Holistic Resource Management for Sustainable and Reliable Cloud Computing:An Innovative Solution to Global Challenge

Minimizing the energy consumption of servers within cloud computing systems is of upmost importance to cloud providers towards reducing operational costs and enhancing service sustainability by consolidating services onto fewer active servers. Moreover, providers must also provision high levels of availability and reliability, hence cloud services are frequently replicated across servers that subsequently increases server energy consumption and resource overhead. These two objectives can present a potential conflict within cloud resource management decision making that must balance between service consolidation and replication to minimize energy consumption whilst maximizing server availability and reliability, respectively. In this paper, we propose a cuckoo optimization-based energy-reliability aware resource scheduling technique (CRUZE) for holistic management of cloud computing resources including servers, networks, storage, and cooling systems. CRUZE clusters and executes heterogeneous workloads on provisioned cloud resources and enhances the energy-efficiency and reduces the carbon footprint in datacenters without adversely affecting cloud service reliability. We evaluate the effectiveness of CRUZE against existing state-of-the-art solutions using the CloudSim toolkit. Results indicate that our proposed technique is capable of reducing energy consumption by 20.1% whilst improving reliability and CPU utilization by 17.1% and 15.7% respectively without affecting other Quality of Service parameters

Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy

Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty

Achieving Adaptation Through Live Virtual Machine Migration in Two-Tier Clouds

This thesis presents a model-driven approach for application deployment and management in two-tier heterogeneous cloud environments. For application deployment, we introduce the architecture, the services and the domain specific language that abstract common features of multi-cloud deployments. By leveraging the architecture and the language, application deployers author a deployment model that captures the high-level structure of the application. The deployment model is then translated into deployment workflows on specific clouds. As a use case, we introduce a live VM migration framework that maintains the application quality of services through VM migrations across two tier-clouds. The proposed framework can monitor the performance of the applications and their underlying infrastructure and plan and executes VM migrations to eliminate hotspots in a datacenter. We evaluate both the application deployment architecture and the live migration on public clouds

Leveraging disaggregated accelerators and non-volatile memories to improve the efficiency of modern datacenters

(English) Traditional data centers consist of computing nodes that possess all the resources physically attached. When there was the need to deal with more significant demands, the solution has been to either add more nodes (scaling out) or increase the capacity of existing ones (scaling-up). Workload requirements are traditionally fulfilled by selecting compute platforms from pools that better satisfy their average or maximum resource requirements depending on the price that the user is willing to pay. The amount of processor, memory, storage, and network bandwidth of a selected platform needs to meet or exceed the platform requirements of the workload. Beyond those explicitly required by the workload, additional resources are considered stranded resources (if not used) or bonus resources (if used). Meanwhile, workloads in all market segments have evolved significantly during the last decades. Today, workloads have a larger variety of requirements in terms of characteristics related to the computing platforms. Those workload new requirements include new technologies such as GPU, FPGA, NVMe, etc. These new technologies are more expensive and thus become more limited. It is no longer feasible to increase the number of resources according to potential peak demands, as this significantly raises the total cost of ownership. Software-Defined-Infrastructures (SDI), a new concept for the data center architecture, is being developed to address those issues. The main SDI proposition is to disaggregate all the resources over the fabric to enable the required flexibility. On SDI, instead of pools of computational nodes, the pools consist of individual units of resources (CPU, memory, FPGA, NVMe, GPU, etc.). When an application needs to be executed, SDI identifies the computational requirements and assembles all the resources required, creating a composite node. Resource disaggregation brings new challenges and opportunities that this thesis will explore. This thesis demonstrates that resource disaggregation brings opportunities to increase the efficiency of modern data centers. This thesis demonstrates that resource disaggregation may increase workloads' performance when sharing a single resource. Thus, needing fewer resources to achieve similar results. On the other hand, this thesis demonstrates how through disaggregation, aggregation of resources can be made, increasing a workload's performance. However, to take maximum advantage of those characteristics and flexibility, orchestrators must be aware of them. This thesis demonstrates how workload-aware techniques applied at the resource management level allow for improved quality of service leveraging resource disaggregation. Enabling resource disaggregation, this thesis demonstrates a reduction of up to 49% missed deadlines compared to a traditional schema. This reduction can rise up to 100% when enabling workload awareness. Moreover, this thesis demonstrates that GPU partitioning and disaggregation further enhances the data center flexibility. This increased flexibility can achieve the same results with half the resources. That is, with a single physical GPU partitioned and disaggregated, the same results can be achieved with 2 GPU disaggregated but not partitioned. Finally, this thesis demonstrates that resource fragmentation becomes key when having a limited set of heterogeneous resources, namely NVMe and GPU. For the case of an heterogeneous set of resources, and specifically when some of those resources are highly demanded but limited in quantity. That is, the situation where the demand for a resource is unexpectedly high, this thesis proposes a technique to minimize fragmentation that reduces deadlines missed compared to a disaggregation-aware policy of up to 86%.(Català) Els datacenters tradicionals consisteixen en un seguit de nodes computacionals que contenen al seu interior tots els recursos necessaris. Quan hi ha una necessitat de gestionar demandes superiors la solució era o afegir més nodes (scale-out) o incrementar la capacitat dels existents (scale-up). Els requisits de les aplicacions tradicionalment són satisfets seleccionant recursos de racks que satisfan millor el seu SLA basats o en la mitjana dels requisits o en el màxim possible, en funció del preu que l'usuari estigui disposat a pagar. La quantitat de processadors, memòria, disc, i banda d'ampla d'un rack necessita satisfer o excedir els requisits de l'aplicació. Els recursos addicionals als requerits per les aplicacions són considerats inactius (si no es fan servir) o addicionals (si es fan servir). Per altra banda, les aplicacions en tots els segments de mercat han evolucionat significativament en les últimes dècades. Avui en dia, les aplicacions tenen una gran varietat de requisits en termes de característiques que ha de tenir la infraestructura. Aquests nous requisits inclouen tecnologies com GPU, FPGA, NVMe, etc. Aquestes tecnologies són més cares i, per tant, més limitades. Ja no és factible incrementar el nombre de recursos segons el potencial pic de demanda, ja que això incrementa significativament el cost total de la infraestructura. Software-Defined Infrastructures és un nou concepte per a l'arquitectura de datacenters que s'està desenvolupant per pal·liar aquests problemes. La proposició principal de SDI és desagregar tots els recursos sobre la xarxa per garantir una major flexibilitat. Sota SDI, en comptes de racks de nodes computacionals, els racks consisteix en unitats individuals de recursos (CPU, memòria, FPGA, NVMe, GPU, etc). Quan una aplicació necessita executar, SDI identifica els requisits computacionals i munta una plataforma amb tots els recursos necessaris, creant un node composat. La desagregació de recursos porta nous reptes i oportunitats que s'exploren en aquesta tesi. Aquesta tesi demostra que la desagregació de recursos ens dona l'oportunitat d'incrementar l'eficiència dels datacenters moderns. Aquesta tesi demostra la desagregació pot incrementar el rendiment de les aplicacions. Però per treure el màxim partit a aquestes característiques i d'aquesta flexibilitat, els orquestradors n'han de ser conscient. Aquesta tesi demostra que aplicant tècniques conscients de l'aplicació aplicades a la gestió de recursos permeten millorar la qualitat del servei a través de la desagregació de recursos. Habilitar la desagregació de recursos porta a una reducció de fins al 49% els deadlines perduts comparat a una política tradicional. Aquesta reducció pot incrementar-se fins al 100% quan s'habilita la consciència de l'aplicació. A més a més, aquesta tesi demostra que el particionat de GPU combinat amb la desagregació millora encara més la flexibilitat. Aquesta millora permet aconseguir els mateixos resultats amb la meitat de recursos. És a dir, amb una sola GPU física particionada i desagregada, els mateixos resultats són obtinguts que utilitzant-ne dues desagregades però no particionades. Finalment, aquesta tesi demostra que la gestió de la fragmentació de recursos és una peça clau quan la quantitat de recursos és limitada en un conjunt heterogeni de recursos. Pel cas d'un conjunt heterogeni de recursos, i especialment quan aquests recursos tenen molta demanda però són limitats en quantitat. És a dir, quan la demanda pels recursos és inesperadament alta, aquesta tesi proposa una tècnica minimitzant la fragmentació que redueix els deadlines perduts comparats a una política de desagregació de fins al 86%.Arquitectura de computador