scholarly journals Towards Analyzing Computational Costs of Spark for SARS-CoV-2 Sequences Comparisons on a Commercial Cloud

2021 ◽  
Author(s):  
Alan L. Nunes ◽  
Alba Cristina Magalhaes Alves de Melo ◽  
Cristina Boeres ◽  
Daniel de Oliveira ◽  
Lúcia Maria de Assumpção Drummond
Keyword(s):  
Fault Tolerance ◽  
South America ◽  
Virtual Machines ◽  
Spot Markets ◽  
Computational Costs ◽  
On Demand ◽  
Financial Costs ◽  
Execution Times ◽  
And Storage ◽  
Spark Framework

In this paper, we developed a Spark application, named Diff Sequences Spark, which compares 540 SARS-CoV-2 sequences from South America in Amazon EC2 Cloud, generating as output the positions where the differences occur. We analyzed the performance of the proposed application on selected memory and storage optimized virtual machines (VMs) at on-demand and spot markets. The execution times and financial costs of the memory optimized VMs outperformed the storage optimized ones. Regarding the markets, Diff Sequences Spark reduced the average execution times and monetary costs when using spot VMs compared to their respective on-demand VMs, even in scenarios with several spot revocations, benefiting from the low overhead fault tolerance Spark framework.

scholarly journals GATECloud.net: a platform for large-scale, open-source text processing on the cloud

2013 ◽  
Vol 371 (1983) ◽  
pp. 20120071 ◽  
Author(s):  
Valentin Tablan ◽  
Ian Roberts ◽  
Hamish Cunningham ◽  
Kalina Bontcheva
Keyword(s):  
Cloud Computing ◽  
Language Processing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cost Benefit Analysis ◽  
Text Processing ◽  
Cost Benefit ◽  
Data Intensive ◽  
On Demand ◽  
Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

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.

Design and Development of Framework for Platform Level Issues in Fog Computing

2021 ◽  
pp. 429-451
Author(s):  
Sejal Atit Bhavsar ◽  
Kirit J Modi
Keyword(s):  
Fault Tolerance ◽  
Data Storage ◽  
Fog Computing ◽  
End Users ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Basic Concepts ◽  
Tolerance Model ◽  
Processing And Storage ◽  
And Storage

Fog computing is a paradigm that extends cloud computing services to the edge of the network. Fog computing provides data, storage, compute and application services to end users. The distinguishing characteristics of fog computing are its proximity to the end users. The application services are hosted on network edges like on routers, switches, etc. The goal of fog computing is to improve the efficiency and reduce the amount of data that needs to be transported to cloud for analysis, processing and storage. Due to heterogeneous characteristics of fog computing, there are some issues, i.e. security, fault tolerance, resource scheduling and allocation. To better understand fault tolerance, we highlighted the basic concepts of fault tolerance by understanding different fault tolerance techniques i.e. Reactive, Proactive and the hybrid. In addition to the fault tolerance, how to balance resource utilization and security in fog computing are also discussed here. Furthermore, to overcome platform level issues of fog computing, Hybrid fault tolerance model using resource management and security is presented by us.

scholarly journals Optimal allocation of virtual machines in multi-cloud environments with reserved and on-demand pricing

2017 ◽  
Vol 71 ◽  
pp. 129-144 ◽  
Author(s):  
José Luis Díaz ◽  
Joaquín Entrialgo ◽  
Manuel García ◽  
Javier García ◽  
Daniel Fernando García
Keyword(s):  
Optimal Allocation ◽  
Virtual Machines ◽  
On Demand ◽  
Cloud Environments ◽  
Multi Cloud

scholarly journals A Load Balancing Scheme Using Federate Migration Based on Virtual Machines for Cloud Simulations

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xiao Song ◽  
Yaofei Ma ◽  
Da Teng
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Distributed System ◽  
Large Scale ◽  
Virtual Machines ◽  
On Demand ◽  
New Type ◽  
Key Techniques ◽  
Large Scale Simulations ◽  
Simulation Systems

A maturing and promising technology, Cloud computing can benefit large-scale simulations by providing on-demand, anywhere simulation services to users. In order to enable multitask and multiuser simulation systems with Cloud computing, Cloud simulation platform (CSP) was proposed and developed. To use key techniques of Cloud computing such as virtualization to promote the running efficiency of large-scale military HLA systems, this paper proposes a new type of federate container, virtual machine (VM), and its dynamic migration algorithm considering both computation and communication cost. Experiments show that the migration scheme effectively improves the running efficiency of HLA system when the distributed system is not saturated.

scholarly journals The Benefits of Enterprise Data Warehouse Implementation in Cloud vs. On-premises

2021 ◽  
Vol 7 (1) ◽  
pp. 67-76
Author(s):  
Darko Golec ◽  
Ivan Strugar ◽  
Drago Belak
Keyword(s):  
Cost Saving ◽  
Data Warehouse ◽  
Normal Level ◽  
The Other ◽  
Peak Demand ◽  
Creative Commons ◽  
On Demand ◽  
Enterprise Applications ◽  
Enterprise Data Warehouse ◽  
And Storage

When we think about running enterprise applications on-premises, enterprises do two things for their servers, databases, and storage. Enterprises provision for peaks and put a lot of infrastructures to handle peak demand, although a lot of this capacity is not used at normal times. The other thing is a few instances that each application needs to have, typically between five and six. Multiplying this number by many times due to various applications causes a lot of costs and creates capacity that is not used. For such reasons, the enterprise applications in the cloud seem reasonable. In the cloud, two things are possible again. Instead of overprovisioning for peaks, enterprises can scale the capacity on on-demand and spin up instances on demand. This means a certain amount of cost-saving by running at a normal level instead of overprovisioning. In this paper, various factors will be considered, and the benefits for enterprise data warehouse implementation in the cloud vs. on-premises will be stated. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

scholarly journals REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT

2020 ◽  
Vol 5 (1) ◽  
pp. 85-89
Author(s):  
Dinkan Patel ◽  
Anjuman Ranavadiya
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Dynamic Scheduling ◽  
Scheduling Algorithms ◽  
Minimum Time ◽  
Cloud Environment ◽  
Static Scheduling ◽  
On Demand

Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines can prove to be useful. Here a review of various task scheduling algorithms is done which can be used to perform the task and allocate resources so that performance can be increased.

scholarly journals Thuật toán MODE giải bài toán lập lịch luồng công việc

2017 ◽  
pp. 51
Author(s):  
Phan Thanh Toàn Phan Thanh Toàn
Keyword(s):  
Information And Communication Technology ◽  
Communication Technology ◽  
Large Scale ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Challenging Problem ◽  
On Demand ◽  
Local Extrema ◽  
Information And Communication ◽  
And Storage

Cloud computing is a new trend of information and communication technology that enables resource distribution and sharing at a large scale. The Cloud consists of a collection of virtual machine that promise to provision on-demand computational and storage resources when needed. End-users can access these resources via the Internet and have to pay only for their usage. Scheduling of scientific workflow applications on the Cloud is a challenging problem that has been the focus of many researchers for many years. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method. This algorithm does not only ensure fast convergence but it also averts getting trapped into local extrema. Our CloudSim-based simulations show that our algorithm is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.

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