Cloud computing is a recent paradigm based around the notion of delivery of resources via a service model over the Internet. Despite being a new paradigm of computation, cloud computing owes its origins to a number of previous paradigms. The term cloud computing is well defined and no longer merits rigorous taxonomies to furnish a definition. Instead this survey paper considers the past, present and future of cloud computing. As an evolution of previous paradigms, we consider the predecessors to cloud computing and what significance they still hold to cloud services. Additionally we examine the technologies which comprise cloud computing and how the challenges and future developments of these technologies will influence the field. Finally we examine the challenges that limit the growth, application and development of cloud computing and suggest directions required to overcome these challenges in order to further the success of cloud computing.
A Cloud Infrastructure Service Recommendation System for Optimizing Real-time QoS Provisioning Constraints
云计算的普及已经彻底改变了基于互联网的应用服务的托管和交付。然而，随着几乎每个月都有大型云计算公司（例如，Amazon Web Service、Microsoft Azure）和小公司（例如Rackspace、Ninefold），决策者（例如应用程序开发人员、CIO）可能会被可用的选择所淹没。由于异构的服务配置和应用程序供应服务质量（QoS）的约束，决策问题进一步复杂化。为了应对这一严峻挑战，在我们以前的工作中，我们开发了一种半自动化的、可扩展的、基于本体的方法，用于仅基于设计时约束（例如，租赁成本、中心位置、服务特色等）。在本文中，我们扩展我们的方法，包括实时的【形】（运行时）的QoS（端到端的消息延迟，端到端的消息吞吐量）的决策过程。在线交互式游戏、大规模传感器分析和云服务上的实时的【形】移动应用领域中的下一代应用的托管需要优化这种实时的【形】QoS约束以满足服务水平协议（SLA）。为此，我们提出了一个实时的【形】的QoS感知的多标准决策技术，建立在众所周知的层次分析法（AHP）的方法。所提出的技术适用于选择基础设施即服务（IaaS）云产品，并且它允许用户定义多个设计时和实时的【形】QoS约束或要求。然后将这些需求与我们的知识库进行匹配，以计算IaaS层云服务的可能最佳组合。我们进行了大量的实验，以证明我们的方法的可行性。
In this article, we describe the PyCloudIoT cloud infrastructure. PyCloudIoT uses a FaaS cloud computing model for offloading numerical computations on a cluster with resource-constrained nodes rather than powerful datacenter. This infrastructure aims at exploiting unused resources of IoT nodes - already deployed at the edge of the network - to reduce latency of user requests. This extra computation must be done without significant energy consumption - IoT nodes being battery-powered. -- Dans cet article, nous décrivons l'infrastructure cloud PyCloudIoT. PyCloudIoT s'appuie sur un modèle de cl…
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure
计算、存储和网络领域的最新技术进步，以及组织在削减成本的同时保持对不断增长的服务需求的响应的需求增加，导致了云计算服务的采用增长。云服务提供了提高敏捷性、弹性、可扩展性和降低总拥有成本（TCO）的承诺。这项研究介绍了一个框架，通过使用线性规划（ILP）方法来优化工作负载分配到Amazon Web Services（AWS）云基础设施上的服务器，从而最大限度地降低成本和提高资源利用率。该模型是基于经典的最小成本流模型，被称为分配模型。
Developing a software service requires a strict software development life cycle and process. This process demands controlling all application code through source control management as well as a rigorous versioning and branching strategy. However, the platform and infrastructure also benefit from this rigor. Software services must be deployed to a target run time environment and provisioning that environment through manual user actions is tedious and error-prone. Provisioning manually also becomes prohibitive as the number of resources grow and spread globally over multiple regions. The answer …
Development of autonomous and self-driving vehicles requires agile and reliable services to manage hazardous road situations. Vehicular Network is the medium that can provide high-quality services for self-driving vehicles. The majority of service requests in Vehicular Networks are delay intolerant (e.g., hazard alerts, lane change warning) and require immediate service. Therefore, Vehicular Networks, and particularly, Vehicle-to-Infrastructure (V2I) systems must provide a consistent real-time response to autonomous vehicles. During peak hours or disasters, when a surge of requests arrives at …
Function as a Service (FaaS) is a new cloud technology with automated resource management. Different from traditional cloud computing, each FaaS cloud function can only run a fixed period of time before being decommissioned. Furthermore, FaaS cloud providers often update their platforms (e.g., idle timeout). These changes and their associated impact are not transparent and could potentially impact the execution of the cloud functions. Hence, in this paper, we develop a methodology to characterize the cloud function idle timeout which is the duration a FaaS cloud provider keeps a cloud function…
High computational time is one of the most important operational issues in centralized dynamic shared ridehailing services. To resolve this issue, we propose a distributed ride-matching system that is based on vehicle to infrastructure (V2I) and infrastructure to infrastructure (I2I) communication. The application on downtown Toronto road network demonstrated that the distributed system resulted in a speed-up of 125 times in terms of computational time and showed high scalability. Moreover, the service rate in the proposed system improved by 7% compared to the centralized. However, the central…
As an application usage grows, its owner scales up vertically by replacing old machines with more powerful ones. This methodology is expensive and leads to resource waste. In response to the business needs, internet giants have developed the microservice architecture, which lets developers divide up their application into smaller units that can be hosted on multiple machines, thus enabling horizontal scale up. We propose a triphasic incremental process to transform a traditional application into a microservice application that guarantees stability during the operation. Then we demonstrated suc…
This study focuses on the feasibility of commercial cloud services for connected vehicle (CV) applications in a Transportation Cyber-Physical Systems (TCPS) environment. TCPS implies that CVs, in addition to being connected with each other, communicates with the transportation and computing infrastructure to fulfill application requirements. The motivation of this study is to accelerate commercial cloud-based CV application development by presenting the lessons learned by implementing a CV mobility application using Amazon Web Services (AWS). The feasibility of the cloud-based CV application i…
This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Neb…
Smart farming is a recent innovation in the agriculture sector that can improve the agricultural yield by using smarter, automated, and data driven farm processes that interact with IoT devices deployed on farms. A cloud-fog infrastructure provides an effective platform to execute IoT applications. While fog computing satisfies the real-time processing need of delay-sensitive IoT services by bringing virtualized services closer to the IoT devices, cloud computing allows execution of applications with higher computational requirements. The deployment of IoT applications is a critical challenge …
Globalized computing infrastructures offer the convenience and elasticity of globally managed objects and services, but lack the resilience to distant failures that localized infrastructures such as private clouds provide. Providing both global management and resilience to distant failures, however, poses a fundamental problem for configuration services: How to discover a possibly migratory, strongly-consistent service/object in a globalized infrastructure without dependencies on globalized state? Limix is the first metadata configuration service that addresses this problem. With Limix, global…
e-Infrastructures have powered the successful penetration of e-services across domains, and form the backbone of the modern computing landscape. e-Infrastructure is a broad term used for large, medium and small scale computing environments. The increasing sophistication and complexity of applications have led to even small-scale data centers consisting of thousands of interconnects. However, efficient utilization of resources in data centers remains a challenging task, mainly due to the complexity of managing physical nodes, network equipment, cooling systems, electricity, etc. This results in…
One of the major restrictions on the practical applications of unmanned aerial vehicles (UAV) is their incomplete self-sufficiency, which makes continuous operations infeasible without human oversights. The more oversight UAVs require, the less likely they are going to be commercially advantageous when compared to their alternatives. As an autonomous system, how much human interaction is needed to function is one of the best indicators evaluating the limitations and inefficiencies of the UAVs. Popular UAV related research areas, such as path planning and computer vision, have enabled substanti…
Compute infrastructure hosted by a cloud provider allows an application to scale without limit. The application developer no longer needs to worry about the up-front investment in a server farm provisioned for a worst-case load scenario. However, managing cloud deployments requires a sophisticated framework that can autoscale the infrastructure and guarantee the up-time of running container images. This paper surveys existing research addressing the management and orchestration of cloud deployments as well as the modelling framework to abstract away the low-level details of the host infrastruc…
The stability and performance of Cloud platforms are essential as they directly impact customers' satisfaction. Cloud service providers use Cloud monitoring tools to ensure that rendered services match the quality of service requirements indicated in established contracts such as service-level agreements. Given the enormous number of resources that need to be monitored, highly scalable and capable monitoring tools are designed and implemented by Cloud service providers such as Amazon, Google, IBM, and Microsoft. Cloud monitoring tools monitor millions of virtual and physical resources and cont…
The European Union is on course to introduce a European Digital Identity that will be available to all EU citizens and businesses. This will have a huge impact on how citizens and businesses interact online. Big Tech companies currently dictate how digital identities are used. As a result, they have amassed vast amounts of private user data. Movements like Self-Sovereign Identity aim to give users control over their online identity. TrustVault is the first data wallet that gives users back control of their identity and all their data. TrustVault allows users to store all their data on their sm…
More and more household appliances connect to the Internet and exchange data freely. This is the foundation for true smart buildings. However, there is still no uniform communication technology available, which can connect all appliances from all vendors. Protocols differ between manufacturers making interoperability difficult or even impossible. Manufacturers cannot rely on a reference for the implementation and real estate developers and operators are reluctant to commit to a system until it is clear which one will prevail. A similar situation is evident in smart grids and applies equally to…
From Quantity to Quality: Massive Molecular Dynamics Simulation of Nanostructures under Plastic Deformation in Desktop and Service Grid Distributed Computing Infrastructure
Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）；University of Pisa（比萨大学）
Predictive machine learning models nowadays are often updated in a stateless and expensive way. The two main future trends for companies that want to build machine learning-based applications and systems are real-time inference and continual updating. Unfortunately, both trends require a mature infrastructure that is hard and costly to realize on-premise. This paper defines a novel software service and model delivery infrastructure termed Continual Learning-as-a-Service (CLaaS) to address these issues. Specifically, it embraces continual machine learning and continuous integration techniques. …
