Study of Energy-Efficient Optimization Techniques for High-Level Homogeneous Resource Management. (27th July 2022)

• Record Type: Journal Article
• Authors: Mann, Suman; Pathak, Nitish; Sharma, Neelam; Kumar, Raju; Porwal, Rabins; Sharma, Sheelesh Kr; Aung, Saw Mon Yee
• Other Names: Rakkesh R Ajay Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-07-27
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/1953510
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Resource management efficiency can be a beneficial step toward optimizing power consumption in software-hardware integrated systems. Languages such as C, C++, and Fortran have been extremely popular for dealing with optimization, memory management, and other resource management. We investigate novel algorithmic architectures capable of optimizing resource requirements and increasing energy efficiency. The experimental results obtained with C++ can be extended to other programming languages as well. We emphasize the inherent drawbacks of memory management operators. These operators are intended to be extremely generic in their application, just as the concept of dynamic memory is. As a result, they are unable to take advantage of the various optimization techniques and opportunities that specific use cases present. Each source code file is modeled after its own distinct memory usage pattern, which can be used to speed up memory management routines. Such concepts are frequently time-consuming and costly to implement; consequently, they are not the primary concern of application developers, as they require manual development and integration. We intend to address this gap by providing a suite of memory management algorithms that enable dramatic performance improvements at the source code level while allowing for seamless integration across multiple use cases. The techniques have been evaluated on several performance parameters, and results have been presented. In this paper, we have compared a variety of memory allocation techniques and compared their space and energy efficiency requirements. Three variants of SSDAM, SSDAM-E, and DLLOM strategies have been evaluated and compared against the base performance of new and delete operators. SSDAM-E, SSDAM with new delete operators, and DDLOM improve the memory consumption by the factors of 8.01, 7.0, and 4.0, respectively. In the worst case, SSDAM-E gave an average running time of 5.650 sec faster than the DLLOM average time of 7.496 sec. As far as energy efficiency is considered, SSDAM-Original and SSDAM-E-Original attain 100%, in comparison with the base efficiency of 12.48% characterized by new/delete operators.