Software-Defined Networks for Resource Allocation in Cloud Computing: A Survey

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation

A manifesto for future generation cloud computing: research directions for the next decade

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing

Green Resource Management in Distributed Cloud Infrastructures

Computing has evolved over time according to different paradigms, along with an increasing need for computational power. Modern computing paradigms basically share the same underlying concept of Utility Computing, that is a service provisioning model through which a shared pool of computing resources is used by a customer when needed. The objective of Utility Computing is to maximize the resource utilization and bring down the relative costs. Nearly a decade ago, the concept of Cloud Computing emerged as a virtualization technique where services were executed remotely in a ubiquitous way, providing scalable and virtualized resources. The spread of Cloud Computing has been also encouraged by the success of the virtualization, which is one of the most promising and efficient techniques to consolidate system's utilization on one side, and to lower power, electricity charges and space costs in data centers on the other. In the last few years, there has been a remarkable growth in the number of data centers, which represent one of the leading sources of increased business data traffic on the Internet. An effect of the growing scale and the wide use of data centers is the dramatic increase of power consumption, with significant consequences both in terms of environmental and operational costs. In addition to power consumption, also carbon footprint of the Cloud infrastructures is becoming a serious concern, since a lot of power is generated from non-renewable sources. Hence, energy awareness has become one of the major design constraints for Cloud infrastructures. In order to face these challenges, a new generation of energy-efficient and eco-sustainable network infrastructures is needed. In this thesis, a novel energy-aware resource orchestration framework for distributed Cloud infrastructures is discussed. The aim is to explain how both network and IT resources can be managed while, at the same time, the overall power consumption and carbon footprint are being minimized. To this end, an energy-aware routing algorithm and an extension of the OSPF-TE protocol to distribute energy-related information have been implemented

On the complexity of configuration and orchestration for enabling disaggregated server provisioning in optical composable data centers

Due to the limitations of traditional data center (DC) architectures, the concept of infrastructure disaggregation has been proposed. DC resources are separated into multiple blades to be exploited independently. As a result, composable DC (CDC) infrastructures are achieved, enhancing the modularity of resource provisioning. However, disaggregation introduces additional challenges that need to be carefully analyzed. One relates to the potential complexity increase on the orchestration and infrastructure configuration that need to be performed when provisioning resources to support services. This aspect is highly influenced by the distribution of resources at the physical infrastructure. As such, when analyzing the performance of a CDC, it becomes essential to also study the related operational complexity of the resource orchestration and configuration phases. Furthermore, the requirements of several tenant services may impose heterogeneous deployments over the shared physical infrastructure in the form of either disaggregated single-server or multi-server distributions. The associated orchestration/configuration cost is again highly influenced by the data plane architecture of the CDC. With these aspects in mind, in this paper, we provide a methodology for analysis of the complexity of resource orchestration for a service deployment and the associated configuration cost in optical CDCs, considering various service deployment setups. A selected set of CDC architectures found in the literature is employed to quantitatively illustrate how the data plane design and service deployment strategies affect the complexity of infrastructure configuration and resource orchestration.This work has been supported by the Spanish Government through project TRAINER-B (PID2020-118011GB-C22) with FEDER contribution.Peer ReviewedPostprint (author's final draft

Low-latency and Resource-efficient Service Function Chaining Orchestration in Network Function Virtualization

© 2014 IEEE. Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing

Efficient Resource Management for Cloud Computing Environments

Cloud computing has recently gained popularity as a cost-effective model for hosting and delivering services over the Internet. In a cloud computing environment, a cloud provider packages its physical resources in data centers into virtual resources and offers them to service providers using a pay-as-you-go pricing model. Meanwhile, a service provider uses the rented virtual resources to host its services. This large-scale multi-tenant architecture of cloud computing systems raises key challenges regarding how data centers resources should be controlled and managed by both service and cloud providers. This thesis addresses several key challenges pertaining to resource management in cloud environments. From the perspective of service providers, we address the problem of selecting appropriate data centers for service hosting with consideration of resource price, service quality as well as dynamic reconfiguration costs. From the perspective of cloud providers, as it has been reported that workload in real data centers can be typically divided into server-based applications and MapReduce applications with different performance and scheduling criteria, we provide separate resource management solutions for each type of workloads. For server-based applications, we provide a dynamic capacity provisioning scheme that dynamically adjusts the number of active servers to achieve the best trade-off between energy savings and scheduling delay, while considering heterogeneous resource characteristics of both workload and physical machines. For MapReduce applications, we first analyzed task run-time resource consumption of a large variety of MapReduce jobs and discovered it can vary significantly over-time, depending on the phase the task is currently executing. We then present a novel scheduling algorithm that controls task execution at the level of phases with the aim of improving both job running time and resource utilization. Through detailed simulations and experiments using real cloud clusters, we have found our proposed solutions achieve substantial gain compared to current state-of-art resource management solutions, and therefore have strong implications in the design of real cloud resource management systems in practice

Efficient Resource Management for Cloud Computing Environments

Cloud computing has recently gained popularity as a cost-effective model for hosting and delivering services over the Internet. In a cloud computing environment, a cloud provider packages its physical resources in data centers into virtual resources and offers them to service providers using a pay-as-you-go pricing model. Meanwhile, a service provider uses the rented virtual resources to host its services. This large-scale multi-tenant architecture of cloud computing systems raises key challenges regarding how data centers resources should be controlled and managed by both service and cloud providers. This thesis addresses several key challenges pertaining to resource management in cloud environments. From the perspective of service providers, we address the problem of selecting appropriate data centers for service hosting with consideration of resource price, service quality as well as dynamic reconfiguration costs. From the perspective of cloud providers, as it has been reported that workload in real data centers can be typically divided into server-based applications and MapReduce applications with different performance and scheduling criteria, we provide separate resource management solutions for each type of workloads. For server-based applications, we provide a dynamic capacity provisioning scheme that dynamically adjusts the number of active servers to achieve the best trade-off between energy savings and scheduling delay, while considering heterogeneous resource characteristics of both workload and physical machines. For MapReduce applications, we first analyzed task run-time resource consumption of a large variety of MapReduce jobs and discovered it can vary significantly over-time, depending on the phase the task is currently executing. We then present a novel scheduling algorithm that controls task execution at the level of phases with the aim of improving both job running time and resource utilization. Through detailed simulations and experiments using real cloud clusters, we have found our proposed solutions achieve substantial gain compared to current state-of-art resource management solutions, and therefore have strong implications in the design of real cloud resource management systems in practice

Integração do paradigma de cloud computing com a infraestrutura de rede do operador

Doutoramento em Engenharia InformáticaThe proliferation of Internet access allows that users have the possibility to use services available directly through the Internet, which translates in a change of the paradigm of using applications and in the way of communicating, popularizing in this way the so-called cloud computing paradigm. Cloud computing brings with it requirements at two different levels: at the cloud level, usually relying in centralized data centers, where information technology and network resources must be able to guarantee the demand of such services; and at the access level, i.e., depending on the service being consumed, different quality of service is required in the access network, which is a Network Operator (NO) domain. In summary, there is an obvious network dependency. However, the network has been playing a relatively minor role, mostly as a provider of (best-effort) connectivity within the cloud and in the access network. The work developed in this Thesis enables for the effective integration of cloud and NO domains, allowing the required network support for cloud. We propose a framework and a set of associated mechanisms for the integrated management and control of cloud computing and NO domains to provide endto- end services. Moreover, we elaborate a thorough study on the embedding of virtual resources in this integrated environment. The study focuses on maximizing the host of virtual resources on the physical infrastructure through optimal embedding strategies (considering the initial allocation of resources as well as adaptations through time), while at the same time minimizing the costs associated to energy consumption, in single and multiple domains. Furthermore, we explore how the NO can take advantage of the integrated environment to host traditional network functions. In this sense, we study how virtual network Service Functions (SFs) should be modelled and managed in a cloud environment and enhance the framework accordingly. A thorough evaluation of the proposed solutions was performed in the scope of this Thesis, assessing their benefits. We implemented proof of concepts to prove the added value, feasibility and easy deployment characteristics of the proposed framework. Furthermore, the embedding strategies evaluation has been performed through simulation and Integer Linear Programming (ILP) solving tools, and it showed that it is possible to reduce the physical infrastructure energy consumption without jeopardizing the virtual resources acceptance. This fact can be further increased by allowing virtual resource adaptation through time. However, one should have in mind the costs associated to adaptation processes. The costs can be minimized, but the virtual resource acceptance can be also reduced. This tradeoff has also been subject of the work in this Thesis.A proliferação do acesso à Internet permite aos utilizadores usar serviços disponibilizados diretamente através da Internet, o que se traduz numa mudança de paradigma na forma de usar aplicações e na forma de comunicar, popularizando desta forma o conceito denominado de cloud computing. Cloud computing traz consigo requisitos a dois níveis: ao nível da própria cloud, geralmente dependente de centros de dados centralizados, onde as tecnologias de informação e recursos de rede têm que ser capazes de garantir as exigências destes serviços; e ao nível do acesso, ou seja, dependendo do serviço que esteja a ser consumido, são necessários diferentes níveis de qualidade de serviço na rede de acesso, um domínio do operador de rede. Em síntese, existe uma clara dependência da cloud na rede. No entanto, o papel que a rede tem vindo a desempenhar neste âmbito é reduzido, sendo principalmente um fornecedor de conectividade (best-effort) tanto no dominio da cloud como no da rede de acesso. O trabalho desenvolvido nesta Tese permite uma integração efetiva dos domínios de cloud e operador de rede, dando assim à cloud o efetivo suporte da rede. Para tal, apresentamos uma plataforma e um conjunto de mecanismos associados para gestão e controlo integrado de domínios cloud computing e operador de rede por forma a fornecer serviços fim-a-fim. Além disso, elaboramos um estudo aprofundado sobre o mapeamento de recursos virtuais neste ambiente integrado. O estudo centra-se na maximização da incorporação de recursos virtuais na infraestrutura física por meio de estratégias de mapeamento ótimas (considerando a alocação inicial de recursos, bem como adaptações ao longo do tempo), enquanto que se minimizam os custos associados ao consumo de energia. Este estudo é feito para cenários de apenas um domínio e para cenários com múltiplos domínios. Além disso, exploramos como o operador de rede pode aproveitar o referido ambiente integrado para suportar funções de rede tradicionais. Neste sentido, estudamos como as funções de rede virtualizadas devem ser modeladas e geridas num ambiente cloud e estendemos a plataforma de acordo com este conceito. No âmbito desta Tese foi feita uma avaliação extensa das soluções propostas, avaliando os seus benefícios. Implementámos provas de conceito por forma a demonstrar as mais-valias, viabilidade e fácil implantação das soluções propostas. Além disso, a avaliação das estratégias de mapeamento foi realizada através de ferramentas de simulação e de programação linear inteira, mostrando que é possível reduzir o consumo de energia da infraestrutura física, sem comprometer a aceitação de recursos virtuais. Este aspeto pode ser melhorado através da adaptação de recursos virtuais ao longo do tempo. No entanto, deve-se ter em mente os custos associados aos processos de adaptação. Os custos podem ser minimizados, mas isso implica uma redução na aceitação de recursos virtuais. Esta compensação foi também um tema abordado nesta Tese