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Service-oriented Resource Orchestration – A Resource Allocation Approach

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Abstract
This thesis is primarily concerned with three main topics in cloud platforms using OpenStack as a case study: allocation of resources to meet the demands of a service requested by remote end-user, migration of virtual machines (VMs) instances to offload the overloaded compute nodes, and monitoring of utilized resources. The overall framework architecture consists of three subsystems: 1) An orchestrator that enables to automate resource management and provisioning in OpenStack, 2) sFlow-based subsystem to monitor resource performance counters of OpenStack compute nodes, and 3) A resource utilization-based subsystem for dynamic VM migration in OpenStack. The proposed orchestrator manages and provisions resources by: 1) exploiting application program interfaces (APIs) provided by the cloud provider in order to control/manage/allocate storage and compute resources; 2) interacting with software-defined networking (SDN) controller to get the details of the available resources, and instructing the changes/rules to manage the network based on the cloud service requirements. For resource allocation, an algorithm is proposed, which allocates resources on the basis of unutilized resources in a pool of virtual machines. An algorithm has been taken into account for comparison with the proposed resource allocation algorithm. The experiment results show that the considered algorithm is outperformed by the proposed algorithm. Furthermore, a monitoring system has been implemented, which collects and stores samples of the performance counters related to infrastructural resources, such as CPU performance, memory faults, and network performance. A framework is proposed for dynamic VM migration in a cloud computing platform. The framework implements the proposed overload detection, VM selection, and VM allocation algorithms for dynamic VM migration in clouds. With the help of experiments, it is shown that the proposed algorithms outperform the algorithms that are considered for the purpose of evaluation.
Author(s)
아팍무하드
Issued Date
2017
Awarded Date
2017. 8
Type
Dissertation
URI
http://dcoll.jejunu.ac.kr/jsp/common/DcLoOrgPer.jsp?sItemId=000000008188
Alternative Author(s)
Muhammad, Afaq
Affiliation
제주대학교 일반대학원
Department
대학원 컴퓨터공학과
Advisor
송왕철
Table Of Contents
I. Introduction 1
1.1 Resrouce Orchestration 3
1.2 VM Migration 5
1.3 Monitoring of Utilized Resources 6
1.4 Research Problems and Objectives 7
1.5 Thesis Organization 9
II. Related Work 11
2.1 Cloud Computing 11
2.1.1 Cloud Computing Methodologies 13
2.1.2 The Cloud Architecture 15
2.1.3 Cloud Services 19
2.1.4 Cloud Applications 21
2.2 Cloud Resource Orchestration 23
2.3 Monitoring of Infrastrcutural Resources 25
2.3.1 Existing Monitoring Frameworks 27
2.4 Dynamic VM Migration. 28
III. Overview of OpenStack and its Deployment 32
3.1 Introduction 32
3.2 Keystone 34
3.2.1 Architecture 35
3.3 Nova 36
3.3.1 Architecture 36
3.4 Neutron 38
3.4.1 Architecture 39
3.5 OpenStack Deployment. 40
3.5.1 OpenStack-ONOS Integration 42
3.5.2 Visualization of OpenStack Deployment in ONOS GUI 43
IV. Resource Orchestration Framework and its Major Components 45
4.1 Proposed Architecture 45
4.2 Service Abstraction Model 46
4.2.1 Service Abstraction Communicator 48
4.3 The Orchestrator 49
4.3.1 Network Resource Communicator 51
4.3.1 Cloud Resource Communicator 52
4.4 sFlow-based Monitoring System 55
4.5 Implementation of Proposed Resource Orchestration Framework 57
4.5.1 Performance Analysis of Proposed MRMC lgorithm 58
V. A Framework for Resource Utilization-based Migration of VMs in Cloud 62
5.1 System Model 64
5.1.1 Stats Aggregator 65
5.1.2 Stats Database 66
5.1.3 Migration Manager. 66
5.1.4 Algorithm Repository 66
5.2 Proposed Structure and Algorithms 67
5.2.1 Overload Detection Algorithm 68
5.2.1 VM Selection Algorithm 70
5.2.1.1 VM Selection Critera 72
5.2.1 VM Allocation Algorithm 73
5.3 Experiment and Results 74
VI. Conclusions 81
Bibliography 84
Degree
Doctor
Publisher
제주대학교 일반대학원
Citation
아팍무하드. (2017). Service-oriented Resource Orchestration – A Resource Allocation Approach
Appears in Collections:
General Graduate School > Computer Engineering
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