Performance Evaluation of Edge Cloud Computing System for Big Data Applications

Information security is a prime goal for every individual and organization. The travelling from client to cloud server can be prone to security issues. The big data storages are available through cloud computing system to facilitate mobile client. The information security can be provided to mobile client and cloud technology with the help of integrated parallel and distributed encryption and decryption mechanism. The traditional technologies include the plaintext stored across cloud and can be prone to security issues. The solution provided by applying the encrypted data upload and encrypted search. The clouds can work in collaboration; therefore, the encryption can also be done in collaboration. Some part of encryption handle by client and other part handled by cloud system. This chapter presents the security scenario of different security algorithms and the concept of mobile and cloud computing. This chapter precisely defines the security features of existing cloud and big data system and provides the new framework that helps to improve the data security over cloud computing and big data security system.

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Automotive Industrial Supply Chain Performance Evaluation under Uncertain Constraints on Cloud Computing System

Concepts, Applications and Emerging Opportunities in Industrial Engineering ◽

10.5772/intechopen.93679 ◽

2021 ◽

Author(s):

Suthep Butdee

Keyword(s):

Cloud Computing ◽

Supply Chain ◽

Performance Evaluation ◽

Real Time ◽

Continuous Process ◽

Computing System ◽

Continuous Process Improvement ◽

Daily Monitoring ◽

Automotive Supply Chain ◽

Cloud Computing System

Performance evaluation is a critical and complex task as well as uncertain demands for automotive supply chain. Several methods are applied and adopted to deal with current situations and maintain competitiveness such as fuzzy logic, neuro fuzzy, agent (multi) based evaluation, etc. However, such systems are not rapid enough to respond customer requirements by real-time on mobile cloud computing system. There are many companies that operate under the first tier company as subcontractors on the same goal. Cloud computing system is capable to monitor real-time production processes for every subcontractor to assist the 1st tier to make decision and respond customer effectively. Daily monitoring data of all subcontractors in the supply chain are stored in the central database and finally the performance evaluation can be done. The implication is cost reduction of the whole supply chain and increase competitiveness as well as continuous process improvement for all.

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Big Data Handling Approach for Unauthorized Access of Cloud Computing

10.20944/preprints202112.0068.v1 ◽

2021 ◽

Author(s):

Abdul Razaque ◽

Shaldanbayeva Nazerke ◽

Bandar Alotaibi ◽

Munif Alotaibi ◽

Akhmetov Murat ◽

...

Keyword(s):

Cloud Computing ◽

Big Data ◽

Access Control ◽

Intrusion Detection ◽

Data Access ◽

Computing System ◽

Data Handling ◽

Unauthorized Access ◽

Data Access Control ◽

Cloud Computing System

Nowadays, cloud computing is one of the important and rapidly growing paradigms that extend its capabilities and applications in various areas of life. The cloud computing system challenges many security issues, such as scalability, integrity, confidentiality, and unauthorized access, etc. An illegitimate intruder may gain access to the sensitive cloud computing system and use the data for inappropriate purposes that may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for Big data in cloud computing. The HUDU aims to restrict illegitimate users from accessing the cloud and data security provision. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. HUDH involves three algorithms; Advanced Encryption Standards (AES) for encryption, Attribute-Based Access Control (ABAC) for data access control, and Hybrid Intrusion Detection (HID) for unauthorized access detection. The proposed scheme is implemented using Python and Java language. Testing results demonstrate that the HUDH can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% high accuracy.

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Cryptography in Big Data Security

Cloud Security ◽

10.4018/978-1-5225-8176-5.ch032 ◽

2019 ◽

pp. 639-656

Author(s):

Navin Jambhekar ◽

Chitra Anil Dhawale

Keyword(s):

Cloud Computing ◽

Big Data ◽

Information Security ◽

Data Security ◽

Computing System ◽

Mobile Client ◽

Security Issues ◽

Cloud Computing System ◽

Cloud Server ◽

Encrypted Search

Information security is a prime goal for every individual and organization. The travelling from client to cloud server can be prone to security issues. The big data storages are available through cloud computing system to facilitate mobile client. The information security can be provided to mobile client and cloud technology with the help of integrated parallel and distributed encryption and decryption mechanism. The traditional technologies include the plaintext stored across cloud and can be prone to security issues. The solution provided by applying the encrypted data upload and encrypted search. The clouds can work in collaboration; therefore, the encryption can also be done in collaboration. Some part of encryption handle by client and other part handled by cloud system. This chapter presents the security scenario of different security algorithms and the concept of mobile and cloud computing. This chapter precisely defines the security features of existing cloud and big data system and provides the new framework that helps to improve the data security over cloud computing and big data security system.

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Performance Evaluation of Parallel Structure from Motion (SfM) Processing with Public Cloud Computing and an On-Premise Cluster System for UAS Images in Agriculture

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10100677 ◽

2021 ◽

Vol 10 (10) ◽

pp. 677

Author(s):

Anjin Chang ◽

Jinha Jung ◽

Jose Landivar ◽

Juan Landivar ◽

Bryan Barker ◽

...

Keyword(s):

Cloud Computing ◽

Performance Evaluation ◽

Structure From Motion ◽

Computing System ◽

Unmanned Aircraft ◽

High Temporal Resolution ◽

Public Cloud ◽

3D Point Clouds ◽

Cloud Computing System ◽

Agricultural Applications

Thanks to sensor developments, unmanned aircraft system (UAS) are the most promising modern technologies used to collect imagery datasets that can be utilized to develop agricultural applications in these days. UAS imagery datasets can grow exponentially due to the ultrafine spatial and high temporal resolution capabilities of UAS and sensors. One of the main obstacles to processing UAS data is the intensive computational resource requirements. The structure from motion (SfM) is the most popular algorithm to generate 3D point clouds, orthomosaic images, and digital elevation models (DEMs) in agricultural applications. Recently, the SfM algorithm has been implemented in parallel computing to process big UAS data faster for certain applications. This study evaluated the performance of parallel SfM processing on public cloud computing and on-premise cluster systems. The UAS datasets collected over cropping fields were used for performance evaluation. We used multiple computing nodes and centralized network storage with different network environments for the SfM workflow. In single-node processing, an instance with the most computing power in the cloud computing system performed approximately 20 and 35 percent faster than in the most powerful machine in the on-premises cluster. The parallel processing results showed that the cloud-based system performed better in speed-up and efficiency metrics for scalability, although the absolute processing time was faster in the on-premise cluster. The experimental results also showed that the public cloud computing system could be a good alternative computing environment in UAS data processing for agricultural applications.

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