Current Trends in Cloud Computing for Data Science Experiments

Current Trends in Cloud Computing for Data Science Experiments

Syed Imran Jami, Siraj Munir
Copyright: © 2021 |Pages: 20
DOI: 10.4018/IJCAC.2021100105
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Abstract

Recent trends in data-intensive experiments require extensive computing and storage resources that are now handled using cloud resources. Industry experts and researchers use cloud-based services and resources to get analytics of their data to avoid inter-organizational issues including power overhead on local machines, cost associated with maintaining and running infrastructure, etc. This article provides detailed review of selected metrics for cloud computing according to the requirements of data science and big data that includes (1) load balancing, (2) resource scheduling, (3) resource allocation, (4) resource sharing, and (5) job scheduling. The major contribution of this review is the inclusion of these metrics collectively which is the first attempt towards evaluating the latest systems in the context of data science. The detailed analysis shows that cloud computing needs research in its association with data-intensive experiments with emphasis on the resource scheduling area.
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1. Introduction

Cloud computing is best known for its pay-as-you-go service provided by renowned service including Google, Microsoft and many more. IaaS (Infrastructure as a Service), PaaS (Platform as a Service) and SaaS (Software as a service) are the major services of cloud computing. Cloud computing is also described as distributed infrastructure provided by any third party, which enable us to categorize it as a distributed system. In distributed cloud computing environment there are several areas that are crucial when cloud computing comes into action with services like IaaS, PaaS and SaaS. Cloud computing comes under the areas of distributed systems. Modern cloud computing has changed the computing paradigm with tool like i.e. Azure ML services, Amazon AWS, CV (Computer Vision) and DL (Deep Learning) services, Google Cloud, CV and DL services. In this work, we provide a survey of latest research trends in cloud computing on the basis of factors involved in distributed systems. One of the factors include Resource Allocation, that allow us to allocate cloud resources in dynamic environment effectively. Another factor on which we surveyed the existing systems is Load Balancing, which aims to optimize resource usage, maximize throughput while response time minimum, and avoid overloading of individual resource. Parameters on Fault Tolerance including Reliability and Availability can be achieved through redundancy. Load balancing usually involves dedicated resources, as shown in (Wikipedia, 2018) through a multilayer switch or a DNS server process. Resource Sharing and Scheduling are the two other factors on the basis of which we surveyed existing systems. Resource Sharing is the capability of cloud computing to share its resources on demand basis while Resource scheduling refers to the use of different algorithms to deliver and allocate different resources in a dynamic environment. The last factor is Job Scheduling, which is the process of prioritizing jobs for maximization of throughput, resource utilization, performance and availability (Y. P. Dave, Shelat, Patel, & Jhaveri, 2014). The document is organized as follows. The next section deals with the related work on survey of cloud computing framework. Subsequent sub-sections deal with survey of latest frameworks presented in last two years. The paper ends with conclusion citing open research areas in Cloud Computing.

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