Development Of Cloud Computing Research Articles

Enhancing cybersecurity in cloud computing and WSNs: A hybrid IDS approach

The evolution of cloud computing has revolutionized how users access services, simplifying the development and deployment of applications across various industries. With its pervasive adoption, robust security measures become imperative. Integrating Intrusion Detection Systems (IDSs) into cloud computing and Wireless Sensor Networks (WSNs) addresses these challenges. IDSs serve as attentive protectors, monitoring network traffic and responding to breaches promptly, enhancing security across industries reliant on cloud services. Similarly, IDS integration in WSNs ensures the security of mission-critical operations, despite resource constraints and dynamic topologies, facilitated by cloud computing. This research proposes a hybrid IDS approach, leveraging the NSL-KDD dataset and methodologies like Intrusion Support Scalar Impact Rate (ISSIR), Optimized Support Vector Machine (OSVM), Extended Long-Short-Term Memory (ELSTM), and Multilayer Perceptron Neural Network (MLPNN), enhancing intrusion detection efficacy. ISSIR aids in feature selection, OSVM mitigates localization errors, ELSTM enables precise anomaly detection, and MLPNN provides robust defense mechanisms. Each method is integrated into a collaborative framework to address specific challenges in detecting intrusions with higher accuracy and reduced false positives. The interplay between these methodologies strengthens the overall intrusion detection framework, addressing the dynamic nature of cybersecurity threats. Results demonstrate the superior performance of MLPNN across various metrics, showcasing its effectiveness in accurately predicting outcomes compared to other models. The proposed MLPNN hybrid system achieves an accuracy of 99.9%, surpassing state-of-the-art methods. This study underscores the significance of advancing IDSs in cloud computing and WSNs, offering insights into enhancing security and mitigating vulnerabilities in an interconnected digital landscape.

OPTIMAL ENERGY CONSUMPTION AND COST PERFORMANCE SOLUTION WITH DELAY CONSTRAINTS ON FOG COMPUTING

Cloud computing plays an essential role in development of the Internet of Things, which provides data processing and storage services. Fog computing is the evolution of cloud computing, which helps provide solutions to cloud computing challenges such as latency, location awareness, and real-time mobility support. Fog computing fills the gap between the cloud and IoT devices within the close vicinity of IoT devices. So, computation, networking, storage, data management, and decision making occur along the path between the cloud and IoT devices. The automatic and intelligent management of fog node resources and achieving an effective scheduling policy in the computing model is a necessary requirement and will lead to the improvement of the overall performance of fog computing. Some optimization problems are modeled by mixed-integer nonlinear programming (MINLP). In this paper, a model, i.e. an MINLP optimization problem on fog computing, is designed. Our model has two goals: to increase Cost Performance as well as to reduce energy consumption. Cost Performance is the price, users are charged as benefit/revenue. In other words Cost Performance is defined as the ratio of the average data rate of each user to its cost. Then the exact mathematical method with the GAMS program was used to prove its logical process. In the next step, we solved the model with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing+GA (SA+GA), Teaching–Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), and random method. According to the TOPSIS comparison, the SA+GA method with a value of 0.23 is the best compared to other methods. Then GWO, GA, TLBO, PSO, and GOA methods are better, respectively.

Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture

Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F -measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key.

Fault aware hybrid harmony search technique for optimal resource allocation in cloud

The evolution of cloud computing is increasing exponentially which provides everything as a service. Clouds made it possible to move a huge amount of data over the networks on-demand. It removed the physical necessity of resources as resources are available virtually over the networks. Emerge of new technologies improvising the cloud system and trying to overcome cloud computing challenges like resource optimization, securities etc. Proper utilization of resources is still a primary target for the cloud system as it will increase the cost and time efficiency. Cloud is a pay-per-uses basis model which needs to perform in a flexible manner with the increase and decrease in demand on every level. In general, cloud is assumed to be non-faulty but faulty is a part of any system. This article focuses on the hybridization of Neural networks with the harmony Search Algorithm (HSA). The hybrid approach achieves a better optimal solution in a feasible time duration in the faulty environment to improve the task failure and improve reliability. The harmony Search approach is inspired from the music improvisation technique, where notes are adjusted until perfect harmony is matched. HS (Harmony search) is chosen, as it is capable to provide an optimal solution in a feasible time, even for complex optimization problems. An ANN-HS model is introduced to achieve optimal resource allocation. The presented model is inspired by Harmony Search and ANN. The proposed model considers multi-objective criteria. The performance criteria include execution time, task failure count and power consumption(Kwh).

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. This technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of Things (IoT), several challenges have emerged with these new technologies. Therefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem.

PFPMine: A parallel approach for discovering interacting data entities in data-intensive cloud workflows

With the evolution of cloud computing, communities and companies deployed their workflows on cloud to support end-to-end business processes that are usually syndicated with other external services. To improve the efficiency of the system as well as reducing energy consumption, data placement and backup strategies should be carefully designed. One of the most challenging problems is the discovery of interacting data entities in date-intensive workflows. To tackle this challenge, this paper presents a frequent pattern-based approach named FPMine for interacting data entity discovery in cloud workflows. A direct discriminative mining algorithm is first proposed to determine the minimum support threshold, based on which FP-tree is constructed to formulate the frequent item pairs. Next, FP-matrix is applied to avoid traversing the FP-trees during data entity discovery, and a pruning approach is introduced to reduce the redundancy of frequent item pairs. Furthermore, we propose a parallel data entity mining algorithm using MapReduce framework, namely PFPMine, and then design a primitive data placement and backup strategy. Finally, we evaluate the efficiency of our approach by experiments using real-life data, based on which we show that our approach can facilitate the discovery of interacting data entities with efficiency for cloud workflows. Comparing with traditional FP-growth approach, we pay only a multiplicative factor for making our approach able to extract fine-grained frequent item pairs rather than frequent patterns, which can bring significant advantages to data placement. After parallelization, the PFPMine algorithm performs better with high efficiency for both sparse datasets and dense datasets than FP-growth. The results show that PFPMine can reduce the running time by at least 25%, and preforms with significantly higher efficiency than FP-growth approach.

Advances in motion and electromyography based wearable technology for upper extremity function rehabilitation: A review

Study DesignScoping review. IntroductionWith the recent advances in technologies, interactive wearable technologies including inertial motion sensors and e-textiles are emerging in the field of rehabilitation to monitor and provide feedback and therapy remotely. Purpose of the StudyThis review article focuses on inertial measurement unit motion sensor and e-textiles–based technologies and proposes approaches to augment these interactive wearable technologies. MethodsWe conducted a comprehensive search of relevant electronic databases (eg, PubMed, the Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, The Cochrane Central Register of Controlled Trial, and the Physiotherapy Evidence Database). The scoping review included all study designs. ResultsCurrently, there are a numerous research groups and companies investigating inertial motion sensors and e-textiles–based interactive wearable technologies. However, translation of these technologies to the clinic would need further research to increase ease of use and improve clinical validity of the outcomes of these technologies. DiscussionThe current review discusses the limitations of the interactive wearable technologies such as, limited clinical utility, bulky equipment, difficulty in setting up equipment inertial motion sensors and e-textiles. ConclusionThere is tremendous potential for interactive wearable technologies in rehabilitation. With the evolution of cloud computing, interactive wearable systems can remotely provide intervention and monitor patient progress using models of telerehabilitation. This will revolutionize the delivery of rehabilitation and make rehabilitation more accessible and affordable to millions of individuals.

History, Deployment and Service Models Towards the Evolution of Cloud Computing

The network concerned usually, though certainly not always, is the Internet. The sources provisioned cover a stable of services consisting of information, software program, storing, safety, etc. As an example, when we utilize a post including Gmail, enjoy a film on YouTube, shop at Amazon.com, or establishment files making use of DropBox, our team are utilizing cloud-based information (The Google Chrome Group, 2010). Within this phase, we examine the development of Cloud Computing from its own very early roots in mainframe-based computing to the present day and also clarify the different companies provided by Cloud Computing in today's company and also private computing situations. This paper provides an overview of the history, deployment and service models towards the evolution of cloud computing.

Research on the Development of China University Library Alliances

With the continuous development of information technology, the university library alliances (ULA) have gradually changed from “entity alliance” in the last century to the “entity body combined with digitizing brain” in the present. In the near future, smart libraries (SL) and cloud service platforms (CSP) will be the real protagonists of the library alliances. In this process, the use of artificial intelligence (AI) and cloud computing (CC) technology will become an important trend. The continuous development of China University Library Alliance (CULA) is the result of using emerging information technology to continuously satisfying the needs of readers. In the era of big data, the analysis of mass document data will become a new topic in the development of ULA, and its service focuses will gradually be transferred from traditional business to document data. With the development of AI and CC, focusing on the intelligent, specialization and individuality of the service is the inevitable trend. This article mainly adopts the research method of comparative library science and historical analysis to explain the important characteristics of different development stages of CULA, to analyze the problems of “entity alliances” and “hybrid alliances”, and then to explain why cloud libraries of big data (BD) and smart libraries will be the mainstream of CULA.

Load Balancing Mechanisms in Amazon Web Services using Meta Heuristic Rules

Cloud computing is defined as the resource that can be delivered or accessed by the local host from the remote server via the internet. Cloud providers typically use a "pay-as-you-go" model. The evolution of cloud computing has led to the evolution of modern environment due to abundance and advancement of computing and communication infrastructure. During user request, and system response generation, an amount load will be assigned in the cloud computing, where it may be over or under load. Due to heavy load, power consumption and energy management problems are created, and it makes system failure and lead data loss. Though, an efficient load balancing method is compulsory to overcome all mentioned problems. The objective of this work is to develop a metaheuristic load balancing algorithm to migrate multi-server for load balancing and machine learning techniques is used to increase the cloud resource utilization and minimize the make-span time of the task. Using an unsupervised machine learning technique, it is possible to predict the correct response time and waiting time of the servers by getting the prior knowledge about the virtual machines and its clusters. And this work involves to calculate the accuracy rate of the Round-Robin load balancing algorithm and then compared it with a proposed algorithm. By this work, the response time and waiting time can be minimized and also it increases the resource utilization and minimize the make- span time of the task.

Fog Computing Architectures: A Reference for Practitioners

Soon after realizing that cloud computing could indeed help several industries overcome classical product-centric approaches in favor of more affordable service-oriented business models, we are witnessing the rise of a new disruptive computing paradigm, namely fog computing. Essentially, fog computing can be considered as an evolution of cloud computing, in the sense that the former extends the latter to the edge of the network (i.e., where the connected devices - the things - are) without discontinuity, realizing the so-called “cloud-to-thing continuum.” Since its infancy, fog computing has been considered as a necessity within several Internet of Things (IoT) domains (one for all: Industrial IoT) and, more generally, wherever embedded artificial intelligence and/or more advanced distributed capabilities were required. Fog computing cannot be considered only a fancy buzzword: according to separate, authoritative analyses, its global market will reach $18 billion by 2022, while nearly 45 percent of the world's data will be moved to the network edge by 2025. In this article, we take stock of the situation, summarizing the most modern and mature fog computing initiatives from the standardization, commercial, and open source communities' perspectives.

Cloud Computing in Lebanese Enterprises: Applying the Technology, Organization, and Environment (TOE) Framework

In the last few years, cloud computing (CC) has grown from being a promising business concept to one of the fastest growing segments of the IT industry. Many businesses including small, medium (SMEs) and large enterprises are migrating to this technology. The objective of this paper was to describe the opinions of enterprises about the benefits and challenges of cloud computing services in the private and public companies in South Lebanon. During 2019, a cross-sectional study which enrolled 29 enterprises used CC was conducted. The survey included questions on socio-demographic characteristics of representative of companies, and companies’ factors in reference to the technology, organization, and environment (TOE) framework. Most (58.6%) of companies were private and micro and SMEs sized (86.8%). The cost saving (75.0%), scalability and flexibility (75.9%), security (44.8%), and improved service delivery were the main benefits that cloud offer to the business. The security aspect, the cost, and the limited provision of infra-structure remain a challenge for the adoption of CC. In conclusion, the research reveals the potential for the development of CC and obstacles for successful implementation of this new technology.

Power Efficient Clustering Scheme for 5G Mobile Edge Computing Environment

Mobile edge computing (MEC), which is an evolution of cloud computing, is acknowledged as a promising technology for meeting low latency and bandwidth efficiency required in fifth generation (5G) era. Accordingly, the enlargement of distributed MEC installments will be realized and their power consumption might be a significant problem in terms of operating costs for service providers. Thus, this paper proposes a theoretical framework for MEC server clustering to minimize power consumption of the MEC environment. To do this, considering power consumption behavior of MEC servers using CPUs with dynamic voltage frequency scaling, we propose a power-efficient clustering scheme (PECS) that minimizes power consumption of MEC servers by obtaining the optimal number of clusters through convex optimization. Numerical results reveal the proposed PECS reduces power consumption of the MEC environment by 12.32% relative to an existing scheme while sustaining average delay of inflows processed in MEC servers at the acceptable level without turning off MEC servers.

Delay-based virtual congestion control in multi-tenant datacenters

With the evolution of cloud computing and virtualization, the congestion control of virtual datacenters has become the basic issue for multi-tenant datacenters transmission. Regarding to the friendly conflict of heterogeneous congestion control among multi-tenant, this paper proposes a delay-based virtual congestion control, which translates the multi-tenant heterogeneous congestion control into delay-based feedback uniformly by setting the hypervisor translation layer, modifying three-way handshake of explicit feedback and packet loss feedback and throttling receive window. The simulation results show that the delay-based virtual congestion control can effectively solve the unfairness of heterogeneous feedback congestion control algorithms.

A Survey on Virtual Machine Migration: Challenges, Techniques, and Open Issues

When users flood in cloud data centers, how to efficiently manage hardware resources and virtual machines (VMs) in a data center to both lower economical cost and ensure a high service quality becomes an inevitable work for cloud providers. VM migration is a cornerstone technology for the majority of cloud management tasks. It frees a VM from the underlying hardware. This feature brings a plenty of benefits to cloud providers and users. Many researchers are focusing on pushing its cutting edge. In this paper, we first give an overview of VM migration and discuss both its benefits and challenges. VM migration schemes are classified from three perspectives: 1) manner; 2) distance; and 3) granularity. The studies on non-live migration are simply reviewed, and then those on live migration are comprehensively surveyed based on the three main challenges it faces: 1) memory data migration; 2) storage data migration; and 3) network connection continuity. The works on quantitative analysis of VM migration performance are also elaborated. With the development and evolution of cloud computing, user mobility becomes an important motivation for live VM migration in some scenarios (e.g., fog computing). Thus, the studies regarding linking VM migration to user mobility are summarized as well. At last, we list the open issues which are waiting for solutions or further optimizations on live VM migration.

Cloud Federation and the Evolution of Cloud Computing

To satisfy the demand for collective and collaborative cloud use, academia and industry want to interconnect heterogeneous clouds to form a federated system. This approach is promising but also faces significant challenges.

<inline-formula> <tex-math notation="LaTeX">$2DCrypt$ </tex-math> </inline-formula>: Image Scaling and Cropping in Encrypted Domains

The evolution of cloud computing and a drastic increase in image size are making the outsourcing of image storage and processing an attractive business model. Although this outsourcing has many advantages, ensuring data confidentiality in the cloud is one of the main concerns. There are state-of-the-art encryption schemes for ensuring confidentiality in the cloud. However, such schemes do not allow cloud datacenters to perform operations over encrypted images. In this paper, we address this concern by proposing 2DCrypt, a modified Paillier cryptosystem-based image scaling and cropping scheme for multi-user settings that allows cloud datacenters to scale and crop an image in the encrypted domain. To anticipate a high storage overhead resulted from the naive per-pixel encryption, we propose a space-efficient tiling scheme that allows tile-level image scaling and cropping operations. Basically, instead of encrypting each pixel individually, we are able to encrypt a tile of pixels. 2DCrypt is such that multiple users can view or process the images without sharing any encryption keys-a requirement desirable for practical deployments in real organizations. Our analysis and results show that 2DCrypt is INDistinguishable under Chosen Plaintext Attack secure and incurs an acceptable overhead. When scaling a 512×512 image by a factor of two, 2DCrypt requires an image user to download approximately 5.3 times more data than the un-encrypted scaling and need to work approximately 2.3 s more for obtaining the scaled image in a plaintext.

Accelerated K-means Clustering Algorithm

Optimizing K-means is still an active area of research for purpose of clustering. Recent developments in Cloud Co mputing have resulted in emergence of Big Data Analytics. There is a fresh need of simp le, fast yet accurate algorithm for clustering huge amount of data. This paper proposes optimization of K-means through reduction of the points which are considered for re- clustering in each iteration. The work is generalizat ion of earlier work by Poteras et al who proposed this idea. The suggested scheme has an improved average runtime. The cost per iteration reduces as number of iterations grow which makes the proposal very scalable. Index Terms—Clustering, k-means, Optimizat ion of k- means, Distance metrics, Poteras et als Scheme.

OpenID Connect as a security service in cloud-based medical imaging systems.

The evolution of cloud computing is driving the next generation of medical imaging systems. However, privacy and security concerns have been consistently regarded as the major obstacles for adoption of cloud computing by healthcare domains. OpenID Connect, combining OpenID and OAuth together, is an emerging representational state transfer-based federated identity solution. It is one of the most adopted open standards to potentially become the de facto standard for securing cloud computing and mobile applications, which is also regarded as "Kerberos of cloud." We introduce OpenID Connect as an authentication and authorization service in cloud-based diagnostic imaging (DI) systems, and propose enhancements that allow for incorporating this technology within distributed enterprise environments. The objective of this study is to offer solutions for secure sharing of medical images among diagnostic imaging repository (DI-r) and heterogeneous picture archiving and communication systems (PACS) as well as Web-based and mobile clients in the cloud ecosystem. The main objective is to use OpenID Connect open-source single sign-on and authorization service and in a user-centric manner, while deploying DI-r and PACS to private or community clouds should provide equivalent security levels to traditional computing model.

A fad or future of IT?: A comprehensive literature review on the cloud computing research

Cloud computing is now a global trend and during the past decade, has drawn attention from both academic and business communities. Although the evolution of cloud computing has not reached the maturity level, there is still adequate research about the topic. The main purpose of this paper is to examine the development and evolution of cloud computing over time. A content analysis was conducted for 236 scholarly journal articles, which were published between 2009 and 2014 in order to (i) identify the possible trends and changes in cloud computing over the six years, (ii) compare publishing productivity of journals about the cloud computing subject, and (iii) guide future research about cloud computing. The results show that the majority of the cloud computing research is about “cloud computing adoption” (19%), and it was followed by the “legal and ethical issues” of cloud computing (15%). It is also found that “cloud computing for mobile applications” (6%), “benefits & challenges of cloud computing” (5%) and “energy consumption dimension of cloud computing” (4%) are the least attention-grabbing themes in the literature. However, “cloud computing for mobile applications” and “energy consumption dimension of cloud computing” themes have become popular in the last two years, so they are expected to be trendy topics of the near future. Finally, another finding of this study is that the majority of the articles were published by engineering, information systems or technical journals such as “IT Professional Magazine”, “International Journal of Information Management” and “Mobile Networks and Applications”. It seems as if this topic is generally ignored by the managerial and organizational journals even though the impact of cloud computing on organizations and institutions is immense and is in need of investigation.