Cloud Computing Services Research Articles

Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

Background : To design an effective workflow scheduling optimization of web servers that will bring good trade-offs to meet the workflow task delay prerequisite and performance requirement. Methods: This study presents a delayaware and performance-efficient energy optimization (DAPEEO) technique for workflow execution in a heterogeneous environment (i.e., edge-cloud environment). This technique provides a workflow execution model which meets the application delay prerequisite and performance requirement. Findings: Our model has been designed to reduce the energy consumption, increase throughput, reduce computational cost and computational time to provide a delay-aware and performance-efficient workflow model for the web servers in hybrid cloud computing. Our model Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has reduced the energy consumption by 4.217%, increased the throughput by 19.51%, and reduce the computational cost by 62.38% when compared with the existing Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH) models. Furthermore, the average energy consumption showed a reduction of 40.993% and 90.384% when compared with the DCOH and Self-Configuring and Self-Healing of Cloud-based Resources (RADAR) workload model respectively. Experiment outcome shows the DAPEEO technique achieves much superior energy efficiency, throughput and computation cost reduction when compared with the existing workflow execution model. Novelty: Existing model failed to balance reducing cost, and meeting workflow execution deadlines under a heterogeneous environment. On the other side, the DAPEEO is efficient in bringing trade-offs in reducing energy dissipation and meeting task deadlines with reduced cost under the edge-cloud computing model. Keywords: Cloud Computing; EdgeCloud; DAPEEO; Energy Efficiency; Throughput; Cost

CoPaM: Cost-aware VM Placement and Migration for Mobile services in Multi-Cloudlet environment: An SDN-based approach

Edge Cloud Computing (ECC) is a new approach for bringing Mobile Cloud Computing (MCC) services closer to mobile users in order to facilitate the complicated application execution on resource-constrained mobile devices. The main objective of the ECC solution with the cloudlet approach is mitigating the latency and augmenting the available bandwidth. This is basically done by deploying servers (a.k.a. “Cloudlets”) close to the user’s device on the edge of the cellular network. When considering the users’ mobility along with the limited resource of the cloudlets serving them, the user-cloudlet communication may need to go through multiple hops, which may seriously affect the communication delay between them and the quality of services (QoS). To reduce as much as possible the negative effects of such a QoS degradation, service execution must be dynamically migrated to a better placement. This study proposes a novel Cost-aware Virtual Machine (VM) placement and migration (CoPaM) framework for mobile services in a network of cloudlets. A Mixed Integer Linear Programming (MILP) model is proposed to select the least cost cloudlets for serving mobile users to a given trajectory in advance based on path prediction methods. Since the proposed model is NP-hard, it is not applicable within large-scale environments with a centralized approach and the use of Software Defined Networking (SDN) technology. Therefore, a heuristic algorithm is presented. Experiments are conducted by emulating the proposed framework in Mininet-WiFi with the Floodlight usage as the SDN controller. The simulation results show the superiority of the performance of the proposed architecture in terms of the service rate and the costs imposed on the operator in comparison to existing approaches.

NLP-based platform as a service: a brief review

Natural language processing (NLP) refers to the field of study that focuses on the interactions between human language and computers. It has recently gained much attention for analyzing human language computationally and has spread its applications for various tasks such as machine translation, information extraction, summarization, question answering, and others. With the rapid growth of cloud computing services, merging NLP in the cloud is a significant benefit. It allows researchers to conduct NLP-related experiments on large amounts of data handled by big data techniques while harnessing the cloud’s vast, on-demand computing power. However, it has not sufficiently spread its tools and applications as a service in the cloud and there is little literature available that discusses the scope of interdisciplinary work. NLP, cloud Computing, and big data are vast domains and contain their challenges and potentials. By overcoming those challenges and integrating these fields, great potential for NLP and its applications can be unleashed. This paper presents a survey of NLP in cloud computing with a key focus on the comparison of cloud-based NLP services, challenges of NLP and big data while emphasizing the necessity of viable cloud-based NLP services. In the first part of this paper, an overview of NLP is presented by discussing different levels of NLP and components of natural language generation (NLG), followed by the applications of NLP. In the second part, the concept of cloud computing is discussed that highlights the architectural layers and deployment models of cloud computing and cloud-hosted NLP services. In the third part, the field of big data in the cloud is discussed with an emphasis on NLP. Furthermore, information extraction via NLP techniques within big data is introduced.

Blockchain & Multi-Agent System: A New Promising Approach for Cloud Data Integrity Auditing with Deduplication

Recently, data storage represents one of the most important services in Cloud Computing. The cloud provider should ensure two major requirements which are data integrity and storage efficiency. Blockchain data structure and the efficient data deduplication represent possible solutions to address these exigencies. Several approaches have been proposed, some of them implement deduplication in Cloud server side, which involves a lot of computation to eliminate the redundant data and it becomes more and more complex. Therefore, this paper proposed an efficient, reliable and secure approach, in which the authors propose a Multi-Agent System in order to manipulate deduplication technique that permits to reduce data volumes thereby reduce storage overhead. On the other side, the loss of physical control over data introduces security challenges such as data loss, data tampering and data modification. To solve similar problems, the authors also propose Blockchain as a database for storing metadata of client files. This database serves as logging database that ensures data integrity auditing function.

Exploiting Blockchains to improve Data Upload and Storage in the Cloud

Cloud computing is an information technology that enables different users to access a shared pool of configurable system resources and different services without physically acquiring them. Most industries nowadays such as banking, healthcare and education are migrating to the cloud due to its efficiency of services especially when it comes to data security and integrity. Cloud platforms encounter numerous challenges such as Data deduplication, Data Transmission, Data Integrity, VM Security, Data Availability, Bandwidth usage… etc. In this paper we have adopted the Blockchain technology - which is a relatively new technology - that emerged for the first time as the cryptocurrency Bitcoin and proved its efficiency in securing data and assuring data integrity. It is mostly a distributed public ledger that holds transactions data in case of Bitcoin. In our work blockchains are adopted in a different way than its regular use in bitcoin. Three of the major challenges in Cloud Computing and Cloud services are Data Deduplication, Storage and Bandwidth usage are discussed in this paper.

Security Issues in IoT and Cloud Computing Service Models with Suggested Solutions

Cloud computing is a new model for providing computing services, where computing is delivered as a service over the Internet. Cloud computing is a very popular choice among SMEs because computing services are provided at much lower prices compared to their own IT infrastructure. In the cloud computing model, computing services and data storage are outsourced to cloud service providers. Customers do not have full control over applications and their data. Therefore, an added overhead of security risks comes along, and the security of data becomes the primary concern for cloud customers when considering cloud services. This paper explores various deployment models of cloud computing services and IoT (Internet of Things), figures out associated data security issues with them, and suggests a metric-based solution to assess security provided by cloud services.

A Case Study of Cloud Computing Service Models for the General Computing Environment in a University

Cloud computing technology has emerged as the basic infrastructure technology in the 4th industrial revolution and has been utilized in various fields. Therefore, the authors analyze the requirements of academic and administrative affairs, education affairs, and affiliated institutions in the university. Proposed cloud computing service models in the universities are implemented using an OpenStack in CentOS 7.3 and Windows. To evaluate the performance, the author compares various performance factors including launching time, booting time, ping time, and software installation time. Lastly, the economic benefits in the university will be evaluated by comparing the adoption cost, maintenance cost, and total cost between the existing client-server system and cloud computing system. Future work aims to implement a cloud computing system that can be applied to the entire university. Finally, based on these studies, the authors will implement a regional base cloud system that can connect all universities in the region with one cloud computing system.

Analysis of Data Security and Privacy Protection in Cloud Computing Services

Abstract: Cloud computing will become a successful paradigm for statistics computing and storage. Increasing worries about information security and privateness in the cloud, however, have emerged. Ensuring protection and privacy for facts management and question processing in the cloud is imperative for better and broader uses of the cloud. Usually cloud computing services are delivered with the aid of a third celebration issuer who owns the infrastructure. It blessings to point out but a few encompass scalability, resilience, flexibility, effectivity and outsourcing non- core activities. Cloud computing provides an progressive commercial enterprise model for corporations to undertake IT services without upfront investment. Despite the possible positive aspects achieved from the cloud computing, the businesses are sluggish in accepting it due to safety issues and challenges related with it. Security is one of the primary problems which bog down the growth of cloud. The notion of handing over essential facts to any other organization is worrisome; such that the buyers want to be vigilant in understanding the risks of records breaches in this new environment. This paper introduces a specific evaluation of the cloud computing safety problems and challenges focusing on the cloud computing. Keywords: Cloud Computing, Privacy, data security, Monotoring

Migration of a Research Library's ICT-Based Services to a Cloud Platform

Libraries have been at the forefront in adopting emerging technologies to manage the library’s operations and provide information services to the user community they serve. With the emergence of cloud computing (CC) technology, libraries are exploring and adopting CC service models to make their own services more efficient, reliable, secure, scalable, and cost-effective. In this article, the authors share their experience migrating some of the library’s locally hosted ICT-based services onto the Microsoft Azure cloud platform. The migration of services to a cloud platform has helped the library significantly reduce the downtime of its services due to power or network or system outages.

The influence of dependability in cloud computing adoption

Cloud computing (CC) has many benefits, so its use has spread rapidly, particularly in the business sector. An important consideration in the acceptance of CC is whether the CC system is dependable, and it can differ among industry and service type. However, little research has considered the effect of dependability (composed of availability, reliability, security, maintainability) on CC acceptance. Especially, group comparisons between high IT-intensive (Hi-ITi) and low IT-intensive (Lo-ITi) industries have not been reported, nor have comparisons between software-as-a-service (SaaS) and platform-as-a-service (PaaS)/infrastructure-as-a-service (IaaS). This study aims to explore how the dependability of CC affects users’ intent to accept it, with focus on how this intent is affected by intensity of IT use (by industry) and by the type of CC service used. To validate the proposed model, this study applied structural equation modeling and conducted multi-group analysis. A total of 230 business managers in South Korea represent the sample for our study. For the full dataset, the three dependability attributes (availability, reliability, security) do not affect the usefulness of CC, but do affect the ease of use of CC. The usefulness of CC is a determinant for positive intention to accept CC, whereas the ease of use of CC is not. Maintainability is the strongest determinant of CC adoption for the full dataset, and for all individual groups, except those that use SaaS. For Hi-ITi and Lo-ITi industries, results show that managers show no differences in their perceptions of the effect of dependability attributes (availability, reliability, security) on the usefulness and the ease of CC. The absence of such a difference in managers’ perception also applies to the relationship between two core variables of TAM (i.e., perceived usefulness, perceived ease of use) and behavioral intention to accept CC. For SaaS and PaaS/IaaS, managers have different perceptions of security on the usefulness of CC, and the effect of the usefulness of CC on the intention to accept CC. The findings can provide academic researchers and industry practitioners with a differentiated and in-depth perspective on the understanding and the spread of CC.

Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

Attacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed in this study (CCAID: cloud computing, attack, and intrusion detection) aims to improve the performance of intrusion detection systems (IDS) operating in CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). The selected features used to train the ML (machine learning) model of the intrusion detection component comprised a binary detection engine for the identification of malicious/attack packets and a multiclassification detection engine for the identification of the type of attack. Both detection engines deploy ensemble classifiers. Experiments were carried out using the NSL KDD dataset. The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). These results compare very favourably with the results reported in the literature and indicate the feasibility of the framework implementation.

Use of cloud computing services in micro and small enterprises: a fit perspective

Micro and Small Enterprises (MSEs) require the right Organizational Information and Communication Technology Infrastructure (OICTI) to provide them with the essential functionalities to support their business processes. In order to have the right OICTI, MSEs are expected to make huge investments in financial and human resources, to purchase, deploy and maintain Information and Communications Technologies (ICTs). Cloud Computing Services (CCS) avail OICTI, for a fraction of the resources required to own private ICT infrastructure. The purpose of this study was to assess the fit between the MSEs’ OICTI needs and the information processing capabilities of CCS and how this fit influences CCS adoption in the Kenyan MSEs. The research was quantitative in nature, in which, a theory-based model grounded on the task technology fit, organization information processing and technology-organization-environment theories, was developed and validated. Study findings suggest strong correlations between MSEs’ tasks and CCS and between MSEs’ information processing needs and CCS information processing capabilities. Other factors identified as influencing CCS procurement are affordability and the relationship between the CCS providers and the MSEs. The study contributes to the academic literature on technology adoption in MSEs by showing that there exists a multidimensional fit between CCS and MSEs’ OICTI requirements.

Challenges and opportunities for the energy management of sustainable data centers in smart grids

With the increase of cloud computing and internet services, data centers are emerging to satisfy the requirement, leading to incremental energy consumption demand and emissions of green house gases. Thus, integration of renewable energies with the traditional power grid is preferred to reduce the environmental impact and increase energy efficiency, which lead to a demand of energy management strategies to coordinate the energy demand and generation. In this paper, we review the challenges for the sustainable data centers in smart grids with regards to energy management strategies, integration with renewable energies and cyber-attacks and propose possible solutions. Through the analysis of the data centers from the perspective of both smart grids level and micro-grid level, the research challenges and potential research directions in the energy management of sustainable data centers have been discussed.

A Review on Cloud Computing Technology, Cloud Deployment and Service Delivery Models

Abstract: With the advent of Cloud Computing technology, organizations are rapidly shifting their IT systems from traditional client/server to cloud computing model where everything is offered as a service whether it’s a software, hardware or storage capacity. Cloud computing uses the concept of pay as per use basis where users do not need to pay for infrastructure, installation and its maintenance. Anyone can access the desired service from the cloud anytime, anywhere in the world on demand basis. The utilization of this innovative technology makes collaboration easier among companies or organizations and has the potential to create financial and operational benefits. This study helps organizations and individuals understand how cloud computing can provide them with reliable, personalized and cost-efficient services in a wide variety of applications. Keywords: Cloud Computing; Public Cloud; Private Cloud; Hy

Simplified and Detailed Analysis of Data Center Particulate Contamination at Server and Room Level Using Computational Fluid Dynamics

Abstract Continuous rise in cloud computing and other web-based services propelled the data center proliferation seen over the past decade. Traditional data centers use vapor-compression-based cooling units that not only reduce energy efficiency but also increase operational and initial investment costs due to involved redundancies. Free air cooling and airside economization can substantially reduce the information technology equipment (ITE) cooling power consumption, which accounts for approximately 40% of energy consumption for a typical air-cooled data center. However, this cooling approach entails an inherent risk of exposing the ITE to harmful ultrafine particulate contaminants, thus, potentially reducing the equipment and component reliability. The present investigation attempts to quantify the effects of particulate contamination inside the data center equipment and ITE room using computational fluid dynamics (CFD). An analysis of the boundary conditions to be used was done by detailed modeling of ITE and the data center white space. Both two-dimensional and three-dimensional simulations were done for detailed analysis of particle transport within the server enclosure. An analysis of the effect of the primary pressure loss obstructions like heat sinks and dual inline memory modules inside the server was done to visualize the localized particle concentrations within the server. A room-level simulation was then conducted to identify the most vulnerable locations of particle concentration within the data center space. The results show that parameters such as higher velocities, heat sink cutouts, and higher aspect ratio features within the server tend to increase the particle concentration inside the servers.

RiboChat: a chat-style web interface for analysis and annotation of ribosome profiling data.

The increasing volume of ribosome profiling (Ribo-seq) data, computational complexity of its data processing and operational handicap of related analytical procedures present a daunting set of informatics challenges. These impose a substantial barrier to researchers particularly with no or limited bioinformatics expertise in analyzing and decoding translation information from Ribo-seq data, thus driving the need for a new research paradigm for data computation and information extraction. In this knowledge base, we herein present a novel interactive web platform, RiboChat (https://db.cngb.org/ribobench/chat.html), for direct analyzing and annotating Ribo-seq data in the form of a chat conversation. It consists of a user-friendly web interface and a backend cloud-computing service. When typing a data analysis question into the chat window, the object-text detection module will be run to recognize relevant keywords from the input text. Based on the features identified in the input, individual analytics modules are then scored to find the perfect-matching candidate. The corresponding analytics module will be further executed after checking the completion status of the uploading of datasets and configured parameters. Overall, RiboChat represents an important step forward in the emerging direction of next-generation data analytics and will enable the broad research community to conveniently decipher translation information embedded within Ribo-seq data.

Assessment of readiness for launching cloud computing services: The case of Ethio‐Telecom in Ethiopia

AbstractA nation's telecom system plays an important role in its socio‐economic development. This case study assesses the technical and organizational readiness of Ethio‐Telecom to implement Cloud Computing services to serve Africa's fast‐growing demand for Cloud Computing. The study used the STOPE (Strategy, Technology, Organization, People, and Environment) model as a theoretical base. A structured survey questionnaire was used to gather data from 100 mid and operational level employees. An interview was conducted with top‐level officials to elicit information about the strategic direction of the company. We used structural equation modeling software for analyses and model testing. The SMART PLS results point to the appropriateness of the STOPE model for the assessment of Ethio‐Telecom's Cloud Computing readiness. Additional analysis revealed that Ethio‐Telecom's Strategy, Technology, Organization, and Environmental dimensions need only minor improvements while the People dimension requires significant improvements to successfully implement Cloud Computing services. We mapped interview and document analysis data to the STOPE model constructs. This qualitative approach further validated the results we got in the quantitative analysis. We believe the mixed methodology, SMART PLS for quantitative analysis and e‐Learning Readiness Survey to determine individual dimension readiness, and quantitative analysis, used in this study have added to the body of knowledge.

Security Utilization of Cloud Computing in The World of Business For Small Medium Enterprises (SMEs)

At this time the business world began to compete globally with the aim of satisfying customers, so companies need access and information that is fast and accurate. To access information quickly, companies need to build information and communication technology infrastructure which is certainly not easy with the need for a large enough capital, besides that there is cloud computing which can be an alternative. The progress of information and communication technology that we have achieved now has really been recognized and felt to provide a lot of convenience and comfort for human life. Information Technology (IT) solutions, cloud computing, are now starting to be discussed a lot by business people, including in Indonesia. Even though it's popular, it doesn't mean cloud computing is easily accepted. Cloud computing will actually save business operations much more, compared to using traditional methods among Small and Medium Enterprises (SMEs). There are various kinds of services provided by cloud computing, including hardware, infrastructure, platforms, and applications. Cloud computing has considerable benefits, this is because the services of cloud computing can reduce computing costs, increase reliability and provide considerable opportunities for the infrastructure world. The purpose of this research is to increase business competitiveness in the business sector by using the Salesfoorce App Cloud application. The results of this application can make business models more flexible and know market needs and consumer needs.

Federated Learning in Edge Computing: A Systematic Survey.

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.

Analysis of Critical Factors in Manufacturing by Adopting a Cloud Computing Service

The advantages of a cloud computing service are cost advantages, availability, scalability, flexibility, reduced time to market, and dynamic access to computing resources. Enterprises can improve the successful adoption rate of cloud computing services if they understand the critical factors. To find critical factors, this study first reviewed the literature and established a three-layer hierarchical factor table for adopting a cloud computing service based on the Technology-Organization-Environment framework. Then, a hybrid method that combines two multi-criteria decision-making tools—called the Fuzzy Analytic Network Process method and the concept of VlseKriterijumska Optimizacija I Kompromisno Resenje acceptable advantage—was used to objectively identify critical factors for the adoption of a cloud computing service, replacing the subjective decision of the authors. The results of this study determined five critical factors, namely data access security, information transmission security, senior management support, fallback cloud management, and employee acceptance. Finally, the paper presents the findings and implications of the study.