ASTAW: Auto-Scaling Threshold-based Approach for Web Application in Cloud Computing Environment

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

Cloud computing and big data: Technologies and applications

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

Privacy-preserving multi-party logistic regression in cloud computing

Cloud computing is a highly scalable and cost - effective infrastructure for running High Performance Computing, enterprise and Web applications. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. Hence, energy efficient solutions are required to minimize this energy consumption. The energy efficient solutions aim at lowering the energy usage of data centers because computing applications and data are growing so quickly that increasingly larger servers and disks are needed to process them fast enough within the required time period so here we reduce the energy consumption by an average of 40% over previously introduced methods. So in datacenters, the number of physical machines can be reduced using virtualization by consolidating virtual machines onto shared servers and enabling them to migrate according to migration policy. This paper presents virtual machines migration and selection policies to boost Cloud Computing Environment energy efficiency and performance.

Resources are provided mainly in the form of services in cloud computing. In the distribute environment of cloud computing, how to find the needed services efficiently and accurately is the most urgent problem in cloud computing. In cloud computing, services are the intermediary of cloud platform, services are connected by lots of service providers and requesters and construct the complex heterogeneous network. The traditional recommendation systems only consider the functional and non-functional requirements of services but ignore the links between providers and requesters of service, which result to the service position is not accurate. Focus on the problems, this study intends to model the relationship of the cloud services participants with the format of heterogeneous information network, which intend to mine the hidden relationships between services participants in the cloud computing environment. In theoretical research, we proposed a cloud service heterogeneous network extraction and automatic maintenance model, proposed a new service recommendation system based on heterogeneous service network ranking and clustering.

Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments

The migration of train control systems to cloud computing is an important trend of urban rail transit. A traditional redundant structure (T-RS) is a safety related vital part of the train control system. In this paper, the safety of the redundant structure in cloud computing environment (CC-RS) is researched. Firstly, a deployment scheme of the CC-RS is presented, the types of failures, especially those related to cloud computing, are analyzed. Then, a model is setup based on graph theory which takes the physical nodes, virtual machines and fault-tolerant mechanisms of cloud computing into consideration. After that, the safety of the redundant structure is analyzed using a minimal spanning tree method and Bayesian algorithm. Lastly, some optimization measures on the deployment of the redundant structure are proposed and verified through numerical analysis, according to which the redundant structure in cloud computing environment can be safer than the traditional redundant structure.

The concept of virtual machines is critical to manage the scalable utilization of the resources in cloud computing. Hence the scheduling and balancing the assigned job loads to virtual machines is essential and exclusive need of the cloud computing environment, which is a critical research objective since, improving the service provision in cloud computing is not only through scheduling the individual resources to the target jobs, also the scheduling the virtual machines and balancing the load at virtual machines. In the context of this argument, a method called, Hierarchical Tabu Search with Fuzzy Reasoning (HTSFR) is proposed to escalate the optimality of resource scheduling and load balancing that deals the virtual machines as resources. The performance analysis was done on simulated environment established by using Cloud SIM. The results obtained from proposal were compared to other contemporary models, which evinced the significant performance advantage of the proposal on other contemporary models compared.

 Abstract—Cloud computing is an emerging technology that is still unclear to many security problems. Ensuring the security ofstored data in cloud servers is one of the most challenging issues in such environments. Accordingly, this schema presents a Hybrid Encryption algorithm that is based on RSA Small-e and Efficient RSA (HE-RSA) for improving the reliability in cloud computing environments. The main aim of this project is to use the cryptography concepts in cloud computing communications and to increase the security of encrypted data in cloud servers with the least consumption of time and cost at the both of encryption and decryption processes.In the proposed model, the number of key generation exponents has been increased in comparison to the original RSA. Moreover,a dual encryption process has been applied in this algorithm to prevent common attacks against RSA algorithm. In addition, this schema also presents the comparison between HE-RSA and original RSA in terms of security and performance. For this purpose, Key Generation, Encryption and Decryption Time in Original RSA and HE-RSA have been compared according to the different size of exponents. Moreover, some of the common attacks against RSA algorithm have been analysed to detect the resistance of the proposed algorithm against possible attacks. Finally, the effects of using HE-RSA on cloud computing environments have been reviewed in more depth and details.

Cloud computing service providers offer their users the flexibility of selecting the processing capacity that is affordable. The selected processing capacity is often used to execute a workflow. The makespan of the user's workflow depends on the structure and size of the workflow and the number of virtual machines (VMs) that share the selected processing capacity. However, determining the number of VMs used for executing the workflow under user's quality of services (QoS) is still a big challenge. In this paper, we proposed an approach with four variants to find the number of VMs required for each workflow, and assist in splitting the selected processing capacity onto the VMs for makespan and utilization optimization. The evaluation results show that the proposed approach can help in finding a good trade off between the makespan, utilization and VM set size.

The Cloud Computing is a most widely spreading platform for executing tasks using virtual machines (VMs) as processing elements. Therefore, implementing HPC using Cloud Computing is considered a powerful approach by isolating tasks, reducing execution time, as well as, price, and satisfying load balance. In this paper, an enhancement task scheduling algorithm on the Cloud Computing environment has been introduced to reduce the make-span, as well as, decrease the price of executing the independent tasks on the cloud resources. The principles of the algorithm is based on calculating the total processing power of the available resources (i.e., VMs) and the total requested processing power by the users' tasks, then allocating a group of users' tasks to each VM based on the ratio of its needed power relative to the total processing power of all VMs. The power of VMs has been defined based on Amazon EC2 and Google pricing models. To evaluate the performance of the enhancement algorithm, a comparative study has been done among this enhancement algorithm, the default FCFS algorithm, and the existed GA, and PSO algorithms. The experimental results show that the enhancement algorithm outperforms other algorithms by reducing make-span and the price of the running tasks.

Today, Cloud Computing become one of the most promising an emerging technology which will has a significant impact on IT Infrastructure. Still, Cloud Computing is infancy. In the current cloud computing environment there is numerous of application, consist of millions of module, these application serve from large quantity of users and user request becomes dynamic. So there must be provision that all resources are dynamically made available to satisfy the needs of requesting a user in a situation where the resource contention fierce is occurs. In this paper, we propose scheduling heuristic to allocate resource and schedule request for cloud system. An aim of proposed scheduling heuristic is to schedule request on Virtual Machine (VM) based on agreed Service Level Agreements (SLA) and creating new VM on physical resources based on availabilities of resources which considered priority based VM allocation, Load Balancing and Preemption Mechanism. And the experimental results of proposed heuristic helps to optimize performance and at the same time reduce the possibilities of SLA violations.

INTRODUCTION: Cloud services are becoming increasingly important as advanced technology changes. In these kinds of cases, the volume of work on the corresponding server in public real-time data virtualized environment can vary based on the user’s needs. Cloud computing is the most recent technology that provides on-demand access to computer resources without the user’s direct interference. Consequently, cloud-based businesses must be scalable to succeed.OBJECTIVES: The purpose of this research work is to describe a new virtual cluster architecture that allows cloud applications to scale dynamically within the virtualization of cloud computing scale Using auto-scaling, resources can be dynamically adjusted to meet multiple demands. METHODS: An auto-scaling algorithm based on the current implementation sessions will be initiated for automated provisioning and balancing of virtualized resources. The suggested methodology also considers the cost of energy.RESULTS: The proposed research work has shown that the suggested technique can handle sudden load demands while maintaining higher resource usage and lowering energy costs efficiently.CONCLUSION: Auto-scaling features are available in measures in order groups, allowing you to automatically add or remove instances from a managed instance group based on changes in load. This research work provides an analysis of auto-scaling mechanisms in cloud services that can be used to find the most efficient and optimal solution in practice and to manage cloud services efficiently.

Managing Cloud Computing Environment: Gaining Customer Trust with Security and Ethical Management