A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in Cloud Computing Environment

Pradeep, K.; Jacob, T. Prem

doi:10.1007/s11277-018-5816-0

Cited by 63 publications

(19 citation statements)

References 14 publications

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“…Nevertheless, the two hybrid algorithms require considerable computational time. The researchers in [20,21] introduced dynamic voltage and frequency scaling that describes the quality-of-service requirements of tasks with minimum frequency. The energy consumption ratio is used and evaluated to determine different frequencies.…”

Section: Related Workmentioning

confidence: 99%

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

et al. 2019

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Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

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Section: Related Workmentioning

confidence: 99%

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

et al. 2019

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“…In [11] An hybridised method of two algorithms namely the cuckoo search (CS) and harmony search (HS) algorithm is performed to attain optimization. New objectives are represented in the multiobjective function of this technique: cost, energy consumption, memory usage, credit and penalty.…”

Section: Related Workmentioning

confidence: 99%

OLOA: Based Task Scheduling in Heterogeneous Clouds

Pradeep¹,

Jacob²

2019

IJIES

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Cloud computing provides increased performance, scalability and is both cost efficient as well as low accounts for maintenance, which makes it a preferred choice when the dynamic allocation of resources is considered. Of the various advantages that cloud computing provides, task scheduling is an essential feature that helps to boost performance and reduce operation cost. In the proposed OLOA, a solution is provided for optimization, taking the makespan and cost as major constraints. This is accomplished using the two algorithms, Lion optimization algorithm (LOA) and the Opposition Based Learning (OBL) algorithm; and creating a hybrid Oppositional Lion optimization algorithm (OLOA). The given solution is simulated and demonstrated in the cloudsim programming environment, where the obtained results show drastic improvement in performance, in comparison to that of the previously used other existing algorithms such as Particle Swarm Optimization (PSO) algorithm, oppositional learning based grey wolf optimizer (OGWO) and the Genetic algorithm (GA), all of which do not match the performance rates of the proposed hybrid algorithm.

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“…From all the above discussed, the following disadvantages have been found. They are Interference that cannot be reduced completely 25,26 . For WEHCRN network design, a framework has been developed for inspecting the distributed nodes 27,28 …”

Section: Related Workmentioning

confidence: 99%

Efficient spectrum sensing framework for cognitive networks

Jacob

Pravin

Nagarajan

2019

Concurrency and Computation

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Summary Cognitive network is a modern networking technology that utilizes both the radio spectrum and the wireless station. The resources can be effectively utilized based on availability of knowledge gathered from the past experiences. By using cognitive radio technology, the new network is constructed to every node. Wireless devices' necessity is being increased nowadays, which leads to the demand for spectrum. The problems are high fluctuations of the different spectrum bands and the disconnections of the network. The cognitive radio network is proposed to be an optimal solution for both the spectrum scarcity and the spectrum inefficiency problems. Using the cognitive radio network, the wireless channel is shared with the primary users. The cognitive radio network provides high bandwidth for the mobile users by using specialized heterogeneous wireless architecture. These challenges are overcome by CR networks that define the concept of spectrum sensing. While using the spectrum, the performance of the network should not degrade. Hence, the framework planned will able to do this situation.

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A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in Cloud Computing Environment

Cited by 63 publications

References 14 publications

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

OLOA: Based Task Scheduling in Heterogeneous Clouds

Efficient spectrum sensing framework for cognitive networks

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