Self-characteristics based Energy-Efficient Resource Scheduling for Cloud

Dewangan, Bhupesh Kumar; Agarwal, Amit; Venkatadri, M.; Pasricha, Ashutosh

doi:10.1016/j.procs.2019.05.044

Cited by 23 publications

(7 citation statements)

References 20 publications

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“…The following restrictions are placed on the model: Constrained processing allocation by eqn (14) 𝛼𝑋 𝑘𝑛 ≥ 𝛿 𝑘𝑛 ∀𝑘 ∈ 𝐾, 𝑛 ∈ 𝑃𝑁 𝑋 𝑘𝑛𝑛 ≤ 𝛼𝛿 𝑘𝑛 ∀𝑘 ∈ 𝐾, 𝑛 ∈ 𝑃𝑁 ∑ 𝑛∈𝑃𝑁 𝛿 𝑘𝑛 = 1 ∀𝑘 ∈ 𝐾 (14) assures that one processing node will be given to each of the task k tasks. Constrained processing node capacity by eqn (15) ∑ 𝑘∈𝐾 𝑋 𝑘𝑡 ≤ 𝐶 𝑛𝑡 ∀𝑛 ∈ 𝑃𝑁 (15) Every task k given to a processing node n does not go above that node's processing limit.…”

Section: Virtual Machine Based Markov Model Infused Wavelength Divisi...mentioning

confidence: 99%

“…In work [13], a dynamic scheduling technique for context-aware, SOA-based applications' response time SLA was developed. In order to prevent overtaxing the service tier resources as measured by a SLA metric, the developers of [14] have suggested rate-limiting requests. In their study [15], the authors devised a dynamic scheduling technique that can ensure SLAs for CPU service share in server clusters.…”

Section: Introductionmentioning

confidence: 99%

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Mobile Cloud IoT for Resource Allocation with Scheduling in Device- Device Communication and Optimization based on 5G Networks

Pise¹

2022

ijfrcsce

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Internet of Things (IoT) is revolutionising technical environment of traditional methods as well as has applications in smart cities, smart industries, etc. Additionally, IoT enabled models' application areas are resource-constrained as well as demand quick answers, low latencies, and high bandwidth, all of which are outside of their capabilities. The above-mentioned issues are addressed by cloud computing (CC), which is viewed as a resource-rich solution. However, excessive latency of CC prevents it from being practical. The performance of IoT-based smart systems suffers from longer delay. CC is an affordable, emergent dispersed computing pattern that features extensive assembly of diverse autonomous methods. This research propose novel technique resource allocation and task scheduling for device-device communication in mobile Cloud IoT environment based on 5G networks. Here the resource allocation has been carried out using virtual machine based markov model infused wavelength division multiplexing. Task scheduling is carried out using meta-heuristic moath flame optimization with chaotic maps. So, by scheduling tasks in a smaller search space, system resources are conserved. We run simulation tests on benchmark issues and real-world situations to confirm the effectiveness of our suggested approach. The parameters measured here are resource utilization of 95%, response time of 89%, computational cost of 35%, power consumption of 38%, QoS of 85%.

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Section: Virtual Machine Based Markov Model Infused Wavelength Divisi...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobile Cloud IoT for Resource Allocation with Scheduling in Device- Device Communication and Optimization based on 5G Networks

Pise¹

2022

ijfrcsce

View full text Add to dashboard Cite

show abstract

“…Integrating Equation (18) with the historical data, the state transition probability matrix P can be finally acquired.…”

Section: B State Transition Probability Matrix Derivationmentioning

confidence: 99%

“…Moreover, for the resource scheduling and without violating the SLA protocol, a multi-objective task scheduling algorithm was applied to improve the data centre throughput and reduce the data center cost [13]. In addition, Juarez et al in [14] applied the dynamic energy perception to parallel task scheduling and Bhupesh et al in [18] propose a self-optimized energy efficient resource management strategy, while Alsarhan et al in [15] studied the adaptive allocation and configuration of resources in a multi-service cloud environment. Besides, to balance the workload among all virtual machines with resilient resource provisioning and de-provisioning, one dynamic k-interval based scheduling algorithm was proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

WebIDE Cloud Server Resource Allocation With Task Pre-Scheduling in IoT Application Development

Li¹,

Tian²,

Wang³

2020

IEEE Access

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WebIDE is leveraged for IoT application development, which could adapt to the rapid growth of IoT applications and meanwhile facilitate the rapid development. Resource allocation is of vital significance in the WebIDE cloud service system. Existing resource allocation approaches may encounter issues such as unbalanced resource assignments, which could lead to the reduced system resource utilization or extended system response time. Existing methods are typically on the basis of predetermined resource demands for each task, and not applicable to the case that the resource demands are dynamic and unknown. This article predicts the tasks to be performed by the WebIDE cloud service through task pre-scheduling, and then applies the existing resource allocation methods. Firstly, all tasks are classified, based on the execution state, execution operations and WebIDE cloud server resource requirements. Secondly, the grouped tasks are mapped to different system states, with the Markov state transition probability matrix leveraged to model the transition probability between tasks, followed by the prediction model constructed. Finally, integrating task pre-scheduling with ant colony algorithm, WebIDE cloud server resource allocation is carried out. Experiment results show that adding the task prediction model could significantly not only reduce the task response time, but also improve the cloud server resource utilization.

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“…To make sure adjustment to non-basic disappointment, tries desirous to purchase a load of masterminded computer instrumentality and a helper uninterruptible power provide device. The target is to stay the crash of key frameworks and systems, concentrating on problems known with up time and period (Dewangan et al, 2019). Adaptation to internal failure is offerprogramming put in instrumentality, or by some mixture of the two.…”

Section: Introductionmentioning

confidence: 99%

Cloud Resource Optimization System Based on Time and Cost

Dewangan¹,

Agarwal²,

Tanupriya³

et al. 2020

Int J Math, Eng, Manag Sci

Self Cite

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Resource management in cloud could be a time and cost-effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud. In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource-management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.

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Self-characteristics based Energy-Efficient Resource Scheduling for Cloud

Cited by 23 publications

References 20 publications

Mobile Cloud IoT for Resource Allocation with Scheduling in Device- Device Communication and Optimization based on 5G Networks

Mobile Cloud IoT for Resource Allocation with Scheduling in Device- Device Communication and Optimization based on 5G Networks

WebIDE Cloud Server Resource Allocation With Task Pre-Scheduling in IoT Application Development

Cloud Resource Optimization System Based on Time and Cost

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