SLA-Aware Profit Optimization in Cloud Services via Resource Scheduling

2010 ◽  
Author(s):  
Hyun Jin Moon ◽  
Yun Chi ◽  
Hakan Hacigümüs
Keyword(s):  
Resource Scheduling ◽  
Cloud Services ◽  
Profit Optimization

Effective Resource Aware Health Care Monitoring in Body Sensor Network Platform Using Modified Particle Swarm Optimization

2021 ◽  
Vol 11 (12) ◽  
pp. 3054-3061
Author(s):  
S. Sureshu ◽  
R. Vijayabhasker
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Network ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Cloud Services ◽  
Swarm Optimization ◽  
Body Sensor Network ◽  
Modified Particle Swarm Optimization ◽  
Network Platform

Real-time physiological data may be gathered using wearable medical sensors based on a network of body sensors. We do not however have an effective, trustworthy and secure body sensor network platform (BSN) that can satisfy growing e-health requirements. Many of these applications require BSN to provide the dependable and energy efficient data transfer of many data speeds. Cloud computing is giving assets to patient dependent on application request at SLA (service level agreement) rules. The service providers are focusing on giving the necessity based asset to satisfy the QoS (quality of service) prerequisites. Therefore, it has become an assessment to adapt service-oriented assets because of vulnerability and active interest for cloud services. The task scheduling is an option in contrast to appropriating asset by evaluating the inconsistent outstanding task at hand. the allocation of tasks given by the microprocessor Subsequently, a productive asset scheduling method needs to disseminate proper VMs (Virtual Machines). The swarm intelligence is appropriate to deal with such vulnerability issues carefully. In this paper, an effective resource scheduling strategy Utilizing Modified Particle Swarm Optimization approach (MPSO) is presented, with a target to limit execution cost that gives an approach for the microprocessor to deal with the multiple number of tasks gives to the controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of things technology (Iot), energy consumed, bandwidth consumption, speed and execution cost. The near investigation of results has been exhibited that the presented scheduling scheme performed better when contrasted with existing evaluation. In this manner, the presented resource scheduling approach might be utilized to enhance the viability of cloud resources.

scholarly journals Performance Evaluation of Cloud Services with Profit Optimization

2015 ◽  
Vol 54 ◽  
pp. 24-30 ◽  
Author(s):  
M. Jaiganesh ◽  
B. Ramadoss ◽  
A. Vincent Antony Kumar ◽  
S. Mercy
Keyword(s):  
Performance Evaluation ◽  
Cloud Services ◽  
Profit Optimization

scholarly journals AN ADAPTIVE RESOURCE SCHEDULING FRAMEWORK WITH SCHEDULE INTERVAL FILLING FOR CLOUD SERVICES

2021 ◽  
Vol 9 (2) ◽  
pp. 968-977
Author(s):  
B.SivaRama Krishna, Et. al.
Keyword(s):  
Cloud Computing ◽  
Resource Scheduling ◽  
Cloud Services ◽  
Task Completion ◽  
Proposed Model ◽  
Schedule Interval ◽  
Significant Research ◽  
Interval Filling ◽  
Adaptive Resource ◽  
Distributed Cloud

Application level resource scheduling in distributed cloud computing is a significant research objective that grabbed the attention of many researchers in recent literature. Minimal resource scheduling failures, robust task completion and fair resource usage are the critical factors of the resource scheduling strategies. Hence, this manuscript proposed a scalable resource-scheduling model for distributed cloud computing environments that aimed to achieve the scheduling metrics. The proposed model called " Modified Resource Scheduling with Schedule Interval Filling " schedules the resource to respective task such that the optimal utilization of resource idle time achieved. The proposed model performs the scheduling in hierarchical order and they are optimal idle resource allocation, if no individual resource is found to allocate then it allocates optimal multiple idle resources with considerable schedule intervals filling. The experimental results evincing that the proposed model is scalable and robust under the adapted metrics.

Task scheduling algorithm based on reliability perception in cloud computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Kuang Yuejuan ◽  
Luo Zhuojun ◽  
Ouyang Weihao
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Pso Algorithm ◽  
Cloud Services ◽  
Task Scheduling Algorithm ◽  
Cloud Resource ◽  
Communication Links ◽  
The Impact

Background: In order to obtain reliable cloud resources, reduce the impact of resource node faults in cloud computing environment and reduce the fault time perceived by the application layer, a task scheduling model based on reliability perception is proposed. Methods: The model combines the two-parameter weibull distribution and analyzes various interaction relations between parallel tasks to describe the local characteristics of the failure rules of resource nodes and communication links in different periods.The model is added into the particle swarm optimization (pso) algorithm, and an adaptive inertial weighted pso resource scheduling algorithm based on reliability perception is obtained. Results: Simulation results show that when A increases to 0.3, the average scheduling length of the task increases rapidly.When it is 0.4-0.6, the growth rate is relatively slow.When greater than 0.8, the average scheduling length increases sharply.It can be seen that the r-pso algorithm proposed in this paper can accurately estimate the relevant parameters of cloud resource failure rule, and the generated resource scheduling scheme has better fitness, and the optimization effect is more significant with the increase of the number of tasks. Conclusion: With only a small amount of time added, the reliability of cloud services is greatly improved.

A Survey on Utilizing Cloud Services in the E-learning Process

2015 ◽  
Vol 6 (9) ◽  
pp. 1606-1612
Author(s):  
Zaydoon Mohammad Hatamleh ◽  
Eslam Najim Badran ◽  
Bilal Mohammad Hatamleh
Keyword(s):  
Learning Process ◽  
Cloud Services ◽  
E Learning

Implementaon of digital economy elements in electric power industry

2018 ◽  
Vol 11 (2) ◽  
pp. 94-102 ◽  
Author(s):  
A. G. Filimonov ◽  
N. D. Chichirova ◽  
A. A. Chichirov ◽  
A. A. Filimonovа
Keyword(s):  
Electric Power ◽  
Energy Production ◽  
Predictive Analytics ◽  
Digital Economy ◽  
Digital Transformation ◽  
Cloud Services ◽  
Small Scale ◽  
Economic Activities ◽  
Industrial Enterprises ◽  
Automation Systems

Energy generation, along with other sectors of Russia’s economy, is on the cusp of the era of digital transformation. Modern IT solutions ensure the transition of industrial enterprises from automation and computerization, which used to be the targets of the second half of the last century, to digital enterprise concept 4.0. The international record of technological and structural solutions in digitization may be used in Russia’s energy sector to the full extent. Specifics of implementation of such systems in different countries are only determined by the level of economic development of each particular state and the attitude of public authorities as related to the necessity of creating conditions for implementation of the same. It is shown that a strong legislative framework is created in Russia for transition to the digital economy, with research and applied developments available that are up to the international level. The following digital economy elements may be used today at enterprises for production of electrical and thermal energy: — dealing with large amounts of data (including operations exercised via cloud services and distributed data bases); — development of small scale distributed generation and its dispatching; — implementation of smart elements in both electric power and heat supply networks; — development of production process automation systems, remote monitoring and predictive analytics; 3D-modeling of parts and elements; real time mathematic simulation with feedback in the form of control actions; — creating centres for analytical processing of statistic data and accounting in financial and economic activities with business analytics functions, with expansion of communication networks and computing capacities. Examples are presented for implementation of smart systems in energy production and distribution. It is stated in the paper that state-of art information technologies are currently being implemented in Russia, new unique digital transformation projects are being launched in major energy companies. Yet, what is required is large-scale and thorough digitization and controllable energy production system as a multi-factor business process will provide the optimum combination of efficient economic activities, reliability and safety of power supply.

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