State-of-the-Art Survey on Cloud Computing Resource Scheduling Approaches

Sohani, Mayank; Jain, Shobhit

doi:10.1007/978-981-10-7386-1_53

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Our work builds on earlier research by evaluating cutting-edge algorithms both from a modelling standpoint and through investigational comparisons. It also evaluates the positives and negatives of the examined algorithms to make recommendations for future study in relevant fields [20]. One application that allows for the use of several web services is cloud computing.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison and Evaluation of Energy-Efficient Cloud Computing Techniques With Load Balancing Approaches

Shah,

Jain,

Bandhu

2024

AEJ

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The advent of Cloud Computing has revolutionized the IT landscape by offering computing resources as a service, similar to conventional utilities like electricity. This paradigm shift has made cloud computing a cornerstone of the contemporary digital economy, attracting substantial focus from both academic and industrial sectors. Its unique pay-as-you-go model provides customers with on-demand resource availability, enhancing operational flexibility. However, this convenience is offset by the growing energy demands of cloud data centers, which not only escalate operational expenses but also contribute to environmental degradation through increased carbon footprints. To combat these issues, Green cloud computing has been introduced, striving for energy-efficient and sustainable operations. This involves employing strategies that minimize energy consumption and resource utilization through the application of energy-conscious algorithms. Although numerous algorithms based on server consolidation have been proposed to optimize energy use in cloud environments, they often lack uniform evaluative comparisons and vary in performance due to differing experimental conditions. This variance presents a challenge in selecting the most effective algorithm tailored to specific needs. This study aims to provide a nuanced analysis of existing energy-efficient algorithms, assisting researchers in identifying the algorithm that best suits their requirements. We undertake an exhaustive comparison of various algorithms, examining their architecture, modelling approaches, and performance metrics. These algorithms are then implemented and tested under uniform conditions using the CloudSim toolkit. Our findings offer an in-depth comparative analysis of these algorithms, shedding light on their respective advantages and shortcomings. Additionally, we delve into a thorough discussion of each algorithm's features and their implications for cloud computing environments.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison and Evaluation of Energy-Efficient Cloud Computing Techniques With Load Balancing Approaches

Shah,

Jain,

Bandhu

2024

AEJ

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“…As this author proposed, the resources are to be allocated to appropriate tasks to decrease the execution time and increase resource utilization. Scheduling of tasks is the best approach to achieve maximum utilization of resources and economic efficiency [27]. The author presents a Generalized Critical Task Anticipation (GCA) algorithm in a heterogeneous computing environment for weighted directed acyclic graph (DAG) scheduling.…”

Section: B Contributionmentioning

confidence: 99%

A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

Sohani

Jain

2021

IEEE Access

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“…To protect from SLA violations, we have to do the allocation of VMs from underloaded host machines, which turn in idle host conditions during under load conditions. We can save energy consumption to start new resources for the workload request allocation purpose [27,28]. Some comparing factors of existing and proposed algorithm heuristics are considered for comparison and represented by Table 1, including the following parameters: Response Time, Execution Time, Resource Utilization, Load Balancing, QoS, Scalability, and Energy Consumption.…”

Section: Related Workmentioning

confidence: 99%

Threshold based VM Placement Technique for Load Balanced Resource Provisioning using Priority Scheme in Cloud Computing

Sohani¹,

Jain²

2021

IJCNC

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The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.

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State-of-the-Art Survey on Cloud Computing Resource Scheduling Approaches

Cited by 4 publications

References 20 publications

Comparison and Evaluation of Energy-Efficient Cloud Computing Techniques With Load Balancing Approaches

Comparison and Evaluation of Energy-Efficient Cloud Computing Techniques With Load Balancing Approaches

A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

Threshold based VM Placement Technique for Load Balanced Resource Provisioning using Priority Scheme in Cloud Computing

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