Emerging Research Directions

7.1 Task and Resource Scheduling

https://ieeexplore.ieee.org/document/9519636 Q. Luo, S. Hu, C. Li, G. Li and W. Shi, "Resource Scheduling in Edge Computing: A Survey," in IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2131-2165, Fourthquarter 2021, doi: 10.1109/COMST.2021.3106401. keywords: {Edge computing;Processor scheduling;Task analysis;Resource management;Cloud computing;Job shop scheduling;Internet of Things;Internet of things;edge computing;resource allocation;computation offloading;resource provisioning},

https://www.sciencedirect.com/science/article/abs/pii/S014036641930831X Congfeng Jiang, Tiantian Fan, Honghao Gao, Weisong Shi, Liangkai Liu, Christophe Cérin, Jian Wan, Energy aware edge computing: A survey, Computer Communications, Volume 151, 2020, Pages 556-580, ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2020.01.004. (https://www.sciencedirect.com/science/article/pii/S014036641930831X) Abstract: Edge computing is an emerging paradigm for the increasing computing and networking demands from end devices to smart things. Edge computing allows the computation to be offloaded from the cloud data centers to the network edge and edge nodes for lower latency, security and privacy preservation. Although energy efficiency in cloud data centers has been broadly investigated, energy efficiency in edge computing is largely left uninvestigated due to the complicated interactions between edge devices, edge servers, and cloud data centers. In order to achieve energy efficiency in edge computing, a systematic review on energy efficiency of edge devices, edge servers, and cloud data centers is required. In this paper, we survey the state-of-the-art research work on energy-aware edge computing, and identify related research challenges and directions, including architecture, operating system, middleware, applications services, and computation offloading. Keywords: Edge computing; Energy efficiency; Computing offloading; Benchmarking; Computation partitioning

https://onlinelibrary.wiley.com/doi/10.1002/spe.3340 https://www.sciencedirect.com/science/article/abs/pii/S0167739X18319903 Wazir Zada Khan, Ejaz Ahmed, Saqib Hakak, Ibrar Yaqoob, Arif Ahmed, Edge computing: A survey, Future Generation Computer Systems, Volume 97, 2019, Pages 219-235, ISSN 0167-739X, https://doi.org/10.1016/j.future.2019.02.050. (https://www.sciencedirect.com/science/article/pii/S0167739X18319903) Abstract: In recent years, the Edge computing paradigm has gained considerable popularity in academic and industrial circles. It serves as a key enabler for many future technologies like 5G, Internet of Things (IoT), augmented reality and vehicle-to-vehicle communications by connecting cloud computing facilities and services to the end users. The Edge computing paradigm provides low latency, mobility, and location awareness support to delay-sensitive applications. Significant research has been carried out in the area of Edge computing, which is reviewed in terms of latest developments such as Mobile Edge Computing, Cloudlet, and Fog computing, resulting in providing researchers with more insight into the existing solutions and future applications. This article is meant to serve as a comprehensive survey of recent advancements in Edge computing highlighting the core applications. It also discusses the importance of Edge computing in real life scenarios where response time constitutes the fundamental requirement for many applications. The article concludes with identifying the requirements and discuss open research challenges in Edge computing. Keywords: Mobile edge computing; Edge computing; Cloudlets; Fog computing; Micro clouds; Cloud computing

https://www.sciencedirect.com/science/article/abs/pii/S1383762121001570 Akhirul Islam, Arindam Debnath, Manojit Ghose, Suchetana Chakraborty, A Survey on Task Offloading in Multi-access Edge Computing, Journal of Systems Architecture, Volume 118, 2021, 102225, ISSN 1383-7621, https://doi.org/10.1016/j.sysarc.2021.102225. (https://www.sciencedirect.com/science/article/pii/S1383762121001570) Abstract: With the advent of new technologies in both hardware and software, we are in the need of a new type of application that requires huge computation power and minimal delay. Applications such as face recognition, augmented reality, virtual reality, automated vehicles, industrial IoT, etc. belong to this category. Cloud computing technology is one of the candidates to satisfy the computation requirement of resource-intensive applications running in UEs (User Equipment) as it has ample computational capacity, but the latency requirement for these applications cannot be satisfied by the cloud due to the propagation delay between UEs and the cloud. To solve the latency issues for the delay-sensitive applications a new network paradigm has emerged recently known as Multi-Access Edge Computing (MEC) (also known as mobile edge computing) in which computation can be done at the network edge of UE devices. To execute the resource-intensive tasks of UEs in the MEC servers hosted in the network edge, a UE device has to offload some of the tasks to MEC servers. Few survey papers talk about task offloading in MEC, but most of them do not have in-depth analysis and classification exclusive to MEC task offloading. In this paper, we are providing a comprehensive survey on the task offloading scheme for MEC proposed by many researchers. We will also discuss issues, challenges, and future research direction in the area of task offloading to MEC servers. Keywords: Multi-access edge computing; Task offloading; Mobile edge computing; Survey

7.4 Energy-Efficient Edge Architectures

The exponential growth of the Internet of Things (IoT) devices, coupled with the emergence of artificial intelligence (AI) and high-speed communication networks (5G/6G), has led to the proliferation of edge computing. In an edge computing paradigm, data processing is distributed away from centralized cloud data centers and relocated closer to the data source or end-users~\cite{Satyanarayanan2019}. This architectural shift offers benefits such as reduced network latency, efficient bandwidth usage, and real-time analytics~\cite{Shi2020}. However, the distribution of processing to a multiplicity of geographically dispersed devices has profound implications for energy consumption. Although large-scale data centers have been the subject of extensive research concerning energy efficiency, smaller edge nodes, including micro data centers, IoT gateways, and embedded systems, also generate significant carbon emissions~\cite{Abdollahi2019,Zhou2019}.