schedulingworkflows in this cloud computing era might as well be the way to go, given that resource allocation will be significantly improved, besides reduced execution time and costs. Most conventional scheduling al...
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schedulingworkflows in this cloud computing era might as well be the way to go, given that resource allocation will be significantly improved, besides reduced execution time and costs. Most conventional scheduling algorithms lack the potential for optimal performance among conflicting objectives like performance, cost-efficiency, and resource utilization. The paper proposes a new multi-objective workflow scheduling framework, where the Spider Monkey Optimization algorithm will be combined with the Fuzzy Self-Defense Algorithm. SMO algorithm emulates the foraging behavior of spider monkeys for a compelling exploration of the complex solution space to find superior task-resource mappings. Besides this, a fuzzy selfdefense strategy tackles the inherent uncertainties of dynamic cloud environments to make the framework more adaptable and resilient against failures and performance degradation. The proposed framework will be multi-objective, including the optimization of minimizing execution time, optimization of resource utilization, and energy consumption. Therefore, the model will significantly improve the balance of those competing goals, drawing strengths from SMO and fuzzy logic. The effectiveness is further validated through extensive experiments using synthetic and real-world workflow applications in a simulated cloud environment. Indeed, notable improvements have been observed along all the key performance indicators related to execution time, energy efficiency, and resource utilization. Besides, the hybrid framework is much more scalable and flexible in handling massive workflows, establishing its value as a practical resource management solution in cloud computing.
Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple c...
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Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be addressed properly. The existing multi-objective workflow scheduling approaches are still limited in many ways, e.g., encoding is restricted by prior experts' knowledge when handling a dynamic real-time problem, which strongly influences the performance of scheduling. In this paper, we apply a deep-Q-network model in a multi-agent reinforcement learning setting to guide the scheduling of multi-workflows over infrastructure-as-a-service clouds. To optimize multi-workflow completion time and user's cost, we consider a Markov game model, which takes the number of workflow applications and heterogeneous virtual machines as state input and the maximum completion time and cost as rewards. The game model is capable of seeking for correlated equilibrium between make-span and cost criteria without prior experts' knowledge and converges to the correlated equilibrium policy in a dynamic real-time environment. To validate our proposed approach, we conduct extensive case studies based on multiple well-known scientific workflow templates and Amazon EC2 cloud. The experimental results clearly suggest that our proposed approach outperforms traditional ones, e.g., non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimization, and game-theoretic-based greedy algorithms, in terms of optimality of scheduling plans generated.
The workflowscheduling with multiple objectives is a well-known NP-complete problem, and even more complex and challenging when the workflow is executed in cloud computing system. In this study, an endocrine-based co...
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Cloud computing is emerging as a deployment promising environment for hosting exponentially increasing scientific and social media applications, but how to manage and execute these applications efficiently depends mai...
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ISBN:
(纸本)9781728169262
Cloud computing is emerging as a deployment promising environment for hosting exponentially increasing scientific and social media applications, but how to manage and execute these applications efficiently depends mainly on workflowscheduling. However, schedulingworkflows in the cloud is an NP hard problem and its existing solutions have certain limitations when applied to real-world scenarios. In this paper, a Temporal Fusion Pointer network-based Reinforcement Learning algorithm for multi-objective workflow scheduling (TFP-RL) is proposed. Through adopting reinforcement learning, our algorithm can discover its heuristics over time by continuous learning according to the rewards resulting from good scheduling solutions. To make more comprehensive scheduling decisions as the influence of historical actions, a novel temporal fusion pointer network (TFP) is designed for the reinforcement learning agent, which can improve the quality of our resulting solutions and the ability of our algorithm in dealing with versatile workflow applications. To decrease convergence time, we train the proposed TFP-RL model independently by the Asynchronous Advantage Actor-Critic method and use its resulting model for scheduling workllovvs. Finally, under a multi-agent reinforcement learning framework, a Pareto dominance-oriented criterion for reasonable action selection is established for a multi-objective optimization scenario. We first train our TFP-RL model by taking randomly generated workflows as inputs to validate its effectiveness in scheduling, then compare our trained model with other existing scheduling approaches through practical compute- and data intensive workflows. Experimental results demonstrate that our proposed algorithm outperforms the benchmarking ones in terms of different metrics.
As a promising distributed paradigm, cloud computing provides a cost-effective deploying environment for hosting scientific applications due to its provisioning elastic, heterogeneous resources in a pay-per-use model....
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As a promising distributed paradigm, cloud computing provides a cost-effective deploying environment for hosting scientific applications due to its provisioning elastic, heterogeneous resources in a pay-per-use model. More and more applications modeled as workflows are being moved to the cloud, and time and cost become important for workflow execution. However, schedulingworkflows is still a challenge due to their large-scale and complexity, as well as the cloud's dynamic characteristics and different quotations. In this work, we propose a Weighted Double Deep Q-Network-based Reinforcement Learning algorithm (WDDQN-RL) for schedulingmultiple workflows to obtain near-optimal solutions in a relatively short time with both makespan and cost minimized. Specifically, we first introduce a dynamic coefficient-based adaptive balancing method into WDDQN to improve the accuracy of the target value estimation by making a trade-off between Deep Q-Network (DQN) overestimation and Double Deep Q-Network (DDQN) underestimation. Second, pointer network-based agents and a two-level scheduling strategy are designed, where pointer networks are used to process a variable candidate task set in the first-level and one selected task is fed to agents in the second-level for allocating resources. Third, we present a dynamic sensing mechanism by adjusting the model's attention to each individual objective for increasing the diversity of solutions while guaranteeing their quality. Experimental results show that our algorithm outperforms the benchmarking approaches in various indicators.
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