Cloud-based Software Services Research Articles

Resource Allocation With Workload-Time Windows for Cloud-Based Software Services: A Deep Reinforcement Learning Approach

As the workloads and service requests in cloud computing environments change constantly, cloud-based software services need to adaptively allocate resources for ensuring the Quality-of-Service (QoS) while reducing resource costs. However, it is very challenging to achieve adaptive resource allocation for cloud-based software services with complex and variable system states. Most of the existing methods only consider the current condition of workloads, and thus cannot well adapt to real-world cloud environments subject to fluctuating workloads. To address this challenge, we propose a novel Deep Reinforcement learning based resource Allocation method with workload-time Windows (DRAW) for cloud-based software services that considers both the current and future workloads in the resource allocation process. Specifically, an original Deep Q-Network (DQN) based prediction model of management operations is trained based on workload-time windows, which can be used to predict appropriate management operations under different system states. Next, a new feedback-control mechanism is designed to construct the objective resource allocation plan under the current system state through iterative execution of management operations. Extensive simulation results demonstrate that the prediction accuracy of management operations generated by the proposed DRAW method can reach 90.69%. Moreover, the DRAW can achieve the optimal/near-optimal performance and outperform other classic methods by 3~13% under different scenarios.

On optimization efficiency of scalability and availability of cloud-based software services using scale rate limiting algorithm

Internet applications nowadays combine globally shared resources into a single software platform. It's a difficult technology issue to supply reports for the resource consumption among those World Wide Web applications. The formulation and simulation of spread levels were introduced in this report. Spread frequency operates together again to impose a worldwide speed restriction over revenue aggregated at several locations allowing for the synchronized monitoring of an internet company's activity. Assures that traffic delays mass transit streams act as passing through a unique, common limitation network. We describe two models overall and the other TCP-optimized—that permits network operators to expressly balance away transmission cost with network correctness, speed, and scaling. All these approaches could speed restrict 1000s of streams with little expense (less than 3% in the tested configuration). We show that with us TCP-centric architecture could scale to hundreds of servers whilst remaining resilient towards both outages and transmission postponement, suitable for countrywide telecom operators.

Measuring Impact of Cloud Computing and Knowledge Management in Software Development and Innovation

For organizations, knowledge is a valuable intangible asset that should be obtained, preserved, shared, and applied to assist decision-making. Cloud computing and knowledge management systems bring together technology and organizational methods to help organizations manage their knowledge and enhance their performance. This work investigates how organizations adopt software development and distribution innovation models using ubiquitous computing more specifically cloud-based software services appraise knowledge management (KM) practices. A combined hybrid research model has been developed using extended Technology Acceptance Model (TAM) and Technology-Organization-Environment (TOE) with KM practices. Series of statistical results using structural equation modeling performed on data collected through an online survey from 983 professionals working in distributed software development (DSD) and innovation worldwide are used to put hypotheses to the test. The results of the study indicate that KM practices (knowledge accessibility, storage, application, and sharing) along with TOE (complexity, compatibility, relative advantage, security, privacy and trust, and reputation) have a substantial and positive impact on the adoption of cloud-based software services. Coordination and communication concerns are the backbones of successful practicing distributed development and innovation. Furthermore, results indicate acceptance of the moderating role of geographical (cultural) difference in examining the impact of KM practices and cloud services.

RETRACTED: Optimizing Scalability and Availability of Cloud Based Software Services Using Modified Scale Rate Limiting Algorithm

Testing and measuring the performance and scalability of cloud-based software services is essential for the further development and improvement of cloud computing. The criteria for the performance of cloud-based software services are intertwined with scalability, flexibility, and efficiency. Especially in the context of quickly growing the quantity of service delivery, the performance evaluation and testing of cloud-based software services is crucial to support the Service Level Agreement (SLA) compliant quality of delivery of these services. In this work, we use the Token bucket method, a scale rate-limiting algorithmic technique, to maximize the efficiency of cloud-based service performance. Increased availability and scalability in distributed systems has been a major obstacle for a long time. For cloud computing businesses to keep their customers happy, keep their trust, and keep their money, they need to make their services easy to use. The Spring Cloud-based software service uses Zuul as its gateway, therefore it must execute rate-limiting and guarantee service stability in the face of rapid growth. Inability to guarantee availability of essential services while using token bucket rate restriction. This research created a method for protecting against token overuse by using a URI configuration file in tandem with the Zuul gateway to potentially filter requests before receiving tokens. The token-bucket rate-limiting technique was created to perform the traffic-limiting function and guarantee the availability of the cloud platform's services. Measuring both scalability and availability helps illustrate the quality of service provided to customers. The performance of cloud-based software services is evaluated with regards to scalability using elasticity measurements. A growth in demand for cloud-based software services is a potential outcome of ongoing research and development in the field of cloud computing.

Scalability resilience framework using application-level fault injection for cloud-based software services

This paper presents an investigation into the effect of faults on the scalability resilience of cloud-based software services. The study introduces an experimental framework using the Application-Level Fault Injection (ALFI) to investigate how the faults at the application level affect the scalability resilience and behaviour of cloud-based software services. Previous studies on scalability analysis of cloud-based software services provide a baseline of the scalability behaviour of such services, allowing to conduct in-depth scalability investigation of these services. Experimental analysis on the EC2 cloud using a real-world cloud-based software service is used to demonstrate the framework, considering delay latency of software faults with two varied settings and two demand scenarios. The experimental approach is explained in detail. Here we simulate delay latency injection with two different times, 800 and 1600 ms, and compare the results with the baseline data. The results show that the proposed approach allows a fair assessment of the fault scenario’s impact on the cloud software service’s scalability resilience. We explain the use of the methodology to determine the impact of injected faults on the scalability behaviour and resilience of cloud-based software services.

Türkiye’de Otellerin Çevrimiçi Pazarlamasında Kullanılan Bulut Tabanlı Güncel Yazılım Hizmetleri

Hotels need new technologies in order to spread their knowledge and establish superiority over their competitors by creating strategies in a fast, efficient, and secure manner in complex processes. Cloud-based software services are one of the major areas where these technologies are applied. Thanks to cloud-based software, the way hotels do their business has been significantly affected, and this effect continues to grow. The aim of this research is to identify the current cloud-based software services offered to hotel businesses in marketing activities and to contribute to the literature on this subject. Within the framework of the Quality Management Awards, which are held annually in the tourism sector in Turkiye, the companies that received awards in 2019 and provide cloud software services to hotels in online marketing were selected as research samples and content analysis was carried out on the websites of these companies. Based on the context of content analysis, it is found out that the cloud-based services offered to hotels in the field of online marketing are provided with nineteen service sizes in five different categories in terms of purpose and scope. Information about the types of services related to the detected categories are provided in the findings section of the research.

Integration of On-Premises and Cloud-Based Software: The Product Bundling Perspective

Firms in the cloud-computing era are facing the critical issue of integrating on-premises software and cloud- based software services. To date, the software industry has designed two different types of integration methods—an enterprise service bus (ESB) and an integration platform as a service (iPaaS). However, there are conflicting views on when and how to use these different integration methods to fulfill integration needs. This study aims to resolve this confusion through economic modeling. By focusing on the indirect network effect and following a two-product bundling framework, we establish a stylized model to investigate the optimal pricing and bundling decisions and the best integration choice. Our findings contradict common perceptions that an iPaaS is the better choice for integration and show that firms can derive higher value by adopting ESB to integrate on-premises software and cloud-based software services. Conceptually, given that the overall network value received from a cloud-based software service is higher, the use of ESB can contribute toward significantly improving the total value of the two software applications. We also find that unbundling is the best marketing strategy for firms because software integration can improve the valuation of individual software. Our findings have important implications for software vendors. It suggests that vendors can leverage a higher network value from the software service by integrating it with on-premises software applications through ESB instead of iPaaS and eventually realize a higher profit by extracting more value from consumers. Since integration increases the value of individual products, the vendors must sell them separately instead of selling them as a bundle.

Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems.

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.

Resource Allocation for Cloud-Based Software Services Using Prediction-Enabled Feedback Control With Reinforcement Learning

With time-varying workloads and service requests, cloud-based software services necessitate adaptive resource allocation for guaranteeing Quality-of-Service (QoS) and reducing resource costs. However, due to the ever-changing system states, resource allocation for cloud-based software services faces huge challenges in dynamics and complexity. The traditional approaches mostly rely on expert knowledge or numerous iterations, which might lead to weak adaptiveness and extra costs. Moreover, existing RL-based methods target the environment with the fixed workload, and thus they are unable to effectively fit in the real-world scenarios with variable workloads. To address these important challenges, we propose a Prediction-enabled feedback Control with Reinforcement learning based resource Allocation (PCRA) method. First, a novel Q-value prediction model is designed to predict the values of management operations (by Q-values) at different system states. The model uses multiple prediction learners for making accurate Q-value prediction by integrating the Q-learning algorithm. Next, the objective resource allocation plans can be found by using a new feedback-control based decision-making algorithm. Using the RUBiS benchmark, simulation results demonstrate that the PCRA chooses the management operations of resource allocation with 93.7 percent correctness. Moreover, the PCRA achieves optimal/near-optimal performance, and it outperforms the classic ML-based and rule-based methods by 5<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>&#x223C;</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq1-2992537.gif"/></alternatives></inline-formula>7&#x0025; and 10<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>&#x223C;</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq2-2992537.gif"/></alternatives></inline-formula>13&#x0025;, respectively.

Digital Design and Manufacturing on the Cloud: A Review of Software and Services – RETRACTION

This paper (Wu 2016), which was published in AI EDAM online on August 22, 2016, has been retracted by Cambridge University Press as it is very similar in content to a published ASME Conference Proceedings paper. The article in question and the ASME Conference Proceedings paper were submitted for review with AI EDAM and the ASME at similar times, but copyright was assigned to ASME before the paper was accepted in AI EDAM and therefore the article in AI EDAM is being retracted. (In recent years, industrial nations around the globe have invested heavily in new technologies, software, and services to advance digital design and manufacturing using cyber-physical systems, data analytics, and high-performance computing. Many of these initiatives, such as cloud-based design and manufacturing, fall under the umbrella of what has become known as Industry 4.0 or Industrial Internet and are often hailed as pillars of a new industrial revolution. While an increasing number of companies are developing or already offer commercial cloud-based software packages and services for digital design and manufacturing, little work has been reported on providing a review of the state of the art of these commercial software and services as well as identifying research gaps in this field. The objective of this paper is to present a state-of-the-art review of digital design and manufacturing software and services that are currently available on the cloud. The focus of this paper is on assessing to what extent engineering design, engineering analysis, manufacturing, and production across all phases of the product development lifecycles can already be performed based on the software and services accessed through the cloud. In addition, the key capabilities and benefits of these software packages and services are discussed. Based on the assessment of the core features of commercial software and services, it can be concluded that almost all phases of product realization can be conducted through digital design and manufacturing software and services on the cloud. Finally, existing research gaps and related challenges to overcome are identified. The state-of-the-art review serves to provide a technology guide for decision makers in their efforts to select suitable cloud-based software and services as alternatives to existing in-house resources as well as to recommend new research areas.)

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make runtime decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services. The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods.

Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model

Emerging cloud-based software services have proposed request for self-adaptive resource allocation that provides to dynamically adjust resources on demand. Traditional self-adaptive resource allocation methods are rule-driven that they combine expert knowledge with a separate set of management rules for each software, leading to high implementation complexity and administrative cost. Recent researches on self-adaptive resource allocation mainly focus on machine learning and control theory. However, machine learning techniques require plenty training data, which in many cases is insufficient and leads to low accuracy of QoS prediction model as well as the ineffectiveness of resource allocation, while resource allocation based on control theory needs a large number of iterations, resulting in a high overhead such as frequent virtual machine switches.This paper proposes a self-adaptive resource allocation method that is actually a framework composed of feedback loops, each of which goes through our designed iterative QoS prediction model and PSO-based runtime decision algorithm. In contrast to previous QoS prediction models which predict a QoS value once and for all, ours improves the predicted QoS value in iterations towards the best one. In the prediction, we first use the workload, the allocated resource, the real QoS value and an operation of resource allocation to produce a QoS value. Then we employ PSO-based runtime decision algorithm together with the predicted QoS value to determine future resource allocation operations. The loops iterate until the PSO-based algorithm suggests no further improvement over the current resource allocation. We evaluate our approach on RUBiS benchmark, illustrating that based on the same historical data, our method can achieve a better QoS prediction accuracy that is 15% higher than the current state of the art. Moreover, it is also shown the effectiveness of cloud application resource allocation is improved by roughly 5%–6%.

Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model

Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model

Scalability analysis comparisons of cloud-based software services

Performance and scalability testing and measurements of cloud-based software services are necessary for future optimizations and growth of cloud computing. Scalability, elasticity, and efficiency are interrelated aspects of cloud-based software services’ performance requirements. In this work, we use a technical measurement of the scalability of cloud-based software services. Our technical scalability metrics are inspired by metrics of elasticity. We used two cloud-based systems to demonstrate the usefulness of our metrics and compare their scalability performance in two cloud platforms: Amazon EC2 and Microsoft Azure. Our experimental analysis considers three sets of comparisons: first we compare the same cloud-based software service hosted on two different public cloud platforms; second we compare two different cloud-based software services hosted on the same cloud platform; finally, we compare between the same cloud-based software service hosted on the same cloud platform with two different auto-scaling policies. We note that our technical scalability metrics can be integrated into a previously proposed utility oriented metric of scalability. We discuss the implications of our work.

Cloud-based software services delivery from the perspective of scalability

Measuring and testing the scalability and performance of cloud-based software services is critical for the delivery of such services, and the development of cloud computing. There are three interconnected Cloud-based software services’ performance aspects; both elasticity and efficiency are depending on the delivery of a sufficient level of scalability performance. In this work, we focused on testing and measuring the cloud-based software services scalability from a technical perspective. This paper uses technical scalability metrics that address both volume and quality scaling, inspired by earlier technical metrics of elasticity. We demonstrate the application of the metrics using a practical example and three demand scenarios and discuss the importance of these metrics. We show how our technical scalability metrics integrated into an earlier utility-oriented metric of scalability, in order to enable the scalability analysis from technical and production-driven perspectives. This work is related to the delivery of cloud-based software services from scalability perspective. We demonstrate our metrics by testing a cloud-based software service connecting with both AWS auto-scaling and load-balancing services. Measurements have been collected and analysis, and then been used to calculate the values of our scalability metrics.

Self-Adaptive and Online QoS Modeling for Cloud-Based Software Services

In the presence of scale, dynamism, uncertainty and elasticity, cloud software engineers faces several challenges when modeling Quality of Service (QoS) for cloud-based software services. These challenges can be best managed through self-adaptivity because engineers’ intervention is difficult, if not impossible, given the dynamic and uncertain QoS sensitivity to the environment and control knobs in the cloud. This is especially true for the shared infrastructure of cloud, where unexpected interference can be caused by co-located software services running on the same virtual machine; and co-hosted virtual machines within the same physical machine. In this paper, we describe the related challenges and present a fully dynamic, self-adaptive and online QoS modeling approach, which grounds on sound information theory and machine learning algorithms, to create QoS model that is capable to predict the QoS value as output over time by using the information on environmental conditions, control knobs and interference as inputs. In particular, we report on in-depth analysis on the correlations of selected inputs to the accuracy of QoS model in cloud. To dynamically selects inputs to the models at runtime and tune accuracy, we design self-adaptive hybrid dual-learners that partition the possible inputs space into two sub-spaces, each of which applies different symmetric uncertainty based selection techniques; the results of sub-spaces are then combined. Subsequently, we propose the use of adaptive multi-learners for building the model. These learners simultaneously allow several learning algorithms to model the QoS function, permitting the capability for dynamically selecting the best model for prediction on the fly. We experimentally evaluate our models in the cloud environment using RUBiS benchmark and realistic FIFA 98 workload. The results show that our approach is more accurate and effective than state-of-the-art modelings.

Microservices Architecture for Tenant-oriented Cloud-based Control Software Services: POC and Proposals

Microservices architecture, where application is composed of a set of independently deployable services is a popular modern approach for building enterprise-level applications, meanwhile most of the cloud solutions for control software (including IoT) are designed in so called monolithic way. LXC-based (docker) microservices allow developers to seamlessly migrate current software solutions from VMs to containers. Nowadays, such container-based applications are used primarily for simple semi-automated deployments, when containers are deployed only at the launch time of the whole system and modified when needed. In this paper we analyze application of docker-microservices as functional blocks for control software, propose on-demand tenant-oriented microservices approach (where entire independent topologies of microservices are being deployed on per-tenant basis) for building clouds of control software, review requirements for management system of such cloud and investigate contemporary container-management systems. Constructed test cluster highlighted some disadvantages of Kubernetes regarding multi-tenant container administration. Advanced Container Management System (AMS) is designed as one more level of orchestration on top of Kubernetes. Conducted demo showed practical value and flexibility of the introduced tenant-oriented microservices method. Described design will be helpful for cloud providers of control software who want to organize elastic on-demand service provisioning.

Digital design and manufacturing on the cloud: A review of software and services—RETRACTED

AbstractThis paper (Wu 2016), which was published in AI EDAM online on August 22, 2016, has been retracted by Cambridge University Press as it is very similar in content to a published ASME Conference Proceedings paper. The article in question and the ASME Conference Proceedings paper were submitted for review with AI EDAM and the ASME at similar times, but copyright was assigned to ASME before the paper was accepted in AI EDAM and therefore the article in AI EDAM is being retracted. (In recent years, industrial nations around the globe have invested heavily in new technologies, software, and services to advance digital design and manufacturing using cyber-physical systems, data analytics, and high-performance computing. Many of these initiatives, such as cloud-based design and manufacturing, fall under the umbrella of what has become known as Industry 4.0 or Industrial Internet and are often hailed as pillars of a new industrial revolution. While an increasing number of companies are developing or already offer commercial cloud-based software packages and services for digital design and manufacturing, little work has been reported on providing a review of the state of the art of these commercial software and services as well as identifying research gaps in this field. The objective of this paper is to present a state-of-the-art review of digital design and manufacturing software and services that are currently available on the cloud. The focus of this paper is on assessing to what extent engineering design, engineering analysis, manufacturing, and production across all phases of the product development lifecycles can already be performed based on the software and services accessed through the cloud. In addition, the key capabilities and benefits of these software packages and services are discussed. Based on the assessment of the core features of commercial software and services, it can be concluded that almost all phases of product realization can be conducted through digital design and manufacturing software and services on the cloud. Finally, existing research gaps and related challenges to overcome are identified. The state-of-the-art review serves to provide a technology guide for decision makers in their efforts to select suitable cloud-based software and services as alternatives to existing in-house resources as well as to recommend new research areas.)

Nonprofits increasingly adopting cloud-based software services

Nonprofits increasingly adopting cloud-based software services

A security policy oracle

Even experienced developers struggle to implement security policies correctly. For example, despite 15 years of development, standard Java libraries still suffer from missing and incorrectly applied permission checks, which enable untrusted applications to execute native calls or modify private class variables without authorization. Previous techniques for static verification of authorization enforcement rely on manually specified policies or attempt to infer the policy by code-mining. Neither approach guarantees that the policy used for verification is correct. In this paper, we exploit the fact that many modern APIs have multiple, independent implementations. Our flow- and context-sensitive analysis takes as input an API, multiple implementations thereof, and the definitions of security checks and security-sensitive events. For each API entry point, the analysis computes the security policies enforced by the checks before security-sensitive events such as native method calls and API returns, compares these policies across implementations, and reports the differences. Unlike code-mining, this technique finds missing checks even if they are part of a rare pattern. Security-policy differencing has no intrinsic false positives: implementations of the same API must enforce the same policy, or at least one of them is wrong! Our analysis finds 20 new, confirmed security vulnerabilities and 11 interoperability bugs in the Sun, Harmony, and Classpath implementations of the Java Class Library, many of which were missed by prior analyses. These problems manifest in 499 entry points in these mature, well-studied libraries. Multiple API implementations are proliferating due to cloud-based software services and standardization of library interfaces. Comparing software implementations for consistency is a new approach to discovering "deep" bugs in them.