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1. Experimenting in a Global Multi-Domain Testbed

   https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484588  

   Municio, Esteban ; Cevik, Mert ; Ruth, Paul ; Marquez-Barja, Johann M. ( January 2021 , IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

   Upcoming AI-based and 5G applications are demanding new network management approaches that are capable to cope with unprecedented levels of flexibility, scalability and energy efficiency. In order to make these use cases tangible and feasible, network management solutions aim to rely on multi-domain, multi-tier architectures that permit complex end-to-end orchestration of network resources. However, current research on scheduling functions and task-offloading algorithms often focus on one single-domain, and the exploration of large-scale inter-operable solutions becomes a challenge. Fortunately for the networking research community, a number of available testing facilities deployed at different geographical location along the world can be integrated to be used as a single joint multi-domain infrastructure. In this demo paper, we present a hands-off experience of how to integrate different high-performance testbeds, located in USA, Belgium and The Netherlands, in order to enable multi-domain large-scale experimentation. We demonstrate end-to-end performance characteristics of the testbed integration and we describe the main takeaways and lessons learned to drive researchers towards successful deployments in such end-to-end global infrastructure. 

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2. Intelligence Augmentation for Collaborative Learning

   Roschelle, Jeremy ( January 2021 , HCI International)

   null (Ed.)

   Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps us see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years. 

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3. Equivalence-Enhanced Microservice Workflow Orchestration to Efficiently Increase Reliability

   https://doi.org/10.1109/ICWS.2019.00076  

   Song, Zheng ; Tilevich, Eli ( July 2019 , 2019 IEEE International Conference on Web Services (ICWS))

   The applicability of the microservice architecture has extended beyond traditional web services, making steady inroads into the domains of IoT and edge computing. Due to dissimilar contexts in different execution environments and inherent mobility, edge and IoT applications suffer from low execution reliability. Replication, traditionally used to increase service reliability and scalability, is inapplicable in these resourcescarce environments. Alternately, programmers can orchestrate the parallel or sequential execution of equivalent microservices— microservices that provide the same functionality by different means. Unfortunately, the resulting orchestrations rely on parallelization, synchronization, and failure handing, all tedious and error-prone to implement. Although automated orchestration shifts the burden of generating workflows from the programmer to the compiler, existing programming models lack both syntactic and semantic support for equivalence. In this paper, we enhance compiler-generated execution orchestration with equivalence to efficiently increase reliability. We introduce a dataflow-based domain-specific language, whose dataflow specifications include the implicit declarations of equivalent microservices and their execution patterns. To automatically generate reliable workflows and execute them efficiently, we introduce new equivalence workflow constructs. Our evaluation results indicate that our solution can effectively and efficiently increase the reliability of microservice-based applications. 

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4. FAM-Graph: Graph Analytics on Disaggregated Memory

   https://doi.org/10.1109/IPDPS53621.2022.00017  

   Zahka, Daniel ; Gavrilovska, Ada ( May 2022 , IEEE)

   Disaggregated memory is being proposed as a way to provide efficient memory scaling for data intensive applications. High performance interconnect technologies, such as CXL, make disaggregated, fabric-attached-memory (FAM) a viable secondary tier of memory. Previous work on remote memory relies on extending kernel level paging to utilize FAM as an additional storage tier after local memory. These approaches have the advantage of exposing remote memory in application transparent ways that do not require code changes, but they incur large overheads due to the mismatch between the abstraction of a flat virtual address space and the reality of the tiered nature of FAM. In this paper, we present an alternative approach to remote memory based on application-specific objects. We design FAM-Graph - a semi-external graph processing system that leverages application-level properties, such as read only edge data, to efficiently tier data between local and remote memory, and prefetch remote data for local computation. Using several graph algorithms and datasets, we demonstrate that FAM-Graph achieves end-to-end performance within factors of 1–6× of Galois, the state of the art shared memory graph processing system, while using up to 20× less local memory. When Galois is used in conjunction with an OS-level FAM solution, we show that FAM-Graph achieves better end-to-end performance by up to 9× when both systems are configured with the same amount of local memory. 

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5. Demo: EdgeVPN.io: Open-source Virtual Private Network for Seamless Edge Computing with Kubernetes

   https://doi.org/10.1109/SEC50012.2020.00032  

   Figueiredo, Renato ; Subratie, Kensworth ( November 2020 , 2020 IEEE/ACM Symposium on Edge Computing (SEC))

   null (Ed.)

   Edge and fog computing encompass a variety of technologies that are poised to enable new applications across the Internet that support data capture, storage, processing, and communication across the networking continuum. These environments pose new challenges to the design and implementation of networks-as membership can be dynamic and devices are heterogeneous, widely distributed geographically, and in proximity to end-users, as is the case with mobile and Internet-of-Things (IoT) devices. We present a demonstration of EdgeVPN.io (Evio for short), an open-source programmable, software-defined network that addresses challenges in the deployment of virtual networks spanning distributed edge and cloud resources, in particular highlighting its use in support of the Kubernetes container orchestration middleware. The demo highlights a deployment of unmodified Kubernetes middleware across a virtual cluster comprising virtual machines deployed both in cloud providers, and in distinct networks at the edge-where all nodes are assigned private IP addresses and subject to different NAT (Network Address Translation) middleboxes, connected through an Evio virtual network. The demo includes an overview of the configuration of Kubernetes and Evio nodes and the deployment of Docker-based container pods, highlighting the seamless connectivity for TCP/IP applications deployed on the pods. 

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