Innovative Concepts and Techniques of Data Analytics in Edge Computing Paradigms

2021 ◽  
pp. 134-152
Author(s):  
Soumya K. ◽  
Margaret Mary T. ◽  
Clinton G.
Keyword(s):  
Cloud Computing ◽  
Financial Services ◽  
Response Times ◽  
Edge Computing ◽  
Analytical Computation ◽  
Digital Customer Experience ◽  
Data Store ◽  
Data Collection And Analysis ◽  
Network Switch ◽  
Improve Reliability

Edge analytics is an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, network switch, or other device instead of waiting for the data to be sent back to a centralized data store. Cloud computing has revolutionized how people store and use their data; however, there are some areas where cloud is limited; latency, bandwidth, security, and a lack of offline access can be problematic. To solve this problem, users need robust, secure, and intelligent on-premise infrastructure for edge computing. When data is physically located closer to the users who connected to it, information can be shared quickly, securely, and without latency. In financial services, gaming, healthcare, and retail, low levels of latency are vital for a great digital customer experience. To improve reliability and faster response times, combing cloud with edge infrastructure from APC by Schneider electrical is proposed.

scholarly journals A Review of Machine Learning Algorithms for Cloud Computing Security

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1379
Author(s):  
Umer Ahmed Butt ◽  
Muhammad Mehmood ◽  
Syed Bilal Hussain Shah ◽  
Rashid Amin ◽  
M. Waqas Shaukat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Storage ◽  
Response Times ◽  
Edge Computing ◽  
Machine Learning Algorithms ◽  
Information Storage ◽  
Future Research ◽  
Computer Algorithms ◽  
Advantages And Disadvantages

Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edge computing is an evolving computing paradigm that brings computation and information storage nearer to the end-users to improve response times and spare transmission capacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones. However, CC and edge computing have security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Machine learning (ML) is the investigation of computer algorithms that improve naturally through experience. In this review paper, we present an analysis of CC security threats, issues, and solutions that utilized one or several ML algorithms. We review different ML algorithms that are used to overcome the cloud security issues including supervised, unsupervised, semi-supervised, and reinforcement learning. Then, we compare the performance of each technique based on their features, advantages, and disadvantages. Moreover, we enlist future research directions to secure CC models.

scholarly journals Recent Advances and Future Research Directions in Edge Cloud Framework

2019 ◽  
Vol 9 (2) ◽  
pp. 439-444 ◽  
Keyword(s):  
Cloud Computing ◽  
Service Time ◽  
Response Times ◽  
Edge Computing ◽  
Future Research ◽  
Simulation Environment ◽  
Research Directions ◽  
Great Idea ◽  
Cloud Framework ◽  
Future Research Directions

Recent years have shown the explosive emergence of Cloud computing in the industry and it is now the need of the hour. It is a great idea to go to utilize 5G remote advancement and man-made thinking to engage speedier response times, lower latency, improved upkeep in figuring. The cloud has at no other time been so essential to the undertaking beforehand. This is where Edge Computing came into picture — seen as an expansion to the cloud, yet interesting in a couple of crucial ways. Empowering data to be taken care of, explored and moved at the edge of the framework, edge enlisting will enable undertakings to gather and assessments data closer to where it is taken care of, consistently, without idleness. Thus it can take into consideration snappy substance conveyance and information preparing that ought to be the eventual fate of registering. In this paper we will extensively study the necessity of Edge Cloud simulation environment and simulate it through EdgeCloudSim. We find that the utilization based, fuzzy competitor based and hybrid based methodologies incline toward offloading the assignments to the edge, so they give better outcomes whereas the average service time of the Fuzzy-Based methodology is least in contrast with the others

scholarly journals Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1506 ◽  
Author(s):  
Jonghwa Choi ◽  
Sanghyun Ahn
Keyword(s):  
Cloud Computing ◽  
Response Times ◽  
Edge Computing ◽  
Time Interval ◽  
Service Provisioning ◽  
Cloud Data ◽  
Service Demand ◽  
Optimal Service ◽  
Cloud Data Centers ◽  
Iot Devices

In recent years, we observed the proliferation of cloud data centers (CDCs) and the Internet of Things (IoT). Cloud computing based on CDCs has the drawback of unpredictable response times due to variant delays between service requestors (IoT devices and end devices) and CDCs. This deficiency of cloud computing is especially problematic in providing IoT services with strict timing requirements and as a result, gives birth to fog/edge computing (FEC) whose responsiveness is achieved by placing service images near service requestors. In FEC, the computing nodes located close to service requestors are called fog/edge nodes (FENs). In addition, for an FEN to execute a specific service, it has to be provisioned with the corresponding service image. Most of the previous work on the service provisioning in the FEC environment deals with determining an appropriate FEN satisfying the requirements like delay, CPU and storage from the perspective of one or more service requests. In this paper, we determined how to optimally place service images in consideration of the pre-obtained service demands which may be collected during the prior time interval. The proposed FEC environment is scalable in the sense that the resources of FENs are effectively utilized thanks to the optimal provisioning of services on FENs. We propose two approaches to provision service images on FENs. In order to validate the performance of the proposed mechanisms, intensive simulations were carried out for various service demand scenarios.

scholarly journals A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

scholarly journals Cloud Computing Strategy and Impact in Banking/Financial Services

2021 ◽  
Author(s):  
Prudhvi Parne
Keyword(s):  
Cloud Computing ◽  
Financial Services ◽  
New Technologies ◽  
Credit Unions ◽  
History Of Computing ◽  
Cloud Technology ◽  
The Past ◽  
Business Agility ◽  
History Of ◽  
Trustworthy Service

With recent advances in technology, internet has drastically changed the computing world from the concept of parallel computing to distributed computing to grid computing and now to cloud computing. The evolution of cloud computing over the past few years is potentially one of the major advances in the history of computing. Unfortunately, many banks are still hesitant to adopt cloud technology. New technologies such as cloud and AI will have the biggest impacts on the banking industry. For banks and credit unions wanting to achieve greater business agility, cloud technology enables organizations to respond instantly to changing market conditions, leveraging data and applied analytics to achieve customer experience and operational productivity benefits. As a result, cloud computing comes in to provide a solution to such challenges making banking a reliable and trustworthy service. This paper aims at cloud computing strategy, impact in banking and financial institutions and discusses the significant reliance of cloud computing.

Accelerating Mobile-Cloud Computing

2015 ◽  
pp. 1933-1955
Author(s):  
Tolga Soyata ◽  
He Ba ◽  
Wendi Heinzelman ◽  
Minseok Kwon ◽  
Jiye Shi
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
High Speed ◽  
Mobile Cloud Computing ◽  
Response Times ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Computational Resources ◽  
Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today's mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

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