Open Access

ARTICLE

Data-Oriented Operating System for Big Data and Cloud

Selwyn Darryl Kessler, Kok-Why Ng^*, Su-Cheng Haw^*

Faculty of Computing and Informatics, Multimedia University, Cyberjaya, 63100, Malaysia

* Corresponding Authors: Kok-Why Ng. Email: ; Su-Cheng Haw. Email:

Intelligent Automation & Soft Computing 2024, 39(4), 633-647. https://doi.org/10.32604/iasc.2024.054154

Received 20 May 2024; Accepted 22 July 2024; Issue published 06 September 2024

Abstract

Operating System (OS) is a critical piece of software that manages a computer’s hardware and resources, acting as the intermediary between the computer and the user. The existing OS is not designed for Big Data and Cloud Computing, resulting in data processing and management inefficiency. This paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud Computing purposes. The proposed algorithm utilizes the performance benefits from the improved Input/Output (I/O) performance. The performance engineering runs the data-oriented design on traditional data management to improve data processing speed by reducing memory access overheads in conventional data management. The OS incorporates a data-oriented design to “modernize” various Data Science and management aspects. The resulting OS contains a basic input/output system (BIOS) bootloader that boots into Intel 32-bit protected mode, a text display terminal, 4 GB paging memory, 4096 heap block size, a Hard Disk Drive (HDD) I/O Advanced Technology Attachment (ATA) driver and more. There are also I/O scheduling algorithm prototypes that demonstrate how a simple Sweeping algorithm is superior to more conventionally known I/O scheduling algorithms. A MapReduce prototype is implemented using Message Passing Interface (MPI) for big data purposes. An attempt was made to optimize binary search using modern performance engineering and data-oriented design.

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Keywords

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