A series of previous Working Groups has met at ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula, including mapping industry job skills to knowledge areas (KAs) and KAs to student learning objectives (LOs). The importance of industry-standard learning content and certification, produced by cloud vendors and others, was apparent throughout this work. This Working Group will focus on the role of certification within cloud computing curricula, from the viewpoints of a range of stakeholders: students, graduates, institutions, vendors and other certification providers; and employers. Areas for study will include: the scope of available certifications and their mapping to our KAs and LOs; approaches to integrating certification in academic cloud curricula, and challenges involved in doing so; and perceptions of the stakeholders of the role and value of certifications in evidencing employability. The outcome of the work will include a set of recommendations for best practice.

Three previous Working Groups (WG) have met at ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula by mapping industry job skills to knowledge areas (KAs) and KAs to student learning objectives (LOs) and using these as the framework for a repository of learning materials and course exemplars \citefoster2018, foster2019, adams2020. The ongoing value of the work of these WGs will be enhanced by validating the KAs and LOs and their mapping to current job skills and continuing to build a community of educators who will contribute to and benefit from the repository.

The accelerating evolution and adoption of cloud computing services is generating increased demand for job skills in this domain. To address this growth, higher education has identified the importance of cloud computing courses that are practical and compatible with this rapidly changing field. This is especially relevant as cloud services are becoming common computing resources for many new computational approaches and advanced subjects such as machine learning and data science. The ability to incorporate specific components of cloud computing teaching content into a variety of courses has become important. However, the lack of availability of high-quality teaching material that is easy to integrate, when teaching rapidly evolving cloud-related concepts continues to be a challenge for instructors. This working group will try to address this challenge.

Cloud Computing is a rapidly evolving field that is triggering a wave of innovations in various domains such as machine learning and artificial intelligence. Cloud skills are becoming essential for any technology-related profession. Furthermore, the accelerated adoption of cloud technologies by industry is increasing the demand for cloud-trained professionals. Thus, higher education institutions are offering training opportunities and programs in cloud computing, however, the lack of well-rounded and high-quality curricular materials continues to be a challenge for educators. A 2018 Working Group (WG) created a report that --among other artifacts-- described fourteen Knowledge Areas (KAs), with numerous Learning Objectives (LOs) for each KA, to teach cloud concepts. Expanding on that work, this WG will focus on providing a collection of resources that would eventually constitute a model cloud curriculum. By relying on two particular surveys: one that looks at the existing curricular offerings, and another one that maps knowledge areas to job titles in cloud computing, we plan to provide a dynamic and configurable curricular exemplars repository for the cloud community-at-large, following the popularity of hands-on, project-based learning methodologies as our primary focus.

A recent proposed update to TCP congestion control, TCP-newCWV, has targeted congestion control for rate-limited applications. These methods need to be explored in the context of rate-adaptive applications, such as DASH. The new method enables a client to exploit the persistence of a DASH connection and enables the DASH server to rapidly resume transmission of a series of video segments using a single TCP connection. Another technique, called 'Pacing' smoothes DASH bursts when there is no TCP ACK clock, and is shown to significantly reduce burst loss. These two methods in combination can increase the application performance. This paper investigates the effect of implementing these techniques on a DASH flow in different congestion scenarios and whether the method can promote better capacity sharing while minimizing the latency experienced by other flows sharing a common network bottleneck. The results confirm that newCWV with Pacing provides a benefit as a platform for DASH transport.

Multiple description coding (MDC) has been proposed as a possible solution to real-time video delivery over relay-assisted wireless networks to exploit path diversity. In this paper, we study layered multiple description video over relay-assisted mobile networks, such as LTE-A, and develop a framework that routes packets to different relays based on source-channel-relay parameters. The proposed system comprises, besides layered MDC, (i) application-layer forward error correction via Random linear codes (RLC) that are very suitable in the relaying scenarios, due to their rateless nature, (ii) unequal error protection (UEP), using the recently proposed expanding window technique, that relies on probabilistic scheduling of layered packets. We generate layered multiple descriptions using data slicing and data partitioning features of H.264/AVC and conduct simulations by modelling a two-relay LTE-A setup with the finite state Markov chain model. Simulation results show benefits of relaying with optimized routing and UEP.

The error free communication of video data over multi-hop wireless networks is a challenging research problem. Multiple description coding has been proposed as a possible solution to leverage path diversity for error robustness. Forward error correction is an additional protection that can be provided to each description. Random linear codes have had renewed interest fostered by the multi-hop and multi-interface radio receivers. In this study, the descriptions are created using the encoding features of slicing and data partitioning for H.264/AVC video. The unequally protected video is protected with Expanding window-Random linear codes against channel errors. Fading channel error model is used to simulate real-world wireless channels. We also propose an adaptive scheme for video transmission over multiple paths. Such scheme may adapt to the varying channel conditions as is frequently the case in wireless transmission. The results show that the proposed scheme can be used for emerging wireless standards.

Application Layer Forward Error Correction (AL)-FEC is increasingly being employed in the emerging wireless multimedia applications, where the multimedia data is sent along with repair data that can be used at the receiver to recover any losses. Raptor codes and Random Linear Codes (RLC) have emerged as promising rateless coding solutions. The DVB-H standard has adopted Raptor codes for IP datacasting, whereas for real-time communications, Reed-Solomon (RS) codes are used as link-layer FEC. This study compares the performance of Raptor codes and RLC for sending the H.264/AVC compressed video traffic over the DVB-H network. The simulations are performed using error traces depicting physical-layer Transport Stream (TS) packet losses in DVB-H. The study highlights the possibility of using RLC for the AL-FEC by selectively configuring codes for optimum performance.

Rateless codes can be advantageously used to provide Application Layer Forward Error Correction (AL-FEC) with a distinct advantage that an infinite number of packets can be generated on the fly from the source packets. For heterogeneous users and/or variable channel conditions, significant adaptation features and improvement in the video quality can be achieved by partitioning the video data into different priority classes. Such partitioned data can be unequally protected using appropriate FEC scheme based on its contribution to video reconstruction. Expanding Window Random Linear Codes (EW RLC) are a simple unequal error protection fountain coding scheme which can adapt to the prioritized data transmission. In this paper, EW RLC are proposed for broadcasting the H.264/Advanced video coding partitioned with the data partitioning feature. The results show viability of the EW RLC for multimedia broadcast applications to suit different data rates and channel conditions.

Application layer forward error correction is becoming a popular addition to protocols for real-time video delivery over IP-based wireless networks. Since each part of video data is not equally important for video reconstruction, it is beneficial to divide video data based on its importance. Such partitioned data could then be provided with different degree of protection, with the important data having more protection against channel erasures. Data partitioning (DP) is one such low-cost feature in H.264/AVC enabling partitioning of video data based on its importance. In this paper, we propose an Unequal error protection (UEP) scheme to protect the DP H.264/AVC coded video data with Raptor and Random linear codes (RLC). The simulations have been performed using error traces which depict physical-layer Transport Stream (TS) packet losses in DVBH. The results highlight that for broadcasting applications with varying channel conditions, better results can be obtained with dynamic probability of selection of different importance layers.