Proceedings Article10.1109/ICNDC.2013.11
A Cloud Task Scheduling Algorithm Based on Users' Satisfaction
Rongxian Chen,Yaying Zhang,Dongdong Zhang +2 more
- 21 Dec 2013
- pp 1-5
15
TL;DR: A task scheduling algorithm based on user's satisfaction is presented, proposing a suitable resource allocation method according to different users' need, thus improving users' satisfaction with the cloud resources for which they pay.
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Abstract: Scheduling algorithm designed for a bunch of tasks plays a significant role in affecting the performance of the cloud platform. At present, many scheduling algorithms mainly focus on optimizing system performance, whereas whether users are satisfied with the resource allocation is seldom considered. This paper presents a task scheduling algorithm based on user's satisfaction, proposing a suitable resource allocation method according to different users' need, thus improving users' satisfaction with the cloud resources for which they pay. Finally, the scheduling algorithm is implemented and simulated on the CloudSim. The result demonstrates the effectiveness of the proposed algorithm.
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Citations
Optimal Multiserver Configuration for Profit Maximization in Cloud Computing
TL;DR: The definition of customer satisfaction in economics is referred to and a formula for measuringCustomer satisfaction in cloud computing is developed and an analysis is given in detail on how the customer satisfaction affects the profit.
Customer-Satisfaction-Aware Optimal Multiserver Configuration for Profit Maximization in Cloud Computing
TL;DR: The approach is to treat a multiserver system as an M/M/m queuing model, such that the optimization problem can be formulated and solved analytically.
198
A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing
Subhadarshini Mohanty,Prashanta Kumar Patra,Mitrabinda Ray,Subasish Mohapatra +3 more
- 01 Jan 2018
TL;DR: A metaheuristic load balancing algorithm using Particle Swarm Optimization (MPSO) has been proposed by utilizing the benefits of particle swarm optimization ( PSO) algorithm to minimize the task overhead and maximize the resource utilization in cloud computing.
28
Personality-guided Cloud Pricing via Reinforcement Learning
TL;DR: In this article, a RL-based dynamic cloud pricing scheme was developed to optimize both cloud provider's profit and costs of heterogeneous users with distinct personalities, where a personality-guided user perceived value prediction scheme was proposed to proactively capture the dynamics of the users' perceived values with respect to services.
21
MPSO: A Novel Meta-Heuristics for Load Balancing in Cloud Computing
TL;DR: A load balancing algorithm using Multi Particle Swarm Optimization MPSO has been developed by utilizing the benefits of particle swarm optimization PSO algorithm to minimize the task overhead and maximize the resource utilization in a homogenous cloud environment.
8
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