Biblio

Block storage resources are essential in an Infrastructure-as-a-Service(IaaS) cloud computing system. It is used for storing virtual machines' images. It offers persistent storage service even the virtual machine is off. Distribute storage systems are used to provide block storage services in IaaS, such as Amazon EBS, Cinder, Ceph, Sheepdog. Ceph is widely used as the backend block storage service of OpenStack platform. It converts block devices into objects with the same size and saves them on the local file system. The performance of block devices provided by Ceph is only 30% of hard disks in many cases. One of the key issues that affect the performance of Ceph is the three replicas for fault tolerance. But our research finds that replicas are not the real reason slow down the performance. In this paper, we present a new approach to accelerate the IO operations. The experiment results show that by using our storage engine, Ceph can offer faster IO performance than the hard disk in most cases. Our new storage engine provides more than three times up than the original one.

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.

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.