SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing

Li, Kaibin; Peng, Zhiping; Cui, Delong; Li, Qirui

doi:10.3390/app11209360

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…Equitable task scheduling is critical in cloud computing, and our priority-focused method guarantees equitable resource distribution and task completion. The writer presented an online algorithm that enhances task scheduling in cloud computing systems using dynamic load management [21]. The proposed algorithm incorporates a Gaussian distribution of relevant parameters in its state space, ensuring consistency in input dimensions.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

et al. 2023

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Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field.

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Section: Related Workmentioning

confidence: 99%

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

et al. 2023

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“…In [17], the authors focused on minimization of makespan, cost, and average delay when working under restrictions of VMs and sensitive deadlines. They developed a constraint-adaptive online task scheduling method using double deep Q-learning.…”

Section: Related Workmentioning

confidence: 99%

“…Methodology Parameters [17] RL to make a scheduling model for cloud computing Makespan, total power cost in datacenters, energy consumption, migration time [18] RL task scheduling with Q-learning Makespan, total power cost, energy consumption [19] Task scheduling optimization algorithm called MPSO Makespan, energy consumption, packet delivery ratio, trust value [20] FELARE Ontime task completion rate, energy saving [21] RLFTWS Makespan, resource usage [22] WBCNF Computation time of tasks [23] BRCH-GWO Makespan [24] RELIEF Communication delay, reliability [25] PSO-based multipurpose algorithm Turnaround time, makespan [26] Regressive WO algorithm Processing cost, load balancing tasks [27] GO Makespan, resource utilization [28] Dynamic task scheduling algorithm based on an improved GA Total execution time and resource utilization ratio [29] PSO based on an AC algorithm Task completion time, makespan [30] DRL-based task scheduling Makespan, computation time [31] MRLCC is an approach for organizing tasks that are based on Meta RL Energy consumption, total cost, makespan [32] A novel DRL-based framework Cost and throughput, makespan [33] RLFTWS Execution time, degree of imbalance [15] DRL model Response time, makespan, CPU utilization [34] Deep reinforcement learning with PPSO SLA violation, makespan [35] DRL Cloud is an NDR-learning-based RP and TS system Estimated completion time, resource utilization [36] Deep Q-network model Degree of imbalance, cost, makespan [37] SDM reinforcement learning Energy consumption, resource utilization [38] DRLHCE Response time, degree of imbalance [39] DQN Makespan, total cost [40] Reinforcement learning Makespan [41] DDDQN-TS Task response time [31] Q-learning Makespan…”

Section: Authormentioning

confidence: 99%

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Shiva Rama Krishna,

Mangalampalli

2023

Applied Sciences

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Task scheduling poses a wide variety of challenges in the cloud computing paradigm, as heterogeneous tasks from a variety of resources come onto cloud platforms. The most important challenge in this paradigm is to avoid single points of failure, as tasks of various users are running at the cloud provider, and it is very important to improve fault tolerance and maintain negligible downtime in order to render services to a wide range of customers around the world. In this paper, to tackle this challenge, we precisely calculated priorities of tasks for virtual machines (VMs) based on unit electricity cost and these priorities are fed to the scheduler. This scheduler is modeled using a deep reinforcement learning technique which is known as the DQN model to make decisions and generate schedules optimally for VMs based on priorities fed to the scheduler. This research is extensively conducted on Cloudsim. In this research, a real-time dataset known as Google Cloud Jobs is used and is given as input to the algorithm. This research is carried out in two phases by categorizing the dataset as a regular or large dataset with real-time tasks with fixed and varied VMs in both datasets. Our proposed DRFTSA is compared to existing state-of-the-art approaches, i.e., PSO, ACO, and GA algorithms, and results reveal that the proposed DRFTSA minimizes makespan compared to PSO, GA, and ACO by 30.97%, 35.1%, and 37.12%, rates of failure by 39.4%, 44.13%, and 46.19%, and energy consumption by 18.81%, 23.07%, and 28.8%, respectively, for both regular and large datasets for both fixed and varied VMs.

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“…In the proposed model, we have adopted a pricing model similar to the Alibaba cloud resource pricing model in which each resource has a separate pricing model. For example, bandwidth consumption has a cost which differs from memory usage or computing power usage [34]. Let PC j be the processing cost per time unit of, VM j , MC j be the memory cost per storage unit of, VM j , BC be the bandwidth cost per data unit of, VM j , MRT i be the memory requirement of, RT i , and BRT i be the bandwidth requirement of.RT i .…”

Section: Cost Ijmentioning

confidence: 99%

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

2022

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IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms. Graphical Abstract

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SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing

Cited by 9 publications

References 35 publications

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

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