A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

Abstract

Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

“…Machine learning classifiers have also been trained using unigrams and weighted unigrams. The results demonstrate that machine learning methods perform well on weighted unigrams, with SVM achieving the highest level of reliability [ 29 , 30 , 31 , 32 , 33 , 34 ]. The formulae used in the calculation of the classification report are given below: where TP = True Positive, TN = True Negative, FP = False Positive, FN = False Negative.…”

Efficient Middleware for the Portability of PaaS Services Consuming Applications among Heterogeneous Clouds

“…Moreover, one platform could supply some assistance that is not offered by another platform [10,[43][44][45][46][47][48][49]. So, application portability among different clouds or from legacy enterprises to the cloud becomes essential to preclude users from vendor lock-in [50][51][52][53][54][55]. A vast amount of the literature on application portability solutions was reviewed, and a comparative analysis is presented in Table 1.…”

“…The middleware code does not need to be changed to facilitate the change in the configuration file. The modified configuration variables are loaded into the middleware directly via dependency injection [52][53][54].…”

Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions

“…It is a keyless hash function. The SHA-256 follows the same model as SHA-1 and begins by defining several constants [25][26][27][28][29]. Several operating systems frequently use hash methods to secure passwords.…”