Modern Optical Data Centers: Design Challenges and Issues

Singh, Arunendra; Singh, Richa; Bhattacharya, Pronaya; Pathak, Vinay Kumar; Tiwari, Abhishek

doi:10.1007/978-981-15-2369-4_4

Cited by 11 publications

(3 citation statements)

References 28 publications

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“…As per the report (Vuleta, 2020), ≈44 Zettabytes of data was generated and it would increase to 463 exabytes by the year 2025. Big data, cloud computing, and IoT are the main pillars of an emerging Industry 4.0 (Singh et al, 2020). To deal with the storage and easy access to the massive data, there exist differently centralized and decentralized organizations.…”

Section: Energy Consumption In Centralized and Decentralized Systemsmentioning

confidence: 99%

A taxonomy of energy optimization techniques for smart cities: Architecture and future directions

et al. 2021

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There is a drastic increase in urbanization over the past few years, which requires energy-efficient and optimized solutions for transportation, governance, quality of life in a smart city among all the citizens. The Internet-of-Energy (IoE) ecosystem offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoE applications is increased while IoE devices continue to grow. Therefore, smart city solutions must have the ability to utilize energy and handle the associated challenges efficiently. Moreover, energy Optimization (EO) techniques can be used to reduce energy consumption to meet the sustainability goals in IoE. Different techniques have been proposed for EO in various fields by researchers worldwide. Computing systems also need energy optimization. The energy consumption in the data center, clouds, and blockchain (BC)-based architectures are a point of concern at the current time. Due to the enormous energy demands of these systems, we cannot take advantage of the latest technologies to their fullest. Due to the emergence of new technologies and some limitations of proposed techniques, we can still not optimize energy usage more than some extent. There is minimal exploration done in energy optimization in BC-based systems. In this paper, we have proposed a survey on the energy optimization techniques in various systems, including the optimization techniques in BC-based systems. We have proposed a taxonomy that classifies energy optimization techniques. We have also proposed an energy-efficient consensus mechanism, Proof-of-High Performance optimization (named as PoHPo), for High-Performance Computing (HPC) based ecosystems. The open issues and challenges are then discussed in EO. The survey intends to propose future directions for industry professionals, green-energy stakeholders, and researchers worldwide to explore this topic further.

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Section: Energy Consumption In Centralized and Decentralized Systemsmentioning

confidence: 99%

A taxonomy of energy optimization techniques for smart cities: Architecture and future directions

et al. 2021

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“…This new era of data explosion and specifically the rising adoption of AI and IoT data analysis, has made that far dream a reality [15,16]. Not like in the past, novel techniques are very welcomed nowadays for a variety of reasons including cost savings, optimal functioning, and dynamic configurations of data center different resources [17]. It is also crucial for the data center decision makers to have meaningful, precise and real-time information about the current conditions of their data centers state, in order to make the meaningful decisions about a technology renovation, or an administrative strategy for both the time and efforts needed for the business continuity without costly disruptions and recovery time objectives [18].…”

Section: Next-gen Data Centers and Iot Sensor Data-fusion Adoptionmentioning

confidence: 99%

IoT Sensor Data Analysis and Fusion

Sohail¹

2020

Enabling AI Applications in Data Science

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“…6G is expected to support optical wireless communication, where the desired information can be transferred over low sending and receiver antennas. This is specifically useful for underwater and space communications [12]. Moreover, the overall backhaul network is expected to support photonic communication, to support high bandwidth requirements [13].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

et al. 2022

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Recently, unmanned aerial vehicles (UAVs) have gained attention due to increased use-cases in healthcare, monitoring, surveillance, and logistics operations. UAVs mainly communicate with mobile base stations, ground stations (GS), or networked peer UAVs, known as UAV swarms. UAVs communicate with GS, or UAV swarms, over wireless channels to support mission-critical operations. Communication latency, bandwidth, and precision are of prime importance in such operations. With the rise of data-driven applications, fifth-generation (5G) networks would face bottlenecks to communicate at near-real-time, at low latency and improved coverage. Thus, researchers have shifted towards network designs that incorporate beyond 5G (B5G) networks for UAV designs. However, UAVs are resource-constrained, with limited power and battery, and thus centralized cloud-centric models are not suitable. Moreover, as exchanged data is through open channels, privacy and security issues exist. Federated learning (FL) allows data to be trained on local nodes, preserving privacy and improving network communication. However, sharing of local updates is required through a trusted consensus mechanism. Thus, blockchain (BC)-based FL schemes for UAVs allow trusted exchange of FL updates among UAV swarms and GS. To date, limited research has been carried out on the integration of BC and FL in UAV management. The proposed survey addresses the gap and presents a solution taxonomy of BC-based FL in UAVs for B5G networks due to the open problem. This paper presents a reference architecture and compares its potential benefits over traditional BC-based UAV networks. Open issues and challenges are discussed, with possible future directions. Finally, a logistics case study of BC-based FL-oriented UAVs in 6G networks is presented. The survey aims to aid researchers in developing potential UAV solutions with the key integrating principles over a diverse set of application verticals.INDEX TERMS Beyond 5G networks, 6G, blockchain, federated learning, unmanned aerial vehicles. D. ORGANIZATION AND READING MAP

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Modern Optical Data Centers: Design Challenges and Issues

Cited by 11 publications

References 28 publications

A taxonomy of energy optimization techniques for smart cities: Architecture and future directions

A taxonomy of energy optimization techniques for smart cities: Architecture and future directions

IoT Sensor Data Analysis and Fusion

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

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