[hpc-announce] CfP: Special Issue "Energy Efficiency in Edge Computing"

[Lee Gillam]

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Energy Efficiency in Edge Computing

Special Issue in the 
Journal of Low Power Electronics and Applications

https://www.mdpi.com/journal/jlpea/special_issues/Energy_Efficiency_Edge_Computing
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----   IMPORTANT DATES   ----
Nov 15: Submission deadline


----   OVERVIEW   ----
Energy efficiency in cloud and edge computing is becoming increasingly important as the number of connected devices and the volume of data they generate continue to grow. Edge computing is a distributed computing paradigm that enables data processing closer to the data source, such as in Internet of Things (IoT) devices, rather than in a centralized data center. The edge paradigm has several advantages over the traditional cloud service, such as reduced latency, increased bandwidth, and improved reliability. However, an edge infrastructure also requires a lot of energy to operate, which can be a challenge in terms of users' monetary cost and environmental impact. According to an International Energy Agency (IEA) report, data centers worldwide consumed around 205 terawatt-hours (TWh) of electricity in 2020, which accounted for about 1% of global electricity consumption. The report also projected that the energy consumption of data centers could increase by 50% or more by 2030. Edge computing devices are typically smaller and consume less power than cloud data centers. However, edge devices may have limited resources and battery capacity, and the distributed nature of the system may lead to additional energy consumption in data transfer and synchronization. 

Through implementing edge intelligence, the computing tasks can be offloaded from the cloud to the edge devices; therefore, this reduces the amount of data that needs to be transmitted and processed in the cloud. Besides intelligence, other techniques such as workload distribution, resource management, scheduling, and data compression might be helpful in reducing the energy consumption of edge infrastructure. This can save users' costs, increase profit, and reduce network latency. Furthermore, although energy consumption is an issue, edge computing is considered as essential for enabling the next generation of services and applications that require high computational speeds and low latencies. Therefore, it is essential to look for possible approaches to minimize their energy consumption. 

This Special Issue invites original works in all areas of cloud and edge computing, including IoT, with aim to decrease the energy consumption of edge infrastructure. The outcome will be a collection of articles that propose edge and cloud computing models and techniques with impacts on users' costs, service performance, energy consumption (service providers' economics), and ecological sustainability (CO2 emissions). Highly cited and reputable researchers from both academia and industry should be contacted for quality submissions and possible publications. 

The list of possible topics includes, but is not limited to, the following: 
- Optimization of workload distribution;
- Intelligent power management;
- Leveraging of renewable energy;
- Energy-aware algorithms;
- Efficient network communication;
- Load balancing;
- Dynamic resource allocation;
- Serverless computing;
- Energy-aware scheduling;
- Data compression and aggregation;
- Edge-cloud integration;
- Resource placement and orchestration;
- Energy, performance, and cost-efficient IoT, edge, and cloud service offerings;
- Machine learning (ML) and artificial intelligence (AI) techniques and algorithms for intelligent computation over the edge infrastructure.

Guest Editors: Dr. Lee Gillam, Dr. Zakarya Muhammad

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Dr Lee Gillam FBCS CITP FHEA, 
Director of Postgraduate Research (CS)
Reader, Department of Computer Science, 
University of Surrey, UK

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