling Algorithms for Distributed Cloud Environments

Due to the difficulty of existing container scheduling algorithms to adapt to large-scale complex scenarios and meet the diverse application and load requirements, this study delves into a groundbreaking hybrid scheduling approach that melds Deep Deterministic Policy Gradient (DDPG) with a Genetic Algorithm (GA). The proposed method initially employs a container grouping policy to reduce the overhead associated with frequent inter-container calls. Subsequently, to address the computational inefficiency of large-scale scheduling, a genetic algorithm is utilized for rapid global optimization. To overcome the problem of genetic algorithms being highly susceptible to falling into local optimality, the DDPG algorithm is applied for local optimization, with a cross-mutation operation introduced to escape local optima. The experimental outcomes demonstrate that the proposed algorithm enhances cluster load balancing by 81.13%, improves the fitness function by 3.26%, reduces completion time by 19.06%, and decreases container dependency overhead by 2.75%. Furthermore, under the experimental conditions, the system performs best when the group size is 10. This research offers a novel paradigm for the development of container scheduling algorithms in distributed intelligent data clouds, advancing the field of resource management in cloud computing environments.

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

Design and Evaluation of a Quantum-Resilient Cryptographic Framework for Enhancing Security and Efficiency in Distributed Cloud Environments

Design and Evaluation of a Quantum-Resilient Cryptographic Framework for Enhancing Security and Efficiency in Distributed Cloud Environments

The Diagnosis-Effective Sampling of Application Traces

Distributed tracing is cutting-edge technology used for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and the direct storage and utilization of every trace is impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as the simplification of application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine-learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

An intelligent optimized secure blockchain mechanism for cloud auditing

The cloud auditing model was attractive for the cloud storage system to check the integrity of the stored user files. Hence, to maximize the security concern, federated learning and the blockchain were introduced. But, in some cases, security issues arose because of the extensive unstructured data and attack vulnerability. So, to avoid security problems and offer better protection in a cloud environment, a novel Optimized Buffalo-based Homomorphic SHA Blockchain (OBHSB) is proposed. In this model for accessing the cloud storage data with the critical matching method, if any of the unauthenticated users are trying to access the file initially, the system checks the key matching parameter. The proposed model was developed to provide better security in big data presented in the cloud environment. Hence, federated learning was adopted to create the cloud auditing phase in the distributed cloud environment. Therefore, it gives flexibility in training all data types: audio, text, and images. Moreover, the prime performance parameters were calculated and compared with other traditional approaches to justify the efficiency of the proposed model. The attack was considered an event in this research. The performance rate of the proposed model was validated in the performance analysis. Subsequently, the case study was developed in this research to explain the working procedure of the proposed design, and the result of the proposed model was discussed.

Hybrid model-based approach for oral cancer detection in distributed cloud environment

ABSTRACT Oral cancer is the most fatal disease among the overall malignant tumours worldwide. As more than 50% of the patients are diagnosed with advanced stages of the disease, the oral cancer prognosis is still bad. Early detection is one of the most crucial factors in lowering cancer fatality rates. But there is still a significant obstacle in the form of delayed diagnosis and developing a model for accurate oral cancer detection. To precisely detect oral cancer, deep learning models are used to reduce the number of deaths from cancer. In addition, the diagnosis system based on cloud-deep learning aids the telehealth services more probable. Therefore, this paper proposes a model, termed HMOCD (Hybrid Model based Oral Cancer Detection via Distributed Cloud Environment) with four stages: Pre-processing, segmentation, feature extraction, and detection. The input image is first pre-processed using the Weiner filter, and the output is then provided to CLAHE for enhancing the filtered image. Second, Modified Deep Joint segmentation is applied to the pre-processed image. Following the segmentation process, features for MTH, LGXP, and M-LBP are extracted. Ultimately, a hybrid classification method that integrates models such as Deep Maxout and enhanced Bi-LSTM is utilised to diagnose the condition.

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.

Blockchain-enabled Data Governance Framework for Enhancing Security and Efficiency in Multi-Cloud Environments through Ethereum, IPFS, and Cloud Infrastructure Integration

In today's digital landscape, the exponential growth of big data demands secure and efficient processing, particularly in complex multi-cloud environments. This paper proposes an innovative blockchain-enabled data governance framework, revolutionizing data management, processing, and security across diverse cloud infrastructures. The framework integrates cutting-edge technologies, including the Ethereum blockchain, the InterPlanetary File System (IPFS) protocol, and cloud solutions like OpenStack and Red Hat OpenShift. At its core, the framework utilizes Ethereum's robust smart contracts and consensus mechanisms to establish a decentralized and secure data governance model. This ensures data integrity, transparency, and immutability, mitigating risks associated with centralized storage and processing. The IPFS protocol complements blockchain by offering efficient data sharding and retrieval mechanisms, enhancing data accessibility and fault tolerance in distributed cloud environments. Through comprehensive testing and analysis, the proposed framework's value is demonstrated. Performance metrics, including throughput, latency, CPU utilization, and memory utilization, were meticulously evaluated to assess system efficiency and scalability. Results indicate high performance, with Ethereum's Proof of Authority (PoA) consensus mechanism enabling efficient transaction throughput of up to 1000 transactions per second. Additionally, the IPFS protocol exhibits effective data retrieval capabilities, with an average latency of 15 milliseconds for data access operations.

Distributed Resource Management in Cloud Computing: A Review of Allocation, Scheduling, and Provisioning Techniques

This review paper provides an in-depth examination of distributed resource management in cloud computing, focusing on the critical elements of allocation, scheduling, and provisioning. Cloud computing, characterized by its dynamic and scalable nature, necessitates efficient resource management techniques to optimize performance, cost, and service. The study encompasses a comprehensive analysis of various strategies in resource allocation, scheduling methodologies, and provisioning techniques within the cloud computing paradigm. Through comparative analysis, this paper aims to highlight the synergies and trade-offs inherent in these methods, offering a holistic view of distributed resource management. It contributes to the field by bridging the gap in existing literature, presenting a critical, comparative analysis of current strategies and their interplay in distributed cloud environments.

Enhancing Security and Privacy in Distributed Cloud Environments: A Review of Protocols and Mechanisms

As cloud computing becomes increasingly integral to data management and services, security and privacy concerns remain paramount. This article presents a comprehensive review of the current protocols and mechanisms designed to fortify security and privacy in distributed cloud environments. It synthesizes contributions from various research works, each proposing innovative solutions to address these challenges. Among these are a two-layer cryptographic algorithm that combines genetic techniques with logical-mathematical functions, enhancing encryption beyond traditional methods, and a novel symmetric-key block cipher that increases encryption complexity while maintaining flexibility. The paper also discusses machine learning applications for threat detection, the role of blockchain in trust management, and the importance of multi-cloud strategies to secure big data. Through comparative analyses, the reviewed methodologies show promising advancements in encryption, data integrity, and resource management, suggesting a robust framework for tackling the evolving landscape of cyber threats. The article underscores the need for continuous innovation and research to navigate the dynamic domain of cloud security, aiming for a future where data security and privacy are not just reactive safeguards but proactive measures embedded in the fabric of cloud computing.

LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing.

This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and real-world cloud datasets, stressing the need for appropriate drift detectors tailored to the cloud domain. A modified version of Long Short-Term Memory (LSTM) called the LSTM Drift Detector (LSTMDD) is proposed and compared with other top drift detection techniques using prediction error as the primary evaluation metric. LSTMDD is optimized to improve performance in detecting anomalies in non-Gaussian distributed cloud environments. The experiments show that LSTMDD outperforms other methods for gradual and sudden drift in the cloud domain. The findings suggest that machine learning techniques such as LSTMDD could be a promising approach to addressing the problem of concept drift in cloud computing, leading to more efficient resource allocation and improved performance.

Distributed Tracing for Troubleshooting of Native Cloud Applications via Rule-Induction Systems

Diagnosing IT issues is a challenging problem for large-scale distributed cloud environments due to complex and non-deterministic interrelations between the system components. Modern monitoring tools rely on AI-empowered data analytics for detection, root cause analysis, and rapid resolution of performance degradation. However, the successful adoption of AI solutions is anchored on trust. System administrators will not unthinkingly follow the recommendations without sufficient interpretability of solutions. Explainable AI is gaining popularity by enabling improved confidence and trust in intelligent solutions. For many industrial applications, explainable models with moderate accuracy are preferable to highly precise black-box ones. This paper shows the benefits of rule-induction classification methods, particularly RIPPER, for the root cause analysis of performance degradations. RIPPER reveals the causes of problems in a set of rules system administrators can use in remediation processes. Native cloud applications are based on the microservices architecture to consume the benefits of distributed computing. Monitoring such applications can be accomplished via distributed tracing, which inspects the passage of requests through different microservices. We discuss the application of rule-learning approaches to trace traffic passing through a malfunctioning microservice for the explanations of the problem. Experiments performed on datasets from cloud environments proved the applicability of such approaches and unveiled the benefits.

Reliable and efficient webserver management for task scheduling in edge-cloud platform

<span lang="EN-US">The development in the field of cloud webserver management for the execution of the workflow and meeting the quality-of-service (QoS) prerequisites in a distributed cloud environment has been a challenging task. Though, internet of things (IoT) of work presented for the scheduling of the workflow in a heterogeneous cloud environment. Moreover, the rapid development in the field of cloud computing like edge-cloud computing creates new methods to schedule the workflow in a heterogenous cloud environment to process different tasks like IoT, event-driven applications, and different network applications. The current methods used for workflow scheduling have failed to provide better trade-offs to meet reliable performance with minimal delay. In this paper, a novel web server resource management framework is presented namely the reliable and efficient webserver management (REWM) framework for the edge-cloud environment. The experiment is conducted on complex bioinformatic workflows; the result shows the significant reduction of cost and energy by the proposed REWM in comparison with standard webserver management methodology.</span>

Configuration and Placement of Serverless Applications Using Statistical Learning

In the last decade, serverless computing emerged as a new compelling paradigm for the deployment of cloud applications and services. It represents an evolution of cloud computing with a simplified programming model, that aims to abstract away most operational concerns. Running serverless applications requires users to configure multiple parameters, such as memory, CPU, cloud provider, etc. While relatively simpler, configuring such parameters correctly while minimizing cost and meeting delay constraints is not trivial. In this paper, we present, a framework that uses Bayesian Optimization to find the optimal resource configuration and placement for functions in a serverless application. uses statistical learning techniques to intelligently collect samples and predict the cost and execution time of a serverless function across unseen configuration values. Our framework uses the predicted cost and execution time on available locations to select the “best” configuration parameters and placement for running a serverless application while satisfying customer objectives. We evaluate on AWS Lambda with real-world applications consisting of multiple functions (both linear chains and service graphs), where we successfully found optimal/near-optimal configurations. We also evaluate over a wide range of simulated distributed cloud environments that confirm the efficacy of our approach.

Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment

The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge–Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.

Oral cancer detection model in distributed cloud environment via optimized ensemble technique

A new Two-tier m-healthcare oral cancer detection framework is constructed in a distributed cloud environment in this study. The following is the detection model: The supplied data is pre-processed to reduce noise as well as any undesired artifacts. The region of interest is separated from the backdrop via image segmentation. The segmentation in this research is done utilizing the Region Growing Technique. Textural features, Graph features, and Morphological features are also retrieved. A suggested Improved Linear Discriminant Analysis (ILDA) is used to pick the features in this study. For cancer classification, the selected features are exposed to ensemble classifiers (EC). In EC, stage 1 includes the Support Vector Machine (SVM) and Multi-layer Perceptron (MLP)) modeled for disease classification. The stage 2 phase includes the optimized CNN, which makes the final decisions regarding the presence/ absence of oral cancer. The optimized CNN is trained with outcomes acquired from SVM and the input for optimal CNN is MLP, which will provide the final detected outcomes. Since, the CNN is the final decision makes, its weight of it is fine-tuned using a new hybrid optimization model- Aquila Exploration Updated with Local Movement (AEULM), this ensures enhanced detection accuracy. The traditional Aquila Optimizer (AO) and the proposed hybrid optimization method are conceptually combined to form Wildebeest Herd Optimization (WHO). To confirm the effectiveness of the predicted model, a comparative assessment is completed.

A Hybrid Supervised Learning Approach for Detection and Mitigation of Job Failure with Virtual Machines in Distributed Environments

Distributed data processing techniques are very popular nowadays due to high data generation from various resources. To increase work learning outcomes and to reduce consumption, modern massive computational systems divide jobs into several smaller tasks that perform in parallel. Nevertheless, responding with straggler processes, which are sluggish running processes that rise the total response time, is a typical performance issue in such platforms. In this paper, we proposed the detection of struggler nodes in a large distributed environment using a hybrid machine learning technique. Initially, the data has been collected from numerous virtual machine network logs. The entire data set has various fields such as Virtual Machine ID, CPU load, memory load, bandwidth utilization, etc. Memory utilization an input to the proposed system is collected from the garbage collection log files where the memory consumption on each VM and its timestamp is recorded. This is the most efficient way to get the memory consumption in web/desktop applications. Similarly, the CPU, I/O and bandwidth utilization is grabbed from the process monitoring functionality and SAR (System Activity Report) utility from the respective VM boxes. This data set is useful to identify weather-specific virtual machine is heated up or not. In this approach, we proposed three conventional machine learning algorithms and a hybrid machine learning algorithm for the identification of node status. Main purpose of the proposed system is to identify the slow performing node in an efficient way to prevent the other nodes from failures. This can provide effective load balancing and low response time for task execution from available VM’s in distributed cloud environments. To create its training program, several extractions of features approaches were used. TF-IDF, correlational co-occurrence, and density-based features have been mined from the whole data set. With extensive experimental analysis, we evaluate our system with our proposed classification algorithm. As a result, the system produces higher classification accuracy of 94.5% over the traditional machine learning classifiers. If the proposed system is tested against the data set fields, memory load and CPU load on the homogenous machine configurations, we see more efficiency while detecting the underperforming node than the heterogenous machine configurations.