Going Back and Forth: Efficient Multi-deployment and Multi-snapshotting on Clouds

Cloud computing has changed the way people think of using resources. Especially, the IaaS (Infrastructure as a Service) allows users to make use of unlimited resources in pay per use fashion. Virtualization is the technology based on which the cloud service providers are able to provide or share computational resources and data centers to users. Though this approach is practical, it throws certain challenges in terms of designing and development of IaaS middleware. One such challenge is the need for deploying thousands of VM instances to meet the requirements of growing number of users. In the process another challenge is to snapshot multiple images and persisting them towards management tasks like stopping VMs temporarily and resuming them as and when required. The presence of data centers in different configurations enables the simultaneous deployment and snapshotting is important. This capability should be coupled with another feature that is the whole mechanism should be hypervisor independent. To achieve this, a new virtual file system is proposed in this paper. This is basing on lazy transfer scheme with VM optimization and object versioning that takes care of multi-snapshotting and multi-deployment simultaneously and effectively. The experiments have shown that the new filing system and related techniques have improved performance, and bandwidth utilization is reduced by 90%

SEUSS: rapid serverless deployment using environment snapshots

Modern FaaS systems perform well in the case of repeat executions when function working sets stay small. However, these platforms are less effective when applied to more complex, large-scale and dynamic workloads. In this paper, we introduce SEUSS (serverless execution via unikernel snapshot stacks), a new system-level approach for rapidly deploying serverless functions. Through our approach, we demonstrate orders of magnitude improvements in function start times and cacheability, which improves common re-execution paths while also unlocking previously-unsupported large-scale bursty workloads.Published versio

The Group Methodology of Using Cloud Technologies in the Training of Future Computer Science Teachers

The development of cloud computing resources and their implementation in university education require an increase in the ICT-competence of future computer science teachers. The article considers the use of project method as an effective tool of encouraging students’ cooperation while solving practical problems and as a means of developing their essential professional skills. The following pedagogical approaches and techniques were used: partnership of group members, development of group work skills, heterogeneous grouping, combined use of individual and peer assessment, teacher’s monitoring of the students’ work, focus on the task and group work skills, chance for every member to be a leader, essential feedback. The authors suggest using private and public cloud technologies to support the implementation of group methodology in the teaching process. One of such technologies is academic cloud based on the Apache CloudStack platform. This cloud environment is deployed in Physics and Mathematics Department of Ternopil V. Hnatiuk National Pedagogical University. The suggested method has been verified experimentally by using Wilcoxon signed-rank test

BlobCR: Virtual Disk Based Checkpoint-Restart for HPC Applications on IaaS Clouds

International audienceInfrastructure-as-a-Service (IaaS) cloud computing is gaining significant interest in industry and academia as an alternative platform for running HPC applications. Given the need to provide fault tolerance, support for suspend-resume and offline migration, an efficient Checkpoint-Restart mechanism becomes paramount in this context. We propose BlobCR, a dedicated checkpoint repository that is able to take live incremental snapshots of the whole disk attached to the virtual machine (VM) instances. BlobCR aims to minimize the performance overhead of checkpointing by persisting VM disk snapshots asynchronously in the background using a low overhead technique we call selective copy-on-write. It includes support for both application-level and process-level checkpointing, as well as support to roll back file system changes. Experiments at large scale demonstrate the benefits of our proposal both in synthetic settings and for a real-life HPC application

Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots

Cloud Computing strives to be dynamic as a service oriented architecture. The services in the SoA are rendered in terms of private, public and in many other commercial domain aspects. These services should be secured and thus are very vital to the cloud infrastructure. In order, to secure and maintain resilience in the cloud, it not only has to have the ability to identify the known threats but also to new challenges that target the infrastructure of a cloud. In this paper, we introduce and discuss a detection method of malwares from the VM logs and corresponding VM snapshots are classified into attacked and non-attacked VM snapshots. As snapshots are always taken to be a backup in the backup servers, especially during the night hours, this approach could reduce the overhead of the backup server with a self-healing capability of the VMs in the local cloud infrastructure. A machine learning approach at the hypervisor level is projected, the features being gathered from the API calls of VM instances in the IaaS level of cloud service. Our proposed scheme can have a high detection accuracy of about 93% while having the capability to classify and detect different types of malwares with respect to the VM snapshots. Finally the paper exhibits an algorithm using snapshots to detect and thus to self-heal using the monitoring components of a particular VM instances applied to cloud scenarios. The self-healing approach with machine learning algorithms can determine new threats with some prior knowledge of its functionality

An Implementation of Divide and Conquer Clustering Technique for Improving the Interoperability in Hybrid Cloud Environment

Cloud computing provides users with pool of resources ubiquitously on demand. While the resources are provided to the users, interoperability needs to be considered. Interoperability is the ability of the cloud environment to transfer the data internally or between the data centers seamlessly. Interoperability is the least studied issue in the field of cloud computing. This paper implements hybrid cloud as a solution to interoperability. Hybrid cloud is chosen for its powerful combination of high configured and secured private clouds and fast accessible and scalable public clouds. The interoperability is then proposed to be enhanced by implementing divide and conquer algorithm of clustering in hybrid cloud