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1. Service Placement for Real-Time Applications: Rate-Adaptation and Load-Balancing at the Network Edge

   Kassir, Saadallah; de Veciana, Gustavo; Wang, Nannan; Wang, Xi; Palacharla, Paparao (September 2020, International Conference on Edge Computing)

   Mobile Edge Computing may become a prevalent platform to support applications where mobile devices have limited compute, storage, energy and/or data privacy concerns. In this paper, we study the efficient provisioning and man- agement of compute resources in the Edge-to-Cloud continuum for different types of real-time applications with timeliness requirements depending on application-level update rates and communication/compute delays. We begin by introducing a highly stylized network model allowing us to study the salient features of this problem including its sensitivity to compute vs. communication costs, application requirements, and traffic load variability. We then propose an online decentralized service placement algorithm, based on estimating network delays and adapting application update rates, which achieves high service availability. Our results exhibit how placement can be optimized and how a load-balancing strategy c 

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2. An Age Control Transport Protocol for Delivering Fresh Updates in the Internet-of-Things

   https://doi.org/10.1109/WoWMoM.2019.8793011  

   Shreedhar, Tanya; Kaul, Sanjit K.; Yates, Roy D. (June 2019, 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM))

   Internet-of-Things (IoT) applications have sources sense and send their measurement updates over the Internet to a monitor (control station) for real-time monitoring and actuation. Ideally, these updates would be delivered fresh, at a high rate constrained only by the supported sensing rate. However, such a rate may lead to network congestion related delays in delivery of updates at the monitor that make the freshest update at the monitor unacceptably old for the application. Alternately, at low rates, while updates arrive at the monitor with smaller delays, new updates arrive infrequently. Thus, both low and high rates may lead to an undesirably aged freshest update at the monitor. We propose a novel transport layer protocol, namely the Age Control Protocol (ACP), which enables timely delivery of such updates to monitors over the Internet in a network-transparent manner. ACP adapts the rate of updates from a source such that the average age of updates at the monitor is minimized. We detail the protocol and the proposed control algorithm. We demonstrate its efficacy using extensive simulations and realworld experiments, including wireless access for the sources and an end-to-end connection with multiple hops to the monitor. 

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3. Reinforcement Learning for Spacecraft Attitude Control

   Vedant, James T (October 2019, 70th International Astronautical Congress)

   Reinforcement learning (RL) has recently shown promise in solving difficult numerical problems and has discovered non-intuitive solutions to existing problems. This study investigates the ability of a general RL agent to find an optimal control strategy for spacecraft attitude control problems. Two main types of Attitude Control Systems (ACS) are presented. First, the general ACS problem with full actuation is considered, but with saturation constraints on the applied torques, representing thruster-based ACSs. Second, an attitude control problem with reaction wheel based ACS is considered, which has more constraints on control authority. The agent is trained using the Proximal Policy Optimization (PPO) RL method to obtain an attitude control policy. To ensure robustness, the inertia of the satellite is unknown to the control agent and is randomized for each simulation. To achieve efficient learning, the agent is trained using curriculum learning. We compare the RL based controller to a QRF (quaternion rate feedback) attitude controller, a well-established state feedback control strategy. We investigate the nominal performance and robustness with respect to uncertainty in system dynamics. Our RL based attitude control agent adapts to any spacecraft mass without needing to re-train. In the range of 0.1 to 100,000 kg, our agent achieves 2% better performance to a QRF controller tuned for the same mass range, and similar performance to the QRF controller tuned specifically for a given mass. The performance of the trained RL agent for the reaction wheel based ACS achieved 10 higher better reward then that of a tuned QRF controller 

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4. Time Delay Control of a High-DOF Robot Manipulator Through Feedback Linearization Based Predictor

   https://doi.org/https://doi.org/10.1115/DSCC2019-8915  

   Bagheri, Mostafa; Naseradinmousavi, Peiman; Krstic, Miroslav (November 2019, ASME 2019 Dynamic Systems and Control Conference)

   We formulate a predictor-based controller for a high-DOF manipulator to compensate a time-invariant input delay during a pick-and-place task. Robot manipulators are widely used in tele-manipulation systems on the account of their reliable, fast, and precise motions while they are subject to large delays. Using common control algorithms on such delay systems can cause not only poor control performance, but also catastrophic instability in engineering applications. Therefore, delays need to be compensated in designing robust control laws. As a case study, we focus on a 7-DOF Baxter manipulator subject to three different input delays. First, delay-free dynamic equations of the Baxter manipulator are derived using the Lagrangian method. Then, we formulate a predictor-based controller, in the presence of input delay, in order to track desired trajectories. Finally, the effects of input delays in the absence of a robust predictor are investigated, and then the performance of the predictor-based controller is experimentally evaluated to reveal robustness of the algorithm formulated. Simulation and experimental results demonstrate that the predictor-based controller effectively compensates input delays and achieves closed-loop stability. 

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5. Testing Cloud Applications under Cloud-Uncertainty Performance Effects

   Wang, Wei; Tian, Ningjing; Huang, Sunzhou; He, Sen; Srivastava, Abhijeet; Soffa, Mary Lou; Pollock, Lori (April 2018, the IEEE Conference on Software Testing, Validation and Verification (ICST))

   The paradigm shift of deploying applications to the cloud has introduced both opportunities and challenges. Although clouds use elasticity to scale resource usage at runtime to help meet an application’s performance requirements, developers are still challenged by unpredictable performance, little control of execution environment, and differences among cloud service providers, all while being charged for their cloud usages. Application performance stability is particularly affected by multi-tenancy in which the hardware is shared among varying applications and virtual machines. Developers porting their applications need to meet performance requirements, but testing on the cloud under the effects of performance uncertainty is difficult and expensive, due to high cloud usage costs. This paper presents a first approach to testing an application with typical inputs for how its performance will be affected by performance uncertainty, without incurring undue costs of bruteforce testing in the cloud. We specify cloud uncertainty testing criteria, design a test-based strategy to characterize the blackbox cloud’s performance distributions using these testing criteria, and support execution of tests to characterize the resource usage and cloud baseline performance of the application to be deployed. Importantly, we developed a smart test oracle that estimates the application’s performance with certain confidence levels using the above characterization test results and determines whether it will meet its performance requirements. We evaluated our testing approach on both the Chameleon cloud and Amazon web services; results indicate that this testing strategy shows promise as a cost-effective approach to test for performance effects of cloud uncertainty when porting an application to the cloud. 

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