We examine and bring out the architecturally significant characteristics of various virtualization and cloud oriented platforms. The impact of such characteristics on the ability of guest applications to achieve various quality attributes (QA) has also been determined by examining existing body of architecture knowledge. We observe from our findings that efficiency, resource elasticity and security are among the most impacted QAs, and virtualization platforms exhibit the maximum impact on various QAs.
Several researchers have explored different dimensions of virtualization and cloud based platforms. For instance, a dissection of the cloud into five main layers, and illustrating their interrelations and inter-dependency on constituent components has been discussed by Youseff et. al. @cite_3 . The architectural requirements of cloud platforms have been discussed by Rimal et. al. @cite_13 . These works examine different technical dimensions of cloud computing. However, from the standpoint of an application that wants to make exploit capabilities of such modern computing platforms, some questions still do not have clear answers. For example: What are important characteristics of various platforms from the viewpoint of a guest application? How do such characteristics impact various functional and non-functional aspects of guest application?
@cite_13 @cite_3
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In these days, Cloud computing is provided by various service ways, and it is possible that practical implement and service by virtualized environment. With developing of cloud computing techniques, many companies propose the different type of platforms through research the relevant technique. Among other platforms, we are going to talk about the performance comparison analysis of Linux Container and Virtual Machine in this paper. We built Cloud environment first on Docker which is based on Linux Container and Hypervisor which is Virtual Machine, we analyzed each of the size, Boot speed, and CPU performance. With this analysis result, Users will be able to understand characteristic of each platforms, and they will be able to choose the platforms reasonably what they need. Cloud technology is moving towards multi-cloud environments with the inclusion of various devices. Cloud and IoT integration resulting in so-called edge cloud and fog computing has started. This requires the combination of data centre technologies with much more constrained devices, but still using virtualised solutions to deal with scalability, flexibility and multi-tenancy concerns. Lightweight virtualisation solutions do exist for this architectural setting with smaller, but still virtualised devices to provide application and platform technology as services. Containerisation is a solution component for lightweight virtualisation solution. Containers are furthermore relevant for cloud platform concerns dealt with by Platform-as-a-Service (PaaS) clouds like application packaging and orchestration. We demonstrate an architecture for edge cloud PaaS. For edge clouds, application and service orchestration can help to manage and orchestrate applications through containers. In this way, computation can be brought to the edge of the cloud, rather than data from the Internet-of-Things (IoT) to the cloud. We show that edge cloud requirements such as cost-efficiency, low power consumption, and robustness can be met by implementing container and cluster technology on small single-board devices like Raspberry Pis. This architecture can facilitate applications through distributed multi-cloud platforms built from a range of nodes from data centres to small devices, which we refer to as edge cloud. We illustrate key concepts of an edge cloud PaaS and refer to experimental and conceptual work to make that case. Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands. Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud. Multi-tenant cloud, which usually leases resources in the form of virtual machines, has been commercially available for years. Unfortunately, with the adoption of commodity virtualized infrastructures, software stacks in typical multi-tenant clouds are non-trivially large and complex, and thus are prone to compromise or abuse from adversaries including the cloud operators, which may lead to leakage of security-sensitive data. In this paper, we propose a transparent, backward-compatible approach that protects the privacy and integrity of customers' virtual machines on commodity virtualized infrastructures, even facing a total compromise of the virtual machine monitor (VMM) and the management VM. The key of our approach is the separation of the resource management from security protection in the virtualization layer. A tiny security monitor is introduced underneath the commodity VMM using nested virtualization and provides protection to the hosted VMs. As a result, our approach allows virtualization software (e.g., VMM, management VM and tools) to handle complex tasks of managing leased VMs for the cloud, without breaking security of users' data inside the VMs. We have implemented a prototype by leveraging commercially-available hardware support for virtualization. The prototype system, called CloudVisor, comprises only 5.5K LOCs and supports the Xen VMM with multiple Linux and Windows as the guest OSes. Performance evaluation shows that CloudVisor incurs moderate slow-down for I O intensive applications and very small slowdown for other applications.