Operation Of Network Infrastructure Research Articles

Green Computing with Deep Learning for Data Centers

Due to development in cloud services, lots of data is transferred between users and servers of the cloud. This transmission of data consumes huge amounts of energy. This energy consumption occurs during the operation of network infrastructure, the conversion of electrical to optical signals to travel long distances, and signal amplification. As Green computing is the use of computing devices in an environmentally friendly way, i.e., using electrical energy efficiently as Data centers require a significant amount of electricity to operate and cool the servers, leading to carbon emissions from the burning of fossil fuels. Green computing in cloud services is about optimizing energy consumption and by incorporating deep learning algorithms, we can enhance the energy efficiency of cloud infrastructure. These algorithms can analyze real-time data from sensors, optimize resource allocation, and dynamically adjust power usage. Through intelligent workload scheduling, server consolidation, and power management, deep learning enables the reduction of energy waste and carbon emissions. The integration of deep learning in cloud services not only improves energy efficiency but also enhances performance and cost-effectiveness. Here we are using a deep learning model which can be used for workload prediction and resource provisioning. By analyzing historical workload patterns and user behavior, deep learning algorithmscan predict future resource demands and allocate resources accordingly, leading to more efficient resource utilization and energy savings

Demand Side Response: Drivers, Challenges, and Opportunities

With the worldwide government thrust towards reducing carbon footprints, low-carbon technologies such as renewable generations, electric vehicles, and energy storage are encouraged to integrate with existing power systems. Intermittent nature of renewables increases system planning uncertainty and negatively impacts the operation of network infrastructure. In such a scenario, demand-side management has become an essential tool to reduce peak demand and thus facilitate the utilities efficient system operation. Demand response is one of the main activities of demand-side management. This paper presents an extensive literature review on demand-side response concepts and drivers originating requirement of demand flexibility. A comprehensive classification of various demand response schemes based on economic, technical, and technology triggers is presented. Demand-side response encourages users to modify their consumption during peak demand of the system. In the future, various demand response schemes could investigate grid behavior and thus contributes significantly to grid stability and investment saving.

Performance evaluation of revised virtual resources allocation scheme in network function virtualization (NFV) networks

The design, management, and operation of network infrastructure have evolved during the last few years, leveraging on innovative technologies and architectures. With such a huge trend, due to the flexibility and significant economic potential of these technologies, software-defined networking (SDN) and network functions virtualization (NFV) are emerging as the most indispensable key catalysers. SDN/NFV enhancing the infrastructure agility, thus network operators and service providers are able to program their own network functions (e.g., gateways, routers, load balancers) on vendor-independent hardware substrate. One of the most important considerations in NFV deployment is how to allocate the virtual resources that are needed to provide flexible virtual network services in an NFV-based network infrastructure. Thus, the most important prerequisite for NFV deployment is achieved fast, scalable and dynamic composition and allocation of networks functions (NFs) to implement network services (NSs). We have proposed a revised RA algorithm that integrates embedding and scheduling of virtual network functions (VNFs) simultaneously. In this paper, performance evaluation of the revised RA algorithm is performed and the proposed algorithm is applied more effectively in NFV environment where the demand for resources is flexible and concentrated.

Governance issues in financing of public–private partnership organisations in network infrastructure industries

Public–private partnership (PPP) organisational approaches to generation, management and operation of network infrastructure and services have widely followed competitive market forms under different regulatory regimes. Managerial decisions on financing of PPP companies have been governed by regulatory markets with unstable institutions in developing and emerging economies. While debt ownership in the capital structure is often shared by additional financiers like development and multilateral banks, unique agency issues seem emerging from debt and equity ownership arrangements of regulated PPP organisations. Within a theoretical framework of financing of PPP organisations explained by theory of the firm, this research looks into nature, form and unique governance issues in debt and equity arrangements in regulated PPP organisations. Analysis is supported by a survey on debt and equity arrangements in regulated PPP organisations across different infrastructure sectors and developing environments. Findings reveal that debt has not been an effective mechanism to control managers’ behaviour since subordinate financing also functions to address debt agency in the capital structure of those regulated PPP organisations. Thus, results suggest that tying performance of managers with the financial structure of regulated PPP organisation is undermined in developing and emerging economies. These governance issues need to be considered for alternative benchmarks to assess efficiency of infrastructure companies under different regulatory regimes for better infrastructure investment performance in developing environments.