Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/35818
Title: Broker-mediated Multiple-Cloud Orchestration Mechanisms for Cloud Computing
Authors: GANESH NEELAKANTA IYER
Keywords: cloud computing, game theory, cloud broker, auction, pricing, resource allocation
Issue Date: 31-Jul-2012
Source: GANESH NEELAKANTA IYER (2012-07-31). Broker-mediated Multiple-Cloud Orchestration Mechanisms for Cloud Computing. ScholarBank@NUS Repository.
Abstract: With a plethora of Cloud Service Providers (CSPs) offering various kinds of services, it is difficult for a user to choose an appropriate CSP or a set of CSPs for executing its tasks. Users are also concerned about other parameters such as security and trustworthiness of the CSPs. Further some of the user applications have tight requirements such as deadline and budget specifications and they need to be deployed among multiple CSPs to meet such requirements. On the other hand, CSPs currently follow fixed price per resource and they need efficient mechanisms to monitor the market and to develop attractive dynamic pricing strategies based on several parameters including user demand, competition and user profile. In the first part of this thesis, we describe a comprehensive Cloud Broker architecture and focus on designing Broker-mediated Multiple-Cloud Orchestration mechanisms to connect various CSPs and users together. We propose three Broker-based Cloud service arbitrage mechanisms (Incentive based, Sealed-bid continuous double auction based and Risk based) for different types of applications in which the Broker supplies flexibility and opportunistic choices for users and foster the competition between Clouds. Users can consider various parameters such as trust, reputation and security to choose an appropriate CSP. We also propose market-oriented dynamic pricing strategies for CSPs to adapt to market conditions quickly. In the second part of this thesis, we propose two Cloud Broker aggregation mechanisms for IaaS Clouds where one is based on cooperative bargaining games and the other one is based on Markovian queues. In the first case, we employ bargaining solutions propounded in literature to efficiently determine the resource requirements for a set of tasks, requesting for one type of resources, so as to maximize the resource utilization and to handle elastic user requirements. It also introduces an asymmetric pricing mechanism to consider user?s budget requirements. The Markovian queue based approach efficiently aggregates user tasks/data among Clouds with heterogeneous resource capabilities based on user?s deadline and budget specifications. We further address the task scheduling within a Cloud to reduce the makespan and to improve the resource usage after the aggregation is completed. Our Broker can function either as an entity to connect several CSPs and users or as an entity to connect several users to one CSP and incorporates several features suitable for various situations and different types of users.
URI: http://scholarbank.nus.edu.sg/handle/10635/35818
Appears in Collections:Ph.D Theses (Open)

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