Study of Scheduling Techniques in Cloud Computing Environment

Yadav, Anagha; Rathod, Suresh

doi:10.14445/22312803/ijctt-v29p113

Cited by 5 publications

(9 citation statements)

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“…To optimize the resource allocation by using priority-based task scheduling and allocating the tasks to the conflict free resources was proposed [5]. This has a better makespan and improves the throughput of the system.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud

Shetty¹

2021

TURCOMAT

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The flexibility provided by the cloud service provider at reduced cost popularized the cloud tremendously. The cloud service provider must schedule the incoming requests dynamically. In a cloud environment tasks must be scheduled such that proper resource utilization is achieved. Hence task scheduling plays a significant role in the functionality and performance of cloud computing systems. While there exist many approaches for boosting the task scheduling in the cloud, it is still an unresolved issue. In this proposed framework we attempt to optimize the usage of cloud computing resources by applying machine learning techniques. The new proposed framework dynamically selects the scheduling algorithm for the incoming request rather than arbitrary assigning a task to the scheduling algorithm. The scheduling algorithm is predicted dynamically using a neural network which is the best for the incoming request. The proposed framework considers scheduling parameters namely cost, throughput, makespan and degree of imbalance. The algorithms chosen for scheduling are 1) MET 2) MCT 3) Sufferage 4)Min-min 5) Min-mean 6) Min-var. The framework includes 4 neural networks to predict the best algorithm for each scheduling parameters considered for optimization. PCA algorithm is used for extracting relevant features from the input data set. The proposed framework shows the scope for the overall system performance by dynamically selecting precise scheduling algorithms for each incoming request from the user.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud

Shetty¹

2021

TURCOMAT

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“…Considering the constraint allocation in the subsequent requests, there is a request which provides the cost efficiency that is initially allocated. This FCFS is advantageous [19] owing to its simplicity and its minimization of the average time it takes to execute the process. It is probably one of the simplest algorithms as it has to allocate a CPU to finalize the order of the process.…”

Section: First Come First Served (Fcfs) Schedulingmentioning

confidence: 99%

An Energy and Deadline Aware Scheduling Using Greedy Algorithm for Cloud Computing

Venuthurumilli¹,

Mandapati²

2019

ISI

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Cloud computing has been providing various services to different users by means of an aid of large and scalable virtualized resources on the internet. Owing to all the recent and inventive developments that are found in the field, there are several scheduling algorithms which were developed in a cloud computing environment with the intention of decreasing the services given in cloud computing. For a very enormous gauge, assorted and the multiuser atmosphere in the cloud scheme where maximization of profit for that of the Cloud Service Provider (CSP) has been the primary objective. For the purpose of this work, the inclusive optimization problem in the operation of the cloud system by means of lowering the cost of procedure and by maximizing the efficiency of energy. At the same time, it satisfies the deadlines that are definite in Service Level Agreements (SLA) that has been addressed from a CSP perspective. The work proposes a Greedy algorithm for the environment of the cloud and this is compared to the scheduling of a First Come First Served (FCFS) and the Min-Min scheduling procedure. This system exploits the tasks and their heterogeneity and also the resources using a scheduler unit that schedules and allocates the tasks which are deadline-constrained which is delimited to the nodes that are energy conscious. After this, the CSP capitalizes on the parallelisms of data for every user workload and also effectively manages all collective user requests and also apply the custom optimization that creates a cost of global energy and a cloud platform which is deadline aware. The results of the experiment prove that this proposed Greedy algorithm which achieves a performance which is better (a guarantee ratio, utilization of resources and energy saving) compared to the FCFS and the Min-Min scheduling algorithm.

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“…The cloud definition provided by the National Institute of Standards and Technology (NIST).The NIST cloud computing definition:"Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. [3]The reliability and performance of cloud services depends up on various factors like scheduling of tasks. Scheduling can be done at task level or resource level or workflow level.…”

Section: Introductionmentioning

confidence: 99%

“…The main benefits of scheduling algorithm are to attain a high performance computing and high system throughput. [3] Scheduling instruct availability of CPU memory and gives maximum utilization of resource. Scheduling of task is based on the individual parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of the scheduling algorithms in cloud computing environment is to employ the resources accurately while controlling the load between the resources so that to get the minimum execution time. [3] Datacenters are essential parts of cloud computing environment. In a single datacenter commonly hundreds and thousands of virtual machines run at any instance of time, hosting many tasks and simultaneously the cloud system keeps accepting the batches of task requests.…”

Section: Introductionmentioning

confidence: 99%

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Scheduling Algorithms in Cloud Computing

Kaur¹,

Sharma²

2019

IJCA

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Cloud Computing has come to be perception for large scale of distributed computing and parallel processing. Cloud computing is a form of internet based computing that provides shared computer processing resources and data to computers and other devices on demand. The execution and suitability of cloud computing services always depends upon the completion of the user tasks affirmed to the cloud system. Task scheduling is one of the main types of scheduling performed. Scheduling is the major issue in establishing cloud computing system. The scheduling algorithms should order the jobs in a way where balance between improving the performance and quality of service and at the same time maintaining the efficiency and fairness among the jobs. This paper aims at studying various scheduling methods. A good scheduling technique also helps in proper and efficient utilization of the resources. Many scheduling techniques have been developed by the researchers like GA (

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Study of Scheduling Techniques in Cloud Computing Environment

Cited by 5 publications

References 0 publications

Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud

Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud

An Energy and Deadline Aware Scheduling Using Greedy Algorithm for Cloud Computing

Scheduling Algorithms in Cloud Computing

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