This paper tries to introduce the notion of edge computing and cloud computing technologies. To tackle huge amounts of information, also called Big Data, Cloud computing technologies along with Edge Computing technologies were introduced. Edge computing technology helps in storing data. The information can further be analyzed and processed in proximity to the source itself due to its location on the edges. Edge computing technology and cloud computing are dissimilar in terms of speed of processing, the quantity of analyzed data, and the location where analysis and processing take place, etc. Challenges such as the restricted availability of data in edge computing can be solved by Cloud Computing since it was designed for Big Data and provides a large coverage. This paper also discusses the future trends and developing technologies such as software-defined networking (SDN) and network functions virtualization (NFV). Due to edge computing technology, the efficiency of cloud computing can be increased and the response time can be reduced significantly. Cloud offloading, smart cities, and video analytics are some of the applications of edge computing.
Revolutions in the industry have accelerated the constant increase in the amount of data and
information which is acquired. The main contributor to this is the Internet of Technology or IoT.
Manufacturing challenges started to erupt due to Big Data, which is this huge amount of raw data that
is generated. Powerful software applications need to be built for the storage, analysis, and processing
of these complex and large datasets. These software applications perform data analysis and thus help
in providing information to manufacturing companies enhancing their production [
Cloud computing is a term used to describe a type of data storage and processing technology. Cloud computing refers to centralized, distributed, and parallel computer systems that are dynamically provided [20]. Data analytics, or the analysis of data, is greatly aided by cloud computing. Big Data Analytics refers to data analytics performed on large amounts of data [22].
Edge computing refers to technology that enables computation near data sources at the network's
edge. On behalf of cloud and IoT services, it works with both downstream and upstream data
[23][24]. Computational or networking devices that lie between cloud-based data centers and
information sources are known as edge devices. A mobile phone, for example, might be placed
between cloud and body sensors, or a cloudlet or mDC could be used to segregate a mobile device
from the cloud [
Edge has the ability to supply a host of advantages in real-world applications. Researchers have
already shown that offloading computing operations for wearable assistance systems which are
cognitive, utilizing cloudlets enhance reply time by 80 to 200 milliseconds while reducing energy
consumption by 30 to 40%. Thanks to edge computing, CloneCloud technology improves reaction
time and energy consumption by 95% for evaluated applications [
A number of challenges, including system reliability, should be considered by anyone working with edge computing. Insufficient wireless connections or limited battery life may also cause energyconstrained edge devices to fail. Developers should nonetheless allow the system to perform its fundamental operations in these circumstances. [23]. Security and privacy are other important factors to consider. Edge computing, on the one hand, safeguards data better than cloud computing because preparation takes place near the source. Due to network topology, the vast number of low-cost personal smartphone equipment in the system, and sensor inconsistency, edge computing makes security and privacy more difficult [31][32].
The methods of storing and processing information are comparable with respect to Cloud Computing technologies and Edge Computing technologies. The differences among these techniques are dependent on the location of storing and processing and time of response as shown in Figure 1. Because of such differences and challenges, some computer technologies open up opportunities for others, and vice versa. Thus, this compares the challenges and potential in Cloud Computing and Edge Computing using various key variables chosen based on Data Analytics requirements presented in Table 1 [23]. 4. Edge Computing’s Challenges and Cloud Computing’s Opportunities in
The development of Edge Computing technology is still in its early phases. Foundations such as
AWS, Azure, and App Engine [
The power of Edge Computing will process a limited amount of data because data storage and processing are done in the same location. Devices based on Edge Computing are designed to perform the processing of data on small databases as a result. However, in the case of Industrial 4.0, where large amounts of data are generated on a daily basis, data analysis requires additional computational capacity, which can be brought about by Cloud Computing technology. The data processed amount is directly proportional to computing power, and Edge Computational technology lacks sufficient computing energy for data processing due to hardware hindrance. Notably, Cloud Processing has the ability to provide greater computing capacity, resulting in cloud computing jobs being more efficient. Standardization is the process of creating an open environment in which industry and academia can collaborate on a common platform [34]. However, as a novel technology, Edge Computing is not completely adopted because of the unavailability of a standardized IoT ecosystem that will result in an uninterrupted and proficient homogenization of all devices through a defined agreement inside the environment of Industry 4.0 [19] [21].
Edge computing was introduced for increasing the efficiency of cloud computing. With the help
from edge computing, cloud computing performance can be increased in the following ways:
• Decreased response time - When the data is produced at the source, it is processed/ prepared
at the edge, and this decreases the response time and thus, increasing the processing
efficiency and reduction of pressure on the network [
The Internet is fast-moving, looking ahead "Internet of Things," which might link billions or perhaps hundreds of trillions of edge devices to create massive amounts of data at a high rate, with certain applications requiring extremely low latency.[25] Due to the compact computer storage, and network connectivity for the few numbers of data service centers, as well as across long distances between peripheral devices and secluded data service centers, typical cloud architecture will face a number of challenges. To address this issue, edge computing and edge cloud appear as viable options, since they bring assets near to assets-constrained edge IoT projects and may help to foster the latest IoT ecosystem, i.e. open innovation. New developing technologies, such as software-defined networking (SDN) and network functions virtualization (NFV) made this possible. Figure 3 depicts a basic depiction of the link between open innovation, SDN, and NFV.
NFV and SDN are extremely complementary from a technological standpoint for edge cloud
potential. Software vendors, device suppliers, ASPs, and ISPs are among non-technical players in the
upcoming edge cloud and market of application. Upcoming 5G technology and the "Network
Softwarization" trend put NFV and SDN integration into a larger perspective. By 2024, 5G networks
are expected to provide greatly upgraded and programmable network architecture, with video traffic
dominating cell networks, IoT and large data processing, Virtual Reality (VR), and exploding being
widely available and delivered with minimal latency [
To further adorn our vision of edge computing, we provide several real-world examples where edge computing can be applied very efficiently and meaningfully.
• Concept of cloud offloading: In a typical cloud computing model, the majority of
calculations are performed in the cloud environment, which implies that requests and
information are handled in a centralized environment. Because the edge possesses specific
computation resources, edge computing enables a portion of the burden to be offloaded from
the cloud. This is because the content provider, as has been the case for decades, makes the
data available on the Internet. At the edge, the IoT generates and consumes data.[
Edge Computing, along with Cloud Computing provides many applications and future opportunities. However, over time, many challenges and issues have developed. Some of them are as follows [16] [27]: • Benchmark Rules: Mobile Edge Computing, or the MEC, has evolved over time. Some standards are needed with the help of industrial collaboration and the agreement of researchers. • •
Well-planned implementation: With efficient MEC deployment, it is possible to reduce latencies by making the best use of bandwidth. However, because of the reliance on complex system components, optimizing spectrum consumption is challenging.
Availability and Security: For assuring consistent service delivery, server capacity and wireless access medium are the most important factors to consider. Along with availability, physical steps should be taken to protect data and applications from intruders [28] [33][34][35].
Cloud computing and Edge computing basically complement each other and are interdependent on
each other, thus benefiting one another mutually. The challenges faced in cloud computing can be
solved using edge computing. Similarly, the limitations of edge computing can be overcome using
cloud computing. The accession problem of Big Data and data storage and processing through the
cloud can be solved with the help of these technologies. Detailed analysis and real-time feedback can
be provided at the user end while performing complex functionalities on the Cloud on a remote basis
[
10.Acknowledgements
We wish to acknowledge the help provided by the technical and support staff of the CCET ACM Student Chapter and the Department of Computer Science & Engineering, CCET (Degree Wing), Chandigarh. We would also like to show our deep appreciation to our supervisors who helped us in the finalization of our research paper.
11.References
J. Pan and J. McElhannon, "Future Edge Cloud and Edge Computing for Internet of Things Applications," in IEEE Internet of Things Journal, vol. 5, no. 1, pp. 439-449, Feb. 2018, doi: 10.1109/JIOT.2017.2767608.
J. Ren, G. Yu, Y. He and G. Y. Li, "Collaborative Cloud and Edge Computing for Latency Minimization," in IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 5031-5044, May 2019, doi: 10.1109/TVT.2019.2904244.
Shahzadi, S., Iqbal, M., Dagiuklas, T., & Qayyum, Z. U. (2017). Multi-access edge computing: open issues, challenges and future perspectives. Journal of Cloud Computing, 6(1), 1-13.
Gou, Z., Yamaguchi, S., et al. (2017). Analysis of various security issues and challenges in the cloud computing environment: a survey. In Identity Theft: Breakthroughs in Research and Practice (pp. 221-247). IGI global.
A. Dahiya (2021), Integration of Cloud and Fog Computing for Energy Efficient and Scalable Services, Insights2Techinfo, pp.1
A Dahiya, B. Gupta (2021), Edge Intelligence: A New Emerging Era, Insights2Techinfo, pp.1
Mishra, A., Gupta, N., & et al. (2020). Security threats and recent countermeasures in cloud computing. In Modern principles, practices, and algorithms for cloud security (pp. 145161). IGI Global.
Gupta, B. B., Yamaguchi, S., & Agrawal, D. P. (2018). Advances in security and privacy of multimedia big data in mobile and cloud computing. Multimedia Tools and Applications, 77(7), 9203-9208.
Zheng, Q., Wang, X., Khan, M. K., Zhang, W., Gupta, B. B., & Guo, W. (2017). A lightweight authenticated encryption scheme based on chaotic scml for railway cloud service. IEEE Access, 6, 711-722.
Negi, P., Mishra, A., et al. (2013). Enhanced CBF packet filtering method to detect DDoS attack in cloud computing environment. arXiv preprint arXiv:1304.7073.
Singh, K. S. (2021). Linux Yourself (1st ed.). Routledge.