On Evaluating Commercial Cloud Services: A Systematic Review

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons

CloudMoV: Cloud-based Mobile Social TV

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Cloud Computing for Next-Generation Sequencing Data Analysis

High-throughput next-generation sequencing (NGS) technologies have evolved rapidly and are reshaping the scope of genomics research. The substantial decrease in the cost of NGS techniques in the past decade has led to its rapid adoption in biological research and drug development. Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis

Cost Effective Analysis of Big Data

Executive Summary Big data is everywhere and businesses that can access and analyze it have a huge advantage over those who can’t. One option for leveraging big data to make more informed decisions is to hire a big data consulting company to take over the entire project. This method requires the least effort, but is also the least cost effective. The problem is that the know-how for starting a big data project is not commonly known and the consulting alternative is not very cost effective. This creates the need for a cost effective approach that businesses can use to start and manage big data projects. This report details the development of an advisory tool to cut down on consulting costs of big data projects by taking an active role in the project yourself. The tool is not a set of standard operating procedures, but simply a guide for someone to follow when embarking on a big data project. The advisory tools has three steps that consist of data wrangling, statistical analysis, and data engineering. Data wrangling is the process of cleaning and organizing data into a format that is ready for statistical analysis. The guide recommends using the open source software and programming language of R. The next step is the statistical analysis portion of the process which takes the form of exploratory data analysis and the use of existing models and algorithms. The use of existing methods should always be attempted to the highest performance before justifying the costs to pay for big data analytics and the development of new algorithms. Data engineering consists of creating and applying statistical algorithms, utilizing cloud infrastructure to distribute processing, and the development of a complete platform solution. The experimentation for the design of our advisory toolwas carried out through analysis of many large data sets. The data sets were analyzed to determine the best explanatory variables to predict a selected response. The iterative process of data wrangling, statistical analysis, and model building was carried out for all the data sets. The experience gained, through the iterations of data wrangling and exploratory analysis, was extremely valuable in evaluating the usefulness of the design. The statistical analysis improved every time the iterative loop of wrangling and analysis was navigated. In house data wrangling, before submission to a data scientist, is the primary cost justification of using the advisory tool. Data wrangling typically occupies 80% of data scientist’s time in big data projects. So, if data wrangling is self-performed before a data scientist receives the data, then less time will be spent wrangling by the data scientist. Since data scientists are paid very high hourly wages, extra time saved wrangling equates to direct cost savings. This is assuming that the data wrangling performed before a data scientist takes over is of adequate quality. The results of applying the advisory tool may vary from case to case, depending on the critical skills the user possesses and the development of such skills. The critical skills begin with coding in R and Python as well as knowledge in the statistical methods of choice. Basic knowledge of statistics, and any programming language is a must to begin utilizing this guide. Statistical proficiency is the limiting factor in the advisory tool. The best start for doing a big data project on one’s own is to first learn R and become familiar with the statistical libraries it contains. This allows data wrangling and exploratory analysis to be performed at a high level. This project pushed the boundaries of what can be done with big data using traditional computer framework without cloud usage. Storage and processing limits of traditional computers were tested and in some cases reached, which verified the eventual need to operate in the cloud environment

Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments

Multi-Tenant Cloud FPGA: A Survey on Security

With the exponentially increasing demand for performance and scalability in cloud applications and systems, data center architectures evolved to integrate heterogeneous computing fabrics that leverage CPUs, GPUs, and FPGAs. FPGAs differ from traditional processing platforms such as CPUs and GPUs in that they are reconfigurable at run-time, providing increased and customized performance, flexibility, and acceleration. FPGAs can perform large-scale search optimization, acceleration, and signal processing tasks compared with power, latency, and processing speed. Many public cloud provider giants, including Amazon, Huawei, Microsoft, Alibaba, etc., have already started integrating FPGA-based cloud acceleration services. While FPGAs in cloud applications enable customized acceleration with low power consumption, it also incurs new security challenges that still need to be reviewed. Allowing cloud users to reconfigure the hardware design after deployment could open the backdoors for malicious attackers, potentially putting the cloud platform at risk. Considering security risks, public cloud providers still don't offer multi-tenant FPGA services. This paper analyzes the security concerns of multi-tenant cloud FPGAs, gives a thorough description of the security problems associated with them, and discusses upcoming future challenges in this field of study

From Single to Multi-clouds Computing Privacy and Fault Tolerance

AbstractSecurity issues of data hosted in a Cloud Computing provider remain hidden seen excessive marketing that led to a totally unrealistic view of cloud computing security. Although Cloud Computing has not yet reached the level of maturity expected by its customers, and that the problems of confidentiality, integrity, reliability and consistency (CIRC) are still open, the researchers in this field have already considered a future cloud strategy which aims: a better QoS, reliability and high availability, it is the Multi-Clouds, Cloud of Clouds or Interclouds.This paper will present the security limitations in the single Cloud and the usefulness of adopting rather Multi-Clouds strategy to reduce security risks, through the use of DepSky which is a virtual storage system that ensures better availability and high confidentiality of data

Executing Large Scale Scientific Workflows in Public Clouds

Scientists in different fields, such as high-energy physics, earth science, and astronomy are developing large-scale workflow applications. In many use cases, scientists need to run a set of interrelated but independent workflows (i.e., workflow ensembles) for the entire scientific analysis. As a workflow ensemble usually contains many sub-workflows in each of which hundreds or thousands of jobs exist with precedence constraints, the execution of such a workflow ensemble makes a great concern with cost even using elastic and pay-as-you-go cloud resources. In this thesis, we develop a set of methods to optimize the execution of large-scale scientific workflows in public clouds with both cost and deadline constraints with a two-step approach. Firstly, we present a set of methods to optimize the execution of scientific workflow in public clouds, with the Montage astronomical mosaic engine running on Amazon EC2 as an example. Secondly, we address three main challenges in realizing benefits of using public clouds when executing large-scale workflow ensembles: (1) execution coordination, (2) resource provisioning, and (3) data staging. To this end, we develop a new pulling-based workflow execution system with a profiling-based resource provisioning strategy. Our results show that our solution system can achieve 80% speed-up, by removing scheduling overhead, compared to the well-known Pegasus workflow management system when running scientific workflow ensembles. Besides, our evaluation using Montage workflow ensembles on around 1000-core Amazon EC2 clusters has demonstrated the efficacy of our resource provisioning strategy in terms of cost effectiveness within deadline