Deployment Of Cloud Services Research Articles

SYSTEM APPROACH TO THE DEVELOPMENT OF CLOUD SERVICES AND ITS IMPLEMENTATION PROPOSAL IN AN ACADEMIC ENVIRONMENT

The article provides a comprehensive overview and practical contribution to the development, implementation, and deployment of cloud services in an academic environment. A systematic approach is applied throughout the paper. The result of the solution is a cloud service design methodology and an example of cloud service deployment in an academic environment.

Leveraging Cloud Infrastructure for Rapid Application Development and Deployment of Cloud Services

Backend as a service (BaaS) is service model in which developers outsource all the behind-the-scenes aspects of a web or mobile application so that they only have to write and maintain only the frontend. This paper explores representative BaaS platforms and the limitations they present to users. Using the feedback received from the developer community, the author presents how the research project addresses the weaknesses earlier identified in existing BaaS platform. Some of these concerns include security aspects that a BaaS platform should present as a bare minimum. Finally, the paper discusses a summary of the benefits that businesses and developers employing a BaaS platform could derive from using a BaaS platform that addresses the limitations of existing platforms. The platform, referred to as DbAPI symbolizes the ways in which databases and Application Programming Interfaces (APIs) present a powerful tool that provides cloud services to users in a way that abstracts the complexity of deploying cloud services while still addressing security concerns. This tool enhances the software development process through the acceleration of the software development life cycle, the reduction of associated costs, and the abstraction of computing infrastructure complexity.

Research Paper on Cloud Computing

Abstract: We are in the middle of a powerful era of cloud computing in IT. Rooted in the wide reach of the Internet, this technology provides a robust computing architecture that combines networked hardware, software, and Internet infrastructure. Its advantages go beyond traditional computing methods and go beyond grid computing and others. A key moment in the maturation of cloud computing occurred in 2006 when Amazon introduced the first cloud services. Hong Kong is particularly suitable for the deployment of cloud services due to its extensive daily data processing across various sectors.

Efficient Auto‐scaling for Host Load Prediction through VM migration in Cloud

SummaryThe expeditious deployment of Cloud applications and services on wide‐ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over‐utilized or under‐utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto‐scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on‐demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time‐series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over‐utilized and under‐utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real‐time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over‐utilization and under‐utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead.

Configuration and Placement of Serverless Applications Using Statistical Learning

In the last decade, serverless computing emerged as a new compelling paradigm for the deployment of cloud applications and services. It represents an evolution of cloud computing with a simplified programming model, that aims to abstract away most operational concerns. Running serverless applications requires users to configure multiple parameters, such as memory, CPU, cloud provider, etc. While relatively simpler, configuring such parameters correctly while minimizing cost and meeting delay constraints is not trivial. In this paper, we present, a framework that uses Bayesian Optimization to find the optimal resource configuration and placement for functions in a serverless application. uses statistical learning techniques to intelligently collect samples and predict the cost and execution time of a serverless function across unseen configuration values. Our framework uses the predicted cost and execution time on available locations to select the “best” configuration parameters and placement for running a serverless application while satisfying customer objectives. We evaluate on AWS Lambda with real-world applications consisting of multiple functions (both linear chains and service graphs), where we successfully found optimal/near-optimal configurations. We also evaluate over a wide range of simulated distributed cloud environments that confirm the efficacy of our approach.

Model-based cloud service deployment optimisation method for minimisation of application service operational cost

Many currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis.

Computer Intelligent Cloud Server Platform of Coal Preparation Plant Based on Big Data Cloud Technology

This article combines the actual situation of the coal preparation plant and uses big data cloud technology to carry out detailed system requirements analysis and monitoring platform design. The entire design includes server framework design, database design, cloud service deployment and mobile client prototype design. According to the on-site working conditions of electromechanical equipment, the article uses ER model and 3NF method to abstract it and establishes a MySQL real-time database of equipment operating status. Finally, the ECS cloud server is used to complete the deployment, and the JSP technology is used to realize the display of the system’s man-machine interface, as well as remote monitoring services for equipment monitoring, information management, and user management. Practical application shows that the system can improve the comprehensive automation and information level of the enterprise, reduce the operating cost of the enterprise, and improve the production efficiency of the enterprise.

Serverless Empowered Video Analytics for Ubiquitous Networked Cameras

Ubiquitous deployment of networked cameras has boosted the prevalence of video analytics. Leveraging machines to automatically analyze captured videos has become the driving force behind a variety of contemporary applications. However, it is known to be resource-hungry with highly dynamic demands, which mismatches the existing monolithic cloud service deployment with coarse-grained resource allocation. Recent advances in serverless computing, which offers ultra-fast and fine-grained autoscaling, would become a game-changer. This article closely examines the potential and challenges of serverless computing in building modern video analytics applications. We accordingly present an integrated framework with geo-distributed resources, and identify the critical design issues toward its implementation. We further discuss a series of promising research directions in this field.

A Delivery Robot Cloud Platform Based on Microservice

Delivery robots face the problem of storage and computational stress when performing immediate tasks, exceeding the limits of on-board computing power. Based on cloud computing, robots can offload intensive tasks to the cloud and acquire massive data resources. With its distributed cluster architecture, the platform can help offload computing and improve the computing power of the control center, which can be considered the external “brain” of the robot. Although it expands the capabilities of the robot, cloud service deployment remains complex because most current cloud robot applications are based on monolithic architectures. Some scholars have proposed developing robot applications through the microservice development paradigm, but there is currently no unified microservice-based robot cloud platform. This paper proposes a delivery robot cloud platform based on microservice, providing dedicated services for autonomous driving of delivery robot. The microservice architecture is adopted to split the monomer robot application into multiple services and then implement automatic orchestration and deployment of services on the cloud platform based on components such as Kubernetes, Docker, and Jenkins. This enables containerized CI/CD (continuous integration, continuous deployment, and continuous delivery) for the cloud platform service, and the whole process can be visualized, repeatable, and traceable. The platform is prebuilt with development tools, and robot application developers can use these tools to develop in the cloud, without the need for any customization in the background, to achieve the rapid deployment and launch of robot cloud service. Through the cloud migration of traditional robot applications and the development of new APPs, the platform service capabilities are continuously improved. This paper verifies the feasibility of the platform architecture through the delivery scene experiment.

A port-based forwarding load-balancing scheduling approach for cloud datacenter networks

Today’s datacenter networks (DCNs) scale is rapidly increasing because of the wide deployment of cloud services and the rapid rise of edge computing. The bandwidth consumption and cost of a DCN are growing sharply with the extensions of network size. Thus, how to keep the traffic balanced is a key and challenging issue. However, the traditional load balancing algorithms such as Equal-Cost Multi-Path routing (ECMP) are not suitable for high dynamic traffic in cloud DCNs. In this paper, we propose a port-based forwarding load balancing scheduling (PFLBS) approach for Fat-tree based DCNs with some new features which can overcome the disadvantages of the existing load balancing methods in the following aspects. Firstly, we define a port-based source-routing addressing scheme, which decreases the switch complexity and makes the table-lookup operation unnecessary. Secondly, based on this addressing scheme, we proposed an effective routing mechanism which can obtain multiple available paths for flow scheduling based in Fat-tree. All the path information is saved in servers and each server only needs to maintain its own path information. Thirdly, we propose an efficient algorithm to implement large flows scheduling dynamically in terms of current link utilization ratio. This method is suitable for cloud DCNs and edge computing, which can reduce the complexity of the switches and the power consumption of the whole network. The experiment results indicate that the PFLBS approach has better performance compared with the ECMP, Hedera and MPTCP approaches, which decreases the flow completion time and improves the average throughput significantly. PFLBS is simple and can be implemented with a few signaling overheads.

A secure cloud service deployment framework for DevOps

<span>The advancements in cloud computing and leveraging the benefits from cloud computing to the service providers have increased the deployment of traditional applications to the cloud. The applications once deployed on the cloud, due to various reasons, need migration from development infrastructure to operational infrastructure, one operational instance to other operational instances due to load balancing and the cycle continues due to the use of DevOps as development strategies for cloud computing applications. Advocates of hybrid and public clouds observe cloud computing makes it possible for organizations to avert or minimize upfront IT infrastructure expenses. Proponents also assert that cloud computing systems permit businesses to receive their software up and running faster, using improved manageability and less maintenance, so it empowers IT teams to rapidly adapt tools to meet the varying and unpredictable requirements. DevOps is a lot of practices that mechanizes the procedures between programming improvement and IT groups, all together that they can fabricate, test, and discharge programming quicker and even more dependably. The idea of DevOps is established on building a culture of a joint effort between groups that generally worked in relative siloes. The guaranteed advantages incorporate expanded trust, quicker programming discharges, capacity to explain basic issues rapidly and better oversee impromptu work. Thus, this work identifies the need for providing multiple security protocols during the complete life cycle of cloud application development and deployment. This work proposes a novel framework for automatic selection and deployment of the security protocols during cloud service deployments. The framework identifies the need for security aspects and selects the appropriate security algorithms for virtual machines. The proposed framework demonstrates nearly 80% improvement over the security policy deployment time. </span>

Duplicacy: A New Generation of Cloud Backup Tool Based on Lock-Free Deduplication

The pervasive deployment of cloud services poses an ever-increasing demand for cross-client deduplication solutions to save network bandwidth, lower storage costs, and improve backup speeds. However, existing solutions typically depend on lock-based approaches relying on a centralized chunk database, which tends to hinder performance scalability. In this article, we present a new cross-client cloud backup solution, named Duplicacy, based on a Lock-Free Deduplication approach. Lock-Free Deduplication stores chunks to network or cloud storage using content hashes as file names. It then adopts a two-step fossil deletion algorithm to solve the hard problem of deleting unreferenced chunks in the presence of concurrent backups, without the need for any locks. Experiments demonstrate that Duplicacy enables significant performance improvement for backups over previous well-known backup tools. In addition, Duplicacy can work with many general-purpose network or cloud storage services which only support a basic set of file operations, and turn them into sophisticated deduplication-aware storage servers without server-side changes.

A behavior-aware SLA-based framework for guaranteeing the security conformance of cloud service

As cloud computing technology turning to mature, cloud services have become a trust-based service. Users’ distrust of the security and performance of cloud services will hinder the rapid deployment and development of cloud services. So cloud service providers (CSPs) urgently need a way to prove that the infrastructure and the behavior of cloud services they provided can be trusted. The challenge here is how to construct a novel framework that can effective verify the security conformance of cloud services, which focuses on fine-grained descriptions of cloud service behavior and security service level aggreements (SLAs). In this paper, we propose a novel approach to verify cloud service security conformance, which reduces the description gap between the CSP and users through modeling cloud service behavior and security SLA, these models enable a systematic integration of security constraints and service behavior into cloud while using UPPAAL to check the performance and security conformance. The proposed approach is validated through case study and experiments with real cloud service based on Open-Stack, which illustrates CloudSec approach effectiveness and can be applied on realistic cloud scenario.

A multifaceted framework for adoption of cloud computing in Malaysian SMEs

PurposeThe purpose of this study is to develop a model to understand the relationships among technology, organizational and environmental (TOE) contexts, intention to adopt cloud computing (IACC) and actual usage of cloud computing (AUCC) in small and medium enterprises (SMEs) in Malaysia as a developing country. More specifically, this paper seeks to explore the mediation effect of IACC on the relationship between TOE context and AUCC.Design/methodology/approachA positivist research approach was selected for this study. Drawing largely upon the TOE framework, this study uses survey data from 209 Malaysian SMEs. Structural equation modelling (SEM) based on partial least squares (PLS) was used to assess the structural relations of the research model.FindingsThe results of the structural model show that data security, technology readiness, top management support, competitive pressure and innovativeness are the most significant factors in predicting the adoption of cloud computing in Malaysian SMEs. Further, the results indicate that intention to adopt cloud computing can play a mediating role between TOE factors and the actual usage of cloud computing.Research limitations/implicationsThe focus upon Malaysian SMEs may diminish the generalizability of the findings. This study provides profound insight into the management and foundation of cloud computing, different types of cloud services and deployment models that could facilitate the management of enterprise strategic resources and contribute to the performance improvement. This study also provides another important implication for practitioners regarding the absolute necessity of value drivers’ identification within enterprise and understand the causal relationships, which are vital in driving those values.Practical implicationsThis study provides several practical guidance for practitioners in deploying cloud services which are most suitable option for their specific technology requirement in their enterprise to enjoy the full benefits of their intangible assets. Another significant implication of this study lies in the fact that it may require a different emphasis on nature and adoption design when there is a higher level of stress on technology-related and cloud computing resources.Originality/valueThis study contributes to the extant literature by developing an integrative model to identify how a wide set of contextual factors can determine the intention to adopt cloud computing and, in turn, influence the actual usage of cloud computing in SMEs in Malaysia as a developing country.

Impact of Factors Affecting Pre-copy Virtual Machine Migration Technique for Cloud Computing

Abstract Cloud managers trigger Virtual Machine (VM) migration to achieve the challenges of server consolidation, load balancing, resource management, and server fault situation. However, the performance of VM migration is affected by VM size, running application’s dirty rate and the number of migration iterations. Therefore, to measure VM migration performance by considering all these parameters becomes a challenging issue. The main contribution of this study is to evaluate the performance impact of these migration control parameters. Simulation results demonstrate that: (i) Migration time and downtime increases of two orders of magnitude while increasing the VM size, (ii) As the application dirty rate increases the migration time and downtime also increases, (iii) Half iterations of pre-copy migration are enough to reduce the migration time and downtime. In particular, the results obtained can help cloud service providers to find the performance impact of these factors on VM migration technique and meet the required goals of efficient deployment of cloud services.

An Effective Integrity Verification Scheme of Cloud Data Based on BLS Signature

Cloud storage services allow users to outsource their data remotely to save their local storage space and enable them to manage resources on demand. However, once users outsourced their data to the remote cloud platform, they lose the physical control of the data. How to ensure the integrity of outsourced data is the major concern of cloud users and also is the main challenge in the cloud service deployment. Limited by the communication and computation overheads, traditional hash-based integrity verification solutions in the stand-alone systems cannot be directly adopted in remote cloud storing environment. In this paper, we improve the previous privacy preserving model and propose an effective integrity verification scheme of cloud data based on BLS signature (EoCo), which ensures public audition and data privacy preserving. In addition, EoCo also supports batch auditing operations. We conducted theoretical analysis of our scheme, demonstrated its correctness and security properties, and evaluated the system performance as well.

Selection of Cloud Delivery and Deployment Models

Research interests on cloud computing adoption and its effectiveness in terms of cost and time has been increasing. However, one of the challenging decisions facing management in adopting cloud services is taking on the right combinations of cloud service delivery and deployment models. A comprehensive review of literature revealed a lack of research addressing this selection decision problem. To fill this research gap, this article proposes an expert system approach for managers to decide on the right combination of service delivery and deployment model selection. The article first proposes a rule-based expert system prototype, which provides advice based on a set of factors that represent the organizational conditions and requirements pertaining to cloud computing adoption. Next, the authors evaluate the system prototype. Lastly, the article concludes with a discussion of the results, its practical implications, limitations, and further research directions.

Virtual Private Cloud and Authentication: A Review

Cloud computing can be called as service through which computer resources could be used online. It helps to eliminate the need of usage of physical hardware rather it offers to use them on the cloud server. Different Types of cloud service models and deployment models make the cloud access to its users. The cloud supports characteristic such as on demand services, resource pooling, elasticity .While authentication plays important role in the security as it protects the data from the unauthorized people and helps to achieve trust between the service provider and the user. In this Paper, we’ve reviewed the existing work so as tounderstand the concept of cloud computing, virtual private cloud and proposed a multifactor authentication technique which uses biometric trait along with the identity password and MAC address as its factors.

Guest Editorial: Cloud Services Meet Big Data

The papers in this special issue are designed to solicit innovative and promising methods and techniques related closely to cloud services in the era of Big Data. The concept of Cloud Service represents a prime facility and feature of services in a cloud computing environment that can be made available to users on demand. Due to the flexibility of cloud computing in scaling IT resources up and down, cloud services gradually become valuable to attract the gaze of researchers and engineers from both academia and industry when they are faced with dynamically changing business requirements. Different stakeholders, such as consumers, providers, and operators, are generating a vast amount of data on such services per minute on the Internet, which increasingly comes to show the “4V” characteristics of big data. Therefore, new methodologies and techniques are urgently required for designing, validating, developing, testing, and deploying cloud services on demand in this specific scenario based on big data, as well as for efficiently being adaptive to business dynamics and users’ explicit and implicit requirements.

Dynamic Deployment and Cost-Sensitive Provisioning for Elastic Mobile Cloud Services

As mobile customers gradually occupying the largest share of cloud service users, the effective and cost-sensitive provisioning of mobile cloud services quickly becomes a main theme in cloud computing. The key issues involved are much more than just enabling mobile users to access remote cloud resources through wireless networks. The resource limited and intermittent disconnection problems of mobile environments have intrinsic conflict with the continuous connection assumption of the cloud service usage patterns. We advocate that seamless service provisioning in mobile cloud can only be achieved with full exploitation of all available resources around mobile users. An elastic framework is proposed to automatically and dynamically deploy cloud services on data center, base stations, client units, even peer devices. The best deployment location is dynamically determined based on a context-aware and cost-sensitive evaluation model. To facilitate easy adoption of the proposed framework, a service development model and associated semi-automatic tools are provided such that cloud service developers can easily convert a service for execution on different platforms without porting. Prototype implementation and evaluation on the Google Cloud and Android platforms demonstrate that our mechanism can successfully maintain seamless services with very low overhead.