Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

MEC vs MCC: performance analysis of real-time applications

Hoje em dia, numerosas são as aplicações que apresentam um uso intensivo de recursos empurrando os requisitos computacionais e a demanda de energia dos dispositivos para além das suas capacidades. Atentando na arquitetura Mobile Cloud, que disponibiliza plataformas funcionais e aplicações emergentes (como Realidade Aumentada (AR), Realidade Virtual (VR), jogos online em tempo real, etc.), são evidentes estes desafios directamente relacionados com a latência, consumo de energia, e requisitos de privacidade. O Mobile Edge Computing (MEC) é uma tecnologia recente que aborda os obstáculos de desempenho enfrentados pela Mobile Cloud Computing (MCC), procurando solucioná-los O MEC aproxima as funcionalidades de computação e de armazenamento da periferia da rede. Neste trabalho descreve-se a arquitetura MEC assim como os principais tipos soluções para a sua implementação. Apresenta-se a arquitetura de referência da tecnologia cloudlet e uma comparação com o modelo de arquitetura ainda em desenvolvimento e padronização pelo ETSI. Um dos propósitos do MEC é permitir remover dos dispositivos tarefas intensivas das aplicações para melhorar a computação, a capacidade de resposta e a duração da bateria dos dispositivos móveis. O objetivo deste trabalho é estudar, comparar e avaliar o desempenho das arquiteturas MEC e MCC para o provisionamento de tarefas intensivas de aplicações com uso intenso de computação. Os cenários de teste foram configurados utilizando esse tipo de aplicações em ambas as implementações de MEC e MCC. Os resultados do teste deste estudo permitem constatar que o MEC apresenta melhor desempenho do que o MCC relativamente à latência e à qualidade de experiência do utilizador. Além disso, os resultados dos testes permitem quantificar o benefício efetivo tecnologia MEC.Numerous applications, such as Augmented Reality (AR), Virtual Reality (VR), real-time online gaming are resource-intensive applications and consequently, are pushing the computational requirements and energy demands of the mobile devices beyond their capabilities. Despite the fact that mobile cloud architecture has practical and functional platforms, these new emerging applications present several challenges regarding latency, energy consumption, context awareness, and privacy enhancement. Mobile Edge Computing (MEC) is a new resourceful and intermediary technology, that addresses the performance hurdles faced by Mobile Cloud Computing (MCC), and brings computing and storage closer to the network edge. This work introduces the MEC architecture and some of edge computing implementations. It presents the reference architecture of the cloudlet technology and provides a comparison with the architecture model that is under standardization by ETSI. MEC can offload intensive tasks from applications to enhance computation, responsiveness and battery life of the mobile devices. The objective of this work is to study and evaluate the performance of MEC and MCC architectures for provisioning offload intensive tasks from compute-intensive applications. Test scenarios were set up with use cases with this kind of applications for both MEC and MCC implementations. The test results of this study enable to support evidence that the MEC presents better performance than cloud computing regarding latency and user quality of experience. Moreover, the results of the tests enable to quantify the effective benefit of the MEC approach

Containerization in Cloud Computing: performance analysis of virtualization architectures

La crescente adozione del cloud è fortemente influenzata dall’emergere di tecnologie che mirano a migliorare i processi di sviluppo e deployment di applicazioni di livello enterprise. L’obiettivo di questa tesi è analizzare una di queste soluzioni, chiamata “containerization” e di valutare nel dettaglio come questa tecnologia possa essere adottata in infrastrutture cloud in alternativa a soluzioni complementari come le macchine virtuali. Fino ad oggi, il modello tradizionale “virtual machine” è stata la soluzione predominante nel mercato. L’importante differenza architetturale che i container offrono ha portato questa tecnologia ad una rapida adozione poichè migliora di molto la gestione delle risorse, la loro condivisione e garantisce significativi miglioramenti in termini di provisioning delle singole istanze. Nella tesi, verrà esaminata la “containerization” sia dal punto di vista infrastrutturale che applicativo. Per quanto riguarda il primo aspetto, verranno analizzate le performances confrontando LXD, Docker e KVM, come hypervisor dell’infrastruttura cloud OpenStack, mentre il secondo punto concerne lo sviluppo di applicazioni di livello enterprise che devono essere installate su un insieme di server distribuiti. In tal caso, abbiamo bisogno di servizi di alto livello, come l’orchestrazione. Pertanto, verranno confrontate le performances delle seguenti soluzioni: Kubernetes, Docker Swarm, Apache Mesos e Cattle

Models to evaluate service Provisioning over Cloud Computing Environments - A Blockchain-As-A-Service case study

ThestrictnessoftheServiceLevelAgreements(SLAs)ismainlyduetoasetofconstraintsrelated to performance and dependability attributes, such as availability. This paper shows that system’s availability values may be improved by deploying services over a private environment, which may obtain better availability values with improved management, security, and control. However, how much a company needs to afford to keep this improved availability? As an additional activity, this paper compares the obtained availability values with the infrastructure deployment expenses and establishes a cost × benefit relationship. As for the system’s evaluation technique, we choose modeling; while for the service used to demonstrate the models’ feasibility, the blockchain-as-a-service was the selected one. This paper proposes and evaluate four different infrastructures hosting blockchains: (i) baseline; (ii) double redundant; (iii) triple redundant, and (iv) hyper-converged. The obtained results pointed out that the hyper-converged architecture had an advantage over a full triple redundant environment regarding availability and deployment cost

Orchestration in the Cloud-to-Things Compute Continuum: Taxonomy, Survey and Future Directions

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems has been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, and heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis.Comment: Journal of Cloud Computing Pages: 2

Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

A fully SDN enabled all-optical architecture for data centre virtualisation with time and space multiplexing

© 2018 [2018 Optical Society of America.]. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited.Virtual Data Centre (VDC) solutions provide an environment that is able to quickly scale-up and where virtual machines and network resources can be quickly added on-demand through self-service procedures. VDC providers must support multiple simultaneous tenants with isolated networks on the same physical substrate. The provider must make efficient use of their available physical resources whilst providing high bandwidth and low-latency connections to tenants with a variety of VDC configurations. This paper utilises state of the art optical network elements to provide high bandwidth optical interconnections and develop an VDC architecture to slice the network and the compute resources dynamically, to efficiently divide the physical network between tenants. We present a Data Centre Virtualisation architecture with an SDN-controlled all-optical data plane combining Optical Circuit Switching (OCS) and Time Shared Optical Network (TSON). Developed network orchestration dynamically translates and provisions VDCs requests onto the optical physical layer. The experimental results show the provisioned bandwidth can be varied by adjusting the number of time slots allocated in the TDM network. These results lead to recommendations for provisioning TDM connections with different performance characteristics. Moreover, application level optical switch reconfiguration time is also evaluated to fully understand the impact on application performance in VDC provision. The experimental demonstration confirmed the developed VDC approach introduces negligible delay and complexity on the network side.Peer ReviewedPostprint (author's final draft