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1. Building a Student-to-Workforce Pipeline for 21st Century Cloud Industry Careers

   https://doi.org/10.18260/1-2--36764  

   Billionniere, Elodie; Meyer, Lawrence (July 2021, 2021 ASEE Virtual Annual Conference Proceedings)

   Cloud migration has accelerated and companies around the world are investing their technological future with the cloud. Given the growing industry demand for cloud related skills, Miami Dade College partnered with the industry leader in cloud computing solutions Amazon Web Services (AWS). In conjunction with AWS, we developed a new cloud-based learning curriculum designed to provide an academic gateway for the next generation of computing technicians to meet local and national workforce demands. The recruitment population focused on predominantly minoritized and low-income populations. This certificate-based curriculum is designed as stackable for both the successful completion of a College Credit Certificate and/or an Associate of Science in Networking Technology with a concentration in Enterprise Cloud Computing. The curriculum was developed in alignment with industry needs and utilizes high impact educational practices. The cloud computing programs prepare students to potentially earn an academic credential and globally recognized industry certifications at the entry (college credit certificate) and associate levels. Ensuring students are offered the highest standard of training, faculty members completed professional development in cloud computing, earning industry-recognized credentials to become AWS-accredited instructors. The current cohort of faculty certified to teach in the AWS Academy is the largest group of certified instructors in the academic arena. The inclusion of industry standard certifications from major cloud providers allows for consistency of program evaluation and instructors assessment of student work and course comprehension. The net effect of these certifications is not only earned degrees, but employer ability to validate prospective employee skill and knowledge outside of an academic environment. This paper presents the approach followed in developing in-depth, project-based learning opportunities using cutting-edge technology for the new academic pathway in cloud literacy and the program outcomes. A discussion on the best practices and lessons learned while implementing the first year of the program is included. 

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2. Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives

   Wang, Jianyu; Pan, Jianli; Esposito, Flavio; Calyam, Prasad; Yang, Zhicheng; Mohapatra, Prasant. (October 2018, ACM computing surveys)

   Mobile devices supporting the "Internet of Things" (IoT), often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality (VR), augmented reality (AR), multimedia delivery and artificial intelligence (AI), which could require broad bandwidth, low response latency and large computational power. Edge cloud or edge computing is an emerging topic and technology that can tackle the deficiency of the currently centralized-only cloud computing model and move the computation and storage resource closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and “offloading” algorithms are needed to allow the mobile devices and the edge cloud to work together smoothly. In this survey paper, we investigate the key issues, methods, and various state-of-the-art efforts related to the offloading problem. We adopt a new characterizing model to study the whole process of offloading from mobile devices to the edge cloud. Through comprehensive discussions, we aim to draw an overall “big picture” on the existing efforts and research directions. Our study also indicates that the offloading algorithms in edge cloud have demonstrated profound potentials for future technology and application development. 

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3. Attacking and Protecting Data Privacy in Edge-Cloud Collaborative Inference Systems

   https://doi.org/10.1109/JIOT.2020.3022358  

   He, Zecheng; Zhang, Tianwei; Lee, Ruby B. (September 2020, IEEE Internet of Things Journal)

   null (Ed.)

   Benefiting from the advance of Deep Learning technology, IoT devices and systems are becoming more intelligent and multi-functional. They are expected to run various Deep Learning inference tasks with high efficiency and performance. This requirement is challenged by the mismatch between the limited computing capability of edge devices and large-scale Deep Neural Networks. Edge-cloud collaborative systems are then introduced to mitigate this conflict, enabling resource-constrained IoT devices to host arbitrary Deep Learning applications. However, the introduction of third-party clouds can bring potential privacy issues to edge computing. In this paper, we conduct a systematic study about the opportunities of attacking and protecting the privacy of edge-cloud collaborative systems. Our contributions are twofold: (1) we first devise a set of new attacks for an untrusted cloud to recover arbitrary inputs fed into the system, even if the attacker has no access to the edge device’s data or computations, or permissions to query this system. (2) We empirically demonstrate that solutions that add noise fail to defeat our proposed attacks, and then propose two more effective defense methods. This provides insights and guidelines to develop more privacy-preserving collaborative systems and algorithms. 

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4. CloudShield: Real-time Anomaly Detection in the Cloud

   Zecheng He, Ruby B. (August 2021, arXiv Computer Science Cryptography and Security)

   In cloud computing, it is desirable if suspicious activities can be detected by automatic anomaly detection systems. Although anomaly detection has been investigated in the past, it remains unsolved in cloud computing. Challenges are: characterizing the normal behavior of a cloud server, distinguishing between benign and malicious anomalies (attacks), and preventing alert fatigue due to false alarms. We propose CloudShield, a practical and generalizable real-time anomaly and attack detection system for cloud computing. Cloudshield uses a general, pretrained deep learning model with different cloud workloads, to predict the normal behavior and provide real-time and continuous detection by examining the model reconstruction error distributions. Once an anomaly is detected, to reduce alert fatigue, CloudShield automatically distinguishes between benign programs, known attacks, and zero-day attacks, by examining the prediction error distributions. We evaluate the proposed CloudShield on representative cloud benchmarks. Our evaluation shows that CloudShield, using model pretraining, can apply to a wide scope of cloud workloads. Especially, we observe that CloudShield can detect the recently proposed speculative execution attacks, e.g., Spectre and Meltdown attacks, in milliseconds. Furthermore, we show that CloudShield accurately differentiates and prioritizes known attacks, and potential zero-day attacks, from benign programs. Thus, it significantly reduces false alarms by up to 99.0%. 

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5. A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm

   https://doi.org/10.1007/978-3-031-45878-1_5  

   Jamali, Hossein; Shill, Ponkoj Chandra; Feil-Seifer, David; Harris, Frederick C; Dascalu, Sergiu M (October 2023, Springer Nature Switzerland)

   Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan. 

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