Job Scheduling for Cloud Computing Integrated with Wireless Sensor Network

Zhu, Chunsheng; Li, Xiuhua; Leung, Victor C. M.; Hu, Xiping; Yang, Laurence T.

doi:10.1109/cloudcom.2014.106

Cited by 22 publications

(14 citation statements)

References 13 publications

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“…There are two basic algorithms Min-Min [40], [41] and least connection (termed LC) [42], to compare with MIPSM. The Min-Min algorithm has better completion time, and the LC algorithm has better load balancing because it prefers idle resources every time.…”

Section: A Experiments Setupmentioning

confidence: 99%

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Zuo

Dong

Shu

et al. 2018

IEEE Systems Journal

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Abstract-In cloud computing, resources are dynamic, and the demands placed on the resources allocated to a particular task are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called interlacing peak is proposed. First, the resource load information, such as CPU, I/O, and memory usage, is periodically collected and updated, and the task information regarding CPU, I/O, and memory is collected. Second, resources are sorted into three queues according to the loads of the CPU, I/O, and memory: CPU intensive, I/O intensive, and memory intensive, according to their demands for resources. Finally, once the tasks have been scheduled, they need to interlace the resource load peak. Some types of tasks need to be matched with the resources whose loads correspond to a lighter types of tasks. In other words, CPUintensive tasks should be matched with resources with low CPU utilization; I/O-intensive tasks should be matched with resources with shorter I/O wait times; and memory-intensive tasks should be matched with resources that have low memory usage. The effectiveness of this method is proved from the theoretical point of view. It has also been proven to be less complex in regard to time and place. Four experiments were designed to verify the performance of this method. Experiments leverage four metrics: 1) average response time; 2) load balancing; 3) deadline violation rates; and 4) resource utilization. The experimental results show that this method can balance loads and improve the effects of resource allocation and utilization effectively. This is especially true when resources are limited. In this way, many tasks will compete for the same resources. However, this method shows advantage over other similar standard algorithms.

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Section: A Experiments Setupmentioning

confidence: 99%

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Zuo

Dong

Shu

et al. 2018

IEEE Systems Journal

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“…Towards offering more useful data reliably to mobile cloud from WSN, a WSN and cloud integration scheme named TPSS is presented in [13], which consists of TPSDT (Time and Priority based Selective Data Transmission) and PSS (Priority-based Sleep Scheduling). To achieve shorter expected completion time of cloud tasks related to WSN during WSN and cloud integration, two job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase Max-Min (PTAM), are proposed for CC integrated with WSN in [14]. In both PTMM and PTAM, cloud tasks related to WSN are executed first in phase 1, followed by the execution of other ordinary cloud tasks in phase 2.…”

Section: B Effectively Integrate Wsn and Cloudmentioning

confidence: 99%

Trust assistance in Sensor-Cloud

Zhu

Leung

Yang

et al. 2015

2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Incorporating 1) the ubiquitous data gathering ability of wireless sensor networks (WSNs) and 2) the powerful data storage and data processing capabilities of cloud computing (CC), Sensor-Cloud (SC) is attracting increasing attention from both academia and industry. In this paper, focusing on improving the quality of service (QoS) of SC for users to obtain sensory data from the cloud, we propose Trust-Assisted SC (TASC). In TASC, trusted sensors (i.e., sensors with trust values exceeding a threshold) collect and transmit sensory data to the cloud. Then the cloud selects the trusted data centers (i.e., data centers with trust values exceeding a threshold) to store, process the sensory data and further transmit the processed sensory data to users on demand. With extensive simulation results, we show that the TASC can substantially improve the throughput and response time for users to obtain sensory data from the cloud, compared with SC without trust assistance (SCWTA).

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“…A priority‐based two‐phase min–min scheduling policy (PTMM) is employed on clustered tasks by Zhu et al . in to minimize the expected completion time. Cardonha et al .…”

Section: Introductionmentioning

confidence: 99%

Maximizing quality of experience through context‐aware mobile application scheduling in cloudlet infrastructure

et al. 2016

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Summary Application software execution requests, from mobile devices to cloud service providers, are often heterogeneous in terms of device, network, and application runtime contexts. These heterogeneous contexts include the remaining battery level of a mobile device, network signal strength it receives and quality‐of‐service (QoS) requirement of an application software submitted from that device. Scheduling such application software execution requests (from many mobile devices) on competent virtual machines to enhance user quality of experience (QoE) is a multi‐constrained optimization problem. However, existing solutions in the literature either address utility maximization problem for service providers or optimize the application QoS levels, bypassing device‐level and network‐level contextual information. In this paper, a multi‐objective nonlinear programming solution to the context‐aware application software scheduling problem has been developed, namely, QoE and context‐aware scheduling (QCASH) method, which minimizes the application execution times (i.e., maximizes the QoE) and maximizes the application execution success rate. To the best of our knowledge, QCASH is the first work in this domain that inscribes the optimal scheduling problem for mobile application software execution requests with three‐dimensional context parameters. In QCASH, the context priority of each application is measured by applying min–max normalization and multiple linear regression models on three context parameters—battery level, network signal strength, and application QoS. Experimental results, found from simulation runs on CloudSim toolkit, demonstrate that the QCASH outperforms the state‐of‐the‐art works well across the success rate, waiting time, and QoE. Copyright © 2016 John Wiley & Sons, Ltd.

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Job Scheduling for Cloud Computing Integrated with Wireless Sensor Network

Cited by 22 publications

References 13 publications

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Trust assistance in Sensor-Cloud

Maximizing quality of experience through context‐aware mobile application scheduling in cloudlet infrastructure

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