scholarly journals Integration Model between Heterogeneous Data Services in a Cloud

2021 ◽  
Vol 27 (4) ◽  
pp. 387-412
Author(s):  
Marcelo Aires Vieira ◽  
Elivaldo Lozer Fracalossi Ribeiro ◽  
Daniela Barreiro Claro ◽  
Babacar Mane
Keyword(s):  
Heterogeneous Data ◽  
Cloud Services ◽  
Test Case ◽  
Integration Model ◽  
Data Services ◽  
On Demand ◽  
Database As A Service ◽  
And Performance ◽  
Data As A Service ◽  
Integrate Data

With the growth of cloud services, many companies have begun to persist and make their data available through services such as Data as a Service (DaaS) and Database as a Service (DBaaS). The DaaS model provides on-demand data through an Application Programming Inter- face (API), while DBaaS model provides on-demand database management systems. Different data sources require efforts to integrate data from different models. These model types include unstructured, semi-structured, and structured data. Heterogeneity from DaaS and DBaaS makes it challenging to integrate data from different services. In response to this problem, we developed the Data Join (DJ) method to integrate heterogeneous DaaS and DBaaS sources. DJ was described through canonical models and incorporated into a middleware as a proof-of-concept. A test case and three experiments were performed to validate our DJ method: the first experiment tackles data from DaaS and DBaaS in isolation; the second experiment associates data from different DaaS and DBaaS through one join clause; and the third experiment integrates data from three sources (one DaaS and two DBaaS) based on different data type (relational, NoSQL, and NewSQL) through two join clauses. Our experiments evaluated the viability, functionality, integration, and performance of the DJ method. Results demonstrate that DJ method outperforms most of the related work on selecting and integrating data in a cloud environment.

scholarly journals HPC Cloud Architecture to Reduce HPC Workflow Complexity in Containerized Environments

2021 ◽  
Vol 11 (3) ◽  
pp. 923
Author(s):  
Guohua Li ◽  
Joon Woo ◽  
Sang Boem Lim
Keyword(s):  
High Performance ◽  
Cloud Services ◽  
Workload Management ◽  
Job Management ◽  
Security Issues ◽  
Cloud Architecture ◽  
Management Efficiency ◽  
Complexity Problem ◽  
And Performance ◽  
Hpc Cloud

The complexity of high-performance computing (HPC) workflows is an important issue in the provision of HPC cloud services in most national supercomputing centers. This complexity problem is especially critical because it affects HPC resource scalability, management efficiency, and convenience of use. To solve this problem, while exploiting the advantage of bare-metal-level high performance, container-based cloud solutions have been developed. However, various problems still exist, such as an isolated environment between HPC and the cloud, security issues, and workload management issues. We propose an architecture that reduces this complexity by using Docker and Singularity, which are the container platforms most often used in the HPC cloud field. This HPC cloud architecture integrates both image management and job management, which are the two main elements of HPC cloud workflows. To evaluate the serviceability and performance of the proposed architecture, we developed and implemented a platform in an HPC cluster experiment. Experimental results indicated that the proposed HPC cloud architecture can reduce complexity to provide supercomputing resource scalability, high performance, user convenience, various HPC applications, and management efficiency.

scholarly journals Context aware benchmarking and tuning of a TByte-scale air quality database and web service

2021 ◽  
Author(s):  
Clara Betancourt ◽  
Björn Hagemeier ◽  
Sabine Schröder ◽  
Martin G. Schultz
Keyword(s):  
Air Quality ◽  
Web Service ◽  
Statistical Processing ◽  
Quality Metrics ◽  
Context Aware ◽  
Near Surface ◽  
On Demand ◽  
Air Quality Measurements ◽  
Present Context ◽  
And Performance

AbstractWe present context-aware benchmarking and performance engineering of a mature TByte-scale air quality database system which was created by the Tropospheric Ozone Assessment Report (TOAR) and contains one of the world’s largest collections of near-surface air quality measurements. A special feature of our data service https://join.fz-juelich.de is on-demand processing of several air quality metrics directly from the TOAR database. As a service that is used by more than 350 users of the international air quality research community, our web service must be easily accessible and functionally flexible, while delivering good performance. The current on-demand calculations of air quality metrics outside the database together with the necessary transfer of large volume raw data are identified as the major performance bottleneck. In this study, we therefore explore and benchmark in-database approaches for the statistical processing, which results in performance enhancements of up to 32%.

scholarly journals A Fast Optimization Method for Reliability and Performance of Cloud Services Composition Application

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhao Wu ◽  
Naixue Xiong ◽  
Yannong Huang ◽  
Qiong Gu ◽  
Chunyang Hu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
State Space ◽  
Generating Function ◽  
Performance Optimization ◽  
Optimization Method ◽  
Cloud Services ◽  
Services Composition ◽  
Fast Optimization ◽  
And Performance ◽  
Universal Generating Function

At present the cloud computing is one of the newest trends of distributed computation, which is propelling another important revolution of software industry. The cloud services composition is one of the key techniques in software development. The optimization for reliability and performance of cloud services composition application, which is a typical stochastic optimization problem, is confronted with severe challenges due to its randomness and long transaction, as well as the characteristics of the cloud computing resources such as openness and dynamic. The traditional reliability and performance optimization techniques, for example, Markov model and state space analysis and so forth, have some defects such as being too time consuming and easy to cause state space explosion and unsatisfied the assumptions of component execution independence. To overcome these defects, we propose a fast optimization method for reliability and performance of cloud services composition application based on universal generating function and genetic algorithm in this paper. At first, a reliability and performance model for cloud service composition application based on the multiple state system theory is presented. Then the reliability and performance definition based on universal generating function is proposed. Based on this, a fast reliability and performance optimization algorithm is presented. In the end, the illustrative examples are given.

scholarly journals An Open-Source Cloud-FPGA Gene Regulatory Accelerator

2021 ◽  
Author(s):  
Lucas Bragança ◽  
Jeronimo Penha ◽  
Michael Canesche ◽  
Dener Ribeiro ◽  
José Augusto M. Nacif ◽  
...  
Keyword(s):  
Open Source ◽  
Gene Regulatory Networks ◽  
High Performance ◽  
Regulatory Networks ◽  
Cloud Services ◽  
On Demand ◽  
Speed Up ◽  
Gene Regulatory ◽  
High Level ◽  
Better Than

FPGAs are suitable to speed up gene regulatory network (GRN) algorithms with high throughput and energy efficiency. In addition, virtualizing FPGA using hardware generators and cloud resources increases the computing ability to achieve on-demand accelerations across multiple users. Recently, Amazon AWS provides high-performance Cloud's FPGAs. This work proposes an open source accelerator generator for Boolean gene regulatory networks. The generator automatically creates all hardware and software pieces from a high-level GRN description. We evaluate the accelerator performance and cost for CPU, GPU, and Cloud FPGA implementations by considering six GRN models proposed in the literature. As a result, the FPGA accelerator is at least 12x faster than the best GPU accelerator. Furthermore, the FPGA reaches the best performance per dollar in cloud services, at least 5x better than the best GPU accelerator.

scholarly journals Scalability and Performance Management of Internet Applications in the Cloud

2013 ◽  
pp. 434-464
Author(s):  
Wesam Dawoud ◽  
Ibrahim Takouna ◽  
Christoph Meinel
Keyword(s):  
Performance Management ◽  
Resource Provisioning ◽  
Infrastructure As A Service ◽  
Internet Applications ◽  
Application Performance ◽  
Total Cost ◽  
On Demand ◽  
And Performance ◽  
The Impact

Elasticity and on-demand are significant characteristics that attract many customers to host their Internet applications in the cloud. They allow quick reacting to changing application needs by adding or releasing resources responding to the actual rather than to the projected demand. Nevertheless, neglecting the overhead of acquiring resources, which mainly is attributed to networking overhead, can result in periods of under-provisioning, leading to degrading the application performance. In this chapter, the authors study the possibility of mitigating the impact of resource provisioning overhead. They direct the study to an Infrastructure as a Service (IaaS) provisioning model where application scalability is the customer’s responsibility. The research shows that understanding the application utilization models and a proper tuning of the scalability parameters can optimize the total cost and mitigate the impact of the overhead of acquiring resources on-demand.

Cloud solutions for GxP laboratories: considerations for data storage

Bioanalysis ◽  
2021 ◽  
Author(s):  
Scott Davis ◽  
Joel Usansky ◽  
Shibani Mitra-Kaushik ◽  
John Kellie ◽  
Kimberly Honrine ◽  
...  
Keyword(s):  
Pharmaceutical Industry ◽  
Best Practices ◽  
Drug Development ◽  
Data Storage ◽  
Real World ◽  
Cloud Storage ◽  
Low Cost ◽  
Cloud Services ◽  
Significant Shift ◽  
On Demand

Challenges for data storage during drug development have become increasingly complex as the pharmaceutical industry expands in an environment that requires on-demand availability of data and resources for users across the globe. While the efficiency and relative low cost of cloud services have become increasingly attractive, hesitancy toward the use of cloud services has decreased and there has been a significant shift toward real-world implementation. Within GxP laboratories, the considerations for cloud storage of data include data integrity and security, as well as access control and usage for users around the globe. In this review, challenges and considerations when using cloud storage options for the storage of laboratory-based GxP data are discussed and best practices are defined.

Performance Evaluation of Public IaaS Clouds for Web 2.0 Applications Using CloudStone Benchmark

2018 ◽  
pp. 292-314
Author(s):  
Sanjay P. Ahuja ◽  
Neha Soni
Keyword(s):  
Web 2.0 ◽  
Web Application ◽  
Cloud Service ◽  
Cloud Services ◽  
Public Cloud ◽  
Data Intensive ◽  
Social Events ◽  
Amazon Ec2 ◽  
And Performance ◽  
Workload Generator

Web 2.0 applications have become ubiquitous over the past few years because they provide useful features such as a rich, responsive graphical user interface that supports interactive and dynamic content. Social networking websites, blogs, auctions, online banking, online shopping and video sharing websites are noteworthy examples of Web 2.0 applications. The market for public cloud service providers is growing rapidly, and cloud providers offer an ever-growing list of services. As a result, developers and researchers find it challenging when deciding which public cloud service to use for deploying, experimenting or testing Web 2.0 applications. This study compares the scalability and performance of a social-events calendar application on two Infrastructure as a Service (IaaS) cloud services – Amazon EC2 and HP Cloud. This study captures and compares metrics on three different instance configurations for each cloud service such as the number of concurrent users (load), as well as response time and throughput (performance). Additionally, the total price of the three different instance configurations for each cloud service is calculated and compared. This comparison of the scalability, performance and price metrics provides developers and researchers with an insight into the scalability and performance characteristics of the three instance configurations for each cloud service, which simplifies the process of determining which cloud service and instance configuration to use for deploying their Web 2.0 applications. This study uses CloudStone – an open-source, three-tier web application benchmarking tool that simulates Web 2.0 application activities – as a realistic workload generator and to capture the intended metrics. The comparison of the collected metrics indicates that all of the tested Amazon EC2 instance configurations provide better scalability and lower latency at a lower cost than the respective HP Cloud instance configurations; however, the tested HP Cloud instance configurations provide a greater storage capacity than the Amazon EC2 instance configurations, which is an important consideration for data-intensive Web 2.0 applications.

Examining IoT's Applications Using Cloud Services

2018 ◽  
pp. 147-163 ◽  
Author(s):  
Saravanan K ◽  
P. Srinivasan
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Distributed Computing ◽  
Knowledge Sharing ◽  
Effective Communication ◽  
Cloud Services ◽  
On Demand ◽  
Computing Paradigm ◽  
Iot Devices ◽  
And Storage

Cloud IoT has evolved from the convergence of Cloud computing with Internet of Things (IoT). The networked devices in the IoT world grow exponentially in the distributed computing paradigm and thus require the power of the Cloud to access and share computing and storage for these devices. Cloud offers scalable on-demand services to the IoT devices for effective communication and knowledge sharing. It alleviates the computational load of IoT, which makes the devices smarter. This chapter explores the different IoT services offered by the Cloud as well as application domains that are benefited by the Cloud IoT. The challenges on offloading the IoT computation into the Cloud are also discussed.

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