Cloud Computing Framework Research Articles

A Survey on Intrusion Detection Systems for Fog and Cloud Computing

The rapid advancement of internet technologies has dramatically increased the number of connected devices. This has created a huge attack surface that requires the deployment of effective and practical countermeasures to protect network infrastructures from the harm that cyber-attacks can cause. Hence, there is an absolute need to differentiate boundaries in personal information and cloud and fog computing globally and the adoption of specific information security policies and regulations. The goal of the security policy and framework for cloud and fog computing is to protect the end-users and their information, reduce task-based operations, aid in compliance, and create standards for expected user actions, all of which are based on the use of established rules for cloud computing. Moreover, intrusion detection systems are widely adopted solutions to monitor and analyze network traffic and detect anomalies that can help identify ongoing adversarial activities, trigger alerts, and automatically block traffic from hostile sources. This survey paper analyzes factors, including the application of technologies and techniques, which can enable the deployment of security policy on fog and cloud computing successfully. The paper focuses on a Software-as-a-Service (SaaS) and intrusion detection, which provides an effective and resilient system structure for users and organizations. Our survey aims to provide a framework for a cloud and fog computing security policy, while addressing the required security tools, policies, and services, particularly for cloud and fog environments for organizational adoption. While developing the essential linkage between requirements, legal aspects, analyzing techniques and systems to reduce intrusion detection, we recommend the strategies for cloud and fog computing security policies. The paper develops structured guidelines for ways in which organizations can adopt and audit the security of their systems as security is an essential component of their systems and presents an agile current state-of-the-art review of intrusion detection systems and their principles. Functionalities and techniques for developing these defense mechanisms are considered, along with concrete products utilized in operational systems. Finally, we discuss evaluation criteria and open-ended challenges in this area.

Research on Risk Prediction Model of Internet Finance Based on Cloud Computing

With the rapid development of the Internet, the traditional Internet financial risk prediction methods can no longer meet the needs of individuals and enterprises, so the concept of cloud computing arises at the historic moment. Cloud computing has subverted the traditional financial risk prediction method and has been widely studied and applied for its distributed, dynamic and autonomous characteristics. How to efficiently and reasonably schedule the resources of cloud data center and improve the accuracy of financial risk prediction is the focus of current research. How to quantify financial risk and financial risk early warning is one of the urgent problems to be solved. Under the framework of cloud computing, this paper combines the feature extraction and data weighting to study the user’s basic attribute data and a large number of downloaded APP types. After that, linear regression with penalty is used to construct the prediction model to improve insolvency. The accuracy of customer default judgment can realize local optimization, so as to improve the prediction and control of hidden risks of customer commercial bank loans and greatly reduce the default risk of bank loans.

Security Framework Based on Shannon’s Theory and Genetic Cryptography for Cloud Computing Framework

Security Framework Based on Shannon’s Theory and Genetic Cryptography for Cloud Computing Framework

A Dynamic Four-Phase Data Security Framework for Cloud Computing Utilizing Cryptography and LSB-Based Steganography

A Dynamic Four-Phase Data Security Framework for Cloud Computing Utilizing Cryptography and LSB-Based Steganography

Intelligent Library Architecture Based on Edge Computing

The development of the Internet of Things promotes the integration of physical space and information space. In addition, more and more people, machines and things are connected to the information space. The centralized cloud computing model has been gradually hard to accommodate the resulting massive data treating requirements. However, the emerging edge computing provides a new technical means to solve this problem. By comparing and analyzing the basic framework and features of edge computing and cloud computing, taking the typical case of Intelligent Library as an example, this paper constructs an Intelligent library system architecture based on edge computing. It analyzes the functions of each level of the architecture. The architecture can reduce the minimum delay and network bandwidth pressure of Library intelligent service, thus saving the data processing cost of Intelligent Library.

A Scalable and Utility Driven Profit Maximized Auction of Resources Model for Cloudlet Based Mobile Edge Computing

This manuscript discusses about mobile edge computing, mobile cloud computing and various cloudlet-based frameworks used in these computing. Various cloudlet-based computing frameworks are inter-cloudlet communication framework, FMCC and SKYR framework. Various problems faced by these frameworks and solution proposed in improved SKYR framework are discussed here. Improved SKYR framework effectively addresses the problem of dynamic consideration of yield factor of over provisioning and under provisioning. It also establishes relationship between the yield factor of availability and its dependent factors, which helps to improve the yield factor of availability. This paper also discusses about various pricing models suitable for data as a service (DaaS). It mentions the drawbacks of latest pricing model that is profit maximization incentive mechanism (PMIM) pricing model and provides a solution to tackle those problems with the proposed effective pricing model named as scalable and utility driven profit maximized auction of resources. This pricing model effectively considers the problem of granularity of task, cost of execution of offloaded task, utility efficiency (η) and marginal utility (mu) factors for PMIM pricing model and provides effective solution. The proposed pricing model also follows the principle of scalability which suggests that it can extent to accommodate dynamic resource providers and mobile users. This proposed pricing model is incorporated in SKYR framework which enables this framework as complete package to motivate resource providers and mobile devices to use it for various applications such as data as a service (DaaS), software as a service (SaaS) and network as a service (NaaS).

An Automated Self-Healing Cloud Computing Framework for Resource Scheduling

In cloud computing, applications, administrations, and assets have a place with various associations with various goals. Elements in the cloud are self-sufficient and self-adjusting. In such a collaborative environment, the scheduling decision on available resources is a challenge given the decentralized nature of the environment. Fault tolerance is an utmost challenge in the task scheduling of available resources. In this paper, self-healing fault tolerance techniques have been introducing to detect the faulty resources and measured the best resource value through CPU, RAM, and bandwidth utilization of each resource. Through the self-healing method, less than threshold values have been considering as a faulty resource and separate from the resource pool. The workloads submitted by the user have been assigned to the available best resource. The proposed method has been simulated in cloudsim and compared the multi-objective performance metrics with existing methods, and it is observed that the proposed method performs utmost.

Study of Experiences and Issues in the Deployment of Environmental Engineering Sensor Network

Sensor complexexplore is a huge and developing zone of scholastic exertion, inspecting innovative and organization issues in the region of natural checking. These advances are utilized by ecological designers and researchers to screen a variety of conditions and administrations, and, explicit to this paper, vitality and water provided to the constructed condition. Despite the fact that the innovation is created by science pros, the utilization and arrangement is generally act upon by ecological specialists. This article inspects sending from the points of view of natural designers and researchers and asks what PC researchers can do to improve the cycle. The paper utilizes a contextual analysis to show the spry activity of Wireless Sensor Networks inside the Cloud Computing framework, and accordingly the interest driven, joint effort extraordinary worldview of digital environments.

Assessing the sustainability of General Insurance Business through Real Time Monitoring of KPIs using Recurrent Neural Network

A company’s sustainability is driven significantly by its operational efficiency. Operational efficiency plays a significant role in the growth and the profitability of a company. Thus, operational efficiency of a company forms the basis for the metrics known as the Key Performance Indicators(KPIs). These KPIs bridge the concept of performance an operation and a means to measure the same quantitatively. In this work, we used Recurrent Neural Network (RNN) with the Long Short Term Memory(LSTM) cells for projecting the public disclosure data of select General Insurance(GI) companies operating in India to the future. We use this data to calculate the KPIs pertaining to the operations of general insurance companies and calculate how the operations of the GI company affect its performance at various levels. Since this analysis is done for the projected data, we get a framework to assess the sustainability of the GI companies by monitoring these KPIs in real-time. The complex RNN and LSTM algorithms were implemented with the help of the Google Colaboratory platform by using the GPUs of the Google Hardware with the help of the Cloud Computing framework.

Strategic Cloud Computing Framework: A Case Study of South African IT Professionals

In a challenging business environment, organizations are striving to stay competitive. Cloud computing is changing the way IT services is being provided and managed. Cloud computing is becoming more relevant in that the benefits of cloud computing include reducing infrastructure and maintenance costs, increasing operational efficiencies, availability and flexibility. However, like any other technology, both strategically and operationally, the organizations are not very confident in adoption and using it. Therefore, in an attempt to help South African organizations to leverage off the benefits of cloud computing. This paper focuses on how cloud computing may be strategically adopted and use in South Africa from the context of individual Information Technology Professionals. To achieve these South African organizations require a means to adopt and use cloud computing. To this point, the contribution of this paper is a framework, based on Contingency Theory, Stakeholder Theory and Technology-Organization-Environment Framework (TOE), that South African organization can use to strategically position cloud computing as preferred technology in a uniformed and coordinated manner. This study applies interpretivism philosophy and case study to understand the cloud computing adoption and use in South African organizations. The study participants were purposefully selected based on their knowledge, experience and views on cloud computing. The main contribution of this paper is the strategic cloud computing framework that can be used by decision-makers to strategically adopt and use cloud computing in a uniformed and coordinated manner in South African organizations.

Application of mobile computing in big data technologies

Mobile are progressively turning into a basic piece of individuals' day by day life, encouraging to play out an assortment of valuable assignments. Portable distributed computing coordinates versatile and distributed computing to grow their abilities and benefits and beats their impediments, for example, constrained memory, CPU power and battery life. Enormous information examination advancements empower extricating an incentive from information having four Vs: volume, assortment, speed and veracity. This paper talks about organized medicinal services and the job of versatile distributed computing and enormous information investigation in its enablement. The inspiration and advancement of arranged medicinal services applications and frameworks is introduced alongside the appropriation of distributed computing in social insurance. A Cloudlet–based Mobile Cloud Computing framework to be utilized for social insurance large information applications is depicted. The procedures, instruments, and uses of enormous information examination are checked on. Ends are drawn concerning the plan of organized social insurance frameworks utilizing large information and versatile distributed computing advances. A point of view toward organized human services is given.

A Novel Pairing-Free Lightweight Authentication Protocol for Mobile Cloud Computing Framework

The mobile cloud computing (MCC) refers to an infrastructure that integrates cloud computing and mobile computing, and it has changed a great deal, the service provisioning of applications, which requires to get the data processed after collection from vast sensor and Internet-of-Things-based network. The ever increasing number of handheld mobile gadgets has exacerbated the need for robust and efficient authenticated key agreements. We could witness a number of MCC-based multiserver authentication schemes lately to foster the secure adaptation of the technology; however, the demonstrated solutions are either insecure or employing too costly bilinear pairing operations for implementation. In view of limitations, as illustrated in previous studies, we propose a novel pairing-free multiserver authentication protocol for MCC environment based on an elliptic curve cryptosystem that is not only efficient, but also free from security loopholes as demonstrated. The performance evaluation section discusses and distinguishes the findings among latest studies. The strength of the contributed scheme is proved theoretically under formal security model.

A novel energy estimation model for constraint based task offloading in mobile cloud computing

The utility of mobile applications has been increased enormously due to the advancements in science and technology to assist the users for various purposes. The main intention of integrating cloud services and resources with the mobile application is to reduce battery usage and to improve the efficiency of mobile devices. Thus the process of shifting a task that can be run on the cloud resources for assistance is referred to as task offloading. The process of task offloading is critical in the field of Mobile Cloud Computing. The major issue that pairs with task offloading are the communication cost estimation of the devices. To overcome the above-mentioned issues and to create an effective task offloading model, A Novel Mobile Cloud Computing framework called Rule Generation based Energy Estimation Model (RG-EEM) is designed. The energy required for executing the task in the local mobile device and cloud is estimated by implementing an energy estimation algorithm. Then a novel constraints specific rule generation algorithm is used to estimate the task execution time and memory utilization of the task, from which the decision has to be taken for offloading or local execution by considering all the possible affecting characteristics. Further, a novel task clustering and scheduling algorithm are implemented to execute the task in the cloud server effectively which will help in allocating similar tasks to a particular virtual machine in the cloud. The RG-EEM algorithm will also help in partitioning the task and parallel execution. The effectiveness of the RG-EEM technique is evaluated using the parametric measures and compared with the existing techniques.

Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

The synthesis of high-performance computing (particularly graphics processing units), cloud computing services (like Google Colab), and high-level deep learning frameworks (such as PyTorch) has powered the burgeoning field of artificial intelligence. While these technologies are popular in the computer science discipline, the physics community is less aware of how such innovations, freely available online, can improve research and education. In this tutorial, we take the Hopfield network as an example to show how the confluence of these fields can dramatically accelerate physics-based computer simulations and remove technical barriers in implementing such programs, thereby making physics experimentation and education faster and more accessible. To do so, we introduce the cloud, the GPU, and AI frameworks that can be easily repurposed for physics simulation. We then introduce the Hopfield network and explain how to produce large-scale simulations and visualizations for free in the cloud with very little code (fully self-contained in the text). Finally, we suggest programming exercises throughout the paper, geared towards advanced undergraduate students studying physics, biophysics, or computer science.

Q-learning based dynamic task scheduling for energy-efficient cloud computing

High energy consumption has become a growing concern in the operation of complex cloud data centers due to the ever-expanding size of cloud computing facilities and the ever-increasing number of users. It is critical to find viable solutions to cloud task scheduling so that cloud resources can be utilized in an energy-efficient way while still meeting diverse user requirements in real time. In this research we propose a Q-learning based task scheduling framework for energy-efficient cloud computing (QEEC). QEEC has two phases. In the first phase a centralized task dispatcher is used to implement the M/M/S queueing model, by which the arriving user requests are assigned to each server in a cloud. In the second phase a Q-learning based scheduler on each server first prioritizes all the requests by task laxity and task life time, then uses a continuously-updating policy to assign tasks to virtual machines, applying incentives to reward the assignments that can minimize task response time and maximize each server’s CPU utilization. We have conducted simulation experiments, which have confirmed that implementing a M/M/S queueing system in a cloud can help to reduce the average task response time. The experiments have also demonstrated that the QEEC approach is the most energy-efficient as compared to other task scheduling policies, which can be largely credited to the M/M/S queueing model and the Q-learning strategy implemented in QEEC.

A predictive context aware collaborative offloading framework for compute-intensive applications

The resourceful mobile devices with augmented capabilities around human pave the way for utilizing it as delegators for resource-constrained devices to run compute-intensive applications. Such collaborative resource sharing policy among mobile devices throws challenges like identifying competent alternatives for offloading and diminishing time consumption of pre-offload process to accomplish remarkable offloading. This paper presents a Mobile Cloud Computing framework with Predictive Context-Aware Collaborative Offloading Process (PCA-COP) that fixes these challenges through conductive alternative discovery. This context-aware discovery adapts a multi-criteria decision making model of Analytic Hierarchy Process (AHP) accompanied with Fuzzy categorization to rank the alternatives and classify them into Highly, Fairly, Less offload-suitable devices. Moreover, to make alternative selection optimal, a Dataset Curtailment enabled Artificial Neural Network (DCANN) prediction is incorporated on AHP-Fuzzy model, which truncates training dataset using Conditioned Stratified Sampling (CSS). The prototype framework is evaluated with mobile applications in the classroom under dynamic context environments.

Security Framework for Cloud Computing using Fragmentation and Homomorphic Encryption

An invention of cloud computing technology comes with numerous benefits for IT industries and others. The data store in the cloud can be easily shared among stakeholders irrespective of their location, i.e. data availability is very good. Nowadays organizations are switching to cloud platform for storing and sharing data in a decentralized manner. This is significantly reduces the economically burden to the organization. As the data are accessible through network, so major concern is to maintain the data confidentiality. Data breach in any form organization losses their trustworthy, and this affect the reputation of the organization. This is very important to maintain the privacy and security of data all the time. There are so many works has been proposed by many researchers to secure data in the cloud by using various encryption techniques. In this paper, we proposed a security mechanism to maintain confidentiality of data. This method is combination of multiplicative homomorphic encryption algorithms along with vertical fragmentation of data. We have tested our scheme based on crypto delays, communication delays, and query processing delays with an existing work. The results obtained show that our method out-perform the existing work. The results obtained show improvement with the proposed method.

I-Voting on Cloud Framework

With large scale automation gathering pace among various branches and services, the paper proposes an Online Voting System built on a Cloud Framework which combines the fairness and privacy of the existing system which uses Electronic Voting Machines, with the transparency and pliability of the new-age technology by using Cloud Computing Framework wherein a community cloud can secure the required biometrics and couple it with the web framework and mobile application to ensure a free-and-fair elections with the voting right at the voters fingertip. This paper proposes a Cloud Framework which can implement this Internet-Voting (I-Voting) by employing Aadhar UIDAI (Unique Identification Authority of India) secured by encryption techniques which can be deployed by a software or an application where the user can validate his biometrics and vote in quick-time, which can then be encrypted and stored in a database from where the results can be accessed quickly saving time and resources. The proposed application combines Cloud Framework, Data Security and Biometric Verification along with what advantages it offers against the existing systems.

Fog‐cloud distributed intrusion detection and cooperation

AbstractCloud computing is becoming a promoted technology. Over the recent years, it has rapidly grown and its adoption is more and more increasing. Many enterprizes and organizations have immigrated their data to the cloud. However, the security of this technology is considered a serious barrier to guarantee the organizations' trust on it. Therefore, intrusion detection has been set as the best solution to secure the cloud platform. Thus, many research studies have been developed for the aim to create an effective and performing intrusion detection system suitable for the cloud environment. In this paper, we propose a new approach for cloud environment–based multilevel cooperation in terms of intrusion detection. It is based on the cooperation and the correlation between fog nodes at the beginning and then between fog and cloud technology. We also evaluate the performance and the efficiency of this proposed prototype in terms of detection rates and detection response to make real‐time detection in the whole cloud platform. The results show that the proposed architecture introduces low false positive rate compared to the existing hybrid intrusion detection approach in cloud computing (HIDCC) framework.

Serverless Computing Platform for Big Data Storage

This paper describes various challenges faced by the Big Data cloud providers and the challenges encountered by its users. This foreshadows that the Serverless computing as the feasible platform for Big Data application’s data storages. The literature research undertaken focuses on various Serverless computing architectural designs, computational methodologies, performance, data movement and functions. The framework for Serverless cloud computing is discussed and its performance is tested for the metric of scaling in the Serverless cloud storage for Big Data applications. The results of the analyses and its outcome are also discussed. Thus suggesting that the scaling of Serverless cloud storage for data storage during random load increase as the optimal solution for cloud provider and Big Data application user.