A two-stage scheduler based on New Caledonian Crow Learning Algorithm and reinforcement learning strategy for cloud environment

Zade, Behnam Mohammad Hasani; Mansouri, N.; Javidi, Mohammad Masoud

doi:10.1016/j.jnca.2022.103385

Cited by 19 publications

(3 citation statements)

References 38 publications

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“…Other DRL-based scheduling algorithms include HCDRL (Chen et al 2023a), DT (decision transformer) using GPT (Wang et al 2023), CORA (Huang et al 2023), DRAW (Chen et al 2023b), PRLCC (Zade et al 2022), ReCARL (Xu et al 2022), etc. These algorithms still belong to the DRL architecture of Fig.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

Zhou,

Tian,

Buyya

et al. 2024

Artif Intell Rev

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With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling.

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Section: Literature Reviewmentioning

confidence: 99%

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

Zhou,

Tian,

Buyya

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

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“…Zade et.al [18] contributed a two-phase algorithm which resulted in improved performance with first phase dealing with meta scheduling that assigns the tasks to host machine and in the second phase the scheduling is rein enforced using parallel reinforcement learning caledonian crow for optimal local scheduling to present optimal mapping of tasks and VMs. Sudheer Mangalampalli et.al [19] proposed an optimal scheduler which targets reduced energy consumption and power costs at datacenters by considering the priorities of tasks and VM's.…”

Section: Related Workmentioning

confidence: 99%

Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing

2023

IJIES

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Cloud computing benchmarks the dream of rendering computing as a utility, providing high agility and reachability from an existing set of technologies. It facilitates a wider dimension to architect and manage remote resources. Cloud technology with exponential growth is drilling towards issues that tend to lower its explored possibilities. As cloud systems by virtue deal with various virtualized resources, scheduling is opted as an important metric for measuring and leveraging performance. But scheduling efficiency is deteriorated by various parameters that pave scope for our research and projects immense need for improvising the overall makespan of the system. The proposed work aims at projecting a greater drift in the first phase by witnessing a sequence of phases like preprocessing the user tasks for improved accuracy, classifying the tasks with respect to resource demand and execution time using the improved density based clustering method (IDCM). The second phase deals with enhanced coot optimization algorithm for task scheduling (ECOA-TS) that proceeds and proves its novelty by adopting Cauchy mutation overcoming the convergence backdrop for generating an optimal mapping between clustered user tasks and VMs. The overall performance of the proposed work overrides by reduced makespan against existing state-of-the-art optimization algorithms like particle swam optimization (PSO), grey wolf optimization (GWO) and whale optimization algorithm (WOA) by 27.41%, 19.8%, and 15.33% respectively.

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“…Heuristic algorithms can be divided into two categories according to their characteristics: Static ones [3][4][5][6] are generally simple strategies and are suitable for tasks that are known before scheduling. Dynamic algorithms [7][8][9] consider the dynamics of cloud platforms, but there are some problems, such as a large number of iterations and long computation times [9][10][11]. In addition, though static and dynamic algorithms can both deal with tasks with dependency [5,6,8,[12][13][14], dependency also brings task validity issues [15].…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms

Gao

Shi

et al. 2023

Electronics

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Task scheduling algorithms based on reinforce learning (RL) have been important methods with which to improve the performance of cloud platforms; however, due to the dynamics and complexity of the cloud environment, the action space has a very high dimension. This not only makes agent training difficult but also affects scheduling performance. In order to guide an agent’s behavior and reduce the number of episodes by using historical records, a task scheduling algorithm based on adaptive priority experience replay (APER) is proposed. APER uses performance metrics as scheduling and sampling optimization objectives with which to improve network accuracy. Combined with prioritized experience replay (PER), an agent can decide how to use experiences. Moreover, this algorithm also considers whether a subtask is executed in a workflow to improve scheduling efficiency. Experimental results on Tpc-h, Alibaba cluster data, and scientific workflows show that a model with APER has significant benefits in terms of convergence and performance.

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A two-stage scheduler based on New Caledonian Crow Learning Algorithm and reinforcement learning strategy for cloud environment

Cited by 19 publications

References 38 publications

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing

Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms

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