Abstract

Cloud-based multimedia application has emerged as a popular service, delivering on demand media computing and storage to millions of users. Though widely deployed, the quality of service (QoS) in current cloud-based multimedia service is not satisfying, due to the varying user demands and strict response time requirements. This thesis investigates resource allocation approaches to improve QoS for cloud-based multimedia services. A service model is desired to quantify the user demands and resource allocation. To meet this need, we propose a queueing model to characterize the cloud service process, based on which we investigate the response time minimization problem and the resource cost minimization problem in single-service scenario, multi-service scenario, and priority service scenario, respectively. Dynamic workload causes the unbalanced resource utilization and local congestion in multimedia cloud. To address this issue, we propose a two-time-scale resource configuration (TRC) scheme to dynamically allocate virtual machines (VMs) to adapt to varying workload. Based on the TRC scheme, we solve the optimal VM configuration problems to minimize the resource cost or minimize the average response time for the single-site cloud scenario and the multi-site cloud scenario, respectively. We propose optimal workload scheduling schemes at user level and task level, respectively. At user level, we optimize the workload assignment to minimize the response time or minimize the resource cost. At task level, we introduce a directed acyclic graph to model the precedence constraints among tasks, and then solve the execution time minimization problems for sequential structure, parallel structure, and mixed structure, respectively. Cloud gaming is an emerging interactive multimedia service. However, current cloud gaming suffers from a high bandwidth consumption and a large response delay. We propose a hybrid streaming framework to provide a high quality cloud gaming experience. We solve the delay-rate-distortion (d-R-D) optimization problem to minimize the overall distortion under the bandwidth and response delay constraints.

Highlights

  • 1.1 MotivationRecent years have witnessed the rapid advances in cloud computing

  • In order to accommodate the burst of workload, multimedia service providers (MSPs) traditionally need to over-provision computation and bandwidth resources to guarantee the quality of service (QoS)

  • In order to address this issue, we propose the optimal resource reconfiguration in t2 scale, in which we reconfigure the virtual machines (VMs) allocated in time scale t1 to minimize the average response time

Read more

Summary

6.12 Comparison of the PSNR performance under different bandwidths: (a)

Orc warrior scene, (b) Human warrior scene, and (c) Angry Bots scene. 6.14 Comparison of the temporal PSNR performance when Rmax = 6 Mbps: (a) Orc warrior scene, (b) Human warrior scene, and (c) Angry Bots scene. 6.19 Comparison of the PSNR performance under different bandwidths: (a) Orc warrior scene, (b) Human warrior scene, and (c) Angry Bots scene. 6.19 Comparison of the PSNR performance under different bandwidths: (a) Orc warrior scene, (b) Human warrior scene, and (c) Angry Bots scene. . 120

Motivation
Objective
Challenges in Multimedia Cloud Computing
Main Contributions
Chapter 6: Delay-rate-distortion Optimization for Cloud
Thesis Organization
Background
Cloud Computing Definition
Service Models
Cloud Computing Issues
Multimedia Cloud Computing
Resource Allocation and Workload Scheduling
Resource Allocation
Workload Scheduling
Cloud Gaming
Queueing Theory in Resource Allocation
Chapter Summary
Introduction
Results or media data
Queueing Model
Results
Cost Model
Queueing Model based Resource Optimization
Single-service Scenario
Multi-service Scenario
Priority-service Scenario
Simulations
Simulations in Single-service Scenario
Simulations in Multi-service Scenario
Simulations in Priority-service Scenario
Multimedia Cloud Architecture
VM Pricing Plan
Two-time-scale Resource Configuration (TRC) Scheme
Workload Prediction Model
Resource Allocation for Single-site Cloud
Resource Reconfiguration for Single-site Cloud
Resource Allocation for Multi-site Cloud
Resource Reconfiguration for Multi-site Cloud
10: Reallocate VMs among services according to the Heuristic for Resource
Performance Evaluation
Simulations for Single-site Cloud
Simulations for Multi-site Cloud
Data Center 2
User Level Workload Scheduling Model
Response Time Minimization Problem
Resource Cost Minimization Problem
Task Level Workload Scheduling Model
Optimal Task Level Scheduling for Sequential Structure
Optimal Task Level Scheduling for Parallel Structure
Optimal Task Level Scheduling for Mixed Structure
Heuristic for Optimal Task Level Scheduling
As demonstrated in
Hybrid Streaming Framework for Cloud Gaming
Optimal Rate Allocation for Hybrid Streaming Framework
Problem Formulation
Gaming Process
Rate-Distortion (R-D) Analysis
Response Delay Analysis
Rate Allocation Algorithm
Experiment Setup
Comparison with Video Streaming Approach
Comparison with Graphics Streaming Approach
Mbps 2 Mbps 6 Mbps 6 Mbps
Evaluation of Rate Allocation Algorithm
Conclusions
Big Data on Multimedia Cloud
Collaborative Media Computing and Rendering
Joint Resource Allocation of Cloud and Network
Optimization of Internal Traffic Management in Cloud
Mobile multimedia applications
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.