Proactive multi-tenant cache management for virtualized ISP networks

The content delivery market has mainly been dominated by large Content Delivery Networks (CDNs) such as Akamai and Limelight. However, CDN traffic exerts a lot of pressure on Internet Service Provider (ISP) networks. Recently, ISPs have begun deploying so-called Telco CDNs, which have many advantages, such as reduced ISP network bandwidth utilization and improved Quality of Service (QoS) by bringing content closer to the end-user. Virtualization of storage and networking resources can enable the ISP to simultaneously lease its Telco CDN infrastructure to multiple third parties, opening up new business models and revenue streams. In this paper, we propose a proactive cache management system for ISP-operated multi-tenant Telco CDNs. The associated algorithm optimizes content placement and server selection across tenants and users, based on predicted content popularity and the geographical distribution of requests. Based on a Video-on-Demand (VoD) request trace of a leading European telecom operator, the presented algorithm is shown to reduce bandwidth usage by 17% compared to the traditional Least Recently Used (LRU) caching strategy, both inside the network and on the ingress links, while at the same time offering enhanced load balancing capabilities. Increasing the prediction accuracy is shown to have the potential to further improve bandwidth efficiency by up to 79%

Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

BACKGROUND: Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. METHODS: Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. RESULTS: Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. CONCLUSION: Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions

Management Application Interactions in Software-Based Networks

IEEE To support the next wave of networking technologies and services, which will likely involve heterogeneous resources and requirements, rich management functionality will need to be deployed. This raises questions regarding the interoperability of such functionality in an environment where potentially interacting applications operate in parallel. Interactions can cause configuration instabilities and subsequently network performance degradation, especially in the presence of contradicting objectives. Detecting and handling these interactions is therefore essential. In this article we present an overview of the interaction management problem, a critical issue in software-based networks. We review and compare existing solutions proposed in the literature and discuss key challenges toward the development of a generic framework for the automated and real-time management of these interactions

Providing proportional TCP performance by fixed-point approximations over bandwidth on demand satellite networks

In this paper we focus on the provision of propor- tional class-based service differentiation to transmission control protocol (TCP) flows in the context of bandwidth on demand(BoD) split-TCP geostationary (GEO) satellite networks. Our approach involves the joint configuration of TCP-Performance Enhancing Proxy (TCP-PEP) agents at the transport layer and the scheduling algorithm controlling the resource allocation at the Medium Access Control (MAC) layer. We show that the two differentiation mechanisms exhibit complementary behavior in achieving the desired differentiation throughout the traffic load space: the TCP-PEPs control differentiation at low and medium system utilization, whereas the MAC scheduler becomes the dominant differentiation factor under high traffic load. The main challenge for the satellite operator is to appropriately configure those two mechanisms to achieve a specific differentiation target for the different classes of TCP flows. To this end, we propose a fixed-point framework to analytically approximate the achieved differentiated TCP performance. We validate the predictive capacity of our analytical method via simulations and show that our approximations closely match the performance of different classes of TCP flows under various scenarios for the network traffic load and configuration of the MAC scheduler and TCP-PEP agent. Satellite network operators could use our approximations as an analytical tool to tune their network

Decentralized Solutions for Monitoring Large-Scale Software-Defined Networks

Software-Defined Networking (SDN) technologies offer the possibility to automatically and frequently reconfigure the network resources by enabling simple and flexible network programmability. One of the key challenges to address when developing a new SDN-based solution is the design of a monitoring framework that can provide frequent and consistent updates to heterogeneous management applications. To cope with the requirements of large-scale networks (i.e. large number of geographically dispersed devices), a distributed monitoring approach is required. This PhD aims at investigating decentralized solutions for resource monitoring in SDN. The research will focus on the design of monitoring entities for the collection and processing of information at different network locations and will investigate how these can efficiently share their knowledge in a distributed management environment

CacheMAsT: Cache Management Analysis and Visualization Tool

Recent approaches have proposed to empower Internet Service Providers (ISPs) with caching capabilities that can allow them to implement their own cache management strategies and as such have better control over the utilization of their resources. In this demo paper, we present CacheMAsT (Cache Management Analysis and Visualization Tool), a decision support tool to visualize the configuration and performance of in-network cache management approaches. CacheMAsT is aimed at assisting researchers and engineers in analyzing and evaluating the different factors that can affect the performance of a cache management strategy and ultimately decide on the optimal approach to apply

A native content discovery mechanism for the information-centric networks

Recent research has considered various approaches for discovering content in the cache-enabled nodes of an Autonomous System (AS) to reduce the costly inter-AS traffic. Such approaches include i) searching content opportunistically (on-path) along the default intra-AS path towards the content origin for limited gain, and ii) actively coordinate nodes when caching content for significantly higher gains, but also higher overhead. In this paper, we try to combine the merits of both worlds by using traditional opportunistic caching mechanisms enhanced with a lightweight content discovery approach. Particularly, a content retrieved through an inter-AS link is cached only once along the intra-AS delivery path to maximize network storage utilization, and ephemeral forwarding state to locate temporarily stored content is established opportunistically at each node along that path during the processing of Data packets. The ephemeral forwarding state either points to the arriving or the destination face of the Data packet depending on whether the content has already been cached along the path or not. The challenge in such an approach is to appropriately use and maintain the ephemeral forwarding state to minimize inter-AS content retrieval, while keeping retrieval latency and overhead at acceptable levels. We propose several forwarding strategies to use and manage ephemeral state and evaluate our mechanism using an ISP topology for various system parameters. Our results indicate that our opportunistic content discovery mechanism can achieve near-optimal performance and significantly reduce inter-AS traffic

On the Placement of Management and Control Functionality in Software Defined Networks

In order to support reactive and adaptive operations, Software-Defined Networking (SDN)-based management and control frameworks call for decentralized solutions. A key challenge to consider when deploying such solutions is to decide on the degree of distribution of the management and control functionality. In this paper, we develop an approach to determine the allocation of management and control entities by designing two algorithms to compute their placement. The algorithms rely on a set of input parameters which can be tuned to take into account the requirements of both the network infrastructure and the management applications to execute in the network. We evaluate the influence of these parameters on the configuration of the resulting management and control planes based on real network topologies and provide guidelines regarding the settings of the proposed algorithms

Analysis of clustered data when the cluster size is informative

Clustered data arise in many scenarios. We may wish to fit a marginal regression model relating outcome measurements to covariates for cluster members. Often the cluster size, the number of members, varies. Informative cluster size (ICS) has been defined to arise when the outcome depends on the cluster size conditional on covariates. If the clusters are considered complete then the population of all cluster members and the population of typical cluster members have been proposed as suitable targets for inference, which will differ between these populations under ICS. However if the variation in cluster size arises from missing data then the clusters are considered incomplete and we seek inference for the population of all members of all complete clusters. We define informative covariate structure to arise when for a particular member the outcome is related to the covariates for other members in the cluster, conditional on the covariates for that member and the cluster size. In this case the proposed populations for inference may be inappropriate and, just as under ICS, standard estimation methods are unsuitable. We propose two further populations and weighted independence estimating equations (WIEE) for estimation. An adaptation of GEE was proposed to provide inference for the population of typical cluster members and increase efficiency, relative to WIEE, by incorporating the intra-cluster correlation. We propose an alternative adaptation which can provide superior efficiency. For each adaptation we explain how bias can arise. This bias was not clearly described when the first adaptation was originally proposed. Several authors have vaguely related ICS to the violation of the ‘missing completely at random’ assumption. We investigate which missing data mechanisms can cause ICS, which might lead to similar inference for the populations of typical cluster members and all members of all complete clusters, and we discuss implications for estimation

Accuracy-Aware Adaptive Traffic Monitoring for Software Dataplanes

Network operators have recently been developing multi-Gbps traffic monitoring tools on commodity hardware, as part of the packet-processing pipelines realizing software dataplanes. These solutions allow the execution of sophisticated per-packet monitoring using the processing power available on servers. Although advances in packet capture have enabled the interception of packets at high rates, bottlenecks can still arise in the monitoring process as a result of concurrent access to shared processor resources, variations of the traffic skew, and unbalanced packet-rate spikes. In this paper we present an adaptive monitoring framework, →ol, which is resilient to bottlenecks while maintaining the accuracy of monitoring reports above a user-specified threshold. →ol dynamically reduces the measurement task sets under adverse conditions, and reconfigures them to recover potential accuracy degradations. To quantify the monitoring accuracy at run time, →ol adopts a novel task-independent technique that generates accuracy estimates according to recently observed traffic characteristics. With a prototype implementation based on a generic packet-processing pipeline, and using well-known measurements tasks, we show that →ol achieves lossless traffic monitoring for a wide range of conditions, significantly enhances the level of monitoring accuracy, and performs adaptations at the time scale of milliseconds with limited overhead