Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Optimizing the performance of optimization in the cloud environment–An intelligent auto-scaling approach

The cloud computing paradigm has gained wide acceptance in the scientific community, taking a significant share from fields previously reserved exclusively for High Performance Computing (HPC). On-demand access to a large amount of computing resources provided by Cloud makes it ideal for executing large-scale optimizations using evolutionary algorithms without the need for owning any computing infrastructure. In this regard, we extended WoBinGO, an existing parallel software framework for genetic algorithm based optimization, to be used in Cloud. With these extensions, the framework is capable of elastically and frugally utilizing the underlying cloud computing infrastructure for performing computationally expensive fitness evaluations. We studied two issues that are pertinent when dealing with large-scale optimization in the elastic cloud environment: the computing instance launching overhead and the price of engaging Cloud for solving optimization problems, in terms of the instances’ cumulative uptime. To explain the usability limits of WoBinGO framework running in the IaaS environment, a comprehensive analysis of the framework’s performance was given. Optimization of both total optimization time and total cumulative uptime, leads to minimizing the cost of cloud resources utilization. In this way, we are proposing an intelligent decision support engine based on artificial neural networks and metaheuristics to provide the user with an assessment of the framework’s behavior on the underlying infrastructure in terms of optimization duration and the cost of resource consumption. According to a given assessment, the user can decide upon faster delivery of results or lower infrastructure costs. The proposed software framework has been used to solve a complex real-world optimization problem of a subsurface rock mass model calibration. The results obtained from the private OpenStack deployment show that by using the proposed decision support engine, significant savings can be achieved in both optimization time and optimization cost

A Process Framework for Managing Quality of Service in Private Cloud

As information systems leaders tap into the global market of cloud computing-based services, they struggle to maintain consistent application performance due to lack of a process framework for managing quality of service (QoS) in the cloud. Guided by the disruptive innovation theory, the purpose of this case study was to identify a process framework for meeting the QoS requirements of private cloud service users. Private cloud implementation was explored by selecting an organization in California through purposeful sampling. Information was gathered by interviewing 23 information technology (IT) professionals, a mix of frontline engineers, managers, and leaders involved in the implementation of private cloud. Another source of data was documents such as standard operating procedures, policies, and guidelines related to private cloud implementation. Interview transcripts and documents were coded and sequentially analyzed. Three prominent themes emerged from the analysis of data: (a) end user expectations, (b) application architecture, and (c) trending analysis. The findings of this study may help IT leaders in effectively managing QoS in cloud infrastructure and deliver reliable application performance that may help in increasing customer population and profitability of organizations. This study may contribute to positive social change as information systems managers and workers can learn and apply the process framework for delivering stable and reliable cloud-hosted computer applications

Big Data Analytics and Application Deployment on Cloud Infrastructure

This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field. Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand. The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies behind clouds, as well as the reasons for the success of this technology and its problems. After the introduction section, I will briefly describe the three main cloud platforms in the market. During this project we developed a simple Social Network. Consequently in the third chapter I will analyze the social network development, with the initial solution realized through Amazon Web Services and the steps we took to obtain the final version using Google Cloud Platform with its charateristics. To conclude, the last section is specific for the data elaboration and contains a initial theoretical part that describes MapReduce and Hadoop followed by a description of our analysis. We used Google App Engine to execute these elaborations on a large dataset. I will explain the basic idea, the code and the problems encountered

A manifesto for future generation cloud computing: research directions for the next decade

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing