Cost-Aware Dynamic Multi-Workflow Scheduling in Cloud Data Center Using Evolutionary Reinforcement Learning

Huang, Victoria; Wang, Chen; Ma, Hui; Chen, Gang; Christopher, Kameron

doi:10.1007/978-3-031-20984-0_32

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…In Reference 66, cost‐aware DMWS in cloud data center using evolutionary reinforcement learning is presented. introduction a novel priority‐based deep neural network scheduling policy that can flexibly adapt to a changing number of VMs and workflows.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

“…According to the above explanations, the classification of workflow scheduling methods has been shown in Figure 4. In the first part, the classification of methods is based on the nature of scheduling algorithms, which includes super-heuristic 9 Meta Heuristic methods [5], [16], [21], [35], [36], [37], [50], [51], [52], [54], [55], [56], [57], [58], [61], [62], [63], [64], [65], [66], [67] 9 Heuristic methods [1], [2], [13], [17], [18], [19], [22], [25], [26], [28], [29], [30], [31], [32], [33], [34], [42], [46], [47], [48], [49] 9 F I G U R E 4 Classification of workflow scheduling methods. 12 methods and heuristic methods.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

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A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

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The advent of distributed computing and mobile clouds made it possible to transfer and distribute the heavy processes of complex workflows to the cloud. Managing and with the help of the mobile cloud, virtualization and shared resources managing the executing large‐scale workflows is possible. Since virtual resources are used, scheduling and resource allocation are important and key research topics in public cloud environment and mobile clouds. The cloud model of mobile devices with public or virtual cloud, which consists of temporary mobile devices, is an interesting topic to be investigated. Workflow scheduling and availability of resources in mobile clouds by high mobility and Energy efficient, it is one of the challenges and problems that must be investigated one of the main challenges in this research area. The purpose of scheduling algorithms is to improve service quality criteria by observing the constraints. In this article, the main goal is to extensively review the scheduling algorithms for the complex scientific workflow in public and mobile clouds. Furthermore, a review of the use of heuristic and meta‐heuristic techniques in dynamic and static states, with various constraints for task scheduling in cloud and mobile computing has been performed. This article accomplishes a review of using existing techniques for scheduling tasks in mobile clouds. We also present a comprehensive analysis and systematic comparison between these scheduling algorithms.

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Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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Section: Introductionmentioning

confidence: 99%

“…Currently, some RL-based approaches presume a fixed and constant number of machines/operations as actions at decision points and learn an end-to-end strategy to solve the JSS problems [101]. Consequently, these approaches face challenges when applied to DFJSS with varying numbers of machines/operations [101]. In response to this challenge, researchers have attempted to use indirect ways to overcome the difficulty of having different numbers of candidate machines/operations at different decision points.…”

Section: Introductionmentioning

confidence: 99%

Advancing Genetic Programming for Learning Scheduling Heuristics

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Dynamic flexible job shop scheduling (DFJSS) has attracted a lot of attention from both academics and industries because of its widespread industrial impact in the real world. The main challenge in DFJSS involves simultaneous machine assignment and operation sequencing under dynamic environments such as new jobs arriving over time. Among the existing methods, scheduling heuristics stand out as effective approaches widely employed because of their simplicity and real-time responsiveness. Nevertheless, designing scheduling heuristics demands domain knowledge and is a time-consuming process. Genetic Programming (GP), as a hyper-heuristic method, has demonstrated notable success in automatically evolving scheduling heuristics for DFJSS. However, there is room for further enhancement in certain aspects to optimise GP's performance for DFJSS, particularly concerning diversity-based parent selection mechanisms, joint decision-making mechanisms, collaborative heuristic generation and selection by GP and reinforcement learning (RL), and multi-objective problem-solving capabilities.The overall goal of this thesis is to enhance the effectiveness of GP in automatically evolving high-quality scheduling heuristics for solving the DFJSS problems. Various machine learning and evolutionary computation techniques, such as lexicase selection, ensemble, semantic, RL, and multi-objective optimisation, are incorporated into GP to further improve the effectiveness of GP for DFJSS.First, three novel diversity-based parent selection mechanisms are designed to enhance the GP's ability to explore the search space effectively, adapt to changes, and maintain a balance between exploration and exploitation for the successful evolution of high-quality scheduling heuristics. The three parent selection mechanisms are cluster selection, diverse partner selection, and lexicase selection. The new mechanisms consider not only the fitness of selected parents but also their diverse strengths, which is crucial for generating high-quality offspring. Experimental results demonstrate that the proposed methods, incorporating these newly designed diversity-based parent selection mechanisms, successfully enhance the quality of scheduling heuristics while promoting increased population diversity.Second, this thesis proposes an innovative ensemble GP method that employs a population comprising both single individuals and ensembles, and allows the evolution of either a single scheduling heuristic for making individual decisions or a group of scheduling heuristics for making joint decisions for solving the DFJSS problem effectively. For creating an ensemble, an ensemble construction and selection strategy is developed for carefully choosing diverse and complementary individuals from the population. Additionally, new crossover and mutation operators are designed to generate offspring from the selected parents, which can be single individuals or ensembles. By maintaining a population that includes both single individuals and ensembles, the proposed method enables effective exploration of the search space through interbreeding. The experiment results show that the proposed method outperforms the compared algorithms including ensemble GP methods in terms of learned scheduling heuristics. Third, in addition to the widely used GP for automatic scheduling heuristics learning in DFJSS, RL has gained attention in recent years within this domain. This thesis proposes a niching GP-assisted RL method, taking advantage of both GP and RL to effectively address the DFJSS problems by intelligently heuristic generation by GP and selection by RL simultaneously. Specifically, instead of relying on manual scheduling heuristics, RL actions are replaced with scheduling heuristics evolved by the niching GP. RL is then employed to optimise and adapt these heuristics based on real-time feedback from the environment. The experiment results demonstrate the effectiveness of the proposed method in comparison to the widely used manual scheduling heuristics and the baseline RL method. This confirms the capability of GP to enhance RL's performance in addressing the DFJSS problem. Last, this thesis proposes two multi-objective GP methods for evolving a Pareto front of scheduling heuristics for solving the multi-objective DFJSS problem. The first method fills the gap by combining a well-known multi-objective evolutionary algorithm based on decomposition with GP, resulting in a novel multi-objective GP based on the decomposition method. This method successfully enhances the spreadability/diversity of the evolved Pareto front of scheduling heuristics. The second method enhances an existing state-of-the-art multi-objective GP approach (NSGPII) by incorporating semantic information. The experiment results demonstrate that NSGPII considering semantic diversity yields better performance compared with the original NSGPII. Moreover, NSGPII incorporating semantic similarity achieves even better performance, highlighting the importance of maintaining a reasonable semantic distance between offspring and their parents.

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An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-Oriented Systems

Metzger,

Bartel,

Laufer

2023

Service-Oriented Computing

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Cost-Aware Dynamic Multi-Workflow Scheduling in Cloud Data Center Using Evolutionary Reinforcement Learning

Cited by 6 publications

References 29 publications

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Advancing Genetic Programming for Learning Scheduling Heuristics

An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-Oriented Systems

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