Towards energy aware cloud computing application construction

Armstrong, Django; Djemame, Karim; Kavanagh, Richard

doi:10.1186/s13677-017-0083-2

Cited by 13 publications

(7 citation statements)

References 17 publications

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“…On the basis of bio-inspired and swarm insight methodologies, a methodical investigation of task scheduling in cloud and network modelling has been familiarized. Tis study should give per-users the ability to select a rational methodology for presenting improved strategies when organizing client's applications by providing them with more options [10].…”

Section: Related Workmentioning

confidence: 99%

“…Obviously, the states' S with action A is focused to obtain reward R. Te Qtables retain the latest updates and actions [26]. Te total reward is computed using the following equation: For {Get the t hours load predictions of the starting VM} (10) VMPreload < -Get-L p LoadPrediction (VMid) (11) {Get load prediction of each VM on host} (12) HRes < -Get_ResFromLoad (VMs, PreLoads, eachhost) (13) endFor (14) For: each server PM in datacenter (15) PM.Tcpu > β (16) workloadBalance in Data center() (17) End Function ALGORITHM 1: Dynamic programming algorithm to load utilization corresponding to bandwidth and network availability.…”

Section: Reinforcement Learning Techniques In Machine Learningmentioning

confidence: 99%

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Dynamic Q-Learning-Based Optimized Load Balancing Technique in Cloud

Muthusamy,

Dhanaraj

2023

Mobile Information Systems

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Cloud computing provides on-demand access to a shared puddle of computing resources, containing applications, storage, services, and servers above the internet. This allows organizations to scale their IT infrastructure up or down as needed, reduce costs, and improve efficiency and flexibility. Improving professional guidelines for social media interactions is crucial to address the wide range of complex issues that arise in today’s digital age. It is imperative to enhance and update professional guidelines regarding social media interactions in order to effectively tackle the multitude of intricate issues that emerge. In this paper, we propose a reinforcement learning (RL) method for handling dynamic resource allocation (DRA) and load balancing (LB) activity in a cloud environment and achieve good scalability and a significant improvement in performance. To address this matter, we propose a dynamic load balancing technique based on Q-learning, a reinforcement learning algorithm. Our technique leverages Q-learning to acquire an optimal policy for resource allocation in real-time based on existing workload, resource accessibility, and user preferences. We introduce a reward function that takes into account performance metrics such as response time and resource consumption, as well as cost considerations. We evaluate our technique through simulations and show that it outperforms traditional load balancing techniques in expressions of response time and resource utilization while also reducing overall costs. The proposed model has been compared with previous work, and the consequences show the significance of the proposed work. Our model secures a 20% improvement in scalability services. The DCL algorithm offers significant advantages over genetic and min-max algorithms in terms of training time and effectiveness. Through simulations and analysis on various datasets from the machine learning dataset repository, it has been observed that the proposed DCL algorithm outperforms both genetic and min-max algorithms. The training time can be reduced by 10% to 45%, while effectiveness is enhanced by 30% to 55%. These improvements make the DCL algorithm a promising option for enhancing training time and effectiveness in machine learning applications. Further research can be conducted to investigate the potential of combining the DCL algorithm with a supervised training algorithm, which could potentially further improve its performance and apply in real-world application.

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Section: Related Workmentioning

confidence: 99%

Section: Reinforcement Learning Techniques In Machine Learningmentioning

confidence: 99%

Dynamic Q-Learning-Based Optimized Load Balancing Technique in Cloud

Muthusamy,

Dhanaraj

2023

Mobile Information Systems

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“…According to the policies, the method enforces access control in BYOD environment. In [7], the author performs evaluation on various algorithms on their security performance. In [8], the author present a taxonomy based approach for the access restriction in cloud environment.…”

Section: Related Workmentioning

confidence: 99%

Real Time Service Level Access Restriction using Dynamic OTUP Generation for Improved Security in Cloud

Ubada*,

Surputheen

2019

IJRTE

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The cloud environment has been identified as the keen platform being used by different cloud users and organizations. The data security has been approached with several techniques like password, attribute based approaches and so on. However, they suffer to achieve higher security performance. To improve the performance, an efficient real time service level access restriction scheme which uses dynamic OTUP (One Time User Password) generation scheme. The method maintains different user information which includes the password details of users. Further, the method collects the one time user password initially. With this, the method restricts the user’s service access by generating OTUP in a dynamic way according to different services. The user has been restricted according to the service and OTUP generation methods. According to this, the method estimates service level trust weight (SLTW) to restrict the user. The methods introduce higher security performance and reduces false ratio.

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“…Zuccaro et al and Zou et al found that using a shared Internet connection or shared databases may also add to the success of information sharing [8,9]. As a result of the exponential growth of technology, information technology (IT) products and services are accessible worldwide every day [10,11]. Bosch-Sijtsema et al and CAPM and Eng stated that this phenomenon pressures building managers to develop, implement, and integrate new technical techniques to accommodate these changes [12,13].…”

Section: Introductionmentioning

confidence: 99%

Success of Implementing Cloud Computing for Smart Development in Small Construction Projects

et al. 2023

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The objective of this study is to ascertain the essential elements that contribute to the successful implementation of cloud computing in small-scale construction projects, with the ultimate goal of promoting intelligent development in Malaysia. The construction sector is undergoing rapid transformation, and the integration of cloud computing technology can make a substantial contribution to the achievement of project objectives and the promotion of sustainable development. Nonetheless, there exists a dearth of comprehension regarding the function of cloud computing in minor construction undertakings within the Malaysian context. In order to bridge this gap, a mixed-methods approach was implemented, which encompassed a comprehensive review of the literature, interviews with experts, and a preliminary survey that involved 160 participants. Utilizing the findings of the pilot survey, the process of Exploratory Factor Analysis (EFA) was employed to discern and eliminate nonessential determinants of success. A survey utilizing primary questionnaires was conducted with a sample size of 230 participants. The subsequent analysis of 16 critical success factors was carried out through the application of Structural Equation Modeling (SEM). The findings indicate that there are four fundamental constructs that play a crucial role in the effective execution of a project. These include cost, quality, and time management (β = 0.352); planning success (β = 0.360); organizational success (β = 0.351); and communication and coordination (β = 0.299). The research results have favorable ramifications for the construction sector in Malaysia. The integration of cloud computing technology in minor construction endeavors has the potential to augment project efficacy and foster sustainable development. This study offers a roadmap for stakeholders in the construction industry to effectively utilize cloud computing technology for smart development by identifying critical success factors.

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Towards energy aware cloud computing application construction

Cited by 13 publications

References 17 publications

Dynamic Q-Learning-Based Optimized Load Balancing Technique in Cloud

Dynamic Q-Learning-Based Optimized Load Balancing Technique in Cloud

Real Time Service Level Access Restriction using Dynamic OTUP Generation for Improved Security in Cloud

Success of Implementing Cloud Computing for Smart Development in Small Construction Projects

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