Cloud Capabilities Research Articles

Elasticity in the cloud related to database autonomies and scalability

Cloud computing has been a very popular paradigm for implementing online applications. Scalability, elasticity, cost of use, and large-scale economies are the main reasons for the effective and widespread acceptance of cloud computing. In this paper, we outline our work to inject the aforementioned "cloud capabilities" into a database system designed to support various applications deployed in the cloud: designing scalable databases using autonomies database and elasticity that enables lightweight resiliency using low-cost live database migrations and an intelligent and autonomous controller designed for system management without human intervention.

A Secure Batch Authentication Scheme for Multiaccess Edge Computing in 5G-Enabled Intelligent Transportation System

With the introduction of 5G technology in the intelligent transportation system (ITS), data bandwidth over communication channel is significantly increased. In the existing cloud-based ITS, a large-scale data is collected, stored, and analyzed to provide precise services to vehicle user, which creates a burden on the cloud service provider that can cause service delays or the inability to provide complex services. Multi-access edge computing (MEC) technology, which extends cloud capabilities to edge servers, is attracting attention in order to reduce the constraint of the cloud-based infrastructure. However, in ITS constructed on MEC, since vehicles exchange information through a wireless communication channel. It is vulnerable to various security attacks. This paper proposes a secure batch authentication scheme for multi-access edge computing in 5G-enabled ITS. The proposed scheme withstands common security attacks including impersonation, replay, man-in-the-middle, physical capture, offline password guessing attacks, and provides secure batch authentication, anonymity and confidentiality. We also consider the vehicle can cross the boundary between MEC servers for continuity of service. We analyze the security of the proposed scheme to show that the proposed scheme is secure against various security attacks, and performance analysis. Lastly, we conduct comparison with other related works.

Migrating a research data warehouse to a public cloud: challenges and opportunities.

ObjectiveClinical research data warehouses (RDWs) linked to genomic pipelines and open data archives are being created to support innovative, complex data-driven discoveries. The computing and storage needs of these research environments may quickly exceed the capacity of on-premises systems. New RDWs are migrating to cloud platforms for the scalability and flexibility needed to meet these challenges. We describe our experience in migrating a multi-institutional RDW to a public cloud.Materials and MethodsThis study is descriptive. Primary materials included internal and public presentations before and after the transition, analysis documents, and actual billing records. Findings were aggregated into topical categories.ResultsEight categories of migration issues were identified. Unanticipated challenges included legacy system limitations; network, computing, and storage architectures that realize performance and cost benefits in the face of hyper-innovation, complex security reviews and approvals, and limited cloud consulting expertise.DiscussionCloud architectures enable previously unavailable capabilities, but numerous pitfalls can impede realizing the full benefits of a cloud environment. Rapid changes in cloud capabilities can quickly obsolete existing architectures and associated institutional policies. Touchpoints with on-premise networks and systems can add unforeseen complexity. Governance, resource management, and cost oversight are critical to allow rapid innovation while minimizing wasted resources and unnecessary costs.ConclusionsMigrating our RDW to the cloud has enabled capabilities and innovations that would not have been possible with an on-premises environment. Notwithstanding the challenges of managing cloud resources, the resulting RDW capabilities have been highly positive to our institution, research community, and partners.

Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser

Cloud computing has been a dominant computing paradigm for many years. It provides applications with computing, storage, and networking capabilities. Furthermore, it enhances the scalability and quality of service (QoS) of applications and offers the better utilization of resources. Recently, these advantages of cloud computing have deteriorated in quality. Cloud services have been affected in terms of latency and QoS due to the high streams of data produced by many Internet of Things (IoT) devices, smart machines, and other computing devices joining the network, which in turn affects network capabilities. Content delivery networks (CDNs) previously provided a partial solution for content retrieval, availability, and resource download time. CDNs rely on the geographic distribution of cloud servers to provide better content reachability. CDNs are perceived as a network layer near cloud data centers. Recently, CDNs began to perceive the same degradations of QoS due to the same factors. Fog computing fills the gap between cloud services and consumers by bringing cloud capabilities close to end devices. Fog computing is perceived as another network layer near end devices. The adoption of the CDN model in fog computing is a promising approach to providing better QoS and latency for cloud services. Therefore, a fog-based CDN framework capable of reducing the load time of web services was proposed in this paper. To evaluate our proposed framework and provide a complete set of tools for its use, a fog-based browser was developed. We showed that our proposed fog-based CDN framework improved the load time of web pages compared to the results attained through the use of the traditional CDN. Different experiments were conducted with a simple network topology against six websites with different content sizes along with a different number of fog nodes at different network distances. The results of these experiments show that with a fog-based CDN framework offloading autonomy, latency can be reduced by 85% and enhance the user experience of websites.

A computing allocation strategy for Internet of things’ resources based on edge computing

In order to meet the demand for efficient computing services in big data scenarios, a cloud edge collaborative computing allocation strategy based on deep reinforcement learning by combining the powerful computing capabilities of cloud is proposed. First, based on the comprehensive consideration of computing resources, bandwidth, and migration decisions, an optimization problem is constructed that minimizes the sum of all user task execution delays and energy consumption weights. Second, a dynamic offloading scheduling algorithm based on Q-learning is proposed based on the optimization problem. This algorithm makes full use of the computing power for cloud and edge, which effectively meets the demand for efficient computing services in Internet of Things’ scenarios. Finally, facing the environment dynamic changes of edge nodes in edge cloud, the algorithm can adaptively adjust the migration strategy. Experiments show that when the number of Internet of Things’ devices is 30, the total energy consumption of Internet of Things’ devices of proposed algorithm is reduced by 24.67% and 19.44%, respectively, compared with other algorithms. The experimental results show that proposed algorithm can effectively improve the success rate of task offloading and execution, which can reduce the local energy consumption.

Hardening the Security of Multi-Access Edge Computing through Bio-Inspired VM Introspection

The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the emerging technologies that enables the evolution to 5G by bringing cloud capabilities near to the end users is Edge Computing or also known as Multi-Access Edge Computing (MEC) that will become pertinent towards the evolution of 5G. This evolution also entails growth in the threat landscape and increase privacy in concerns at different application areas, hence security and privacy plays a central role in the evolution towards 5G. Since MEC application instantiated in the virtualized infrastructure, in this paper we present a distributed application that aims to constantly introspect multiple virtual machines (VMs) in order to detect malicious activities based on their anomalous behavior. Once suspicious processes detected, our IDS in real-time notifies system administrator about the potential threat. Developed software is able to detect keyloggers, rootkits, trojans, process hiding and other intrusion artifacts via agent-less operation, by operating remotely or directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Experimental results of remote VMI on more than 50 different malicious code demonstrate average anomaly detection rate close to 97%. We have established wide testbed environment connecting networks of two universities Kyushu Institute of Technology and The City College of New York through secure GRE tunnel. Conducted experiments on this testbed deliver high response time of the proposed system.

Mass Personalisation as a Service in Industry 4.0: A Resilient Response Case Study

The Fourth Industrial Revolution (Industry 4.0) leads to mass personalisation as an emerging manufacturing paradigm. Mass personalisation focuses on uniquely made products to individuals at scale. Global challenges encourage mass personalisation manufacturing with efficiency competitive to mass production. Driven by individualisation as a trend and enabled by increasing digitalisation, mass personalisation can go beyond today’s mass customisation. This paper aims to introduce Mass Personalisation as a Service (MPaaS) to address unique and complex requirements at scale by harnessing Industry 4.0 technologies, including Internet of Things, Additive Manufacturing, Big Data, Cloud Manufacturing, Digital Twin, and Blockchain. A case study for the implementation of MPaaS in personalised face masks is presented. The workforce with constant exposure to contaminants requires personal protective equipment (PPE), such as facemasks, for longer hours resulting in pressure-related ulcers. This prolonged use of PPE highlights the importance of personalisation to avoid ulcers and other related health concerns. Most studies have used Additive Manufacturing for individualisation and cloud capabilities for large-scale manufacturing. This study develops a framework and mathematical model to demonstrate the capability of the proposed solution to address one of the most critical challenges by making personalised face masks as an essential PPE in the critical industrial environment.

A Systematic Analysis of Internet of Things in Healthcare Applications : A Review

The Internet of Things (IoT) is a network of physical devices, software, and hardware that communicate with one another. As the population ages, healthcare resources become scarce, and medical expenses rise, IoT-based solutions must be adapted to meet these issues in healthcare. To enhance the monitoring efficiency of the IoT-based healthcare system, several studies have been conducted. In this paper, the architecture utilized in the IoT, particularly cloud-integrated systems and security in IoT devices is explored. Factors like accuracy and power consumption are major concerns in the Internet of Things, therefore research projects aimed at enhancing the performance of IoT-based healthcare systems are highlighted. In this work, data management strategies in an IoT-based healthcare system with cloud capabilities are thoroughly examined. The performance of the IoT-based healthcare system is examined, as well as its benefits and drawbacks. Moreover, a comparative analysis is also done on some existing technologies that are utilized in healthcare. It has been observed from past studies that IoT protocol such as 6LoWPAN is mostly utilized in the domain of health care. The majority of research studies are effective in detecting many symptoms and accurately predicting illnesses. The IoT-based healthcare system built specifically for the elderly is an effective way to keep track of their medical concerns. High power consumption, a scarcity of resources, and security concerns major drawbacks of current systems are included in the proposed study.

Recent Developments in Parallel and Distributed Computing for Remotely Sensed Big Data Processing

This article gives a survey of state-of-the-art methods for processing remotely sensed big data and thoroughly investigates existing parallel implementations on diverse popular high-performance computing platforms. The pros/cons of these approaches are discussed in terms of capability, scalability, reliability, and ease of use. Among existing distributed computing platforms, cloud computing is currently the most promising solution to efficient and scalable processing of remotely sensed big data due to its advanced capabilities for high-performance and service-oriented computing. We further provide an in-depth analysis of state-of-the-art cloud implementations that seek for exploiting the parallelism of distributed processing of remotely sensed big data. In particular, we study a series of scheduling algorithms (GSs) aimed at distributing the computation load across multiple cloud computing resources in an optimized manner. We conduct a thorough review of different GSs and reveal the significance of employing scheduling strategies to fully exploit parallelism during the remotely sensed big data processing flow. We present a case study on large-scale remote sensing datasets to evaluate the parallel and distributed approaches and algorithms. Evaluation results demonstrate the advanced capabilities of cloud computing in processing remotely sensed big data and the improvements in computational efficiency obtained by employing scheduling strategies.

Budget optimized dynamic virtual machine provisioning in hybrid cloud using fuzzy analytic hierarchy process

Among various deployment models, Hybrid cloud is the preferred model allowing customers to maximize cost savings and performance by leveraging advantage of quick provisioning capabilities of public cloud. However, due to vast diversity of cloud services, today’s struggle for the customers is to evaluate and discover the best fit Cloud Service Provider (CSP) for an efficient Virtual Machine (VM) provisioning in various hybrid cloud contracts of multi cloud environment. Currently there is no framework that allows customers to evaluate CSPs based on application workload, application environment and employ an budget optimized VM provisioning with dynamically varying factors while adhering to Service Level Agreement (SLA). Hence, the proposed VM Provisioning framework is a Multi Criteria Decision-Making (MCDM) model with an objective to provision the VMs from the well suited CSP in hybrid cloud. The framework initially derives weights for each decision-making input criteria considered as Quality of Service (QoS) Service Measurement Index (SMI) attributes using extent analysis method of Fuzzy Analytical processing (FHP). It ranks the CSP by aggregating weights of decision makers among the alternatives. The framework then proposes a budget optimized algorithm called BOPVM to provision VMs in the hybrid cloud based on the order of assigned rank to CSPs. The framework is evaluated and implemented on real time workload among the CSPs – Amazon, Azure and Openstack. The evaluation results shows significant provisioning cost savings of 50% with reduced provisioning time by 65% when compared with existing algorithms.

A microservices persistence technique for cloud-based online social data analysis

Social data analysis has become a vital tool for businesses and organisations for mining data from social media and analysing for diverse purposes such as customer opinion mining, pattern recognition and predictive analytics. However, the high level of volatility for social data means application updates due to analytical results requires spontaneous integration. In addition, while cloud computing has been hugely utilised to address computational overhead issues due to the volume of social data, results obtained still fall short of expected levels. Hence, a persistence mechanism for rapid deployment and integration of software updates for the analytical process is proposed. The persistence mechanism constitutes a significant component within a novel methodology which also leverages cloud computing, microservices and orchestration for online social data analysis, one which fully maximises cloud capabilities and fosters optimisation of cloud computing resources. The proposed methodology provides means of delivering real-time, persistent social data analytics as a cloud service, thereby facilitating spontaneous integration of solutions to maximise expectations from targeted social media audience.

On the optimality of Concurrent Container Clusters Scheduling over heterogeneous smart environments

Smart environments rely on the cloud for most computation activities; however, leveraging the availability of resources at the edge could complement the cloud capabilities to provide a resources continuum spectrum that takes advantage of the benefits of both technologies, cloud and edge computing. In this scenario, applications are usually decomposed for execution in sets of tasks, which are in turn encapsulated into virtual components such as containers. Containers are lightweight implementations of virtual machines, improving efficiency and portability in distributed applications. Traditional containers scheduling in cloud is a well-known problem, but when the environment is heterogeneous, as it is in the scope of edge computing and edge-cloud systems, the problem becomes more challenging. In this paper we present the Concurrent Container Clusters Scheduling problem (C3S) aimed at optimizing the problem of placing containers in clusters of heterogeneous nodes satisfying a set of resource requirements, quality of service limitations, and considering additional stringent constraints in terms of applications execution in isolation for security guaranteeing. The C3S problem has been formulated using Integer Linear Programming with the dual objective of minimizing the number of applications rejected while minimizing the number of nodes used for computation. We have evaluated the optimality of this approach, analyzed the performance in terms of solving time and, finally, created a heuristic approach to solve the problem in realistic high demanding scenarios.

N-Beats for Backup Routing Optimization in Cloud Acknowledgement Scheme for a Node Network

Advanced machine-learning techniques are recognized as a powerful support tool for routing optimization in node networks. Moreover, the capabilities of cloud computing have grown immensely with advances in hardware. Real-time machine learning is enabled by better connectivity, processing power, and memory units. This paper proposes and evaluates the N-Beats technique for cloud acknowledgement schemes. It provides insights into the transmission trend and the tendencies of particular nodes or groups of neighbouring nodes in a network. We performed a set of experiments, and the results are remarkable for their high accuracy.

A Balance Regulation Algorithm for Reliable Data in Hierarchical Private Cloud Architecture

At present, enterprises need data for data analysis. They mostly use a kind of point-to-point data transmission form, which does not have regulation mechanism in the process. However, it has the problem of low data reliability, including two parts as follows: 1) Faulty sensor affects the collected data amount of the terminal server; 2) offensive data invade the data in transmission. In view of this, we propose the hierarchical private cloud architecture, including three aspects as follows. Firstly, we use distributed computing and virtualization capabilities of cloud computing to realize the hierarchical transmission of data. Secondly, through this mechanism of hierarchical transmission and classification algorithm of machine learning, we realize hierarchical filtering of offensive data. Finally, by combining hierarchical transmission mechanism with threshold value, classification algorithm, and limit tolerance mechanism, we regulate the data amount to monitor fault sensor in real time. Experiments are conducted to assess the proposed architecture's performance. The results show that each layer acts as a protective screen to counterattack the offensive data, which shows good robustness, real-time, and adaptive ability. Moreover, to compare with OM mode, the identification efficiency of fault sensor of TM mode is improved by 2 times. Also, TM mode improves 33.33% identification acuity, which is suitable for the enterprises that are mainly based on streaming computing. In summary, the hierarchical private cloud architecture achieves the filtering of offensive data and the real-time identification of faulty sensor, which guarantees the security, accuracy, and integrity of the data transmission process.

Ranking Cloud Computing Criteria in Developing Electronic Communications Services Using MCDM

According to the results and considering fuzzy calculations related to the capabilities of cloud computing in developing electronic communications services, the most important criteria in the "IT management in steel industries" cluster having (A) network code was "communicating with steel industries` costumers" having (AB) network code and fuzzy network weight equal to 0.096; the most important criteria in "cloud computing capabilities" cluster having (B) network code were "reducing steel industries` costs" having (BA) network code and fuzzy network weight equal to 0.191; and "providing rapid services to steel industries` costumers" having (BB) network code and fuzzy network weight equal to 0.120. on the other hand, the most important criteria in "developing electronic communications services" cluster having (C) network code was "storing the data in electronic communications services" having (CD) network code and fuzzy network weight equal to 0.123, since based on fuzzy logic calculation, they had the highest fuzzy rank in Matlab programming environment regarding cloud computing capabilities in developing electronic communications services.

Utilizing Cloud Capabilities for Face Detection and Face Recognition During COVID-19: Comparative Analysis

Face Recognition and Face detection is one of the most challenging problem in today’s era in the field of Image processing and Computer vision. Various algorithms are introduced using convolutional architecture for face detection using technologies like deep learning and machine learning. Due to this pandemic situation of COVID-19, Face mask detection has become a necessity and also an emerging Research in the field of Computer vision. As the situation in all over India is getting worse and death rates are increasing, so for protection wearing mask has become a necessity for each individual during this pandemic situation. This paper proposes the novel approach of utilizing cloud capabilities for face recognition. This paper then compares and analyse of face detection approach required during COVID-19 using the cloud computing techniques like Google cloud vision API, Amazon Face Rekognition, IBM cloud and Kairos facial recognition. As this Cloud computing techniques are automated there is no need of Deep learning and machine learning expert and it will help in faster computing and getting the results faster by using this cloud platforms.

Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks

Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue for agile learning of healthcare data from IoT devices. (2) We present an effective deep learning model for electrocardiogram (ECG) inference, which can be deployed to run on edge smart devices for low-latency diagnosis. (3) We design a data enhancement method for ECG based on deep convolutional generative adversarial network to expand ECG data volume. (4) We carried out experiments on two representative datasets to evaluate the effectiveness of the deep learning model of ECG classification based on EdgeCNN. EdgeCNN shows superior to traditional cloud medical systems in terms of network Input/Output (I/O) pressure, architecture cost and system high availability. The deep learning model not only ensures high diagnostic accuracy, but also has advantages in aspect of inference time, storage, running memory and power consumption.

Cloud capability maturity model: A study of South African large enterprises

Background: The adoption of cloud services can enable enterprises to realise improved cost structures, agility and productivity, yet the rate of adoption has been measured. Despite the benefits of cloud computing and the fact that the overall adoption of public cloud services is gaining momentum, South African large enterprises are cautious in adopting the services of cloud service providers because of perceived challenges of cloud adoption. Objectives: The objective of this study was to examine how do South African large enterprises assess and advance their cloud readiness and maturity such that cloud service practices contribute positively to business efficiency and agility whilst mitigating against the perceived risks of cloud computing. Method: This research employed a qualitative approach using in-depth interviews. Sixteen South African large enterprise cases were studied by interacting with respondents associated with cloud decision-making. Data were collected from specific cases, utilising non-probability sampling. Results: Reinvention of the organisation can be enabled through the advanced, integrated cloud and analytic features available through the global public cloud providers such as artificial intelligence and machine learning. A cloud maturity framework and a cloud capability maturity model to optimise and advance cloud maturity status are presented. Conclusion: This article guides information technology (IT) managers to achieve an optimal cloud maturity status level using a proposed cloud capability maturity model. The cloud framework developed in this study will assist IT managers and decision-makers to use evidence-based management principles to determine their maturity of cloud adoption.

E-shape - A cloud-based contribution to Earth Observation

Earth Observation (EO) is the collecting, analysing and presentation of information related to the Earth to improve understanding of our planet. The data that is gathered is usually centered on physical, chemical and biological systems that help researchers identify and understand changes in the natural and manmade environment. Observing Earth from a vantage point in and around space - often through satellites - enables scientists to access a bird's eye view of the planet, providing a unique perspective on a wide range of subjects, such as extreme weather events and their effect on the environment. Improved observations have come together with the explosion of modelling and cloud computing, which helps make better, and more sustainable, decisions. As technological capabilities have increased, so too has the focus on EO. Countries around the world are developing means of observing the Earth in an attempt to reap the related rewards, such as improved understanding, but also the creation of jobs and the achievement of various goals highlighted within international organisations, such as the United Nations' Sustainable Development Goals. Indeed, the potential positive outcomes from sustained EO can hardly be overstated, as it permeates through into an extremely broad range of different areas that will touch and impact humans around the world either now or in the future. It is with this in mind that e-shape has been established. This is a flagship project of the European Commission that falls under the Horizon 2020 programme. It is a unique initiative that brings decades of public investment in EO and cloud capabilities together to form a range of services for decision-makers, members of the public, industry and researchers. With 55 partners from 17 countries, this project has far-reaching impacts.

Unified programming concepts for unobtrusive integration of cloud-based and local parallel computing

The growth in the data and computation need of today’s operations has led to technical solutions that distribute workload over several entities for better performance. To facilitate such a paradigm, research studies have been investigating efficient approaches for combining high performance computing in shared-memory with distributed-memory environments. Meanwhile, the benefits of cloud computing and its modern enhancements have created potentials for applications to leverage the powerful, ubiquitous and cheap resources of cloud infrastructures. Yet, a small portion of the work in this area addresses the programming aspects of cloud-related technologies. Despite the extensive improvements in the fundamental mechanisms of this realm, programming environments offer little support for incorporating the high performance mechanisms of shared-memory computing with cloud computing. This study proposes a solution for an unobtrusive definition and integration of cloud-based and shared-memory parallel computing, in order to further simplify the application of cloud capabilities in local systems. It does so by implementing the proposed concepts in @PT (Annotation Parallel Task), a parallel-programming environment that utilizes native Java annotations as its language constructs. The experimental evaluations discussed here demonstrate that the proposed approach facilitates achieving the potential benefits of cloud computing for performance and energy consumption in local devices.