The need for high reliability, availability and performance has significantly increased in modern applications, that handle rapidly growing demands while providing uninterruptible services. Cloud computing systems fundamentally provide access to large pools of data and computational resources. Eucalyptus is a software framework largely used to implement private clouds and hybrid-style Infrastructure as a Service. It implements the Amazon Web Service (AWS) API, allowing interoperability with other AWS-based services. This article investigates the software aging effects in the Eucalyptus framework, considering workloads composed of intensive requests for remote storage attachment and virtual machine instantiations. We found problems that may be harmful to system dependability and performance, specifically regarding to RAM memory and swap space exhaustion, besides highly excessive CPU utilization by the virtual machines. We also present an approach that applies time series analysis to schedule rejuvenation, so as to reduce the downtime by predicting the proper moment to perform the rejuvenation. We experimentally evaluate our approach using an Eucalyptus test bed. The results show that our approach achieves higher availability, when compared to a threshold-triggered rejuvenation method based on continuous monitoring of resources utilization.

No abstract

Cloud computing is a paradigm that provides services through the Internet. The paradigm has been influenced by previously available technologies (for example cluster, peer-to-peer, and grid computing) and has now been adopted by almost all large organizations. Companies such as Google, Amazon, Microsoft and Facebook have made significant investments in cloud computing, and now provide services with high levels of dependability. The efficient and accurate assessment of cloud-based infrastructure is fundamental in guaranteeing both business continuity and uninterrupted public services, as much as is possible. This paper presents an approach for selecting cloud computing infrastructures, in terms of dependability and cost that best suits both company and customer needs. We use stochastic models to calculate dependability-related metrics for different cloud infrastructures. We then use a Multiple-Criteria Decision-Making (MCDM) method to rank the best cloud infrastructures, taking customer service constraints such as reliability, downtime, and cost into consideration. A case study demonstrates the practicability and usefulness of the proposed approach.

Cloud computing systems fundamentally provide access to large pools of data and computational resources. Eucalyptus is a software framework used to implement private and hybrid-style Infrastructure as a Service clouds. It implements the Amazon Web Service (AWS) API, allowing interoperability with other AWS-based services. Elastic block storage (EBS) is a technology which provides flexible allocation of remote storage volumes to the virtual machines running in a cloud computing environment. Eucalyptus interacts with many software components to provide EBS features to the virtual machines: KVM hypervisor and Eucalyptus Node Controller (NC) are among those components. This work investigates the software aging effects in a Eucalyptus environment, considering workloads composed of intensive requests for attaching remote storage volumes to virtual machines. The results evidenced that memory leaks in Node Controller and the high CPU utilization by the KVM process are strongly correlated. The experimental analysis also show how much the aging effects are related to the performance degradation of a virtualized web server running on this infrastructure.

No abstract