Modified HEFT Algorithm for Task Scheduling in Cloud Environment

Dubey, Kalka; Kumar, Mohit; Sharma, S. C.

doi:10.1016/j.procs.2017.12.093

Cited by 93 publications

(46 citation statements)

References 13 publications

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“…Kalka and Mohit [17] they adjusted the HEFT algorithm that inefficiently distributes the workload between the processor, reduces application rendering time based on their order and assignment of task with the processor. They concluded that the proposed algorithm decreases task time and load balance problems compared to the HEFT and CPOP algorithms.…”

Section: Scheduling Algorithm In Cloud Computingmentioning

confidence: 99%

A cloud computing scheduling and its evolutionary approaches

Abdalkafor

Jihad

Allawi³

2021

IJEECS

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<span>Despite the increasing use of cloud computing technology because it offers unique features to serve its customers perfectly, exploiting the full potential is very difficult due to the many problems and challenges. Therefore, scheduling resources are one of these challenges. Researchers are still finding it difficult to determine which of the scheduling algorithms are appropriate and effective and that helps increases the performance of the system to accomplish these tasks. This paper provides a broad and detailed examination of resource scheduling algorithms in the environment of a cloud computing environment and highlights the advantages and disadvantages of some algorithms to help researchers in selecting the best algorithms to schedule a particular workload to get a satisfy a quality of service, guarantee good utilization of the cloud resources also minimizing the make-span.</span>

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Section: Scheduling Algorithm In Cloud Computingmentioning

confidence: 99%

A cloud computing scheduling and its evolutionary approaches

Abdalkafor

Jihad

Allawi³

2021

IJEECS

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“…However, the performance needs to be checked on real-time large datasets that improve the execution time. In [86], modified Heterogeneous Earliest Finish Time (MHEFT) is proposed for dynamic load balancing in the cloud resulting. The algorithm works well under a smaller number of tasks and did not consider other QoS metrics for performance evaluations.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling an Optimized Approach for Load Balancing in Cloud

et al. 2020

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Despite significant infrastructure improvements, cloud computing still faces numerous challenges in terms of load balancing. Several techniques have been applied in the literature to improve load balancing efficiency. Recent research manifested that load balancing techniques based on metaheuristics provide better solutions for proper scheduling and allocation of resources in the cloud. However, most of the existing approaches consider only a single or few QoS metrics and ignore many important factors. The performance efficiency of these approaches is further enhanced by merging with machine learning techniques. These approaches combine the relative benefits of load balancing algorithm backed up by powerful machine learning models such as Support Vector Machines (SVM). In the cloud, data exists in huge volume and variety that requires extensive computations for its accessibility, and hence performance efficiency is a major concern. To address such concerns, we propose a load balancing algorithm, namely, Data Files Type Formatting (DFTF) that utilizes a modified version of Cat Swarm Optimization (CSO) along with SVM. First, the proposed system classifies data in the cloud from diverse sources into various types, such as text, images, video, and audio using one to many types of SVM classifiers. Then, the data is input to the modified load balancing algorithm CSO that efficiently distributes the load on VMs. Simulation results compared to existing approaches showed an improved performance in terms of throughput (7%), the response time (8.2%), migration time (13%), energy consumption (8.5%), optimization time (9.7%), overhead time (6.2%), SLA violation (8.9%), and average execution time (9%). These results outperformed some of the existing baselines used in this research such as CBSMKC, FSALB, PSO-BOOST, IACSO-SVM, CSO-DA, and GA-ACO.

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“…The Heterogeneous Earliest Finish Time (HEFT) was designed for heterogeneous multiprocessor systems. Dubey et al [17] proposed a modified version of HEFT, capable of reducing the makespan time compared to existing HEFT and Critical Path on a Processor (CPOP).…”

Section: Related Workmentioning

confidence: 99%

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

Arora¹,

Banyal²

2020

IJACSA

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Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user's tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user's tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data-and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).

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Modified HEFT Algorithm for Task Scheduling in Cloud Environment

Cited by 93 publications

References 13 publications

A cloud computing scheduling and its evolutionary approaches

A cloud computing scheduling and its evolutionary approaches

Modeling an Optimized Approach for Load Balancing in Cloud

HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

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