Cloud Customers Research Articles

Decoding the Cloud Giants: A Comparison of AWS, Azure and GCP

The adoption of cloud services by companies and organizations is increasingly becoming essential for enhancing competitive performance in today's business environment. Cloud services represent a relatively new technological advancement. However, selecting the optimal cloud service to meet specific requirements remains one of the most significant challenges. Major commercial entities, including Amazon AWS, Microsoft Azure, and Google Cloud, provide a variety of cloud services through dedicated, reliable, and cost-effective web applications. These cloud services attract a diverse range of users and organizations across various sectors, such as healthcare, business, and education. This article aims to introduce cloud customers to the most prominent cloud services. Additionally, we explore the concept of cloud computing, defined as the delivery of computing resources over the Internet. Leading cloud service providers such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) offer on-demand cloud solutions, including processing power, memory, and databases, alleviating the need for users to invest in, operate, and maintain physical computers, hardware, and servers. This study compares the performance and services of three major cloud platforms—Google Cloud Platform, Amazon AWS, and Microsoft Azure—as well as their architectures and types of cloud services. Cloud technology allows resources to be dynamically scaled without the need for significant changes in infrastructure, additional staffing, or new software development.

A lightweight performance proxy for deep‐learning model training on Amazon SageMaker

SummaryCloud computing has become popular for training deep‐learning (DL) models, avoiding the costs of acquiring and maintaining on‐premise systems. SageMaker is a cloud service that automates the execution of DL workloads. Its features include automatic hyperparameter optimization and use of spot instances. Nonetheless, it does not assist in selecting the right instance type for a workload. In public clouds, rent price depends on the configuration of the chosen instance type. Advanced and faster instances are typically more expensive, but not always the best choice. To select the optimal instance type, users must compare the workload's relative performance (and hence cost) on several candidates. Building on the execution profiles of multiple DL applications, we model the performance and cost of training DL applications on SageMaker and propose a lightweight technique to estimate these at low temporal and monetary cost. This method is a performance proxy that can be used to replace more expensive performance measurement procedures. So, it could speed up any technique that relies on such measurements. We show how it can help cloud customers seeking suitable instance types to train DL models, and that it can accurately predict the performance of different instance types when training these models on SageMaker.

Optimizing Task Scheduling in Cloud Data Centres with Dynamic Resource Allocation Using Genetic Algorithm (TSOGA)

Nowadays, Massive business applications are increasingly giving attention to cloud computing data centres because of its high potential, adaptability, and efficiency in supplying several sources of both software and hardware to support networked consumers. The criteria for autonomy of virtual machines necessitate a flexible resource allocation strategy for Virtual Machines (VMs) .The majority of resource utilization models were inaccurate, making it impossible to determine the virtual machine's energy usage directly from the hardware. Due to the size of modern data centres and the constantly changing character of their resource supply, efficient scheduling solutions must be developed to oversee these resources and meet the objectives of both cloud service providers and cloud customers. Hence an algorithm called Task Scheduling Optimization based Genetic Algorithm (TSOGA) has been proposed to dynamically allocate the resources in pursuit of scheduling the tasks in cloud data centers. The proposed module initially focuses on task scheduling process, followed by optimized running time of task execution. For data centres with dynamic resource allocation, the goal of TSOGA is to efficiently assign jobs to resources while minimizing execution time and optimizing resource utilization. Thus, to manage the data centres while achieving high levels of efficiency in resource allocation, we constructed a virtual node for our research. Incorporation of Genetic Algorithm is to determine an ideal or nearly ideal schedule for carrying out tasks using the available resources while taking into account a variety of restrictions and goals, such as minimizing execution and waiting time of task during dynamic scheduling process and efficient resource utilization.

Privacy-Preserving Image Storage on Cloud Using An Unified Cryptographic Authentication Scheme

With the proliferation of several cutting-edge technologies such as the Artificial Intelligence (AI), and Machine Learning (ML), Internet of Things (IoT), cloud technology is gaining colossal popularity in recent years. Despite the general publicity on the theme across the digital world, defending user data kept in the cloud database is the most decisive problem. Recent potential cyber attacks reveal that storing private images entails more unique care related to other types of information on the cloud. As the cloud customer who has kept their images has no control over their data the cloud service provider has to ensure better security against cyber threats. Cryptography algorithms are the best choice to secure pictorial data in the cloud. These techniques transform images into an inarticulate form to keep confidentiality over undependable and vulnerable social media .In this paper, we aim to propose an approach for improving image security on the cloud using cryptography algorithms. We developed a cohesive approach, called Unified Cryptographic Image Authentication (UCIA) to protect user images on a cloud platform. The proposed UCIA approach includes two phases: (i)UCIA engenders a cipher text through a Data Encryption Standard (DES) by providing a key and a message as input, and (ii)UCIA implements a Twofish algorithm to encipher the pictures by applying cipher text. The enciphered picture data is then stored in the cloud database and can be recovered when the customer requests it. The effectiveness of both enciphering and deciphering procedures are analyzed using the evaluation metrics including time for enciphering, deciphering, cloud storage, and enciphering throughput. Experimental results reveal the better performance and strength of the UCIA approach

Privacy-Preserving Image Storage on Cloud Using An Unified Cryptographic Authentication Scheme

With the proliferation of several cutting-edge technologies such as the Artificial Intelligence (AI), and Machine Learning (ML), Internet of Things (IoT), cloud technology is gaining colossal popularity in recent years. Despite the general publicity on the theme across the digital world, defending user data kept in the cloud database is the most decisive problem. Recent potential cyber attacks reveal that storing private images entails more unique care related to other types of information on the cloud. As the cloud customer who has kept their images has no control over their data the cloud service provider has to ensure better security against cyber threats. Cryptography algorithms are the best choice to secure pictorial data in the cloud. These techniques transform images into an inarticulate form to keep confidentiality over undependable and vulnerable social media .In this paper, we aim to propose an approach for improving image security on the cloud using cryptography algorithms. We developed a cohesive approach, called Unified Cryptographic Image Authentication (UCIA) to protect user images on a cloud platform. The proposed UCIA approach includes two phases: (i)UCIA engenders a cipher text through a Data Encryption Standard (DES) by providing a key and a message as input, and (ii)UCIA implements a Twofish algorithm to encipher the pictures by applying cipher text. The enciphered picture data is then stored in the cloud database and can be recovered when the customer requests it. The effectiveness of both enciphering and deciphering procedures are analyzed using the evaluation metrics including time for enciphering, deciphering, cloud storage, and enciphering throughput. Experimental results reveal the better performance and strength of the UCIA approach.

Optimizing Performance of Cloud Infrastructure Through Effective Resource Scheduling

Cloud computing has emerged as a very promising technology that has garnered significant interest from both industry professionals and academic researchers. Cloud computing service models refer to the various types of services that are provided, including hardware and software infrastructure, platforms for application development, testing, and deployment, as well as enterprise software that is readily available for usage through subscription. Public cloud computing involves the delegation of IT infrastructure, storage, or applications to an external service provider. The presence of a cloud infrastructure also signifies the existence of geographically distributed computing resources. The utilisation of resources in conjunction with cloud computing is not exclusive to large-scale organisations, as it may be employed by entities of any size. Numerous services are offered based on a fee-for-use model, rendering them cost-effective for organisations of various sizes. Cloud service providers are obligated to provide consumers with cloud services on demand, as there is a growing need for such services. This requirement stems from the necessity to decrease the size of large data volumes, which in turn leads to cost savings in maintaining extensive storage systems. The overall effectiveness of cloud computing environments is directly related to the operational performance of cloud infrastructure. This phenomenon holds substantial significance in the realm of optimization, since it enhances the overall efficiency of the underlying cloud architecture. The proposed technique exhibits a significant effectiveness in enhancing cloud performance, as it manifests improvements for both service providers and cloud customers.
 Key Words: Cloud Computing, Task Scheduling, Cloud Infrastructure, Resource Scheduling, Performance Analysis and Infrastructure as Service.

Cloud computing security: a taxonomy, threat detection and mitigation techniques

ABSTRACT Cloud Computing is a rapidly growing emerging technology in the IT environment. Internet-based computing provides services like sharing resources e.g. network, storage, applications and software through the Internet. Cloud customers can access these services according to their need and pay only for those services which they utilize. Almost every industrial sector like banking, retail, education, healthcare, manufacturing and businesses is adopting cloud techniques within their enterprise either privately or in sharing with other enterprises. However, many security issues must be resolved because they are a barrier in adopting this fast-growing technology. This paper reviews various threats on cloud computing like distributed denial of service attack, spoofing attacks (e.g. ARP Spoofing, IP Spoofing, DNS Spoofing), man-in-the middle attack, data loss, data breach, reused IP address attack, malicious insiders, insecure interfaces & APIs etc. These security issues prevent users from adopting cloud services. This study presents various detection and prevention methods to address cloud security concerns. The effectiveness of each strategy is then analyzed based on its attributes, benefits and drawbacks. Deep learning and machine learning algorithms produce more precise findings for both known and unidentified attack patterns.

Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

Cloud Computing (CC) offers abundant resources and diverse services for running a wide range of consumer applications, although it faces specific issues that need attention. Cloud customers aim to choose the most suitable resource that fulfills the requirements of consumers at a fair cost and within an acceptable timeframe; however, at times, they wind up paying more for a shorter duration. Many advanced algorithms focus on optimizing a single variable individually. Hence, an Optimized Resource Allocation in Cloud Computing (ORA-CC) Model is required to achieve equilibrium between opposing aims in Cloud Computing. The ORA-CC study aims to create a task processing structure with the decision-making ability to choose the best resource in real-time for handling diverse and complicated uses on Virtual Computers (VC). It will utilize a Modified Particle Swarm Optimization (MoPSO) method to meet a deadline set by the user. The fitness value is calculated by combining a base value with the enhanced estimation of resources based on the ORA-CC algorithm to create a robust arrangement. The ORA-CC technique's effectiveness is evaluated by contrasting it with a few current multi-objective restrictions applied to machine scheduling strategies utilizing the Cloudsim simulation. The comparison demonstrates that the suggested ORA-CC strategy offers more efficient resource allocation than other techniques.

Re-thinking the allocation of roles under the GDPR in the context of cloud computing

Abstract • Cloud computing is a well-established and flourishing phenomenon, whose key activity on personal data is the storage thereof. This study analyses the allocation of the controller–processor roles under the General Data Protection Regulation (GDPR) to the cloud computing actors in the context of the storage of personal data. It examines the current controller–processor model and suggests a joint controllership interpretation to the EU regulators, as a more appropriate allocation of roles in the context of cloud computing. • We argue that the prevailing controller–processor interpretation stems from the current primacy of the purpose criterion, as well as the recognition of consent as a criterion to allocate controllership. This article suggests that cloud providers control some of the essential means of the storage of personal data, thereby shifting part of the control from cloud customers to cloud providers. • If the joint controllership approach was to be followed by the EU regulators, this article argues that it would better reflect the economic and technical reality, and provide a more appropriate responsibility and liability framework for cloud providers and customers, which in turn would enhance the level of protection that data subjects should benefit from under the GDPR.

Framework for Data Provenance Assurance in Cloud Environment using Ethereum Blockchain

Ensuring secure data provenance is crucial for maintaining accountability and confidentiality in cloud environments. Cloud data provenance involves recording the history of creation and operations performed on cloud data objects. However, establishing trust between cloud customers and service providers remains a challenge, highlighting the need for assured data provenance models in cloud storage. Blockchain technology has emerged as a solution for designing data provenance assurance mechanisms. It provides a decentralized and distributed ledger to record the provenance of digital assets. In this context, we present a blockchain-based framework for ensuring data provenance in cloud storage. Initially, we develop a cloud storage application using OpenStack swift storage. This application caters to the storage needs of university students and faculty while providing data provenance capabilities. Subsequently, we design a data provenance assurance framework for confidential files of users using the Ethereum blockchain. To evaluate the scalability and performance of the proposed framework, we analyze various factors such as transaction throughput, latency, network size, and load on the blockchain network. The performance of the system is compared under two consensus algorithms: Proof of Work and Proof of Authority. By conducting this analysis, we aim to assess the effectiveness and efficiency of the blockchain-based solution in ensuring data provenance in cloud storage environments.

The Story of AWS Glue

AWS Glue is Amazon's serverless data integration cloud service that makes it simple and cost effective to extract, clean, enrich, load, and organize data. Originally launched in August 2017, AWS Glue began as an extract-transform-load (ETL) service designed to relieve developers and data engineers of the undifferentiated heavy lifting needed to load databases, data warehouses, and build data lakes on Amazon S3. Since then, it has evolved to serve a larger audience including ETL specialists and data scientists, and includes a broader suite of data integration capabilities. Today, hundreds of thousands of customers use AWS Glue every month. In this paper, we describe the use cases and challenges cloud customers face in preparing data for analytics and the tenets we chose to drive Glue's design. We chose early on to focus on ease-of-use, scale, and extensibility. At its core, Glue offers serverless Apache Spark and Python engines backed by a purpose-built resource manager for fast startup and auto-scaling. In Spark, it offers a new data structure --- DynamicFrames --- for manipulating messy schema-free semi-structured data such as event logs, a variety of transformations and tooling to simplify data preparation, and a new shuffle plugin to offload to cloud storage. It also includes a Hivemetastore compatible Data Catalog with Glue crawlers to build and manage metadata, e.g. for data lakes on Amazon S3. Finally, Glue Studio is its visual interface for authoring Spark and Python-based ETL jobs. We describe the innovations that differentiate AWS Glue and drive its popularity and how it has evolved over the years.

Reputation based novel trust management framework with enhanced availability for cloud

To select a cloud service provider (CSP) out of immense number of relevant CSPs available, cloud customer (CC) trusts on the reviews provided by the CCs who have used the services provided by CSPs. However, relying on these feedbacks may be hazardous if the review(s) have been deliberately posted with wicked motive. To mitigate the impact of malicious feedbacks, a framework, namely highly available credible cloud trust management framework (HA2CTMF) has been proposed with the intent of revamping the reputation and availability parameters of QoS by evaluating the trust of cloud service providers (CSPs) based on the credibility of feedbacks furnished by CC. HA2CTMF detects collusion and Sybil attacks on the labeled CloudArmor dataset and increases availability of the trust service. Collusion attack has been detected by comparing review count; service count; & mean of reviews with their respective threshold values. Further, Sybil attack has been uncovered with the help of these aspects: deviation from mean of reviews; pattern detection; & comparison of polarity value from both lower & upper threshold values. Additionally, enhanced availability has been ensured by working on disaster management and diminishing repercussions of single point of failure. The results have been accessed on the following parameters: precision; recall; f1-score & availability percentage on the dataset comprising 8774 data points. This framework has exhibited precision of 85.69%, recall of 89.45%, f1-score of 87.53% and availability of 96.71% compared to CloudArmor framework values on the same parameters as: 44.47%; 62.21%; 51.85% & 70.43% respectively. As per the results attained, we conclude that proposed HA2CTMF has demonstrated better outcome.

Multi‐objective hybrid capuchin search with genetic algorithm based hierarchical resource allocation scheme with clustering model in cloud computing environment

Internet of recent decades considered cloud computing as the most effective and distributed platform. It is a comfortable and quick way to access shared resources over the Internet anytime. The major problem cloud customers face while choosing the resources for a particular application is QoS. In the cloud computing environment, various resources need to be effectively allocated on VMs by reducing makespan and synchronously increasing resource utilization. For that, a novel multi-objective hybrid capuchin search with genetic algorithm (MHCSGA) based hierarchical resource allocation is established in this work. MHCSGA optimizes the multi-objective functions like resource utilization, response time, makespan, execution time and throughput. Initially, partitioning around the K-medoids clustering method is utilized to allocate the resources optimally. During clustering, the tasks are divided into two cluster groups then, the optimization is performed to attain an optimal resource allocation process. The experimental setup is executed using the JAVA tool. For the simulation process, the proposed work uses the GWA-T-12 Bitbrains dataset. The makespan achieved by proposed algorithm for 50, 100, 150, and 200 tasks are found to be 10.45, 17.6, 25.67, and 31.34, respectively. The comparison analysis proves that the developed model attains improved performance than the state-of-the-art works.

Cloud Security for Healthcare Services

The primary goal of this paper is to give the audience a set of principles to follow when purchasing cloud services to deliver healthcare services in order to ensure cybersecurity and the security of personal data processing, as well as a clear awareness of the accompanying obligations. The objectives are to provide a landscape of the applicable EU legislative instruments relevant to cloud services in the healthcare sector as well as an overview of the major cybersecurity and data protection challenges related to the security of personal data processing for cloud customers in the healthcare sector.

Performance Framework for Virtual Machine Migration in Cloud Computing

In the cloud environment, the transfer of data from one cloud server to another cloud server is called migration. Data can be delivered in various ways, from one data centre to another. This research aims to increase the migration performance of the virtual machine (VM) in the cloud environment. VMs allow cloud customers to store essential data and resources. However, server usage has grown dramatically due to the virtualization of computer systems, resulting in higher data centre power consumption, storage needs, and operating expenses. Multiple VMs on one data centre manage share resources like central processing unit (CPU) cache, network bandwidth, memory, and application bandwidth. In multi-cloud, VM migration addresses the performance degradation due to cloud server configuration, unbalanced traffic load, resource load management, and fault situations during data transfer. VM migration speed is influenced by the size of the VM, the dirty rate of the running application, and the latency of migration iterations. As a result, evaluating VM migration performance while considering all of these factors becomes a difficult task. The main effort of this research is to assess migration problems on performance. The simulation results in Matlab show that if the VM size grows, the migration time of VMs and the downtime can be impacted by three orders of magnitude. The dirty page rate decreases, the migration time and the downtime grow, and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VM transfer is completed. All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.

Energy efficient cloud computing using secure virtual machine migration: A taxonomy

Numerous cloud service enterprises have launched a broad variety of cloud services. Several businesses and people use these programs on a daily basis. The efficiency of cloud service depends on efficiency of the service providers. It is also important for cloud customers to analyses and review the available cloud resources thoroughly to pick the best cloud service. Secure virtual machine placement is a big issue during the migration of virtual machines in cloud computing. Secure virtual machine migration maximizes the throughput and reliability of the system and reduces Service Level Agreement (SLA) violations. This article focuses on secure virtual machine migration with energy-efficient techniques

Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning

Cloud computing leads to efficient resource allocation for network users. In order to achieve efficient allocation, many research activities have been conducted so far. Some researchers focus on classical optimisation theory techniques (such as multi-objective optimisation, evolutionary optimisation, game theory, etc.) to satisfy network providers and network users’ service-level agreement (SLA) requirements. Normally, in a cloud data centre network (CDCN), it is difficult to jointly satisfy both the cloud provider and cloud customer’ utilities, and this leads to complex combinatorial problems, which are usually NP-hard. Recently, machine learning and artificial intelligence techniques have received much attention from the networking community because of their capability to solve complicated networking problems. In the current work, at first, the holistic utility satisfaction for the cloud data centre provider and customers is formulated as a reinforcement learning (RL) problem with a specific reward function, which is a convex summation of users’ utility functions and cloud provider’s utility. The user utility functions are modelled as a function of cloud virtualised resources (such as storage, CPU, RAM), connection bandwidth, and also, the network-based expected packet loss and round-trip time factors associated with the cloud users. The cloud provider utility function is modelled as a function of resource prices and energy dissipation costs. Afterwards, a Q-learning implementation of the mentioned RL algorithm is introduced, which is able to converge to the optimal solution in an online and fast manner. The simulation results exhibit the enhanced convergence speed and computational complexity properties of the proposed method in comparison with similar approaches from the joint cloud customer/provider utility satisfaction perspective. To evaluate the scalability property of the proposed method, the results are also repeated for different cloud user population scenarios (small, medium, and large).

Cost-Effective Spot Instances Provisioning Using Features of Cloud Markets

Cloud computing offers a variable-cost payment scheme that allows cloud customers to specify the price they are willing to pay for renting spot instances at much lower costs than fixed payment schemes, and depending on the varying demand from cloud customers, cloud platforms could revoke spot instances at any time. To alleviate the effect of spot instance revocations, applications often employ different fault-tolerance mechanisms to minimize or even eliminate the lost work for each spot instance revocation. However, these fault-tolerance mechanisms incur additional overhead related to application completion time and deployment cost. This article proposes a novel cloud market-based approach for provisioning spot instances using features of cloud markets to reduce the deployment cost and completion time of applications. The simulation results show that the approach reduces the deployment cost and completion time compared to approaches based on fault-tolerance mechanisms.

In-Depth Analysis and Systematic Literature Review on Risk Based Access Control in Cloud

Security in Cloud is one of the most foremost and critical feature, which can ensure the confidence of the Scientific community on Cloud environment. With the dynamic and ever changing nature of the Cloud computing environment, static access control models become obsolete. Hence, dynamic access control models are required, which is still an emergent and underdeveloped domain in Cloud security. These models utilize not only access policies but also contextual and real-time information to determine the access decision. Out of these dynamic models the Risk-based Access control model, estimates the security risk value related to the access request dynamically to determine the access decision. The exclusive working pattern of this access control model makes it an excellent choice for dynamically changing environment that rules the cloud’s environment. This paper provides a systematic literature appraisal and evaluation of risk-based access control models to provide a detailed understanding of the topic. The contributions of selected articles have been summarized. The security risks in cloud environment have been reviewed, taking in the account of both Cloud Service Provider and Cloud Customer perspectives. Additionally, risk factors used to build the risk-based access control model were extracted and analyzed. Finally, the risk estimation techniques used to evaluate the risks of access control operations have also been identified.

An optimized radial bias function neural network for intrusion detection of distributed denial of service attack in the cloud

SummaryCloud system is a set of resources meant to provide cloud customers with on‐demand services. Accessibility to cloud environment is provided through internet services, making data stored on cloud more accessible to attackers internally and externally. Several intrusion detection systems and authentication methods have been developed in previous studies to identify the intruders, however they are generally unsuccessful with certain drawbacks. Numerous existing researchers have focused on machine learning techniques for identifying intrusions. This article intends to introduce a new intrusion detection model in the cloud and it involves three processes like preprocessing, feature extraction and detection. Initially, preprocessing takes place and the preprocessed data are subjected to feature extraction process. The flow‐based features, statistical, and higher‐order statistical features with improved holoentropy features, and the technical indicators are extracted. After extracting the features, they are subjected to a detection process, where the optimized radial bias function neural network (RBF‐NN) is exploited. For precise detection, the weight of RBF‐NN is tuned optimally by (harmonic mean based poor and rich optimization) HMPRO Algorithm. The main aim of the proposed model is to detect attacks in the cloud. Finally, the performance of the presented scheme is computed over the existing approaches using the CICDDoS2019 and UNSW‐NB_15 dataset in terms of different metrics.