Biblio

With the rapid development of cloud computing, IaaS (Infrastructure as a Service) becomes more and more popular. IaaS customers may not clearly know the actual performance of each cloud platform. Moreover, there are no unified standards in performance evaluation of IaaS VMs (virtual machine). The underlying virtualization technology of IaaS cloud is transparent to customers. In this paper, we will design an automated performance benchmarking platform which can automatically install, configure and execute each benchmarking tool with a configuration center. This platform can easily visualize multidimensional benchmarking parameters data of each IaaS cloud platform. We also rented four IaaS VMs from AliCloud-Beijing, AliCloud-Qingdao, UCloud and Huawei to validate our benchmarking system. Performance comparisons of multiple parameters between multiple platforms were shown in this paper. However, in practice, customers' applications running on VMs are often complex. Performance of complex applications may not depend on single benchmarking parameter (e.g. CPU, memory, disk I/O etc.). We ran a TPC-C test for example to get overall performance in MySQL application scenario. The effects of different benchmarking parameters differ in this specific scenario.

Trusted Platform Modules (TPM) have grown to be crucial safeguards from the number of software-based strikes. By giving a restricted range of cryptographic providers by way of a well-defined user interface, divided as a result of the program itself, TPM and Infrastructure as a service (IaaS) can function as a root of loyalty so when a foundation aimed at advanced equal protection methods. This information studies the works aimed at uses on TPM within the cloud computing atmosphere, by journal times composed somewhere among 2013 as well as 2020. It identifies the present fashion as well as goals from these technologies within the cloud, as well as the kind of risks that it mitigates. The primary investigation is being focused on the TPM's association to the IaaS security based on the authorization and the enabling schema for integrity. Since integrity measurement is among the key uses of TPM and IaaS, particular focus is given towards the evaluation of operating period phases as well as S/W levels it's put on to. Finally, the deep survey on recent schemes can be applied on Cloud Environment.

Cloud computing has become the main backend of the IT infrastructure as it provides ubiquitous and on-demand computing to serve to a wide range of users including end-users and high-performance demanding agencies. The users can allocate and free resources allocated for their Virtual Machines (VMs) as needed. However, with the rapid growth of interest in cloud computing systems, several issues have arisen especially in the domain of cybersecurity. It is a known fact that not only the malicious users can freely allocate VMs, but also they can infect victims' VMs to run their own tools that include cryptocurrency mining, ransomware, or cyberattacks against others. Even though there exist intrusion detection systems (IDS), running an IDS on every VM can be a costly process and it would require fine configuration that only a small subset of the cloud users are knowledgeable about. Therefore, to overcome this challenge, in this paper we present a VM introspection based allowlisting method to be deployed and managed directly by the cloud providers to check if there are any malicious software running on the VMs with minimum user intervention. Our middleware monitors the processes and if it detects unknown events, it will notify the users and/or can take action as needed.

Cloud Management Platforms (CMP) have been developed in recent years to set up cloud computing architecture. Infrastructure-as-a-Service (IaaS) is a cloud-delivered model designed by the provider to gather a set of IT resources which are furnished as services for user Virtual Machine Image (VMI) provisioning and management. Openstack is one of the most useful CMP which has been developed for industry and academic researches to simulate IaaS classical processes such as launch and store user VMI instance. In this paper, the main purpose is to adopt a security policy for a secure launch user VMI across a trust cloud environment founded on a combination of enhanced TPM remote attestation and cryptographic techniques to ensure confidentiality and integrity of user VMI requirements.

Recent increases in the field of infrastructure has led to the emerging of cloud computing a virtualized computing platform. This technology provides a lot of pros like rapid elasticity, ubiquitous network access and on-demand access etc. Compare to other technologies cloud computing provides many essential services. As the elasticity and scalability increases the chance for vulnerability of the system is also high. There are many known and unknown security risks and challenges present in this environment. In this research an environment is proposed which can handle security issues and deploys various security levels. The system handles the security of various infrastructure like VM and also handles the Dynamic infrastructure request control. One of the key feature of proposed approach is Dual authorization in which all account related data will be authorized by two privileged administrators of the cloud. The auto scalability feature of the cloud is be made secure for on-demand service request handling by providing an on-demand scheduler who will process the on-demand request and assign the required infrastructure. Combining these two approaches provides a secure environment for cloud users as well as handle On-demand Infrastructure request.

Availability is one of the most important requirements in the production system. Keeping the level of high availability in Infrastructure-as-a-Service (IaaS) cloud computing is a challenge task because of the complexity of service providing. By definition, the availability can be maintain by using fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them focus on the fault detection aspect. In this paper, after a rigorous analysis on the nature of failures, we would like to introduce a technique to identified the failures occurring in IaaS system. By using fuzzy logic algorithm, this proposed technique can provide better performance in terms of accuracy and detection speed, which is critical for the cloud system.

Using heterogeneous clouds has been considered to improve performance of big-data analytics for healthcare platforms. However, the problem of the delay when transferring big-data over the network needs to be addressed. The purpose of this paper is to analyze and compare existing cloud computing environments (PaaS, IaaS) in order to implement middleware services. Understanding the differences and similarities between cloud technologies will help in the interconnection of healthcare platforms. The paper provides a general overview of the techniques and interfaces for cloud computing middleware services, and proposes a cloud architecture for healthcare. Cloud middleware enables heterogeneous devices to act as data sources and to integrate data from other healthcare platforms, but specific APIs need to be developed. Furthermore, security and management problems need to be addressed, given the heterogeneous nature of the communication and computing environment. The present paper fills a gap in the electronic healthcare register literature by providing an overview of cloud computing middleware services and standardized interfaces for the integration with medical devices.

Monitoring is an important issue in cloud environments because it assures that acquired cloud slices attend the user's expectations. However, these environments are multitenant and dynamic, requiring automation techniques to offload cloud administrators. In a previous work, we proposed FlexACMS: a framework to automate monitoring configuration related to cloud slices using multiple monitoring solutions. In this work, we enhanced FlexACMS to allow dynamic and automatic attribution of monitoring configuration tasks to servers without administrator intervention, which was not available in previous version. FlexACMS also considers the monitoring server load when attributing configuration tasks, which allows load balancing between monitoring servers. The evaluation showed that enhancements reduced FlexACMS response time up to 60% in comparison to previous version. The scalability evaluation of enhanced version demonstrated the feasibility of our approach in large scale cloud environments.