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1. MSDBench: Understanding the Performance Impact of Isolation Domains on Microservice-Based IoT Deployments

   Wang, S. ; Bakir, F. ; Ekaireb, T. ; Pearson, J. ; Krintz, C. ; Wolski, R. ( January 2022 , Benchmarking, Measuring, and Optimizing)

   Gainaru, A. ; Zhang, C. ; Luo, C. (Ed.)

   We present MSDBench – a set of benchmarks designed to illuminate the effects of deployment choices and operating system ab- stractions on microservices performance in IoT settings. The microser- vices architecture has emerged as a mainstay set of design principles for cloud-hosted, network-facing applications. Their utility as a design pattern for “The Internet of Things” (IoT) is less well understood. We use MSDBench to show the performance impacts of different deploy- ment choices and isolation domain assignments for Linux and Ambience, an experimental operating system specifically designed to support mi- croservices for IoT. These results indicate that deployment choices can have a dramatic impact on microservices performance, and thus, MSD- Bench is a useful tool for developers and researchers in this space. 

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2. MIRAS: Model-based Reinforcement Learning for Microservice Resource Allocation over Scientific Workflows

   https://doi.org/10.1109/ICDCS.2019.00021  

   Yang, Zhe ; Nguyen, Phuong ; Jin, Haiming ; Nahrstedt, Klara ( July 2019 , 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS))

   Microservice, an architectural design that decomposes applications into loosely coupled services, is adopted in modern software design, including cloud-based scientific workflow processing. The microservice design makes scientific workflow systems more modular, more flexible, and easier to develop. However, cloud deployment of microservice workflow execution systems doesn't come for free, and proper resource management decisions have to be made in order to achieve certain performance objective (e.g., response time) within constraint operation cost. Nevertheless, effective online resource allocation decisions are hard to achieve due to dynamic workloads and the complicated interactions of microservices in each workflow. In this paper, we propose an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning. Our approach (1) assumes little prior knowledge of the microservice workflow system and does not require any elaborately designed model or crafted representative simulator of the underlying system, and (2) avoids high sample complexity which is a common drawback of model-free reinforcement learning when applied to real-world scenarios. We show that our proposed approach automatically achieves effective policy for resource allocation with limited number of time-consuming interactions with the microservice workflow system. We perform extensive evaluations to validate the effectiveness of our approach and demonstrate that it outperforms existing resource allocation approaches with read-world emulated workflows. 

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3. Twister2: Design of a big data toolkit

   https://doi.org/10.1002/cpe.5189  

   Kamburugamuve, Supun ; Govindarajan, Kannan ; Wickramasinghe, Pulasthi ; Abeykoon, Vibhatha ; Fox, Geoffrey ( March 2019 , Concurrency and Computation: Practice and Experience)

   Data‐driven applications are essential to handle the ever‐increasing volume, velocity, and veracity of data generated by sources such as the Web and Internet of Things (IoT) devices. Simultaneously, an event‐driven computational paradigm is emerging as the core of modern systems designed for database queries, data analytics, and on‐demand applications. Modern big data processing runtimes and asynchronous many task (AMT) systems from high performance computing (HPC) community have adopted dataflow event‐driven model. The services are increasingly moving to an event‐driven model in the form of Function as a Service (FaaS) to compose services. An event‐driven runtime designed for data processing consists of well‐understood components such as communication, scheduling, and fault tolerance. Different design choices adopted by these components determine the type of applications a system can support efficiently. We find that modern systems are limited to specific sets of applications because they have been designed with fixed choices that cannot be changed easily. In this paper, we present a loosely coupled component‐based design of a big data toolkit where each component can have different implementations to support various applications. Such a polymorphic design would allow services and data analytics to be integrated seamlessly and expand from edge to cloud to HPC environments.

    

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4. Pushing the Physical Limits of IoT Devices with Programmable Metasurfaces

   Chen, L. ; Hu, W. ; Jamieson, K. ; Chen, X. ; Fang, D. ; Gummeson, J. ( April 2021 , Proceedings of the USENIX NSDI Symposium)

   Small, low-cost IoT devices are typically equipped with only a single, low-quality antenna, significantly limiting communication range and link quality. In particular, these antennas are typically linearly polarized and therefore susceptible to polarization mismatch, which can easily cause 10-15 dBm of link loss on communication to and from such devices. In this work, we highlight this under-appreciated issue and propose the augmentation of IoT deployment environments with programmable, RF-sensitive surfaces made of metamaterials. Our smart meta-surface mitigates polarization mismatch by rotating the polarization of signals that pass through or reflect off the surface. We integrate our metasurface into an IoT network as LLAMA, a Low-power Lattice of Actuated Metasurface Antennas, designed for the pervasively used 2.4 GHz ISM band. We optimize LLAMA’s metasurface design for both low transmission loss and low cost, to facilitate deployment at scale. We then build an end-to-end system that actuates the metasurface structure to optimize for link performance in real time. Our experimental prototype-based evaluation demonstrates gains in link power of up to 15 dBm, and wireless capacity improvements of 100 and 180 Kbit/s/Hz in through-surface and surface-reflective scenarios, respectively, attributable to the polarization rotation properties of LLAMA’s metasurface. 

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5. A Novel ROS2 QoS Policy-enabled Synchronizing Middleware for Co-simulation of Heterogeneous Multi-Robot Systems

   Dey, E ; Walczak, M ; Anwar, M ; Roy, N ; Freeman, J ; Gregory, T ; Suri, N ; Busart, C ( July 2023 , 32nd IEEE International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, USA, July 2023)

   Recent Internet-of-Things (IoT) networks span across a multitude of stationary and robotic devices, namely unmanned ground vehicles, surface vessels, and aerial drones, to carry out mission-critical services such as search and rescue operations, wildfire monitoring, and flood/hurricane impact assessment. Achieving communication synchrony, reliability, and minimal communication jitter among these devices is a key challenge both at the simulation and system levels of implementation due to the underpinning differences between a physics-based robot operating system (ROS) simulator that is time-based and a network-based wireless simulator that is event-based, in addition to the complex dynamics of mobile and heterogeneous IoT devices deployed in a real environment. Nevertheless, synchronization between physics (robotics) and network simulators is one of the most difficult issues to address in simulating a heterogeneous multi-robot system before transitioning it into practice. The existing TCP/IP communication protocol-based synchronizing middleware mostly relied on Robot Operating System 1 (ROS1), which expends a significant portion of communication bandwidth and time due to its master-based architecture. To address these issues, we design a novel synchronizing middleware between robotics and traditional wireless network simulators, relying on the newly released real-time ROS2 architecture with a master-less packet discovery mechanism. Additionally, we propose a ground and aerial agents’ velocity-aware customized QoS policy for Data Distribution Service (DDS) to minimize the packet loss and transmission latency between a diverse set of robotic agents, and we offer the theoretical guarantee of our proposed QoS policy. We performed extensive network performance evaluations both at the simulation and system levels in terms of packet loss probability and average latency with line-of-sight (LOS) and non-line-of-sight (NLOS) and TCP/UDP communication protocols over our proposed ROS2-based synchronization middleware. Moreover, for a comparative study, we presented a detailed ablation study replacing NS-3 with a real-time wireless network simulator, EMANE, and masterless ROS2 with master-based ROS1. Our proposed middleware attests to the promise of building a largescale IoT infrastructure with a diverse set of stationary and robotic devices that achieve low-latency communications (12% and 11% reduction in simulation and reality, respectively) while satisfying the reliability (10% and 15% packet loss reduction in simulation and reality, respectively) and high-fidelity requirements of mission-critical applications. 

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