Abstract

As the multitude and complexity of cloud market increases the evaluation and selection of cloud services becomes a burdensome task for the users. With the increased rise of available services from various Cloud Service Providers (CSP), the role of cloud brokers becomes more and more important. In this thesis, the challenge of optimally allocating multiple cloud system resources to multiple mobile user’s requests with different requirements is investigated and an optimal Cloud Broker model is proposed. The cloud brokering mechanism is formulated as a Semi-Markov Decision Process (SMDP) model under the average system cost criteria, taking into consideration the cost of the occupying computing resources, the communication costs, the request traffic, and some security risk degrees and resource requirements from the multiple mobile users. Through minimizing the overall system cost, the optimal resource allocation policy is derived by using the Value Iteration Algorithm. Simulation results are provided, demonstrating the efficiency of the proposed Cloud Broker design.

Highlights

  • 1.1 Context and MotivationCloud computing has become a widely used computing infrastructure based on which many existing cloud vendors

  • We have proposed a novel an Semi-Markov Decision Process (SMDP)-based formulation of the cloud Broker problem in an Mobile cloud computing (MCC) environment considering the minimization of the overall system cost as target objective

  • The results show that the Cloud Service Providers (CSP) can ensure a better QoS and gain a higher revenue when they cooperate

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Summary

Bibliography vii

43 4.4 The structure of the optimal policy for service class 2 when p12 = p22= 0.6 MU. 43 4.5 The structure of the optimal policy for service class 1 when p12 = p22= 0.4 MU, 4.6 The structure of the optimal policy for service class 2 when p12 = p22 = 0.4 MU, 4.7 The structure of the optimal policy for Service Class 1 when p12 = p22= 0.4 MU, 4.8 The structure of the optimal policy for Service Class 2 when p12 = p22= 0.4 MU, ix.

Context and Motivation
Cloud Service Models
Cloud Architectures
Mobile Cloud Computing
Cloud Service Brokering
Research Problem
Proposed Approach
Thesis Contributions
Thesis Outline
Cloud Market
Related Work
With Respect to Cloud Market Models
With Respect to Cloud Broker designs
System Model
Optimal Control Problem
State Variables, System State, and State Space
Actions
Transition probabilities
Departure Events
Cost Structure
Optimization Problem and Value Iteration Algorithm
Simulation Setup
Local Processing Probability
Impact of the Access Price
Impact of the Number of Requested VMs
Impact of the Arrival Rate
Impact of the Departure Rate
Impact of the Cloud Capacity
Impact of the access price on the structure of the optimal policy
Impact of the departure rate on the structure of the optimal policy
Impact of the arrival rate on the structure of the optimal policy
Conclusion
Full Text
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