| Titre : | OPTIMIZING RESOURCE ALLOCATION FOR CLOUD COMPUTING |
| Auteurs : | Karima Amer, Auteur ; Fayçal Guerrouf, Directeur de thèse |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2024 |
| Format : | 1 vol. (52 p.) / couv. ill. en coul / 30 cm |
| Langues: | Anglais |
| Résumé : |
Cloud computing is a way of delivering IT services over the Internet, which allows customers to access and use computer resources on demand without the need for local infrastructure. Virtual machines (VMs) represent an important concept in cloud computing that provides flexible and scalable computing resources. Virtual machines are software-based computing environments that can be easily created, controlled, and moved between real servers in the cloud. To efficiently utilize cloud resources, load balancing techniques are used to distribute workloads across multiple virtual machines and servers. Load balancing aims to optimize resource usage, increase throughput, reduce response times, and avoid overloading individual servers. In this regard, various load balancing algorithms have been proposed, including static algorithms that allocate resources based on predefined rules, and dynamic algorithms that adapt to changes in load in real time. Load balancing in cloud computing provides many benefits, such as improved performance, increased reliability, and cost savings through efficient use of resources. However, implementing effective load balancing in cloud environments poses challenges due to factors such as resource heterogeneity, dynamic workloads, and the need for real-time monitoring and adaptation. |
| Sommaire : |
Contents i
List of Figures iv List of Tables v General Introduction 1 1 Resource Allocation in Cloud Computing 3 1.1 Introduction . . . . . . . . 3 1.2 Cloud computing . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2 Roots of cloud computing . . . . . . .. . 4 1.2.3 Key Characteristics . . . . . . . . . . . 5 1.2.4 Service Models . . . . . . 6 1.2.5 Benefits . . . . . . . . . . . . . . . . . . . . . . . 7 1.2.6 Challenges . . . . . . . . . . . . . . .. . . . . 7 1.2.7 Server problems and issues in cloud computing 8 1.2.8 Cloud Architecture . . . . . . . . . . . . . . .. . . . 8 1.3 Deployment models in cloud computing . . . . . . . . . . 11 1.3.1 Private Cloud . . . . . . . . . . . . . . . . . . .. . . 11 1.3.2 Cloud Bursting . . . . . . . . . . . . . .. . . 11 1.3.3 Federated Cloud . . . . . . . . . . . . . 11 1.3.4 Multiple Clouds . . . . . . . . 11 1.4 Resource allocation in cloud computing . . . . . . . . . . . . 12 1.4.1 Cloud resource allocation . . . . . . . . . . . . 12 1.5 Resource Allocation Technique . . . . . . . . . . . . . 13 1.5.1 Static Scheduling Algorithm . . . . 14 1.5.2 Dynamic Scheduling Algorithm . .. . . . 14 1.5.3 Heuristic Scheduling Algorithms . . . . . . . . 15 1.5.4 Opportunistic Load Balancing . . . . . . . . . . 15 1.5.4.1 Minimum Execution Time . . . . . . . . 5 1.5.4.2 Minimum Completion Time . . . . . 15 1.5.5 Min-Min Technique . . . . . . . . . . . .. . . . . . . 16 1.5.6 Max-Min Technique . . . . . . . . . . . . . . 16 1.6 Conclusion . . . . . . . . . . . . . . . . 17 2 Virtual Machine in cloud computing 18 2.1 Introduction . . .. . . . . . . . . . . 18 2.2 Virtual Machines . . . . . . . . . . . . .. . . . . . . 18 2.2.1 Review on Virtual Machine . . . . . 18 2.2.2 Reasons for using virtualization . . . . .. . . 20 2.2.3 The importance of virtualization in cloud computing . . . . . 20 2.2.4 Virtual machine monitoring . . . . . . . .. . . 20 2.2.5 Virtual machine migration . . . . . . . 21 2.2.6 Resource Allocation in VMs . . . . . . . . . .. . . 22 2.3 Effects of overuse of VMs on performance . .. . . 23 2.3.1 Resource Contention . . . . . . . . . . . . . . . . 23 2.3.2 I/O bottlenecks . . . . . . . . . . . . . . 24 2.3.3 Virtual machine migration burden . . . . . . . 24 2.4 Virtual Machine Allocation . . . . . . . . . . 24 2.5 VM Scheduling in cloud Environment . . . .. . 25 2.6 Related work . . . . . . . . . . . . . . . . 27 2.7 Conclusion . . . . . . . . . . . . . . 29 3 Design 3 3.1 Introduction . . . . . . . .. . . 30 3.2 Global Design . . . . . . . . . . . . . . . . . 30 3.3 Details Design . . . . . . . . . . . . . . . . 32 3.3.1 Mathematical Notations . . . . . . 32 3.3.2 VMs allocation Engine . . . . . . . . . . . . .. . 33 3.3.3 Stage 1 . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.4 Stage 2 . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3.5 Stage 3 . . . . . . . . . . . . . . . . . . . . . . 37 3.3.6 Class diagram . . . . . . . . . . . . . . 37 3.3.7 state machine diagram . . . . . . . 39 3.4 Conclusion . . . . . . . .. . . 40 4 Implementation and Simulation 41 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . 41 4.2 environment development . . . . . . . . . . . . . . . . 41 4.3 implementation details . . . . . . . . . . . . .. . . . 42 4.4 result and discussion . . . . . . . . . . . .. . . . . 44 4.4.1 Output . . . . . . . . . . . . . . . . . . . . . 44 4.4.2 Comparative algorithm . . . . . . . . . . . . . 46 4.4.3 Comparison . . . . . . . . . . . . . . . . . 47 4.5 Conclusion . . . . . . . . . . . . . .. . . . . . 49 General Conclusion 51 Bibliographie 52 |
| Type de document : | Mémoire master |
Disponibilité (1)
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