Spider Monkey Optimization based Energy-Efficient Resource Allocation in Cloud Environment

The origin of Cloud computing is from the principle of utility computing, which is characterized as a broadband service providing storage and computational resources. It provides a large variety of processing options and heterogeneous tools, allowing it to meet the needs of a wide range of applications at different levels. As a result, resource allocation and management are critical in cloud computing. In this work, the Spider Monkey Optimization (SMO) is used for attaining an optimized resource allocation. The key parameters considered to regulate the performance of SMO are its application time, migration time, and resource utilization. Energy consumption is another key factor in cloud computation which is also considered in this work. The Green Cloud Scheduling Model (GCSM) is followed in this work for the energy utilization of the resources. This is done by scheduling the heterogeneity tasks with the support of a scheduler unit which schedules and allocates the tasks which are deadline-constrained enclosed to nodes which are only energy-conscious. Assessing these methods is formulated using the cloud simulator programming process. The parameter used to determine the energy efficiency of this method is its energy consumption. The simulated outcome of the proposed approach proves to be effective in response time, makespan, energy consumption along with resource utility corresponding to the existing algorithms.

Supervised Curricular Internship II: Experiences of the future chemistry teacher in remote education

The research analyzed the experiences of 20 (twenty) students developed in Remote Teaching (ER) in the 1st semester of 2021 after completing the Supervised Curricular Internship II. A qualitative and quantitative approach was carried out using a virtual questionnaire created on the Google Forms® digital platform, to investigate the choice of the Chemistry Degree course, conditions of access to digital technologies, pedagogical tools used during the internship, adversities encountered by these students during the realization of the internship and the contributions of the remote internship for the formation of the Graduate in Chemistry. After analyzing and tabulating the data, the research presented the following results: the students chose a degree because they like to teach and because of their insertion in the job market, they accessed digital technologies at home, using smartphones through a broadband service with good quality connection, used Google Meet®, WhatsApp® and VLE virtual learning environment as pedagogical tools during the internship. Internet access, lack of knowledge in digital media and adaptation to remote classes were the adversities encountered during the internship. Learning and using different technological resources and digital platforms were the main contributions of the internship to the formation of decent people. The virtual remote internship enabled and provided opportunities for varied everyday experiences, promoting reflection on teaching work.

The Changing Context for Broadband Evaluation

Broadband Internet access is now widely recognized as basic infrastructure, like roads, water, and electricity. Identifying and reaching consensus on what constitutes an appropriate level of broadband service requires ongoing research to evaluate the social and economic impacts of broadband and broadband policies. As broadband has evolved, so too must the research focus. While availability and adoption of ever more advanced broadband will remain a concern, more of the focus should be on understanding how broadband is used and its effects on things like improved healthcare outcomes, educational performance, green/energy efficiency, and improving the quality of life for all society. We need more sector-specific and micro-studies of usage, and we need to tap into the different perspectives of the multiple academic disciplines. This will require multidisciplinary engagement among social scientists, network researchers, and policymakers. The research will require novel and more dynamic and heterogeneous data sets, metrics, and analytics tools.

COMPARATIVE ANALYSIS OF PREDICTION TECHNIQUES ON THE BASIS OF TELECOM CUSTOMER CHURN

Telecommunication customer churn is considered as major cause for dropped revenue and customer baseline of voice, multimedia and broadband service provider. There is strong need on focusing to understand the contributory factors of churn. Now considering factors from data sets obtained from Pakistan major telecom operators are applied for modeling. On the basis of results obtained from the optimal techniques, comparative technical evaluation is carried out. This research study is comprised mainly of proposition of conceptual frame work for telecom customer churn that lead to creation of predictive model. This is trained tested and evaluated on given data set taken from Pakistan Telecom industry that has provided accurate & reliable outcomes. Out of four prevailing statistical and machine learning algorithm, artificial neural network is declared the most reliable model, followed by decision tree. The logistic regression is placed at last position by considering the performance metrics like accuracy, recall, precision and ROC curve. The results from research has revealed main parameters found responsible for customer churn were data rate, call failure rate, mean time to repair and monthly billing amount. On the basis of these parameter artificial neural network has achieved 79% more efficiency as compare to low performing statistical techniques.

A General Framework of LSTM and Transfer Learning Based CFDAMA Strategy in Broadband Satellite System

With the development of the economy and technology, people’s requirement for communication is also increasing. Satellite networks have been paid more and more attention because of its broadband service capability and wide coverage. In this paper, we investigate the framework of a long-short term memory (LSTM) and transfer learning-based Combined Free/Demand Assignment Multiple Access (CFDAMA) scheme (CFDAMA-LSTMTL), which is a new multiple access scheme in the broadband satellite system. Generally, there is a delay time T between sending a request from the user to the satellite and receiving a reply from the satellite. So far, the traditional multiple access schemes has not processed the data traffic generated in this period of time. So, in order to transmit the data traffic in time, we propose a new prediction method, which combines LSTM with transfer learning. We introduce the prediction method into the CFDAMA scheme so that it can reduce data accumulation by the way of sending the sum slots requested by the user and the predicted request slots generated in the delay time T. A comparison with CFDAMA-PA and CFDAMA-PB is provided through simulation results, which gives the effect of the CFDAMA-LSTMTL in a broadband satellite systems.

A CLSTM and transfer learning based CFDAMA strategy in satellite communication networks

With the development of the economy and technology, people’s requirement for communication is also increasing. Satellite communication networks have been paid more and more attention because of their broadband service capability and wide coverage. In this paper, we investigate the scheme of convolutional long short term memory (CLSTM) network and transfer learning (TL) based combined free/demand assignment multiple access (CFDAMA) scheme (CFDAMA-CLSTMTL), which is a new multiple access scheme in the satellite communication networks. Generally, there is a delay time T between sending a request from the user to the satellite and receiving a reply from the satellite. So far, the traditional multiple access schemes have not processed the data generated in this period. So, in order to transmit the data in time, we propose a new prediction method CLSTMTL, which can be used to predict the data generated in this period. We introduce the prediction method into the CFDAMA scheme so that it can reduce data accumulation by the way of sending the slots request which is the sum of slots requested by the user and the predicted slots generated in the delay time. A comparison with CFDAMA-PA and CFDAMA-PB is provided through simulation results, which gives the effect of the CFDAMA-CLSTMTL in a satellite communication network.