Auto-Scaling Network Resources using Machine Learning to Improve QoS and Reduce Cost

Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Hence, auto-scaling (of resources without human intervention) has been receiving attention. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto-scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts Quality of Service (QoS) and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier using real data collected from a private ISP. We report in-depth analysis of the learning process (learning-curve analysis), feature ranking (feature selection, Principal Component Analysis (PCA), etc.), impact of different sets of features, training time, and testing time. Our results show how the proposed methods improve QoS and reduce operational cost for network owners. We also demonstrate a practical use-case example (Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to show that our ML methods save significant cost for network service leasers

Socio-economic impact of CSR activities of an Islamic Banking: A Case of Islami Bank Bangladesh Limited

This paper examines the practices and driving philosophy of corporate social responsibility (CSR) by Islami Bank in Bangladesh and to evaluate the need to modify CSR program of the organization to enhance its effectiveness. The purpose of the paper is to study the underlying drivers of the CSR program undertaken by the Islami Bank Bangladesh Limited and to explore if the CSR activities are planned as a holistic approach to social development. This study covers a period of 5 years ranging from 2010 – 2014 using secondary data from annual reports of the bank, relevant articles, websites, Bangladesh Bank publications, newspaper, journal and magazines. This study found that the Islami Bank Bangladesh had linked its CSR program as core business strategy to grow business with shared prosperity with its surrounding community. However, it has the improvement opportunity to create a synergy by bundling all its CSR activities as a holistic program. If appropriately planned, such program will promote self-sufficiency, create new jobs and enable economic development under alternative livelihood program as suitable in the locality

Ethical Implications of Public Relations in Bangladesh: Islamic Perspective

This paper aims to examine public relation practices in Bangladesh, weighing its ethical implications from an Islamic perspective and investigates whether it comply with Islam’s ethical specifications to facilitate Muslim Marketer’s thoughts and practices. The paper uses Qur’an (Chapter 3, Verse 103) as a theoretical framework to critically evaluate relevant information to ascertain the extent of ethical legitimacy of promotional strategies used in public relations in Bangladesh. It cites relevant references from Qur’an and Sunnãh as interpretive evidences and methodology. Islam puts stress on institutionalizing ethics in every aspects of Business. This complete code of life strongly recommends Muslims to do business which should certainly be in the ethical framework guided by Shari’ah. The existing public relation strategies in Bangladesh are ethically dubious. Undue influence, exertion of too much political power, flattering, fabrication, falsehood, and bribery are very much common practices done by the corporations to build favorable public relations. No ways are these in compliance with Islamic Ethical Values. This paper suggests the necessity for further research into the ethical dimensions of business practices in Bangladesh to promote ethical awareness in the society. This study includes mutual socio-economic and ethical responsibilities among Bangladeshi Marketers to save the society from corruption and moral deterioration

Screening for cervical cancer (By VIA Test) among selected garments worker in Chattogram, Bangladesh

Background: Bangladesh is a densely populated country of South East Asia with low resource setting where cervical cancer is the 2nd leading cause of female cancer. In more than 80% cases are diagnosed at advanced and inoperable stage. Regarding socio demographic context of this country VIA has been introduced as a screening method for cervical cancer which is most simple, cost effective, and acceptable test for all women. In Bangladesh among 3 million garment workers more than 80% are women. The objective of this study was to identify prevalence of VIA positive cases among garment workers. So that it can reduce the incidence of cervical cancer in Bangladesh. Methods: It was a cross–sectional observational study conducted in some selected garment factories in Chattogram city of Bangladesh from January 2021 to July 2021, where we enrolled 534 female workers for VIA test. Results: Among all the respondents 56% were 30 years or younger, 38% were aged between 31 to 40 years. Among 534 participants, 44.9% completed primary education, 37.3% were smoker and 34.5% had their children at early age. Majority (86.7%) had excessive whitish discharge. Post coital bleeding and irregular bleeding was 2.6% and 2.2% respectively. Considering awareness, 61.8% had idea about cervical cancer, only 1.1% had undergone VIA test in the past. In our study we found 2.4% of participants were VIA positive cases. Conclusions: It is important to include the garment workers, while making public health policies and implementation of cervical cancer control program

Traffic-Adaptive Networking Solutions for Next-Generation Wide-Area Optical Networks

As we continually churn out more and more information that must be transmitted over our networks, the networking challenges of handling this large-scale, highly-diverse, and varied- speed traffic opens exciting new research and development problems. More bandwidth and lower latency is required to accommodate the complex applications over wide-area networks. Although a complete traffic-adaptive network is still a long way to go, our networks are grad- ually incorporating changes through flexible network architectures such as Precision Time Synchronization Protocol (PTP), Elastic Optical Networks (EON), C+L bands expansion, Artificial Intelligence (AI), etc. This dissertation comprises of four contributions: i) high- precision time synchronization techniques for optical datacenter networks (Chapter 2); ii) dynamic resource allocation in mixed-grid optical networks (Chapter 3); iii) C+L bands up- grade strategies to sustain capacity crunch (Chapter 4); and iv) C to C+L bands upgrade with resource re-provisioning (Chapter 5). The dissertation concludes with a summary and future research directions (Chapter 6).The main topics of this dissertation are the following: 1. A datacenter, which is a highly-distributed multiprocessing system, needs to keep accurate track of time across a large number of machines. Precise time synchronization is critical due to stringent requirements of time-critical applications such as real-time big-data analytics, high-performance computing, Internet of things (IoT) networks, and financial trading. To achieve this time accuracy, we consider Precision Time Protocol (PTP) to synchronize the server clocks. Zero overhead is maintained by using data traffic to carry the time messages instead of a separate control channel. We showed that microsecond level of time accuracy can be achieved and discussed the dependency of the accuracy on different traffic loads, traffic distributions, and packet lengths. 2. A rapid change in traffic type, volume, and dynamicity is presented by cloud-based services, datacenters, 5G, Internet of Everything (IoE), etc. Although introduced as a promising solution to this change in 2008, EON technologies are not fully deployed yet. Rather a logical and gradual migration technique is adapted which investigates bottle- neck points of the network and takes migration decision while keeping the rest of the fixed-grid network operational. Therefore, a co-existing fixed-grid and flex-grid (which can be called a “mixed-grid”) is a cost-effective solution for current circumstances. However, this introduces new challenges by the interoperability issues between fixed and flex-grid technologies. We proposed a solution to a RSMA problem in a mixed- grid considering interoperability constraints. The solution proposes routes, spectrum, and modulation format to provision a dynamic, heterogeneous traffic on two US-wide network topologies ensuring maximum network throughput and minimum blocking. 3. As more traffic is put into the access network, the backbone network also needs to have higher capacity and better network planning. Although EON helped to maximize the available spectrum utilization in C band, the exploitation of bandwidth potential of single-mode fiber (SMF) can be achieved through opening up other spectrum bands. We investigate cost-efficient upgrade strategies for capacity enhancement in wide-area networks enabled by C+L bands. A multi-period strategy for upgrading network links from C band to C+L bands is proposed, ensuring physical-layer awareness, cost effec- tiveness, and less than 0.1% blocking. Results indicate that performance of an upgrade strategy depends on efficient selection of the sequence of links to be upgraded and on the time instant to upgrade, which are both topology- and traffic-dependent. 4. We study efficient allocation of resources during C to C+L bands network upgrade. After an upgrade, resource allocation may become sub-optimal, leading to lower utilization of spectrum resources causing requests blocking, early upgrade, and higher cost. Thus, we investigate pro-active re-provisioning of lightpaths after each upgrade for cost benefit. Our strategy locates highly-utilized links and upgrades them in batches. After each batch upgrade, existing traffic in C band is re-provisioned to L band. This re-provisioning frees up high-OSNR lightpaths in C band, leading to improved quality of future transmissions, delayed upgrades, and cost benefits. Results show that re- provisioning of a shorter lightpath provides the most cost-effective upgrade strategy

Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Auto-scaling (of resources without human intervention) has been investigated in academia and industry. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto- scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts QoS and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier. We also demonstrate using realistic traffic load traces and optical backbone network that our ML method improves QoS and saves significant cost for network owners as well as leasers