An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Shi, Ting; Yang, Mei; Li, Xiang; Lei, Qing; Jiang, Yingtao

doi:10.1016/j.pmcj.2015.07.005

Cited by 60 publications

(22 citation statements)

References 29 publications

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“…The following experiments compare our proposed QoS‐based resource allocation optimization algorithm for mobile device ( QRA_MD ) with APTSA in mobile cloud proposed by Shi et al and mobile cloud‐based heterogeneous resource allocation algorithm ( MC‐HRA ) proposed by Chen et al . The 4 main performance metrics in the experiments are execution success ratio, response time, job drop ratio, and energy consumption.…”

Section: Methodsmentioning

confidence: 99%

QoS‐based resource allocation across local and public cloud for resource‐constrained mobile device

Liu

Luo

2016

Trans. Emerging Tel. Tech.

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This paper uses the hybrid cloud architecture, including the local cloud and the remote public cloud, to execute the mobile tasks with the objective of optimizing the resource allocation, while meeting the service‐level agreement requirement. Hybrid cloud is a cloud computing environment that uses a mix of on‐premises http://searchcloudcomputing.techtarget.com/definition/private-cloud and third‐party http://searchcloudcomputing.techtarget.com/definition/public-cloud services with orchestration between the 2 platforms. The mobile application could run in private cloud, but use http://searchcloudcomputing.techtarget.com/definition/cloud-bursting to access additional computing resources from a public cloud when computing demands spike. The proposed Quality of Service–based resource allocation optimization scheme across local and public cloud for resource‐constrained mobile device is formulated into a constrained optimization problem and further divided into 2 subproblems, ie, one for resource allocation for the local cloud and one for the remote public cloud, using the Lagrange method. The proposed Quality of Service–based resource allocation optimization algorithm across local and public cloud is proposed. Through the experiments, the performance of proposed algorithm is evaluated with other baseline algorithms. The results of the experiments show a performance improvement when compared to the approaches from the literature.

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Section: Methodsmentioning

confidence: 99%

QoS‐based resource allocation across local and public cloud for resource‐constrained mobile device

Liu

Luo

2016

Trans. Emerging Tel. Tech.

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“…In Shi et al (2016), an adaptive probabilistic scheduler is presented, which optimizes the task power consumption with time restrictions and in Zhou et al (2017), the Context-Aware Offloading Framework is proposed a decision algorithm discharge with recognition of the context that takes into account the context of changes such as: network tiers and heterogeneous mobile cloud resources to provide code discharge decisions. Also, they have provided a general model of cost estimate for the mobile cloud infrastructure resources to estimate the cost of performing the task, including run time, energy consumption.…”

Section: Context-aware Scheduling Algorithmsmentioning

confidence: 99%

“…Resource allocation is not done in a balanced fashion Complies with expectations in terms of access to the service and processing time; Prioritizes tasks by exploring contexts such as battery level and signal quality when scheduling and optimizes user QoE Management is centralized, making it difficult to implement in densely distributed environments such as fog Shi et al (2016), Adaptive Probabilistic Scheduler Allows the reduction of average energy consumption in a successful task and maintains a high task execution rate. It has a high adaptability in both fixed and mobile networks.…”

Section: Maximizes Task Execution Timementioning

confidence: 99%

Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives

Barros

Rocio

Sousa³

et al. 2020

J INFORM SYSTEMS ENG

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Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a systematic review of the literature on the main task by: scheduling algorithms in the existing cloud and fog architecture; studying and discussing their limitations, and we explored and suggested some perspectives for improvement.

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“…This policy is robust to the limited scheduling information. Shi et al [8] have presented an adaptive probabilistic scheduler, which can optimize the energy consumption of tasks with time-constrained. Zhao et al [9] have introduced a cooperative scheduling mechanism over edge-clouds and the cloud.…”

Section: B Task Schedulersmentioning

confidence: 99%

Distributed and Application-Aware Task Scheduling in Edge-Clouds

Xiong

Lin

2018

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)

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Edge computing is an emerging technology which places computing at the edge of the network to provide an ultralow latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the power of edge computing by offloading computation to cloudlets in edge-clouds. However, the task scheduling of computation offloading in edge-clouds faces a two-fold challenge. First, as cloudlets are geographically distributed, it is difficult for each cloudlet to perform load balancing without centralized control. Second, as tasks of computation offloading have a wide variety of types, to guarantee the user quality of experience (QoE) in terms of task types is challenging. In this paper, we present Petrel, a distributed and application-aware task scheduling framework for edge-clouds. Petrel implements a sample-based load balancing technology and further adopts adaptive scheduling policies according to task types. This application-aware scheduling not only provides QoE guarantee but also improves the overall scheduling performance. Trace-driven simulations show that Petrel achieves a significant improvement over existing scheduling strategies.

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An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Cited by 60 publications

References 29 publications

QoS‐based resource allocation across local and public cloud for resource‐constrained mobile device

QoS‐based resource allocation across local and public cloud for resource‐constrained mobile device

Scheduling in Cloud and Fog Architecture: Identification of Limitations and Suggestion of Improvement Perspectives

Distributed and Application-Aware Task Scheduling in Edge-Clouds

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