Self-optimizing autonomic control of geographically distributed collaboration applications

Computers play essential roles in variety of fields to make their timely work efficient, with the help of high-performance computing architecture like Distributed Computing, Grid architecture, Cluster and Cloud technology. Cloud Computing is experiencing rapid development in both Academic and Industry. A Cloud Environment is a computing model with diverse capabilities which enables users with appropriate and need based access to reciprocated pool of computing resources like networking facility, storage servers, application servers and other services. Essential part of Cloud environment is proficient allocation of resources to user with suitable task. The Cloud Computing Environment has anthology of uncountable nodes, variety of resources, with potential challenges in task scheduling and execution. This paper presents a systematic study on Cloud Computing Environment with an assortment of optimization factors and its functionality in Cloud Computing.KeywordsCloud ComputingTask schedulingOptimization factorsTask allocation

Cloud computing is next generation of Internet technology and data centers. As such, Information security (IS) has become one of the most essential issues of data center security on the cloud computing, but we cannot rely solely on single solution to support all of the necessary features of IS. Therefore, we need to a new information security service model, called Security Operation Center (SOC). It can support online interactions and remote monitoring via network operations in data centers. The SOC is required to integrate related IS technologies based on security policies. In this paper, we propose the service level agreement (SLA) strategy for designing the SOC platform to meet the IS requirements of any organization in cloud computing. Therefore, the main purposes of this paper are to propose the framework of SLA strategy and the integration technology for designing an SOC platform. The SOC platform with SLA is to handle and supervise all the security service processes under different levels of cloud security enforcements according to data center scales, business properties, and existing IS functions. Consequently, the SLA strategy and integrating technology of IS can be the reference model for designing a proposed SOC platform in data centers on the cloud computing.

A distributed load balancing and admission control algorithm based on Fuzzy type-2 and Game theory for large-scale SaaS cloud architectures

Cloud computing has emerged as a transformative technology, offering diverse service models to meet varying organizational needs. This study employs the Complex Proportional Assessment (COPRAS) method to comprehensively evaluate and analyze different cloud environments, providing a nuanced approach to cloud service selection. The research investigates five distinct cloud environment types: general-purpose, service-centric, zone-centric, distance-centric, and cost-centric, examining their performance across critical parameters including quality of service, number of available services, availability zones, consumer distance, and hourly cost. The multi-criteria analysis reveals significant variations in cloud environment effectiveness. Zone-centric environments emerged as the top performer, achieving a remarkable quality index of 0.291 and a 100% usability degree. Service-centric environments followed closely, demonstrating a 92.03% usability degree and highlighting the importance of service availability. General-purpose environments showed moderate performance with a 63.97% usability degree, indicating their versatility. Conversely, distance-centric and cost-centric environments exhibited the lowest performance, suggesting limitations in meeting comprehensive organizational requirements. Key findings underscore that cloud computing is not a one-size-fits-all solution but a complex ecosystem requiring strategic selection. The research emphasizes that while cost is important, it should not be the sole determining factor in cloud environment selection. The study provides a robust framework for decision-makers, enabling them to align cloud infrastructure choices with specific organizational objectives. The methodology offers critical insights into the evolving cloud computing landscape, addressing the growing complexity of data management and the increasing demand for scalable, secure computing solutions. By presenting a comprehensive evaluation approach, the research contributes to a more sophisticated understanding of cloud environment selection, encouraging organizations to adopt a strategic, multi-dimensional approach to cloud infrastructure deployment.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

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

Over the last few years, cloud computing has emerged as the best option for offering various applications. It can supply databases, web services, processing, storage, development platforms, and web services to help businesses swiftly expand their infrastructure and service offerings. However, massive amounts of data will severely burden the cloud computing environment. Due to this, load‐balanced task scheduling has remained a crucial aspect of resource distribution from a data center, ensuring that each virtual machine (VM) has a balanced load to fulfill its full potential. Overloading or underloading a host or server can cause issues with processing speed or even cause a system crash. To prevent this, we need an intelligent way to schedule tasks. Therefore, the hybrid optimization algorithm called gazelle coati optimization algorithm (GCOA) is introduced in this paper to schedule tasks in a cloud environment. This algorithm integrates the coati optimization algorithm (COA) and the gazelle optimization algorithm (GOA) to enhance the GOA's exploitation process. The main objective of this hybrid approach is to optimize scheduling, maximize VM throughput and resource utilization, and establish load balancing between VMs based on makespan, energy, and cost. The performance assessment of the proposed approach is conducted on two real‐world workloads, such as Google Cloud Jobs (GoCJ) and the heterogeneous computing scheduling problems (HCSP) datasets, using several performance metrics, and the results are compared with the previous scheduling and load balancing methods. The experiment results show that the suggested strategy produced significant gains in makespan, energy, cost, resource utilization, and throughput—up to 10% and 60%, respectively—making it appropriate for real‐world cloud infrastructures.

The quality of cloud computing services is evaluated based on various performance metrics out of which response time (RT) is most important. Nearly all cloud users demand its application's RT as minimum as possible, so to minimize overall system RT, the authors have proposed request response time prediction-based data center (DC) selection algorithm in this work. Proposed DC selection algorithm uses results of optimization function for DC selection formulated based on M/M/m queuing theory, as present cloud scenario roughly obeys M/M/m queuing model. In cloud environment, DC selection algorithms are assessed based on their performance in practice, rather than how they are supposed to be used. Hence, explained DC selection algorithm with various forecasting models is evaluated for minimum user application RT and RT prediction accuracy on various job arrival rates, real parallel workload types, and forecasting model training set length. Finally, performance of proposed DC selection algorithm with optimal forecasting model is compared with other DC selection algorithms on various cloud configurations.

Resource management strategy in adaptive cloud provisions the needed resources dynamically to the end-users. To improve the runtime performance of adaptive cloud for service-based applications, two aspects of technical issues are required to be addressed. The first one is the balancing of a large amount of data on existing resources and the second is resource provisioning which can adjust the number of resources optimally to adapt the time-varying workload. As the growth of data is increasing tremendously, efficient resource management is the need in cloud computing. We build a cloud framework to process data in automation with adaptive resource and workload management strategy. Numbers of approaches are reviewed and applied for workload prediction. We developed the model Auto-Regressive Integrated Moving Average-workload Prediction for Efficient Resource Provisioning (ARIMA-PERP) and evaluated the results that can satisfy the on-demand need of end-users for efficient resource utilization. To serve the maximum number of user requests, performance metrics of the proposed approach are evaluated. It is observed that our evaluated results achieved an accurate prediction by 91.11%, which meets the efficient resource utilization for the demanded workload. As compared with the exiting approach, we achieved better performance by 0.11% for accurate prediction. The proposed architecture is intended to provide the resources dynamically and efficiently satisfying the demands of the user. To achieve this objective of efficient resource provisioning, algorithms are developed for workload prediction which helps in deciding optimum resource provisioning. Our system uses proactive approach resource management and deployment of the adaptive cloud system. In traditional systems, resources are managed based on demand, availability and the strategy of scheduling which results in delayed response time at large. We configured the ARIMA model to predict the future workload for provisioning the resources dynamically and remove the problem of over-provisioning and under-provisioning in a cloud environment.

With its scalable and cost-effective solutions, cloud computing has taken center stage in modern data management. Nevertheless, this fast growth of cloud services comes with urgent data security and privacy-related dilemmas. In this paper, three novel algorithms are proposed to deal with the above-mentioned problem in the cloud environment, namely Improved Privacy- Preserved Data Security Approach (IPP-DSA), Multi-Authority Scheme Based Privacy Preserving Algorithm (MASPPA) and Anonymous Access-Based Privacy Preserving Algorithm (AABPPA). Key management efficiency is advocated by the new IPP-DSA with small key generating times which are expected to be short enough to run in steadily update phase. The MASPPA uses the multi-authority attribute-based encryption to realize fine-grained access control, and the encryption times are commensurate to its extensive security guarantee. The second is the AABPPA which is appropriate for situations where user anonymity is required, however is slower in computation times than the AABM but does really well in preserving privacy. This section presents the performance metrics, specifically the time taken for key generation, encryption, and decryption and collectively analyse the advantages and disadvantages of each algorithm. Our results show that the algorithms proposed, when compared to FT-RSA, provide very high security and privacy for applications ranging from low to high computational demands. We recommend additional optimizations to make them more effective in real-world applications.

Now-a-days, cloud computing has become a cornerstone of modern technology, driving innovation, efficiency and accessibility across various industries and application. Distributed computing solves the difficulty of using distributed autonomous machines and communicating with each other over a network. Cloud computing provides clients with a range of services and capabilities that enhances productivity, accessibility and scalability while reducing the need for extensive hardware and infrastructure investments. When interest in distributed computing rises, it implies that more individuals, businesses, and organizations are exploring, adopting, and implementing distributed computing solutions. This surge in interest leads to increase in data traffic. There are two solutions for the issue of increase in data traffic, one is to server optimization (or) server performance enhancement (upgrade single server to a high performance server) but upgraded server may exceed its capacity (overload).Another one is multi servers. Ulti-server configurations are common in scenarios where the demands of an application or service exceed the capabilities of a single server, and the distribution of tasks across multiple servers is necessary for optimal performance and reliability. When there are multiple servers, there is an issue i.e. Load Adjusting. [1] Load balancing is one of the critical issues in cloud computing. In a cloud environment, where resources are often dynamically allocated and distributed, load balancing plays a central role in managing workloads efficiently. Load balancing in cloud computing is a technique used to distribute computing workloads and network traffic across multiple servers or resources within a cloud environment. The primary goal of load balancing is to optimize resource utilization, prevent individual servers from becoming overloaded, and ensure that the overall system can handle varying levels of demand efficiently. Here we also discussed about the hybrid of Cat and Mouse Optimization and Grey Wolf Optimization algorithms. This paper refers to Cloud Computing, Load balancing techniques, Load balancing algorithms.

Considering the cost effectiveness, elasticity, and flexibility of virtualized cloud environments, porting HPC applications to those environments and executing them within these settings is becoming more and more popular. For this purpose, traditional HPC applications have to be redesigned as cloud applications. An analysis of the performance of the redesigned applications within the cloud environment is in-dispensable, if the applications should be efficiently executed in the cloud environment.This paper proposes a workflow for the integral performance analysis of cloud applications within a cloud environment using monitoring and tracing techniques. For this, collectd acts as a lightweight monitoring daemon for recording performance data from outside of the applications, Score-P as a profiling and tracing tool for recording the performance data from the inside. Thus, this workflow will help in answering the question How and why an application behaves like this within the cloud environment?.In order to show the usability of the proposed workflow, a parallel client server application was selected and adapted for the execution in a private OpenStack cloud. Performance measurements of the example running in the cloud environment could be successfully done according to the proposed workflow. In particular, performance metrics from both the outside and the inside of the application could be obtained to analyze and evaluate the performance of the application in detail.

In cloud computing, resource provision service plays an important role for operating large-scale datacentres. Conventional resource provision policies or services mainly concentrate on optimising costs and application execution performance. In this paper, we present an integrated and adaptive resource provision framework, which is based on our previous work on performance monitor in cloud environments. In the proposed framework, several novel mechanisms are implemented, aiming at improving the energy-efficiency as well as the execution performance for cloud systems. Extensive experiments are conducted to evaluate the performance of the proposed framework in terms of different metrics. The experimental results show that the proposed framework can significantly improve the energy-efficiency metric, especially when a cloud system is in presence of intensive hybrid workloads.

On one side the Cloud Computing offers scalable virtual computing resources in the form of web-services on pay-as-you-go basis; but on other side, it also raises the concerns regarding the security threats to the Cloud environment from outside and inside the environment. The existing security systems are not able to withstand the dynamic nature of threats, so some sort of augmentations are required to the existing security systems which will enable the security systems to work in more prominent way. Inter-VM attacks possess such kind of threats from inside the Cloud environment. User profiling can be helpful in this situation by analyzing the usage patterns of the users and by providing the plausible interpretation from those usage patterns to the security system and then, the security system, based on those interpretations, act in reactive and proactive manner to the analyzed threat situation. This paper is the based on the findings from our last paper, where we had found that the designing of User Profiling System based on Artificial Intelligence techniques: Fuzzy and Genetic Algorithms are individually not sufficient to deliver a comprehensive User Profiling System but it might be possible to use these two approaches in a hybrid way to design a comprehensive User Profiling System for Cloud Computing security. Hence, we used hybrid approach Fuzzy guided Genetic Algorithm to design a User Profiling System and found that it covered all the research gaps, which we had analyzed in our previous work and even it fulfill all the seven principles of the evaluating framework.

Cloud computing not only provides convenience for users, but also brings certain information security threats to users. How to effectively protect the user’s information security has become one of the core issues of network security. This paper analyzes the problems and reasons of network security in cloud computing environment, and points out the main ways to improve network security. This paper analyzes the network security problems in the cloud computing environment, and discusses the information security technology in the network. This paper studies the definition, characteristics, classification and core technology of cloud computing security, analyzes the security risk of cloud computing network, and puts forward the cloud computing network security model and security level evaluation method from different angles. Level experiment results show that the network security model in cloud environment is feasible, computing can fully meet the development requirements of the current network information age, and effectively standardize the network security operation environment. The results show that: in 2016, the number of articles related to cloud computing is at least 2653, and in 2020, the number of articles related to cloud computing is at most 6438.