Computing Resource Management in a Private Cloud Data Center
| Autor: | Ching-Chi Lin, 林敬棋 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 This dissertation proposes computing resource allocation and scheduling algorithms for the two major issues in a data center hosting cloud, i.e to reduce the energy consumption and to guarantee service performance. We focus on an enterprise-level data center supporting a private cloud in this dissertation. In a data center, tasks and applications are deployed to servers for execution in the form of virtual instances, i.e. virtual machines or containers. Each virtual instance has a specification, which limits the amount of resource the virtual instance can utilize per time unit. Since the resource required by a service may vary over time, we have to dynamically scale the number of virtual instances running a service in order to guarantee the service performance. We can also reduce the energy consumption by adjusting the deployment of virtual instances to servers. We study three topics that are related to service performance and energy saving. These topics are: auto-scaling for web services, energy-efficient virtual instance deployment, and energy-aware task scheduling on a multi-core server. For each topic, we consider different scenarios, and propose solutions accordingly. For auto-scaling for web services, we propose different scaling strategies based on the stability of the workload changes of a web service. For workloads that can change drastically within a short time period, we propose two virtual instance scaling strategies, and compare their performance using real-world workload traces. For workloads with predictable behaviors, we consider the scenario that there are multiple specifications of virtual instances. We propose scaling algorithms that determine the number of each specification of virtual instances for running a web service in each time period, so that the cost is minimized. For energy-efficient virtual instance deployment, we consider two scenarios, virtual instances with fixed specifications, and virtual instances with moldable specifications. For virtual instances with fixed specifications, we propose energy-efficient deployment strategies that consolidate virtual instances onto a subset of servers. The servers that are not assigned with virtual instance are put into sleep mode to save energy. For virtual instances with moldable specifications, we propose algorithms that generate energy-efficient deployment plans. A deployment plan includes: 1) the number of cores allocated to each virtual instance, 2) the operating frequency of each virtual instance, and 3) the deployment of virtual instances to servers. We propose dynamic programming algorithms which generate an optimal deployment plan with minimum energy consumption when the inputs, i.e. the number of virtual instances and servers, are small. We also propose heuristics that generate feasible deployment plans with affordable computing time. The third topic is energy-aware task scheduling on a multi-core server. We consider two types of multi-core platforms, homogeneous and asymmetric. We propose algorithms that determine the assignment of tasks to cores, the execution order of tasks, and the operating frequency of each task for the two types of multi-core platforms. An asymmetric-aware scheduler based on our proposed algorithm is implemented as a proof-of-concept. The scheduler consumes 61:6% and 76:9% energy of the existing Global Task Scheduler with performance and conservative frequency governors respectively. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|