Cloud Vendors Research Articles

Big Data Analytics in the Cloud: A Survey of Architectures and Technologies

In the contemporary generation of burgeoning records, the combination of Big Data analytics with cloud computing has emerged as a paradigm-transferring pressure, facilitating scalable and efficient processing of big datasets. This review paper gives an intensive survey of architectures and technologies that form the bedrock of Big Data analytics inside cloud environments. Tracing the evolution from conventional records processing to dispensed paradigms, the survey explores key architectures, inclusive of Lambda, Kappa, and serverless, shedding mild on their components and scalability attributes. A specified examination of cloud-primarily based Big Data frameworks together with Apache Hadoop and Apache Spark, together with managed services from principal cloud vendors, gives insights into the various alternatives to be had. The position of cloud-local garage answers, data control techniques, and strategies for scalability and overall performance optimization are dissected. Security and privacy issues in cloud-primarily based Big Data analytics are scrutinized, encompassing encryption mechanisms and compliance frameworks. The evaluate contemplates the challenges inherent inside the area and envisions futureinstructions, which includes hybrid cloud architectures and edge computing integration. Industry case studies illustrate practical applications across finance, healthcare, and e-commerce. The end synthesizes key findings, emphasizing the transformative effect of cloud-based totally Big Data analytics on selection-making and innovation. This complete survey serves as a precious resource for researchers, practitioners, and decision-makers navigating the dynamic intersection of Big Data analytics and cloud computing.

CLOUD COMPUTING PRACTICES AND PERCEIVED BENEFITS BY SMES IN MALAYSIA: SOME EMPIRICAL EVIDENCE

Cloud computing can offer many benefits to small and medium-sized enterprises (SMEs) as cloud services can be utilized by SMEs by leasing the services from cloud vendors. Although still lagging behind the larger counterparts, more SMEs are starting to adopt cloud services, particularly in the developed countries. SMEs in Malaysia are also following suit, however, not much empirical studies have been conducted in a comprehensive manner to provide insights on the status and practices of cloud adoption among SMEs in Malaysia. The purpose of this study is to fill this gap. Using the survey method, a total of 387 responses were used for the descriptive analysis. The findings indicate that most Malaysia small businesses in both service and manufacturing sectors have substantial experience with basic cloud services and adopt similar practices like SMEs in the developed nations. The implication of the findings is that Malaysia SMEs are catching up with the global trend of utilizing cloud computing to improve the competitiveness of their business.

Intelliquip acquired by FPX

FPX, the B2B selling cloud vendor, has acquired Intelliquip, the front-end sales CPQ (Configure, Price and Quote) solutions developer for the fluid handling equipment industry.

Intelliquip acquired by FPX

FPX, the B2B selling cloud vendor, has acquired Intelliquip, the front-end sales CPQ (Configure, Price and Quote) solutions developer for the fluid handling equipment industry.

Iprivacy-Performance Measurement of Encrypted Image Over Mobile Cloud

Basically, people can use mobile phones to capture images and upload to cloud storage. While uploaded the data to cloud especially image, untrusted third party cloud vendors may pass to unauthorised users for their profit. And maintaining im-age Privacy is the major current issue in the cloud storage and resource-limited mobile devices. Due to the overcome of these issues, the proposed work introduced the algorithm Privacy-Preserving algorithm for Image Encryption (PPIE) to secure the encrypted image of mobile on cloud. The proposed architecture is handled by three processes such as Divide, Chunk group and Scramble (DCS) for fast execution. For achieving the high per-formance of the mobile system, the proposed algorithm reduced the encryption time to speed up the mobile system with limited mobile resources. The main highlights are keeping metadata on mobile rather than cloud. Even cloud service provider cannot able to retain the original image. The performance measures of PPIE, by various JPEG images, are calculated by various metrics such as throughput, Key sensitivity, Low Complexity, Processing Time and Time Consumption. It was proved to be reduced by 50% of time consumption while compared to AES. The proposed work may prevent users from accessing of private images.

Toward Designing Cost-Optimal Policies to Utilize IaaS Clouds with Online Learning

Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instances and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51 percent when spot and on-demand instances are considered and of up to 43.74 percent when self-owned instances are considered, compared to previously proposed or intuitive policies.

Cloud Resource Management for Analyzing Big Real-Time Visual Data from Network Cameras

Thousands of network cameras stream real-time visual data for different environments, such as streets, shopping malls, and natural scenes. The big visual data from these cameras can be useful for many applications, but analyzing the large quantities of data requires significant amounts of resources. These resources can be obtained from cloud vendors offering cloud instances (referred to as instances in this paper) with different capabilities and hourly costs. It is a challenging problem to manage cloud resources to reduce the cost for analyzing the big real-time visual data from network cameras while meeting the performance requirements. That is because the problem is affected by many factors related to the analysis programs, the cameras, and the instances. This paper proposes a cloud resource manager (referred to as manager in this paper) that aims at solving this problem. The manager estimates the resource requirements of analyzing the data stream from each camera, formulates the resource allocation problem as a 2D vector bin packing problem, and solves it using a heuristic algorithm. The resource manager monitors the allocated instances; it allocates more instances if needed and deallocates existing instances to reduce the cost if possible. The experiments show that the resource manager is able to reduce up to 60 percent of the overall cost. The experiments use multiple analysis programs, such as moving objects detection, feature tracking, and human detection. One experiment analyzes more than 97 million images (3.3 TB of data) from 5,310 cameras simultaneously over 24 hours using 15 Amazon EC2 instances costing $188.

FPGA and GPU-based acceleration of ML workloads on Amazon cloud - A case study using gradient boosted decision tree library

Cloud vendors such as Amazon (AWS) have started to offer FPGAs in addition to GPUs and CPU in their computing on-demand services. In this work we explore design space trade-offs of implementing a state-of-the-art machine learning library for Gradient-boosted decision trees (GBDT) on Amazon cloud and compare the scalability, performance, cost and accuracy with best known CPU and GPU implementations from literature. Our evaluation indicates that depending on the dataset, an FPGA-based implementation of the bottleneck computation kernels yields a speed-up anywhere from 3X to 10X over a GPU and 5X to 33X over a CPU. We show that smaller bin size results in better performance on a FPGA, but even with a bin size of 16 and a fixed point implementation the degradation in terms of accuracy on a FPGA is relatively small, around 1.3%–3.3% compared to a floating point implementation with 256 bins on a CPU or GPU.

RADON: rational decomposition and orchestration for serverless computing

Emerging serverless computing technologies, such as function as a service (FaaS), enable developers to virtualize the internal logic of an application, simplifying the management of cloud-native services and allowing cost savings through billing and scaling at the level of individual functions. Serverless computing is therefore rapidly shifting the attention of software vendors to the challenge of developing cloud applications deployable on FaaS platforms. In this vision paper, we present the research agenda of the RADON project (http://radon-h2020.eu), which aims to develop a model-driven DevOps framework for creating and managing applications based on serverless computing. RADON applications will consist of fine-grained and independent microservices that can efficiently and optimally exploit FaaS and container technologies. Our methodology strives to tackle complexity in designing such applications, including the solution of optimal decomposition, the reuse of serverless functions as well as the abstraction and actuation of event processing chains, while avoiding cloud vendor lock-in through models.

Serverless Computing: An Investigation of Deployment Environments for Web APIs

Cloud vendors offer a variety of serverless technologies promising high availability and dynamic scaling while reducing operational and maintenance costs. One such technology, serverless computing, or function-as-a-service (FaaS), is advertised as a good candidate for web applications, data-processing, or backend services, where you only pay for usage. Unlike virtual machines (VMs), they come with automatic resource provisioning and allocation, providing elastic and automatic scaling. We present the results from our investigation of a specific serverless candidate, Web Application Programming Interface or Web API, deployed on virtual machines and as function(s)-as-a-service. We contrast these deployments by varying the number of concurrent users for measuring response times and costs. We found no significant response time differences between deployments when VMs are configured for the expected load, and test scenarios are within the FaaS hardware limitations. Higher numbers of concurrent users or unexpected user growths are effortlessly handled by FaaS, whereas additional labor must be invested in VMs for equivalent results. We identified that despite the advantages serverless computing brings, there is no clear choice between serverless or virtual machines for a Web API application because one needs to carefully measure costs and factor-in all components that are included with FaaS.

A trust management framework for clouds

In today’s cloud computing platforms, more and more users are now working or collaborating in the multi-cloud environment, in which collaborators, clouds, computing nodes may belong to different institutions or organizations. Those different organizations might have their policies. Security is still a big concern in cloud computing. To help cloud vendors and customers to detect and prevent from being affected by potential attacks, we propose a trust management framework. We consider link/flow’s level trust, node’s level trust, and task/mission’s level trust.We introduced a new security metric trustability (trust–reliability) and a new algorithm to calculate it. Trustability measures how much a system can be trusted under a specific attack vector. Trustability can be used to explore the design space of resource configuration in order be able to choose the right trade-off between trustability and cost of redundancy. We show that our trust management framework can guide the administrators and customers to make decisions. For example, based on the real-time trust information, cloud administrators can migrate tasks from suspect nodes to trustworthy nodes, dynamically allocate a resource, and manage the trade-off between the degree of redundancy and the cost of the resource.

A Scientific Decision Framework for Cloud Vendor Prioritization under Probabilistic Linguistic Term Set Context with Unknown/Partial Weight Information

With the tremendous growth of Cloud Vendors, Cloud vendor (CV) prioritization is a complex decision-making problem. Previous studies on CV selection use functional and non-functional attributes, but do not have an apt structure for managing uncertainty in preferences. Motivated by this challenge, in this paper, a scientific framework for prioritization of CVs is proposed, which will help organizations to make decisions on service usage. Probabilistic linguistic term set (PLTS) is adopted as a structure for preference information, which manages uncertainty better by allowing partial information ignorance. Decision makers’ (DMs) relative importance is calculated using the programming model, by properly gaining the advantage of the partial knowledge and attributes, the weights are calculated using the extended statistical variance (SV) method. Further, DMs preferences are aggregated using a hybrid operator, and CVs are prioritized, using extended COPRAS method under the PLTS context. Finally, a case study on CV prioritization is provided for validating the scientific framework and the results are compared with other methods for understanding the strength and weakness of the proposal.

Data De-Duplication Using SHA (Secure Hash Algorithm)

Now-a-days the number of users using cloud storage has increased so that the data stored has been increased in exponential rates. The data should be secured and the storage should be used efficiently. But a lot of duplicate data is present as two or more users may upload the same data. To make use of the cloud storage efficiently we have to reduce the redundant data hence improving the resources like storage space, disk I/O operations of the cloud vendors. Data De-Duplication is the process to remove redundant data and store only one instance of duplicate data. The objective of the proposed system is to increase the efficient comparison of hash values of a different data blocks and security of data. This paper includes a method for data deduplication using SHA (Secure Hash Algorithm) and AES. SHA is used as it is more secure than other hashing algorithms. The data is encrypted using AES at owner machine itself and using SHA the redundant data will be eliminated.

An empirical study on predicting cloud incidents

With the increasing rate of adoption and growth of cloud computing services, businesses have been shifting their information technology (IT) infrastructure to the cloud. Although cloud vendors promise high availability and reliability of their cloud services, cloud-related incidents involving outages and service disruptions remain a challenge. Understanding cloud incidents and the ability to predict them would be helpful in deciding how to manage and circumvent future incidents. In this study, we propose a hybrid model that employs machine learning and time series methods to forecast cloud incidents. We evaluate the proposed model using a sample of 2261 incidents collected from two cloud providers namely, Netflix and Hulu. Unique to this study is that our model relies solely on historical data that is independent of the underlying cloud infrastructure. Results suggest that the proposed hybrid model outperforms individual forecasting models: neural network, time series and random forest. Results also reveal important temporal insights from the proposed model and highlights the practical relevance of historical data to forecast and manage cloud incidents.

Leveraging business competitiveness by adopting cloud computing in Indonesian creative industries

The purpose of this study is to identify determinants of the cloud computing adoption in creative industries in Indonesia, and the impact of that adoption on business competitiveness. A theoretical model was developed based on the two complementary models, the technology acceptance model (TAM) and technology-organisation-environment (TOE). An e-survey was conducted among firms that had adopted cloud-computing technology in Indonesia creative industries. This study provides sufficient statistical evidence to infer that perceived usefulness, compatibility, security and privacy, management support and competitive pressure were related to the adoption of cloud computing. Cloud vendors must consider incorporating networks that offer more user friendly and affordable solutions to the market. One factor related user friendliness is that the network must be convenient to use without any issues related to performance. Solutions tied to the cloud should be cost effective for small and medium-sized businesses.

Leveraging business competitiveness by adopting cloud computing in Indonesian creative industries

The purpose of this study is to identify determinants of the cloud computing adoption in creative industries in Indonesia, and the impact of that adoption on business competitiveness. A theoretical model was developed based on the two complementary models, the technology acceptance model (TAM) and technology-organisation-environment (TOE). An e-survey was conducted among firms that had adopted cloud-computing technology in Indonesia creative industries. This study provides sufficient statistical evidence to infer that perceived usefulness, compatibility, security and privacy, management support and competitive pressure were related to the adoption of cloud computing. Cloud vendors must consider incorporating networks that offer more user friendly and affordable solutions to the market. One factor related user friendliness is that the network must be convenient to use without any issues related to performance. Solutions tied to the cloud should be cost effective for small and medium-sized businesses.

An Exploratory Study on Use of Cloud Computing for Talent Acquisition in Health Industry

Talent acquisition is a long-term strategy to make hiring process more effective and efficient. It is an important task in every organization to succeed their operations in the competitive environment. Cloud based technology is the emerging practice which solves many problems in real time works especially human resource management. Many health industry experts and human resource managers trust the cloud technology that it can develop the services and research of health care and assist to find the talent candidate. The main objectives of the study are a) to study the invasion of cloud technology and its benefits in talent acquisition and b) to analyze the recent technological changes as well as to identify and overcome the challenges in the health industry in talent acquisitions process. Secondary data was collected from web sources and journals. Interviews with human resource managers of health sectors and cloud vendors were conducted during December 2018 to identify their opinions towards use of cloud in talent acquisition.

An intelligent water drops-based approach for workflow scheduling with balanced resource utilisation in cloud computing

The problem of finding optimal solutions for scheduling scientific workflows in cloud environment has been thoroughly investigated using various nature-inspired algorithms. These solutions minimise the execution time of workflows, however may result in severe load imbalance among Virtual Machines (VMs) in cloud data centres. Cloud vendors desire the proper utilisation of all the VMs in the data centres to have efficient performance of overall system. Thus, load balancing of VMs becomes an important aspect while scheduling tasks in cloud environment. In this paper, we propose an approach based on Intelligent Water Drops (IWD) algorithm to minimise the execution time of workflows while balancing the resource utilisation of VMs in cloud computing environment. The proposed approach is compared with a variety of well-known heuristic and meta-heuristic techniques using three real-time scientific workflows, and experimental results show that the proposed algorithm performs better than these existing techniques in terms of makespan and load balancing.

Pricing policy for a cloud registration service with a novel cloud architecture

In order to attract more users, cloud vendors always provide free service in many cloud applications. If a Non-VIP (Non-Very Important Person) user is satisfied with the free cloud service, the user may well register as a VIP (Very Important Person) user. In this paper, we focus on the registration process. In order to provide a timely registration service, we propose a novel cloud architecture where free service and registration service are deployed on the same VM (Virtual Machine). We present a multiple-server queueing model with a second optional service for analyzing the stochastic behavior of cloud users. By establishing a two-dimensional Markov chain, we derive the steady-state distribution of the queueing model. We construct an individual benefit function to investigate the Nash equilibrium arrival rate of anonymous users, and propose a new intelligent searching algorithm to obtain the socially optimal arrival rate of anonymous users. For coinciding these two arrival rates of anonymous users, we impose an appropriate registration fee with a pricing policy.

New solutions for exposing clustered applications deployed in the cloud

Hosting applications in the cloud has become very popular. In this context, automatic application scaling, health checking and bootstrapping required services has never been easier. However, there are some types of services that require direct communication between a server and a client, bypassing the cloud. Popular examples include data grids (distributed caching solutions) and gaming servers. Most of these services use custom network protocols based on TCP or UDP transport, along with client-side load balancing, for optimizing network traffic. Unfortunately, most cloud vendors use HTTP-based reverse proxies and are unable to route custom protocols effectively. The major contribution of this paper is a proposed method for using a single load balancer per application instance (replica) to solve the problem of effective communication between a clustered application deployed within the cloud, and a client hosted in the outside world.