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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Emmanuel Adetiba
Covenant University
Computer Science & IT Electrical & Electronics Engineering

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Monitoring and resource management taxonomy in interconnected cloud infrastructures: a survey Vingi Patrick Nzanzu; Emmanuel Adetiba; Joke Atinuke Badejo; Mbasa Joaquim Molo; Claude Takenga; Etinosa Noma-Osaghae; Victoria Oguntosin; Sadeeq Suraju
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.20503

Abstract

Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management.
Compact automatic modulation recognition using over-the-air signals and FOS features Emmanuel Adetiba; Folarin Joseph Olaloye; Abdultaofeek Abayomi; Nasir Faruk; Sibusiso Moyo; Obiseye Obiyemi; Surendra Thakur
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.4119

Abstract

The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. The utilization of real-time over-the-air digital radio frequency (RF) data for the development of a digital spectrum sensing model based on the automatic modulation classification (AMC) is presented in this study as a step for incorporating opportunistic spectrum sensing onto the NomadicBTS architecture. Some digital modulation techniques were studied for second-generation (2G) through fourth-generation (4G) technology. The raw RF signal dataset was digitized and curated, while non-complex first-order statistical (FOS) features were used with algorithms based on the Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) to find the best learning algorithm for the generated AMR model. The results show that the developed AMR model has a very high likelihood of correctly classifying signals, with distinct patterns for each of the features of FOS. The results are compared to reveal a least mean square error (MSE) of 0.0131 with a maximum accuracy of 93.5 percent when the model was trained with seventy (70) neurons in the hidden layer using the LM method. The best model's accuracy will allow for the most precise identification of spectrum holes in the bands under consideration.
Co-Authors Abdultaofeek Abayomi Claude Takenga Etinosa Noma-Osaghae Folarin Joseph Olaloye Joke Atinuke Badejo Mbasa Joaquim Molo Nasir Faruk Obiseye Obiyemi Sadeeq Suraju Sibusiso Moyo Surendra Thakur Victoria Oguntosin Vingi Patrick Nzanzu