With the rapid growth of IoT devices and their diverse workloads, container-based microservices deployed at edge nodes have emerged as a lightweight, scalable solution. However, existing microservice scheduling algorithms often assume static resource availability, which is unrealistic when multiple containers are assigned to an edge node. Besides, containers suffer from cold-start inefficiencies during early-stage training in currently popular reinforcement learning (RL) algorithms. In this paper, we propose a hybrid learning framework that combines offline imitation learning (IL) with online Soft Actor-Critic (SAC) optimization to enable cold-start-aware microservice scheduling with dynamic resource allocation. We first formulate a delay-and-energy-aware scheduling problem and construct a rule-based expert to generate demonstration data for behavior cloning. Then, a GRU-enhanced policy network is designed within the policy network to extract correlations among multiple decisions by separately encoding slow-evolving node states and fast-changing microservice features, and an action selection mechanism is provided to speed up convergence. Extensive experiments show that our method significantly accelerates convergence and achieves superior final performance. Compared with baselines, our algorithm improves the total objective by 50% and convergence speed by 70%, and demonstrates the highest stability and robustness across various edge configurations.

With the massive increase of Internet of Things devices and their data, executing applications by using micro-service architecture has emerged as the predominant trend. As the container technology emerges, microservices can be lightweightly deployed in resource-constrained edge nodes. However, existing container scheduling algorithms often overlook the allocation of computing resources on edge servers. When multiple containers are assigned to an edge node, it is usually assumed that they share a CPU frequency, which is obviously unrealistic. In this paper, we first formulate an online container-based microservice scheduling problem with dynamic computing power to minimize the total delay and energy consumption, where we need to determine the assignment between microservices and edge nodes and the allocation of computing power to each microservice in an edge node. Then, we propose a Soft Actor-Critic (SAC) based reinforcement learning algorithm to address this problem, where a GRU unit is designed in the policy network to extract the correlation among multiple decisions, and an action selection mechanism is given to speed up the convergence. Finally, a simulated scheduling system is implemented to validate our algorithm, which demonstrates that our algorithm outperforms the commonly used baselines by up to 65% in terms of the total objective on average.

SUMMARY: We have developed an RNA-Seq analysis workflow for single-ended Illumina reads, termed RseqFlow. This workflow includes a set of analytic functions, such as quality control for sequencing data, signal tracks of mapped reads, calculation of expression levels, identification of differentially expressed genes and coding SNPs calling. This workflow is formalized and managed by the Pegasus Workflow Management System, which maps the analysis modules onto available computational resources, automatically executes the steps in the appropriate order and supervises the whole running process. RseqFlow is available as a Virtual Machine with all the necessary software, which eliminates any complex configuration and installation steps. AVAILABILITY AND IMPLEMENTATION: http://genomics.isi.edu/rnaseq CONTACT: [email protected]; [email protected]; [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.