Round Robin with Load Degree: An Algorithm for Optimal Cloudlet Discovery in Mobile Cloud Computing

Ramasubbareddy, Somula; Sasikala, R.

doi:10.12694/scpe.v19i1.1392

Cited by 11 publications

(4 citation statements)

References 23 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tang et al [9], suggested load-balanced scheduling (DLBS) approach to analyze the load imbalance degree of every data center in the open flow network and then schedule the unbalanced data load to other data centers in order to maximize network throughput. Somula et al [16], proposed load balancing in mobile computing to improvise response time and reduce waiting time and latency. Load balancing is carried out by offloading the load from mobile cloudlets to cloud computing environments using a round-robin algorithm having limited resources like storage, processing capacity, and battery life.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Game theoretical approach for load balancing using SGMLB model in cloud environment

et al. 2020

View full text Add to dashboard Cite

On-demand cloud computing is one of the rapidly evolving technologies that is being widely used in the industries now. With the increase in IoT devices and real-time business analytics requirements, enterprises that ought to scale up and scale down their services have started coming towards on-demand cloud computing service providers. In a cloud data center, a high volume of continuous incoming task requests to physical hosts makes an imbalance in the cloud data center load. Most existing works balance the load by optimizing the algorithm in selecting the optimal host and achieves instantaneous load balancing but with execution inefficiency for tasks when carried out in the long run. Considering the long-term perspective of load balancing, the research paper proposes Stackelberg (leader-follower) game-theoretical model reinforced with the satisfaction factor for selecting the optimal physical host for deploying the tasks arriving at the data center in a balanced way. Stackelberg Game Theoretical Model for Load Balancing (SGMLB) algorithm deploys the tasks on the host in the data center by considering the utilization factor of every individual host, which helps in achieving high resource utilization on an average of 60%. Experimental results show that the Stackelberg equilibrium incorporated with a satisfaction index has been very useful in balancing the loading across the cluster by choosing the optimal hosts. The results show better execution efficiency in terms of the reduced number of task failures by 47%, decreased 'makespan' value by 17%, increased throughput by 6%, and a decreased frontend error rate as compared to the traditional random allocation algorithms and flow-shop scheduling algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

“…By taking the second-order derivative of (9) we obtain a positive value which implies the set of R j is proved to be convex and so the range for R j is defined to be [R min j ; R max j ] in Eq (16).…”

Section: Plos Onementioning

confidence: 99%

Game theoretical approach for load balancing using SGMLB model in cloud environment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The minimum value that leads the account for distortion is the whole point for pooling. So, use these techniques creation of the model can be achieved [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Fruit Recognition Using Deep Learning

Reddy

Ramasubbareddy

Saidulu

et al. 2021

Innovations in Computer Science and Engineering

View full text Add to dashboard Cite

This paper discusses on the fruits classification for which the data is collected from the dataset called Fruits_360. Using this data, training of a neural network which will identify the fruit. Using the deep learning and image processing concepts form a neural networking system. The proposed work uses convolution neural networks in building the model and also used ResNet to get the results of image classification from deep learning concept. To meet the resource requirement for the proposed work, it uses Google cloud vision API which gives us the required GPU to proceed with the process of analyzing the data from the image and also discussed the in depth of how image classification is done using deep learning concepts. Here, in building a deep learning model, which classifies the given image into any of these nine categories: Apple, Avocado, Banana, Cherry, Cocos, Kiwi, Mango, Orange, Lemon. This model can also be implemented into mobile version.

show abstract

“…Due to the same functionality of services that are given differently, it is difficult for end users to choose the best option. This existing research is only suitable for consumers who wish to identify cloud services that are identical to the method they already knew or engage, but does not take into account those who look for the optimal services without background knowledge [36,37,38].…”

mentioning

confidence: 99%

Cloud Broker Recommendation Framework to Provide Trustworthy Cloud Services to the End User

Marimuthu¹,

Akilandeswari²,

Varasree³

et al. 2022

SCPE

View full text Add to dashboard Cite

In recent years, many cloud services have become available on the Website}. Discovering suitable cloud services for the end user is incredibly complex and difficult. The cloud brokerage service is an application that aids in providing solutions for this problem. It recommends suitable cloud service providers to the end users depending on their relevant requirements. The Internet provides access to a wide variety of cloud brokers. As a result, choosing a cloud broker or service provider is both time-consuming and tedious. It is now becoming a necessity to choose a proper cloud brokerage service based on trust. Research works found in the literature address some of the issues and provide feasible solutions by proposing frameworks, optimizations and rule based algorithms. However, those works focus solely on delivering a trustworthy service to the end user through application of techniques and algorithms. There is no proper framework model in place to provide suitable and trustworthy recommended services to the users. This article provides a detailed description of the frameworks that are offered by the researchers, including issues and proposes a trustworthy recommendation framework (TRF) to provide trustworthy services to the end user. This article also presents a Trustworthy Recommended Weighted value (TRWv) approach for determining trustworthy services, and it is discovered that the proposed method achieves high accuracy (91.3%) when compared to similar works.

show abstract

Round Robin with Load Degree: An Algorithm for Optimal Cloudlet Discovery in Mobile Cloud Computing

Cited by 11 publications

References 23 publications

Game theoretical approach for load balancing using SGMLB model in cloud environment

Game theoretical approach for load balancing using SGMLB model in cloud environment

Fruit Recognition Using Deep Learning

Cloud Broker Recommendation Framework to Provide Trustworthy Cloud Services to the End User

Contact Info

Product

Resources

About