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1. Characterizing task completion latencies in multi-point multi-quality fog computing systems

   https://doi.org/https://doi.org/10.1016/j.comnet.2020.107526  

   Gorlatova, Maria; Inaltekin, Hazer; Chiang, Mung. (November 2020, Computer networks)

   Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about the potential of fog computing, few comprehensive studies quantitatively characterizing the properties of fog computing architectures have been conducted. In this paper we examine the statistical properties of fog computing task completion latencies, which are important to understand to develop algorithms that match IoT nodes’ tasks with the best execution points within the fog computing substrate. Towards characterizing task completion latencies, we developed and deployed a set of benchmarks in 6 different locations, which included local nodes of different grades, conventional cloud computing services in two different regions, and Amazon Web Services (AWS) and Microsoft Azure serverless computing options. Using the developed infrastructure, we conducted a series of targeted experiments with a node invoking our benchmarks from different locations and in different conditions. The empirical study elucidated several important properties of task execution latencies, including latency variation across different execution points and execution options, and stability with respect to time. The study also demonstrated important properties of serverless execution options, and showed that statistical structure of computing latencies can be accurately characterized based on a small number (only 10–50) of latency samples. The complete measurement set we have captured as part of this study is publicly available. 

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2. Privacy-aware Task Allocation and Data Aggregation in Fog-assisted Spatial Crowdsourcing

   https://doi.org/10.1109/TNSE.2019.2892583  

   Wu, Hai-Qin; Wang, Liangmin; Xue, Guoliang (June 2019, IEEE Transactions on Network Science and Engineering)

   Spatial crowdsourcing (SC) enables task owners (TOs) to outsource spatial-related tasks to a SC-server who engages mobile users in collecting sensing data at some specified locations with their mobile devices. Data aggregation, as a specific SC task, has drawn much attention in mining the potential value of the massive spatial crowdsensing data. However, the release of SC tasks and the execution of data aggregation may pose considerable threats to the privacy of TOs and mobile users, respectively. Besides, it is nontrivial for the SC-server to allocate numerous tasks efficiently and accurately to qualified mobile users, as the SC-server has no knowledge about the entire geographical user distribution. To tackle these issues, in this paper, we introduce a fog-assisted SC architecture, in which many fog nodes deployed in different regions can assist the SC-server to distribute tasks and aggregate data in a privacy-aware manner. Specifically, a privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption. PTAA supports representative aggregate statistics (e.g.,sum, mean, variance, and minimum) with efficient data update while providing strong privacy protection. Security analysis shows that PTAA can achieve the desirable security goals. Extensive experiments also demonstrate its feasibility and efficiency. 

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3. A Comparison of New Swarm Task Allocation Algorithms in Unknown Environments with Varying Task Density

   Cai, Grace; Harasha, Noble; Lynch, Nancy (January 2023, 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023))

   Task allocation is an important problem for robot swarms to solve, allowing agents to reduce task completion time by performing tasks in a distributed fashion. Existing task allocation algorithms often assume prior knowledge of task location and demand or fail to consider the effects of the geometric distribution of tasks on the completion time and communication cost of the algorithms. In this paper, we examine an environment where agents must explore and discover tasks with positive demand and successfully assign themselves to complete all such tasks. We first provide a new dis- crete general model for modeling swarms. Operating within this theoretical framework, we propose two new task allocation algo- rithms for initially unknown environments – one based on N-site selection and the other on virtual pheromones. We analyze each algorithm separately and also evaluate the effectiveness of the two algorithms in dense vs. sparse task distributions. Compared to the Levy walk, which has been theorized to be optimal for foraging, our virtual pheromone inspired algorithm is much faster in sparse to medium task densities but is communication and agent intensive. Our site selection inspired algorithm also outperforms Levy walk in sparse task densities and is a less resource-intensive option than our virtual pheromone algorithm for this case. Because the perfor- mance of both algorithms relative to random walk is dependent on task density, our results shed light on how task density is impor- tant in choosing a task allocation algorithm in initially unknown environments. 

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4. A Comparison of New Swarm Task Allocation Algorithms in Unknown Environments with Varying Task Density

   Cai, Grace; Harasha, Noble; Lynch, Nancy (May 2023, Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023))

   Task allocation is an important problem for robot swarms to solve, allowing agents to reduce task completion time by performing tasks in a distributed fashion. Existing task allocation algorithms often assume prior knowledge of task location and demand or fail to consider the effects of the geometric distribution of tasks on the completion time and communication cost of the algorithms. In this paper, we examine an environment where agents must explore and discover tasks with positive demand and successfully assign themselves to complete all such tasks. We first provide a new discrete general model for modeling swarms. Operating within this theoretical framework, we propose two new task allocation algorithms for initially unknown environments – one based on N-site selection and the other on virtual pheromones. We analyze each algorithm separately and also evaluate the effectiveness of the two algorithms in dense vs. sparse task distributions. Compared to the Levy walk, which has been theorized to be optimal for foraging, our virtual pheromone inspired algorithm is much faster in sparse to medium task densities but is communication and agent intensive. Our site selection inspired algorithm also outperforms Levy walk in sparse task densities and is a less resource-intensive option than our virtual pheromone algorithm for this case. Because the performance of both algorithms relative to random walk is dependent on task density, our results shed light on how task density is important in choosing a task allocation algorithm in initially unknown environments. 

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5. CPU Energy-Aware Parallel Real-Time Scheduling

   https://doi.org/10.4230/LIPIcs.ECRTS.2020.2  

   Saifullah, Abusayeed; Fahmida, Sezana; Modekurthy, Venkata P.; Fisher, Nathan; Guo, Zhishan (July 2020, Leibniz international proceedings in informatics)

   Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach. 

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