Performance Analysis for Large IaaS Clouds

2016

Abstract: Cloud computing is business infrastructure paradigm that promising to remove the need for organizations to keep up an exclusive computing hardware. Cloud computing provides to users with various capabilities to store and process the data in third-party data centers. Cloud computing maximizes the effectiveness of shared resources. During the use of time sharing and virtualization cloud address with the particular set of material resources in a large scaled user‟s base with different needs. In this paper computing the performance of Platform-as-a-Service (PaaS) model and integrating the mechanisms to capture the virtual machine migrations. We study the cloud services on different large applications. In this paper we are presenting the performance of Platform-as-a-Service (PaaS) by using systematic model to perform end-to-end analysis of a cloud service. The systematic model designed by using scheduling algorithm i.e., Adaptive First Come First Serve under different job sizes...

Cloud Computing makes the dream of computing real as a tool and in the form of service. This internetbased ongoing technology which has brought flexibility, capacity and power of processing has realized service-oriented idea and has created a new ecosystem in the computing world with its great power and benefits. Cloud capabilities have been able to move IT industry one step forward. Nowadays, large and famous enterprise have resorted to cloud computing and have transferred their processing and storage to it. Due to popularity and progress of cloud in different organizations, cloud performance evaluation is of special importance and this evaluation can help users make right decisions. In this paper, we provide an overall perspective on cloud evaluation criteria and highlight it with help of simulation. For this purpose, we present different major factors in cloud computing performance and we analyze and evaluate cloud performance in various scenarios considering these factors.

The popularity of cloud computing and proliferation of the market among its competitors, has created an urgent need for an effective performance analysis methodology best suited for Infrastructure as a Service (Iaas) cloud computing environment consisting of a myriad of physical and virtual Machines. In order for the cloud service providers to be efficient in providing and configuring their computational resources.

International Journal of Engineering Applied Sciences and Technology, 2021

Cloud computing is key for today's fast world. It serves across all the domains. Be it business, educational, public, or private sector, etc. Its application can be observed anywhere. Its overall concept and its analysis of services and model are presented here. Mainly cloud has 3 delivery models. Saas (Software as a service), Paas(Platform as a service), and Iaas (Infrastructure as a service). We explored these with their performances and interdependencies. Cloud adoption has its performance limitation and solutions to overcome these challenges are provided here. Also, cloud delivery models are discussed. Performance factors are very important for as a whole cloud computing success, where it adds its ideal cost of services, its reliability, and its scalability constitutes as its pillar factors.

IJSRD - International Journal for Scientific Research and Development, 2018

— Cloud computing is a method or technique for enabling convenient, on demand network access to a shared pool of computing resources (such as computer networks, servers, applications, storage, and services) that can be continuously provisioned and released with minimum management efforts. In this paper we have used some of the existing performance monitoring tools and techniques popular among variety of users. During this study a number of factors such as response time, bandwidth and latency etc. have been determined. Considering the frequency of factors appearing from the review work it has been inferred that the cloud response time is very crucial for the cloud performance and selected for further study.

2016

The objective of this paper is to perform a comprehensive performance comparison of public cloud services for computing and to analyze the correlation between their prices and performance. Eight representative public cloud providers were divided into two groups using market share: small cloud providers and large cloud providers. Results revealed that these offered computing services vary widely in performance and price; most small cloud providers have more stable and better computing performance than large cloud providers; the performance of CPU impact price significantly.

2013

Cloud Computing is defined as a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers. It is based on service-level agreements that established between service providers and consumers. Cloud Computing opens up many new possibilities for Internet application developers. The VMware is used to integrate the virtual servers which are used for performance analysis.

Cloud service selection can be a complex and challenging task for a cloud engineer. Most current approaches try to identify a best cloud service provider by evaluating several relevant criteria like prices, processing, memory, disk, network performance, quality of service and so on. Nevertheless, the decision making problem involves so many variables, that it is hard to model it appropriately. We present an approach that is not about selecting a best cloud service provider. It is about selecting most similar resources provided by different cloud service providers. This its much better practical needs of cloud service engineers. Especially, if container clusters are involved. EasyCompare, an automated benchmarking tool suite to compare cloud service providers, is able to benchmark and compare virtual machine types of different cloud service providers using an Euclidian distance measure. It turned out, that only 1% of theoretical possible machine pairs have to be considered in practice. These relevant machine types can be identi ied by systematic benchmark runs in less than three hours. We present some expectable but also astonishing evaluation results of EasyCompare used to evaluate two major and representative public cloud service providers: Amazon Web Services and Google Compute Engine. an evolutionary process. So often, the irst cloud service provider is not selected systematically, it is just a "mean available". Therefore, our guiding question in this paper is an engineering one. It is not about selecting virtual machines from a best cloud service provider, but it is about selecting the most similar cloud virtual machines provided by different cloud service providers. Container clusters like Mesos [6], CoreOS or Kubernetes [24] are getting more and more attention in cloud computing due to the fact that they are providing a horizontally scalable concept of one logical virtual machine, that can be deployed on up to thousands of "physical" nodes. These "physical" nodes can be hosted by different public providers and in private clouds or on bare-metal machines as well. Horizontally scalable microservice approaches -widely used by companies like Facebook, Twitter, Net lix, and so on -rely heavily on this kind of cluster solutions . This is done to handle regional workloads, to provide failover and over low capacities and also to avoid cloud vendor lock-in [32], . All provided machines of such kind of cluster should show similar performance characteristics to provide ine-grained resource allocation capabilities . At this point, one obvious question arises: How to identify similar virtual machine types from different providers? None of the above mentioned approaches will answer this question. All of them are trying to identify a best (in terms of performance or cost-effectiveness) resource. These approaches are not helpful to identify most similar resources provided by different providers. A major problem is, that no two cloud service providers provide exactly the same virtual machine types. To answer these questions, we propose an automated benchmarking system called EasyCompare. The core idea is to describe virtual machine type performances by a performance vector. A similarity value between 0 (not similar) and 1 (very similar) can be calculated using these vectors. The presented approach can be used to compare regional differences of providers, to compare machine variants of the same or different cloud service providers, or to de ine machine types for a private cloud to align them to performance of machines types of public cloud service providers, and so on. The remainder of this paper is structured as follows. A proposed benchmarking architecture to collect benchmark data in the cloud is explained in Section 2. Section 3 analyzes related work and existing benchmarking solutions for cloud computing to derive some appropriate benchmarks to measure relevant performance aspects of cloud provided virtual machines. Section 4 presents some data, which has been collected to compare two representative public IaaS Cloud Service Providers (Amazon Web Services, AWS and Google Compute Engine, GCE). By analyzing this data, we will see that different cloud service providers provide a variety of comparable and non comparable machine types, making it complicated (but not impossible) to compare virtual machine types of different cloud service providers. Section 5 applies these insights to derive a similarity measure, making it possible to compare two arbitrary virtual machine types by a normalized Euclidian distance measure. The proposed Euclidian distance measure is evaluated on the collected data. We conclude our indings in Section 6 and show that our approach contributes to do a pragmatic cloud service selection only on relevant subsets of cloud offerings.

Data Driven e-Science, 2011

Infrastructure as a Service cloud provides an open avenue for consumer to easily own computing and storage infrastructure. User can invent new services and have it deploy in a fraction of a time. As services being consumed, user expect consistent performance of their virtual machines. To host an IaaS, virtualization performance and behaviour need to be understood first. In this paper we present virtualization benchmarking methods, results and analysis in various resource utilization scenarios. The scenarios are Performance Overhead; the cost of virtualization compared to physical, Performance Variance; the VM instance performance when more VM is instantiate and Performance Fairness or Isolation; the study of fairness on each of the guest instances under stress environment. These studies provides us the fundamental understanding of virtualization technology.

International Journal of Cloud Computing, 2015

The pay-per-use business model is one of the key factors for the success of the cloud computing paradigm: resources are acquired only when needed and charged on the basis of their actual usage. The execution of applications in the cloud implies costs that depend on the usage of the leased resources and on the resource pricing model adopted by the providers. This paper presents a technique to evaluate the trade-o↵ between costs and performance of cloud applications through the use of benchmarks and simulation. Given a mOSAIC cloud application, it is possible to predict performance indexes and resource consumption under generic workloads. This makes it possible to choose the deployment on the resources of the provider that guarantees the desired performance levels and minimizes the costs for executing the application.