Sim-DRS: a similarity-based dynamic resource scheduling algorithm for microservice-based web systems

Microservice-based Web Systems (MWS), which provide a fundamental infrastructure for constructing large-scale cloud-based Web applications, are designed as a set of independent, small and modular microservices implementing individual tasks and communicating with messages. This microservice-based architecture offers great application scalability, but meanwhile incurs complex and reactive autoscaling actions that are performed dynamically and periodically based on current workloads. However, this problem has thus far remained largely unexplored. In this paper, we formulate a problem of Dynamic Resource Scheduling for Microservice-based Web Systems (DRS-MWS) and propose a similarity-based heuristic scheduling algorithm that aims to quickly find viable scheduling schemes by utilizing solutions to similar problems. The performance superiority of the proposed scheduling solution in comparison with three state-of-the-art algorithms is illustrated by experimental results generated through a well-known microservice benchmark on disparate computing nodes in public clouds.

Multi-heuristic scheduling methods for workflow in credit cloud

In the credit cloud, credit services are sold to applications for credit computing, credit fusion and credit risk estimates. Plenty of services with different performance for the same task may have different execution time and charged by various ways. The users have specific requirements for the workflow completion time or cost. Hence, to meet the user’s satisfaction is an important challenge. In this paper, we propose heuristic scheduling methods for credit workflow with total cost minimization, and the deadline should be satisfied. The problem can be divided into two sub-problems, task-mode mapping and task tabling on renting service instances. For the task-mode mapping problem, a recursive heuristic method is constructed to select appropriate service for each task of the workflow. Then another heuristic algorithm based is established to get a final schema with deadline constraint. We discussed the service instance rented in shareable manner and compared with un-shareable manner. Three renting strategies are discussed in detail. Experimental results show the effectiveness and efficiency of the proposed algorithm.

RLSchert: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction

The job scheduler plays a vital role in high-performance computing platforms. It determines the execution order of the jobs and the allocation of resources, which in turn affect the resource utilization of the entire system. As the scale and complexity of HPC continue to grow, job scheduling is becoming increasingly important and difficult. Existing studies relied on user-specified or regression techniques to give fixed runtime prediction values and used the values in static heuristic scheduling algorithms. However, these approaches require very accurate runtime predictions to produce better results, and fixed heuristic scheduling strategies cannot adapt to changes in the workload. In this work, we propose RLSchert, a job scheduler based on deep reinforcement learning and remaining runtime prediction. Firstly, RLSchert estimates the state of the system by using a dynamic job remaining runtime predictor, thereby providing an accurate spatiotemporal view of the cluster status. Secondly, RLSchert learns the optimal policy to select or kill jobs according to the status through imitation learning and the proximal policy optimization algorithm. Extensive experiments on real-world job logs at the USTC Supercomputing Center showed that RLSchert is superior to static heuristic policies and outperforms the learning-based scheduler DeepRM. In addition, the dynamic predictor gives a more accurate remaining runtime prediction result, which is essential for most learning-based schedulers.

Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.

Multiobjective Heuristic Scheduling of Automated Manufacturing Systems Based on Petri Nets

In practice, automated manufacturing systems usually have multiple, incommensurate, and conflicting objectives to achieve. To deal with them, this paper proposes an extend Petri nets for the multiobjective scheduling of AMSs. In addition, a multiobjective heuristic A* search within reachability graphs of extended Petri nets is also proposed to schedule these nets. The method can obtain all Pareto-optimal schedules for the underlying systems if admissible heuristic functions are used. Finally, the effectiveness of the method is illustrated by some experimental systems.

Scheduling and simulation of maritime traffic in congested waterways: an application to the Strait of Istanbul

The aim of this study is to develop a simulation model which is capable of mimicking actual vessel arrival patterns and vessel entrance decisions (which are made based on expert opinions generally) on congested, narrow waterways. The model is tested on the transit traffic pattern in the Strait of Istanbul. Based on a heuristic scheduling algorithm, this model decides entrance times and vessel types on the strait. The model, with different policies for day and night traffic, is run for a period of seven years with 20 replications for each year. The performance measures of the model are: average interarrival times, number of vessels passed and entrance times for each successive vessel pair in both traffic directions. The model results are congruent with the actual results of performance measures. Therefore, it may be deduced that the proposed algorithm can be a guide for operators regarding scheduling decisions in congested, narrow waterways.