Deployment Of Cloud Applications Research Articles

A formal approach for the correct deployment of cloud applications

The deployment of cloud applications is a complex task. It refers to the enablement of SaaS, PaaS or IaaS solutions that may be accessed on demand by end users. It encompasses all the activities from installation to uninstallation, including reconfiguration, etc. To facilitate the deployment of cloud applications, it is essential to design them as component-based applications in order to favor the design by reusing and reducing the development cost. However, assembling components can be a tedious and error-prone task if sufficient precautions are not taken regarding different constraints, dependencies, and conflicts between components. In this paper, we introduce a formal Event-B-based approach for the modeling and the verification of component-based applications deployment. Our goal is to build correct by-construction systems that fulfill the different constraints regarding the components, the cloud infrastructure, and the deployment process. Basically, our approach starts with an abstract model describing the main concepts of the system. Then different details are gradually introduced through refinement. For each refinement step, proof obligations are produced to ensure the model's correctness. The obtained formal model consists of a precise specification on which mathematical reasoning can be carried out to prove the correctness of our component-based application model and validate its deployment in a cloud environment by using ProB. The presented approach is illustrated through a case study.

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.

Optimization of DevOps Transformation for Cloud-Based Applications

Rapid software development is critical for meeting company objectives and competing more effectively in the competitive IoT infrastructure. DevOps is a growing technique that enables enterprises to provide high-quality software capabilities through automation, to improve team communication, and to increase efficiency across the software product lifecycle. Research problem: Due to the increased demand for new products and technologies, a huge overwork shifted on the organizations for introducing software with pace and to become stable to compete with others. Due to this, the majority of organizations prefer an automated system for product development and require cloud-based applications. The git version control system is used for version management and Docker is used to package code and provide libraries. AWS services are leveraged to deploy an application as a cloud. Jenkins is used as a CI/CD pipeline to manage various phases of development and to make the development process continuous. The ELK stack is used to monitor and visualize the execution of code. In light of the findings, DevOps is an efficient method for cloud application deployment and resource selection based on the relative importance of each optimized objective in terms of value parameters such as cost, memory, and CPU capacity, and that the method can be tailored to specific application requirements. The findings of this analysis indicate that an application can be deployed to the cloud using DevOps techniques. The proposed approach cost 60% less at full weight 1.0 and 11.3% less with no weight compared to the benchmark solution’s 15.078%

Transferable Knowledge for Low-Cost Decision Making in Cloud Environments

Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this article, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60 percent reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenarios.

Frisbee: automated testing of Cloud-native applications in Kubernetes

As more and more companies are migrating (or planning to migrate) from on-premise to Cloud, their focus is to find anomalies and deficits as early as possible in the development life cycle. We propose Frisbee, a declarative language and associated runtime components for testing cloud-native applications on top of Kubernetes. Given a template describing the system under test and a workflow describing the experiment, Frisbee automatically interfaces with Kubernetes to deploy the necessary software in containers, launch needed sidecars, execute the workflow steps, and perform automated checks for deviation from expected behavior. We evaluate Frisbee through a series of tests, to demonstrate its role in designing, and evaluating cloud-native applications; Frisbee helps in testing uncertainties at the level of application (e.g., dynamically changing request patterns), infrastructure (e.g., crashes, network partitions), and deployment (e.g., saturation points). Our findings have strong implications for the design, deployment, and evaluation of cloud applications. The most prominent is that: erroneous benchmark outputs can cause an apparent performance improvement, automated failover mechanisms may require interoperability with clients, and that a proper placement policy should also account for the clock frequency, not only the number of cores.

Cloud Education Chain and Education Quality Evaluation Based on Hybrid Quantum Neural Network Algorithm

This paper proposes the functional model and application service implementation process of the education cloud platform application service architecture. The entire cloud application service architecture mainly includes four parts: cloud service management, cloud application service rapid creation and deployment, dynamic process configuration, and unified identity authentication. Based on the basic theory of workflow, the process status and business services of cloud application services are discussed. The BP neural network weight optimization model based on the improved quantum evolution method is studied, and a method that combines the improved quantum evolution algorithm (IQEA) and the BP algorithm to complete the back propagation neural network training is proposed, that is, the IQEA-BP algorithm. Firstly, the traditional quantum evolution algorithm is improved, and then, the improved quantum evolution algorithm is used to optimize the network weights as a whole to overcome the shortcomings of the BP algorithm that is easy to fall into the local optimum; then, we use the BP algorithm to find the better weight as the initial value to improve the training and prediction accuracy of the network. In order to enrich the school education quality evaluation system, this article adds soft indicators that can reflect school education performance on the basis of the existing “National Education Inspection Team” indicators and uses analytical methods to prove the effectiveness and feasibility of the new evaluation indicators. The X1-X10 index data is selected as the evaluation index of the school education quality evaluation system in this paper. Testing the performance of the BP neural network, the accuracy rate of the school education quality evaluation is 93.3%, the average absolute error is 0.067, and the accuracy and recall rate of the test set grade gradient of 0, 1, 2, 3, 5, 6, and 8 are all 93%, indicating that the IQEA-BP neural network algorithm has a good effect on the evaluation of school education quality.

Enabling Role-Based Orchestration for Cloud Applications

With the rapidly growing popularity of cloud services, the cloud computing faces critical challenges to orchestrate the deployment and operation of cloud applications on heterogenous cloud platforms. Cloud applications are built on a platform model that abstracts away underlying platform-specific details, so that their orchestration can benefit from the abstract view and flexibility of the underlying platform configuration. However, considerable efforts are still required to properly manage complicated cloud applications. This paper proposes a model-driven approach to cloud application orchestration which promotes the concerns of distinct roles for cloud system provisioning and operation. By establishing a set of capabilities as modeling constructs, our approach allows TOSCA-based application topology itself and its orchestration needs to be specified in a way to provide a more targeted support for different needs and concerns of application developers and operators. With novel orchestration features like application topology description, platform capability modeling, and role-awareness for cloud application orchestration, it can significantly reduce the complexity of application orchestration in diverse cloud environments. To show the feasibility and effectiveness of our proposal for cloud application orchestration, we present a proof-of-concept orchestration system implementation and evaluate its deployment and orchestration results in a Kubernetes cluster.

Virtualization in Cloud Computing: Moving from Hypervisor to Containerization—A Survey

Containers emerged as a lightweight alternative to virtual machines that offer better microservice architecture support. They are widely used by organizations to deploy their increasingly diverse workloads derived from modern applications such as big data, IoT, and edge/fog computing in either proprietary clusters or private, public cloud data centers. With the growing interest in container-based virtualization technologies, the requirement to explore the deployment and orchestration of clusters of containers has become a central research problem. Although progress has been made to study containerization, systematic consolidation of the existing literature with a summative evaluation is still missing. To fill this gap, in this paper, we first taxonomically classify the existing research studies on the performance comparison between hypervisor and container technology and then analyze state-of-the-art for container cluster management orchestration systems, its performance monitoring tools, and finally future research trends. This results in a better understanding of container technology with attention to provide summative analysis in terms of (i) how much performance overhead is generated by a hypervisor compared to container-based virtualization, (ii) which container technology is suited for a cloud application deployment based on the type of benchmark executing, (iii) how to provide management of containers deployed in a cluster environment, (iv) container performance monitoring tools, and (v) finally emerging concerns for future research directions.

Auto-scaling techniques for IoT-based cloud applications: a review

Cloud and IoT applications have inquiring effects that can strongly influence today’s ever-growing internet life along with necessity to resolve numerous challenges for each application such as scalability, security, privacy, and reliability. During the deployment of IoT-based Cloud applications, the demand for Cloud tenants is dynamic that makes challenging to maintain scalability of the system. Developing an effective scaling technique is not merely a big concern, but how to achieve autonomic scaling results using future load prediction and migration policies is also a crucial phase. Also, to evaluate such auto-scaling strategy, certain Quality of Service (QoS) metrics must be recognized, explored and leveraged to enhance the performance of the system. Therefore, in this paper, a survey of existing auto-scaling, load prediction and VM migration techniques for IoT-based Cloud applications has been carried out along with the evaluation of various QoS parameters. Further, the future trends have also been discussed for performing auto-scaling in a Cloud environment.

WITHDRAWN: Development and deployment of real-time cloud applications on red hat OpenShift and IBM bluemix

Most computing services, hosted by a number of cloud service providers, are available on demand nowadays. There are a range of cloud computing realms and core fields that have been under scrutiny for a long time due to rising digital infrastructure demands and applications. The cloud service providers bill based on the space or service offered to the customer to get access to cloud data. It is not always possible in research and development to provide the actual cloud technology to conduct the experiments. It is not possible for any research scholar, educator or scientist to employ cloud providers at all times and then perform their algorithms or implementations. For research, production and testing purposes, the real time cloud platforms can be taken by the researchers with free tier. In this research manuscript, the inclusion of IBM Bluemix and Red Hat OpenShift clouds are done for the development and effectual deployment of web apps on cloud. Such services can be used by the researchers and academicians for high performance web applications on cloud with real time logs and analytics on assorted parameters.

Extending TOSCA for Edge and Fog Deployment Support

The emergence of fog and edge computing has complemented cloud computing in the design of pervasive, computing-intensive applications. The proximity of fog resources to data sources has contributed to minimizing network operating expenditure and has permitted latency-aware processing. Furthermore, novel approaches such as serverless computing change the structure of applications and challenge the monopoly of traditional Virtual Machine (VM)-based applications. However, the efforts directed to the modeling of cloud applications have not yet evolved to exploit these breakthroughs and handle the whole application lifecycle efficiently. In this work, we present a set of Topology and Orchestration Specification for Cloud Applications (TOSCA) extensions to model applications relying on any combination of the aforementioned technologies. Our approach features a design-time “type-level” flavor and a run time “instance-level” flavor. The introduction of semantic enhancements and the use of two TOSCA flavors enables the optimization of a candidate topology before its deployment. The optimization modeling is achieved using a set of constraints, requirements, and criteria independent from the underlying hosting infrastructure (i.e., clouds, multi-clouds, edge devices). Furthermore, we discuss the advantages of such an approach in comparison to other notable cloud application deployment approaches and provide directions for future research.

TORCH: a TOSCA-Based Orchestrator of Multi-Cloud Containerised Applications

The growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer’s perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking the service that best accommodate their specific needs is a critical challenge. Once the choice is made, so called “cloud orchestration tools” (orchestrators) are required to take care of the customer application’s life-cycle. While big players offer their customers proprietary orchestrators, in the literature quite a number of open-source initiatives have launched multi-cloud orchestrators capable of transparently managing applications on top of the most representative cloud platforms. In this paper, we propose TORCH, a TOSCA-based framework for the deployment and orchestration of cloud applications, both classical and containerised, on multiple cloud providers. The framework assists the cloud customer in defining application requirements by using standard specification models. Unlike other multi-cloud orchestrators, adopts a strategy that separates the provisioning workflow from the actual invocation of proprietary cloud services API. The main benefit is the possibility to add support to any cloud platforms at a very low implementation cost. In the paper, we present a prototypal implementation of TORCH and showcase its interaction with two different container-based cluster platforms. Preliminary performance tests conducted on a small-scale test-bed confirm the potential of TORCH.

Interoperability of Cloud Using Single API

In the cloud computing model, users access services according to their requirements. Most of the people use cloud since it has low cost, high speed computing, backup and restore, mobility and unlimited flexible storage capacity. Cloud resources are hosted in large datacentres operated by companies such as Amazon, Apple, Google and Microsoft. During cloud application deployment, an application is managed over a single service. Such an approach has several short comings. One side-effect of the lack of interoperability among cloud providers is vendor lock in, which means lack of ability to migrate application components from one cloud provider to another cloud provider. If a user finds some required platforms in new service provider but the user cannot leave the current provider as the resources are present with them. This is known as vendor lock in.
 To solve this issue in achieving interoperability several efforts are underway. Our project is that a user who creates a unique channel that can be used to gain the services provided by different providers. So that the user can use another vendor’s service which is not present in present vendor’s cloud. That is user can use multiple clouds having different resources using a single API without depending on their own APIs.

A GUI-driven prototype for synthesizing self-adaptation decision

The ability to ensure an optimal decision is significant for self-adaptive systems especially when dealing with uncertainty. For this reason, a synthesis-driven approach can be used to capture and synthesize a decision that aims to satisfy the multi-objective properties. Assessing the quality of the synthesis-driven approach is challenging, since it involves a set of activities from modeling, simulating, and analyzing the outcomes. This paper presents the design and implementation of a graphical user interface (GUI)-based prototype for assessing synthesis outcome and performance of an adaptation decision. The prototype is designed and developed based on the component-based development approach that is able to integrate the existing and related libraries from PRISM-games model checker for the synthesis engine, JFreeChart libraries for the chart presentation, and Java Universal Network/Graph Framework libraries for the graph visualization. This paper also presents the implementation of the proposed prototype based on the cloud application deployment scenario to illustrate its applicability. This work contributes to provide a fundamental work towards automated synthesis for self-adaptive systems.

On the modeling of optimal and automatized cloud application deployment

We investigate the problem of modeling the optimal and automatic deployment of cloud applications. We follow an approach based on three main pillars: (i) the specification of the computing resources needed by software components and those provided by the executing environment (e.g. virtual machines or containers), (ii) the declarative description of deployment rules, (iii) and the computation of an optimal deployment that minimizes the total cost by using constraint solving techniques. We experiment with such an approach by applying it to the Abstract Behavioural Specification language ABS, and we validate it by modeling and simulating with ABS (and its tool-suite) the Fredhopper Cloud Services, a worldwide system offering e-Commerce services, currently deployed on Amazon EC2.

Cost-Minimizing Bandwidth Guarantee for Inter-Datacenter Traffic

The emerging deployment of large-scale cloud applications incurs significant inter-datacenter traffic, which makes the scarce wide-area bandwidth across data centers become the performance bottleneck. To achieve the desirable network performance, bandwidth guarantee should be provided for the resulting inter-datacenter traffic. However, the existing bandwidth allocation methods mainly focus on intra-datacenter traffic, and cannot achieve the cost-minimizing bandwidth guarantee for inter-datacenter traffic. In this paper, we focus on the bandwidth guarantee problem for inter-datacenter traffic and present a novel bandwidth allocation model. Our model can ensure the bandwidth guarantee, minimize the resulting network cost, and efficiently avoid the potential traffic overload on low cost links. To solve the large-scale optimization problem in our model, we are motivated to develop a distributed algorithm by blending the advantages of alternating direction method of multipliers (ADMM) and the auxiliary variable method. Specifically, we efficiently decompose the optimization problem into many small sub-problems, which are allowed to be processed in a large-scale computing environment, where each server solves a few small sub-problems. We further present a theoretically proved globally, asymptotically stable algorithm to solve these sub-problems. Extensive evaluation results demonstrate that our bandwidth allocation method can effectively realize the bandwidth guarantee for inter-datacenter traffic with reduced network cost and outperforms the prior method PS-L. In particular, the total network cost is reduced by 59.57 percent on average.

Interoperability in semantic Web of Things: Design issues and solutions

SummaryThe significant improvement in processing power, communication, energy consumption, and the size of computational devices has led to the emergence of the Internet of Things (IoT). IoT projects raise many challenges, such as the interoperability between IoT applications because of the high number of sensors, actuators, services, protocols, and data associated with these systems. Semantics solves this problem by using annotations that define the role of each IoT element and reduces the ambiguity of information exchanged between the devices. This work presents SWoTPAD, a semantic framework that helps in the development of IoT projects. The framework is designer oriented and provides a semantic language that is more user‐friendly than OWL‐S and WSML and allows the IoT designer to specify devices, services, environment, and requests. Following this, it makes use of these specifications and maps them for RESTful services. Additionally, it generates an automatic service composition engine that is able to combine services needed to handle complex user requests. We validated this approach with two case studies. The former concerns a residential security system and the latter, the cloud application deployment. The average time required for service discovery and automatic service composition corresponds to 72.9% of the service execution time in the case study 1 and 64.4% in the case study 2.

Formal Quality of Service assurances, ranking and verification of cloud deployment options with a probabilistic model checking method

Abstract Context: Existing software workbenches allow for the deployment of cloud applications across a variety of Infrastructure-as-a-Service (IaaS) providers. The expected workload, Quality of Service (QoS) and Non-Functional Requirements (NFRs) must be considered before an appropriate infrastructure is selected. However, this decision-making process is complex and time-consuming. Moreover, the software engineer needs assurances that the selected infrastructure will lead to an adequate QoS of the application. Objective: The goal is to develop a new method for selection of an optimal cloud deployment option, that is, an infrastructure and configuration for deployment and to verify that all hard and as many soft QoS requirements as possible will be met at runtime. Method: A new Formal QoS Assurances Method (FoQoSAM), which relies on stochastic Markov models is introduced to facilitate an automated decision-making process. For a given workload, it uses QoS monitoring data and a user-related metric in order to automatically generate a probabilistic model. The probabilistic model takes the form of a finite automaton. It is further used to produce a rank list of cloud deployment options. As a result, any of the cloud deployment options can be verified by applying a probabilistic model checking approach. Results: Testing was performed by ranking deployment options for two cloud applications, File Upload and Video-conferencing. The FoQoSAM method was compared to a baseline Analytic Hierarchy Process (AHP). The results show that the first ranked cloud deployment options satisfy all hard and at least one of the soft requirements for both methods, however, the FoQoSAM method always satisfies at least an additional QoS requirement compared to the baseline AHP method. Conclusions: The proposed new FoQoSAM method is appropriate and can be used in decision-making when ranking and verifying cloud deployment options. Due to its practical utility it was integrated into the SWITCH workbench.

A Comprehensive Survey on Exiting Solution Approaches towards Security and Privacy Requirements of IoT

<em>‘Internet of Things (IoT)’</em>emerged as an intelligent collaborative computation and communication between a set of objects capable of providing on-demand services to other objects anytime anywhere. A large-scale deployment of data-driven cloud applications as well as automated physical things such as embed electronics, software, sensors and network connectivity enables a joint ubiquitous and pervasive internet-based computing systems well capable of interacting with each other in an IoT. IoT, a well-known term and a growing trend in IT arena certainly bring a highly connected global network structure providing a lot of beneficial aspects to a user regarding business productivity, lifestyle improvement, government efficiency, etc. It also generates enormous heterogeneous and homogeneous data needed to be analyzed properly to get insight into valuable information. However, adoption of this new reality (i.e., IoT) by integrating it with the internet invites a certain challenges from security and privacy perspective. At present, a much effort has been put towards strengthening the security system in IoT still not yet found optimal solutions towards current security flaws. Therefore, the prime aim of this study is to investigate the qualitative aspects of the conventional security solution approaches in IoT. It also extracts some open research problems that could affect the future research track of IoT arena.