Energy efficient strategy for task allocation and VM placement in cloud environment

Bharathi, P. Divya; Prakash, P.; Kiran, Mariam

doi:10.1109/ipact.2017.8244950

Cited by 11 publications

(2 citation statements)

References 5 publications

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“…The next three arguments represent the input form's contents and the output form's term and destination. The ‘DepDisk’ should be transmitted to the ad-hoc host just before the VM working; to prepare an (Ad- hoc Cloud Host) for working before the VM execution phase, there is a need for using the ‘Task Creation’, and ‘SCHED CONFIG’ object with data related to the V-BOINC project [ 72 ]. Upon a successful function implementation, the BOINC work-unit was established and saved in the ‘Job Service’ DB, work-unit ID and logs were saved as well, then the ‘Ad-hoc Cloud Guest’ receives the work-unit from the ‘Job Service’ DB.…”

Section: Proposed Modelmentioning

confidence: 99%

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

et al. 2022

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In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the “Ad-hoc Cloud System” idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.

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Section: Proposed Modelmentioning

confidence: 99%

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

et al. 2022

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“…(3) load balancing, [4] which seeks to place the services to different nodes according to the working status of the nodes in terms of workload. However, the main goal of service placement in edge computing is to improve the QoS, such as reducing the user-perceived latency.…”

Section: Introductionmentioning

confidence: 99%

Mobility-aware dynamic service placement for edge computing

Liu¹,

Wang²,

Tian³

et al. 2019

EAI Endorsed Trans IoT

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Edge computing provides capabilities similar to traditional cloud computing at network edge closer to users. Offloading applications from the cloud to the edge of the network can effectively diminish the latency of users waiting for applications to improve their quality of service(QoS). Due to the user mobility, however, the distance between the user and the edge device is constantly increasing, resulting in a degradation of the user's QoS. To satisfy the QoS of mobile users, applications should be dynamically migrated to follow the user mobility.In this paper, we propose a mobility-aware dynamic service placement policy that reduces the number of virtual machine migrations and diminishes user-perceived latency simultaneously. The proposed scheme filters out the invalid migration that the user handoffs to another access point before the migration is completed. The simulation trials demonstrate that our solution improves the QoS of users and reduces the number of application migrations.

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A Novel Coalitional Game-Theoretic Approach for Energy-Aware Dynamic VM Consolidation in Heterogeneous Cloud Datacenters

Xiao

Xia

Zeng

et al. 2019

Lecture Notes in Computer Science

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Energy efficient strategy for task allocation and VM placement in cloud environment

Cited by 11 publications

References 5 publications

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

Mobility-aware dynamic service placement for edge computing

A Novel Coalitional Game-Theoretic Approach for Energy-Aware Dynamic VM Consolidation in Heterogeneous Cloud Datacenters

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