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What are the levels of scheduling in cloud computing since beginning from the job submission to get its response from the cloud data center? 

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Open accessJournal ArticleDOI
Vijindra, Sudhir Shenai 
81 Citations
Thus the comprehensive way of different type of scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling as well as grid scheduling.
Analysis and number results show that our approach for job scheduling system can not only guarantee the QoS requirements of the users’ jobs, but also can make the maximum profits for the Cloud computing service providers.
Simulation results have demonstrated that the proposed technique shows a significant outcome in terms of response time, data center processing time and total cost in cloud computing.
The algorithm for scheduling resources under clouding computing environment is different from that under traditional distributed computing environment because of the high scalability and heterogeneity of computing resources in cloud computing environment.
Task scheduling is a vital area in the cloud computing, and it must be optimized by considering different parameters.
Hence, scheduling the increasing demand of workload in the cloud environments is highly necessary.
We present a novel approach of heuristic-based request scheduling at each server, in each of the geographically distributed data centers, to globally minimize the penalty charged to the cloud computing system.
Task scheduling is one of the most challenging aspects in cloud computing nowadays, which plays an important role to improve the overall performance and services of the cloud such as response time, cost, makespan, throughput etc.
Open accessJournal ArticleDOI
A. Sujitha, K. Gunasekar 
Thus the far reaching way of different type of scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling and grid scheduling.
Scheduling is a very important part of the cloud computing system.
This paper presents a novel low-power task scheduling algorithm(LTSA)for large-scale cloud data centers.
Efficient task scheduling mechanism should meet users' requirements and improve the resource utilization, so as to enhance the overall performance of the cloud computing environment.
For the problem that the energy efficiency of the cloud computing data center is low, from the point of view of the energy efficiency of the servers, we propose a new energy-efficient multi-job scheduling model based on Google’s massive data processing framework.
The experimental results show that job scheduling strategy based on CBFCM resources classification in the cloud computing services have certain advantages.
Theoretical as well as experimental results conclusively demonstrate that the scheduling algorithm has high potential as it takes both preference and fairness into account, and maximises cloud computing system utility by the clustering mechanism in cloud computing environments.
Therefore, we propose implementing artificial neural networks to optimize the job scheduling results in cloud as it can find new set of classifications not only search within the available set.
We hope that our systematic and comprehensive survey work as a stepping stone for new researchers in the field of cloud computing and will be helpful for further development of scheduling technique.

Related Questions

What are best deep learning scheduling algorithms for cloud computing?5 answersDeep reinforcement learning (DRL) algorithms have shown promising results for task scheduling in cloud computing. The DQN-based scheduling approach proposed by Yao et al.effectively addresses the multi-task scheduling problem in cloud manufacturing. Another study by Du et al.introduces the Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning (WDRL) for remote sensing data processing in cloud computing, which optimizes task scheduling efficiency. Additionally, a modified version of the D3QN algorithm, called NoisyD3QN, is proposed by an anonymous authorto reduce power consumption and improve response speed in cloud task scheduling. Furthermore, a novel technique combining a convolutional neural network with modified butterfly optimization (CNN-MBO) is proposed by Badri et al.for effective task scheduling with enhanced security in the cloud computing environment. These studies demonstrate the effectiveness of deep learning-based scheduling algorithms for cloud computing.
What are the future research directions in job scheduling in grid computing?5 answersFuture research directions in job scheduling in grid computing include exploring new scheduling algorithms and strategies to improve resource utilization and ensure efficient execution of jobs. One proposed approach is the First Come First Serve Left Right Hole Scheduling (FCFS-LRH) reservation strategy, which aims to reduce idle time and improve resource allocation. Another direction is the integration of IoT with grid and cloud computing, which requires new scheduling techniques to handle the heterogeneity of resources and management systems in the IoT grid. Additionally, the use of artificial intelligence techniques, such as feed-forward neural networks, shows promise in solving real-life problems related to pattern classification in grid computing environments. These research directions aim to enhance the performance and scalability of job scheduling in grid computing systems.
What is the current state of reasearch into how the machine scheduling problem can be tackled with data analytics?5 answersResearch into how the machine scheduling problem can be tackled with data analytics is currently focused on addressing the heterogeneity of both data analytics jobs and resources. Existing solutions either focus on scheduling a single type of resource without considering coordination between different resources, or they statically reserve resources for executors based on peak demand, leading to low utilization and poor performance. Efforts are being made to bridge the gap between analytical models and learning-based optimizations, with a focus on hybridizing analytical optimization with machine learning methods. In the context of manufacturing industries, big data analytics is being used for fault prediction and prevention in shop-floor scheduling and machining, as well as for optimizing machining processes. Overall, the current state of research aims to improve scheduling and machining by leveraging data analytics techniques to handle complexity, uncertainty, and optimization challenges.
How can data stratification be used on cloud computing?5 answersData stratification can be used in cloud computing to classify and organize data based on its level of security and risk. This helps in determining the required level of security for different types of data and protecting it accordingly. Data classification techniques are widely used in cloud computing to ensure that data is stored and accessed securely. Additionally, data stratification can help in reducing data load in the cloud, thereby reducing storage and management costs for users. By using encryption techniques, data can be protected and stored securely in the cloud. Data mining techniques can also be applied in cloud computing to extract useful information from raw or unused data, allowing users to retrieve meaningful information from integrated data warehouses.
What is Dynamic Scheduling in cloud computing?7 answers
What is scheduling problem in cloud computing?7 answers

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