A hybrid heuristic queue based algorithm for task assignment in mobile cloud

Rashidi, Shima; Sharifian, Saeed

doi:10.1016/j.future.2016.10.014

Cited by 55 publications

(22 citation statements)

References 30 publications

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“…Wu and Huang (2014) proposed a multifactor multisite risk based offloading in MCC to select suitable nodes for offloading by analyzing the overall benefits and risks of the system. Rashidi and Sharifian (2017) proposed a novel scheme of hybrid heuristic queue based algorithm for task assignment by implementing ant colony and genetic optimization algorithms with decreased mean completion time, total energy consumption by evaluation the factors such as time and energy incurred in the video encoding system. Enzai and Tang (2016) implemented hill climbing algorithm to offload computational task in multisite cloud servers with decreased energy consumption, computation time and total computational cost without considering the scalability factor.…”

Section: Review Of Literaturementioning

confidence: 99%

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

Veeraiyan¹,

Kousika²,

Senthilkumar³

et al. 2020

Journal of Computer Science

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Offloading the execution of heavy computational modules from mobile devices to Mobile Cloud Computing (MCC) is inevitable in today's era as it mainly focuses in consuming less battery power and execution time. But, the problem incurred with identifying the most optimal cloud device to map each module still remains a challenge in cloud computing environment. In this paper, a novel MCC offloading framework is proposed to fasten the allocation and execution of high computational modules that runs in the mobile device, effectively on the cloud. The framework employs K-Means clustering algorithm to group the nearest cloud virtual machines that best suits for executing modules of software running in the mobile. The objective of the paper is to maximize the energy savings by extending the battery life and execution speed of mobile device when executing heavy computational modules. The optimal selection of cloud device is attained by grouping the requirements of each module with the nearest cloud devices offering the same requirements using K-Means Algorithm. The proposed framework is compared with the existing mobile computation offloading frameworks with respect to energy saving, execution time and energy consumption. The results show that the proposed work executes the modules of computationally intensive modules in minimum time span with maximized energy savings than the existing frameworks.

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Section: Review Of Literaturementioning

confidence: 99%

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

Veeraiyan¹,

Kousika²,

Senthilkumar³

et al. 2020

Journal of Computer Science

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“…It shows that loss avoidance combines the marginal cost of work in one task with the increase in the workload selected in other tasks in order to determine when multitasking saves compensation costs. Shima Rashid [17] studied mobile cloud task allocation based on a mixed heuristic queue, proposed a multi-server queue model for extracting the system performance parameters, and proposed a resource allocation mechanism for two-level mobile cloud computing architecture with an unloading mechanism.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of the Task Distribution Scheme of Express Center at the End of Modern Logistics

et al. 2019

Electronics

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With the rise and improvement of artificial intelligence technology, the express delivery industry has become more intelligent. At the terminal of modern logistics, each dispatch center has hundreds of express mail deliveries to be dispatched every day, and the number of dispatchers is far less than the number of express mail deliveries. How to assign scientific tasks to each courier dispatch is the main target of this paper. The purpose is to make the number of tasks between the various couriers in the express center roughly the same in each cycle, so that there is a more balanced income between the couriers. In the simulation experiment, the delivery addresses are clustered according to the balanced k-means algorithm. Then, the ant colony algorithm is used to plan the delivery order of the express items in each class. Then, the time cost model is established according to the delivery distance of the express items in each class and the delivery mode of the express items to calculate the delivery time cost. Through a large amount of experimental data, the standard deviation of delivery time cost of each courier gradually decreases and tends to stabilize, which suggests that this method has a good effect on the dispatching task assignment of the express center. It can effectively make the delivery workload between the distributors roughly the same, and improve the delivery efficiency of the courier, save energy, and promote sustainable development.

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“…The primary components of MCC include the migration of computationally intensive tasks In the cloud, the load assigned to every node in the network is similarly distributed with an even quantity of resources over time. This enhances the scheme performance by moving the workloads among various nodes [4] [26]. The primary goal is to expedite the implementation of applications on resources whose workload changes at the runtime in an unpredictable way.…”

Section: Introductionmentioning

confidence: 99%

“…This enhances the scheme performance by moving the workloads among various nodes [4] [26]. The primary goal is to expedite the implementation of applications on resources whose workload changes at the runtime in an unpredictable way.…”

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confidence: 99%

A Krill Herd Behaviour Inspired Load Balancing of Tasks in Cloud Computing

Hasan¹,

Mohammed²

2017

STUD INFORM CONTROL

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A developing trend in the IT environment is mobile cloud computing (MCC) with colossal infrastructural and resource requirements. In the cloud computing environment, load balancing -a way of distributing workloads across numerous computing resources, is a vital aspect. A proficient load balancing guarantees an effective resource usage through the supply of network resources based on the user demands. It can also organize the network clients using the fitting planning criteria. This paper sets forth an advanced load balancing and energy/cost aware technique for a demand-based network resource allocation in cloud computing. The load balancing process in the proposed strategy utilizes a Krill load balancer (Krill LB) which is expected to achieve a well-balanced load over virtual machines. The aim of using the Krill LB as the load balancer is to increase the throughput of the network as much as possible. The speed, task cost, and weight of the tasks were first determined, after which, the Krill herd optimization algorithm was for the load balancing based on the measured parameters. Furthermore, a modified dynamic energy-aware cloudlet-based mobile cloud computing model (MDECM) was introduced for energy cost awareness in load balancing based on the service rate and energy of the mobile users. The proposed work was aimed at optimizing resource allocation in MCC in an energy-efficient manner. The performance of the suggested Krill-LB was benchmarked against that of Honey Bee Behavior Load Balancing (HBB-LB), Kill Herd, and Round Robin algorithms. or applications to servers in cloud domain in order to execute them, and after that, recover the outcome of the execution from these servers [8]. It utilizes communication technology to share information and assets and incorporates locationaware technologies, mobile access to IT, and energy sparing technology specifically designed for mobile devices [15]. As of late, mobile applications have been noticeably copious with different classes, such as entertainment, health, games, business, social networking, travel, andIn the cloud, the load assigned to every node in the network is similarly distributed with an even quantity of resources over time. This enhances the scheme performance by moving the workloads among various nodes [4] [26]. The primary goal is to expedite the implementation of applications on resources whose workload changes at the runtime in an unpredictable way. These are generally discussed in homogeneous conditions such as grids. Fundamentally, there are two ways of load balancing procedures: (i) Static and (ii) dynamic [3]. The load balancing plan and a migration policy are aimed at virtual machine (VM) clustering to brilliantly choose a VM from an over-burdened Studies in Informatics and Control, 26(4) 413-424,

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A hybrid heuristic queue based algorithm for task assignment in mobile cloud

Cited by 55 publications

References 30 publications

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

Design and Analysis of the Task Distribution Scheme of Express Center at the End of Modern Logistics

A Krill Herd Behaviour Inspired Load Balancing of Tasks in Cloud Computing

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