ML-driven classification scheme for dynamic interference-aware resource scheduling in cloud infrastructures

Meyer, Vinícius; Kirchoff, Dionatrã F.; Silva, Matheus L. da; Rose, César A. F. De

doi:10.1016/j.sysarc.2021.102064

Cited by 16 publications

(2 citation statements)

References 51 publications

(91 reference statements)

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“…Vinícius Meyer et al [17] presented a machine learningbased classification technique in 2021 to provide the best possible resource allocation in cloud environments that are aware of dynamic interference. The main objective was to demonstrate how categorization techniques affect resource allocation so that it better accommodates variations in workload.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approach for Workload Prediction and Balancing in Cloud Computing

Karimunnisa,

Pachipala

2024

IJACSA

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Cloud Computing voted as one of the most revolutionized technologies serving huge user demand engrosses a prominent place in research. Despite several parameters that influence the cloud performance, factors like Workload prediction and scheduling are triggering challenges for researchers in leveraging the system proficiency. Contributions by practitioners given workload prophesy left scope for further enhancement in terms of makespan, migration efficiency, and cost. Anticipating the future workload in due to avoid unfair allocation of cloud resources is a crucial aspect of efficient resource allocation. Our work aims to address this gap and improve efficiency by proposing a Deep Max-out prediction model, which predicts the future workload and facilitates workload balancing paving the path for enhanced scheduling with a hybrid Tasmanian Devil-assisted Bald Eagle Search (TES) optimization algorithm. The results evaluated proved that the TES scored efficiency in makespan with 16.342%, and migration efficiency of 14.75% over existing approaches like WACO, MPSO, and DBOA (Weighted Ant Colony Optimization Modified Particle Swarm Optimization, Discrete Butterfly Optimization Algorithm). Similarly, the error analysis during the evaluation of prediction performance has been figured out using different approaches like MSE, RMSE, MAE, and MSLE, among which our proposed method overwhelms with less error than the traditional methods.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Approach for Workload Prediction and Balancing in Cloud Computing

Karimunnisa,

Pachipala

2024

IJACSA

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“…Khan et al [20] proposed a task scheduling method based on a hybrid optimization algorithm, which effectively schedules jobs with the least amount of waiting time. Meyer et al [21] proposed a machine learning-driven classification scheme for dynamic interference-aware resource scheduling in cloud computing environments. ey presented a classification approach to better represents the workload variations for resource scheduling.…”

Section: Related Workmentioning

confidence: 99%

PAS: Performance-Aware Job Scheduling for Big Data Processing Systems

Shen³

et al. 2022

Security and Communication Networks

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Big data analytics has become increasingly vital in many modern enterprise applications such as user profiling and business process optimization. Today’s big data processing systems, such as Hadoop MapReduce, Spark, and Hive, treat big data applications as a batch of jobs for scheduling. Existing schedulers in production systems often maintain fair allocation without considering application performance and resource utilization simultaneously. It is challenging to perform job scheduling in big data systems to achieve both low turnaround time and high resource utilization due to the high complexity in data processing logics and the dynamic variation in workloads. In this article, we propose a performance-aware scheduler, referred to as PAS, which dynamically schedules big data jobs in Hadoop YARN and Spark and autonomously adjusts scheduling policies to improve application performance and resource utilization. Specifically, PAS schedules multiple concurrent jobs using different policies based on the predicted job completion time and employs a greedy approach and a one-step lookahead strategy to opportunistically maximize the average job performance while still maintaining a satisfactory level of resource utilization. We implement PAS in Hadoop YARN and evaluate its performance with HiBench, a well-known big data processing benchmark. Experimental results show that PAS reduces the average turnaround time by 25% and the makespan by 15% in comparison with four state-of-the-art schedulers.

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Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing

Sreelatha,

Reddy,

Hanafiah

et al. 2024

Softw Pract Exp

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The most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model.

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ML-driven classification scheme for dynamic interference-aware resource scheduling in cloud infrastructures

Cited by 16 publications

References 51 publications

Deep Learning Approach for Workload Prediction and Balancing in Cloud Computing

Deep Learning Approach for Workload Prediction and Balancing in Cloud Computing

PAS: Performance-Aware Job Scheduling for Big Data Processing Systems

Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing

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