AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges

Al-Doghman, Firas; Moustafa, Nour; Khalil, Ibrahim; Tari, Zahir; Zomaya, Albert Y.

doi:10.1109/tsc.2022.3155447

Cited by 59 publications

(13 citation statements)

References 127 publications

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“…As computing is getting pushed away from being cloudbased to edge-based, architectures to facilitate DL model training and deployment are also shifting from monolithic entities to graphs of loosely-coupled microservices [276]. Microservices provide a promising way of modularizing DLbased applications at the process level.…”

Section: ) Microservicesmentioning

confidence: 99%

Enabling All In-Edge Deep Learning: A Literature Review

et al. 2023

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In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition, image processing, and natural language understanding. One of the significant contributors to its success is the proliferation of end devices that acted as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference is the main challenge. Usually, central cloud servers are used for the computation, but it opens up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers (a mesh of computing devices near end devices). Moreover, the confluence point of DL and edge has given rise to edge intelligence (EI). International Electrotechnical Commission (IEC) defines EI as the concept where the data is acquired, stored, and processed utilizing edge computing with DL and advanced networking capabilities. Broadly, EI has six levels of categories based on where the training and inference of DL take place, e.g., cloud server, edge server and end devices. This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important such as in mission-critical applications (e.g., health care). Besides, 5G/6G networks are envisioned to use all in-edge. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism, data parallelism, and split learning, which facilitates DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented. INDEX TERMS Artificial intelligence, all in-edge, deep learning, distributed systems, decentralized systems, edge intelligence I. INTRODUCTION T HE global community is increasingly becoming a datadriven environment in which end devices are generating vast quantities of data outside of the traditional data centers. International Telecommunication Union anticipates that global internet traffic per month will reach 607 Exabytes (EB) in 2025 and 5016 EB in 2030 [1]. This enormous amount of data has a positive impact on artificial intelligence (AI) applications. In particular, deep learning (DL) rely on the availability of large quantities of data for its d...

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Section: ) Microservicesmentioning

confidence: 99%

Enabling All In-Edge Deep Learning: A Literature Review

et al. 2023

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“…To further evaluate the effectiveness of CoScal, we also conduct experiments with another Docker-based microservice application, named Stan's Robot Shop 4 , which is an online system composed of 12 microservices for robots sale. We use Locust 5 , a Python-based testing tool similar to JMeter, to generate requests based on Alibaba's trace [17], and send the requests to the homepage of Stan's Robot Shop.…”

Section: Experimental Analysis With Stan's Robot Shopmentioning

confidence: 99%

CoScal: Multifaceted Scaling of Microservices With Reinforcement Learning

Song

Ilager

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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The emerging trend towards moving from monolithic applications to microservices has raised new performance challenges in cloud computing environments. Compared with traditional monolithic applications, the microservices are lightweight, fine-grained, and must be executed in a shorter time. Efficient scaling approaches are required to ensure microservices' system performance under diverse workloads with strict Quality of Service (QoS) requirements and optimize resource provisioning. To solve this problem, we investigate the trade-offs between the dominant scaling techniques, including horizontal scaling, vertical scaling, and brownout in terms of execution cost and response time. We first present a prediction algorithm based on gradient recurrent units to accurately predict workloads assisting in scaling to achieve efficient scaling. Further, we propose a multi-faceted scaling approach using reinforcement learning called CoScal to learn the scaling techniques efficiently. The proposed CoScal approach takes full advantage of datadriven decisions and improves the system performance in terms of high communication cost and delay. We validate our proposed solution by implementing a containerized microservice prototype system and evaluated with two microservice applications. The extensive experiments demonstrate that CoScal reduces response time by 19% to 29% and decreases the connection time of services by 16% when compared with the state-of-the-art scaling techniques.

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“…Subsequent works deal in general with the design of microservice systems in order to ensure their best performance [ 23 , 24 , 25 , 26 ]. Another important aspect that appears in the literature and influences the design of sensor systems is security [ 27 ], in particular in edge computing, and the authors analyzed solutions based on AI.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Representative Microservices Technologies in Terms of Performance for Use for Projects Based on Sensor Networks

Plecinski

Bokla

Klymkovych

et al. 2022

Sensors

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Reading and analyzing data from sensors are crucial in many areas of life. IoT concepts and related issues are becoming more and more popular, but before we can process data and draw conclusions, we need to think about how to design an application. The most popular solutions today are microservices and monolithic architecture. In addition to this choice, there is also the question of the technology in which you will work. There are more and more of them on the market and in each of them it is practically possible to achieve similar results, but the difference lies in how quickly it will be possible and whether the approach invented will turn out to be the most optimal. Making the right decisions at the beginning of application development can determine its path to success or failure. The main goal of this article was to compare technologies used in applications based on microservice architecture. The preparation of a book lending system, whose server part was implemented in three different versions, each using a different type of technology, helped to achieve this goal. The compared solutions were: Spring Boot, Micronaut and Quarkus. The reason for this research was to investigate projects using sensor networks, ranging from telemedicine applications to extensive sensor networks collecting scientific data, or working in an environment with limited resources, e.g., with BLE or WIFI transmitters, where it is critical to supply energy to these transmitters. Therefore, the issue of efficiency and hence energy savings may be a key issue depending on the selected programming technology.

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AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges

Cited by 59 publications

References 127 publications

Enabling All In-Edge Deep Learning: A Literature Review

Enabling All In-Edge Deep Learning: A Literature Review

CoScal: Multifaceted Scaling of Microservices With Reinforcement Learning

Comparison of Representative Microservices Technologies in Terms of Performance for Use for Projects Based on Sensor Networks

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