Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Benchmarking Eventually Consistent Distributed Storage Systems

Cloud storage services and NoSQL systems typically offer only "Eventual Consistency", a rather weak guarantee covering a broad range of potential data consistency behavior. The degree of actual (in-)consistency, however, is unknown. This work presents novel solutions for determining the degree of (in-)consistency via simulation and benchmarking, as well as the necessary means to resolve inconsistencies leveraging this information

Exploiting Data Locality in Dynamic Web Applications

The Internet has grown from a static document retrieval system to a dynamic medium where users are both consumers and producers of information. Users may experience above-average website latencies due to the physical distances information must travel. Because user satisfaction is related to a website\u27s responsiveness, e-commerce may be hindered and prevent online businesses from reaching their full potential. This dissertation analyzes how temporal and relational dependencies in web applications limit their ability to become distributed. Two contributions are made, the first showing the location of data inside a datacenter influences the web system\u27s performance, and secondly, that relaxing strict consistency inside the web application at a fine- grained level can greatly lower latencies for geographically diverse users. Experiments are used to show when and how much these optimizations can benefit a dynamic web application

Control Strategies for Improving Cloud Service Robustness

This thesis addresses challenges in increasing the robustness of cloud-deployed applications and services to unexpected events and dynamic workloads. Without precautions, hardware failures and unpredictable large traffic variations can quickly degrade the performance of an application due to mismatch between provisioned resources and capacity needs. Similarly, disasters, such as power outages and fire, are unexpected events on larger scale that threatens the integrity of the underlying infrastructure on which an application is deployed.First, the self-adaptive software concept of brownout is extended to replicated cloud applications. By monitoring the performance of each application replica, brownout is able to counteract temporary overload situations by reducing the computational complexity of jobs entering the system. To avoid existing load balancers interfering with the brownout functionality, brownout-aware load balancers are introduced. Simulation experiments show that the proposed load balancers outperform existing load balancers in providing a high quality of service to as many end users as possible. Experiments in a testbed environment further show how a replicated brownout-enabled application is able to maintain high performance during overloads as compared to its non-brownout equivalent.Next, a feedback controller for cloud autoscaling is introduced. Using a novel way of modeling the dynamics of typical cloud application, a mechanism similar to the classical Smith predictor to compensate for delays in reconfiguring resource provisioning is presented. Simulation experiments show that the feedback controller is able to achieve faster control of the response times of a cloud application as compared to a threshold-based controller.Finally, a solution for handling the trade-off between performance and disaster tolerance for geo-replicated cloud applications is introduced. An automated mechanism for differentiating application traffic and replication traffic, and dynamically managing their bandwidth allocations using an MPC controller is presented and evaluated in simulation. Comparisons with commonly used static approaches reveal that the proposed solution in overload situations provides increased flexibility in managing the trade-off between performance and data consistency

Computing Without Borders: The Way Towards Liquid Computing

Despite the de-facto technological uniformity fostered by the cloud and edge computing paradigms, resource fragmentation across isolated clusters hinders the dynamism in application placement, leading to suboptimal performance and operational complexity. Building upon and extending these paradigms, we propose a novel approach envisioning a transparent continuum of resources and services on top of the underlying fragmented infrastructure, called liquid computing. Fully decentralized, multi-ownership-oriented and intent-driven, it enables an overarching abstraction for improved applications execution, while at the same time opening up for new scenarios, including resource sharing and brokering. Following the above vision, we present liqo, an open-source project that materializes this approach through the creation of dynamic and seamless Kubernetes multi-cluster topologies. Extensive experimental evaluations have shown its effectiveness in different contexts, both in terms of Kubernetes overhead and compared to other open-source alternatives

Fog Computing

Everything that is not a computer, in the traditional sense, is being connected to the Internet. These devices are also referred to as the Internet of Things and they are pressuring the current network infrastructure. Not all devices are intensive data producers and part of them can be used beyond their original intent by sharing their computational resources. The combination of those two factors can be used either to perform insight over the data closer where is originated or extend into new services by making available computational resources, but not exclusively, at the edge of the network. Fog computing is a new computational paradigm that provides those devices a new form of cloud at a closer distance where IoT and other devices with connectivity capabilities can offload computation. In this dissertation, we have explored the fog computing paradigm, and also comparing with other paradigms, namely cloud, and edge computing. Then, we propose a novel architecture that can be used to form or be part of this new paradigm. The implementation was tested on two types of applications. The first application had the main objective of demonstrating the correctness of the implementation while the other application, had the goal of validating the characteristics of fog computing.Tudo o que não é um computador, no sentido tradicional, está sendo conectado à Internet. Esses dispositivos também são chamados de Internet das Coisas e estão pressionando a infraestrutura de rede atual. Nem todos os dispositivos são produtores intensivos de dados e parte deles pode ser usada além de sua intenção original, compartilhando seus recursos computacionais. A combinação desses dois fatores pode ser usada para realizar processamento dos dados mais próximos de onde são originados ou estender para a criação de novos serviços, disponibilizando recursos computacionais periféricos à rede. Fog computing é um novo paradigma computacional que fornece a esses dispositivos uma nova forma de nuvem a uma distância mais próxima, onde “Things” e outros dispositivos com recursos de conectividade possam delegar processamento. Nesta dissertação, exploramos fog computing e também comparamos com outros paradigmas, nomeadamente cloud e edge computing. Em seguida, propomos uma nova arquitetura que pode ser usada para formar ou fazer parte desse novo paradigma. A implementação foi testada em dois tipos de aplicativos. A primeira aplicação teve o objetivo principal de demonstrar a correção da implementação, enquanto a outra aplicação, teve como objetivo validar as características de fog computing

Management of Temporally and Spatially Correlated Failures in Federated Message Oriented Middleware for Resilient and QoS-Aware Messaging Services.

PhDMessage Oriented Middleware (MOM) is widely recognized as a promising solution for the communications between heterogeneous distributed systems. Because the resilience and quality-of-service of the messaging substrate plays a critical role in the overall system performance, the evolution of these distributed systems has introduced new requirements for MOM, such as inter domain federation, resilience and QoS support. This thesis focuses on a management frame work that enhances the Resilience and QoS-awareness of MOM, called RQMOM, for federated enterprise systems. A common hierarchical MOM architecture for the federated messaging service is assumed. Each bottom level local domain comprises a cluster of neighbouring brokers that carry a local messaging service, and inter domain messaging are routed through the gateway brokers of the different local domains over the top level federated overlay. Some challenges and solutions for the intra and inter domain messaging are researched. In local domain messaging the common cause of performance degradation is often the fluctuation of workloads which might result in surge of total workload on a broker and overload its processing capacity, since a local domain is often within a well connected network. Against performance degradation, a combination of novel proactive risk-aware workload allocation, which exploits the co-variation between workloads, in addition to existing reactive load balancing is designed and evaluated. In federated inter domain messaging an overlay network of federated gateway brokers distributed in separated geographical locations, on top of the heterogeneous physical network is considered. Geographical correlated failures are threats to cause major interruptions and damages to such systems. To mitigate this rarely addressed challenge, a novel geographical location aware route selection algorithm to support uninterrupted messaging is introduced. It is used with existing overlay routing mechanisms, to maintain routes and hence provide more resilient messaging against geographical correlated failures

Decentralized SDN Control Plane for a Distributed Cloud-Edge Infrastructure: A Survey

International audienceToday’s emerging needs (Internet of Things applications, Network Function Virtualization services, Mobile Edge computing, etc.) are challenging the classic approach of deploying a few large data centers to provide cloud services. A massively distributed Cloud-Edge architecture could better fit these new trends’ requirements and constraints by deploying on-demand infrastructure services in Point-of-Presences within backbone networks. In this context, a key feature is establishing connectivity among several resource managers in charge of operating, each one a subset of the infrastructure. After explaining the networking management challenges related to distributed Cloud-Edge infrastructures, this article surveys and analyzes the characteristics and limitations of existing technologies in the Software Defined Network field that could be used to provide the intersite connectivity feature. We also introduce Kubernetes, the new de facto container orchestrator platform, and analyze its use in the proposed context. This survey is concluded by providing a discussion about some research directions in the field of SDN applied to distributed Cloud-Edge infrastructures’ management

Satellite Networks: Architectures, Applications, and Technologies

Since global satellite networks are moving to the forefront in enhancing the national and global information infrastructures due to communication satellites' unique networking characteristics, a workshop was organized to assess the progress made to date and chart the future. This workshop provided the forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. Presentations on overview, state-of-the-art in research, development, deployment and applications and future trends on satellite networks are assembled