Alberto Gonzalez Prieto

A-GAP: An Adaptive Protocol for Continuous Network Monitoring with Accuracy Objectives

We present A-GAP, a novel protocol for continuous monitoring of network state variables, which aims at achieving a given monitoring accuracy with minimal overhead. Network state variables are computed from device counters using aggregation functions, such as SUM, AVERAGE and MAX. The accuracy objective is expressed as the average estimation error. A-GAP is decentralized and asynchronous to achieve robustness and scalability. It executes on an overlay that interconnects management processes on the devices. On this overlay, the protocol maintains a spanning tree and updates the network state variables through incremental aggregation. Based on a stochastic model, it dynamically configures local filters that control whether an update is sent towards the root of the tree. We evaluate A-GAP through simulation using real traces and two different types of topologies of up to 650 nodes. The results show that we can effectively control the trade-off between accuracy and protocol overhead, and that the overhead can be reduced by almost two orders of magnitude when allowing for small errors. The protocol quickly adapts to a node failure and exhibits short spikes in the estimation error. Lastly, it can provide an accurate estimate of the error distribution in real-time.

Adaptive distributed monitoring with accuracy objectives

We present A-GAP, a novel protocol for continuous monitoring of network state variables, which aims at achieving a given monitoring accuracy with minimal overhead. Network state variables are computed from device counters using aggregation functions, such as SUM, AVERAGE and MAX. The accuracy objective is expressed as the average estimation error. A-GAP is decentralized and asynchronous to achieve robustness and scalability. It executes on an overlay that interconnects management processes on the devices. On this overlay, the protocol maintains a spanning tree and updates the network state variables through incremental aggregation. It dynamically configures local filters that control whether an update is sent towards the root of the tree. It reduces the overhead by attempting to minimize the maximum processing load over all management processes. We evaluate A-GAP through simulation using an ISP topology and real traces. The results show that we can effectively control the trade-off between accuracy and protocol overhead, that the overhead can be reduced significantly by allowing small errors, and that an accurate estimation of the error distribution can be provided in real-time.

SLS to DiffServ configuration mappings

This document addresses the problem of mapping Service Level Specifications (SLS) to IP Differentiated Services (DiffServ) configuration. We introduce a two step mapping controlled via a policy-based management system. The two step mapping includes the service specification to intra-domain service mapping (Per-Domain Behavior (PDB) [PDB-DEF]) and the further mapping to the DiffServ mechanism available in the domain. The first step uses an Ndimensional room (e.g. including delay/jitter, loss and throughput) to classify the SLS into a limited set of available intra-domain services. Based on this classification, assignment of the service class, and per service class admission control is performed. The mapping system is implemented on top of a QoS Management API configuring our Linuxbased DiffServ routers.

Toward decentralized probabilistic management

In recent years, data communication networks have grown to immense size and have been diversified by the mobile revolution. Existing management solutions are based on a centralized deterministic paradigm, which is appropriate for networks of moderate size operating in relatively stable conditions. However, it is becoming increasingly apparent that these management solutions are not able to cope with the large dynamic networks that are emerging. In this article, we argue that the adoption of a decentralized and probabilistic paradigm for network management will be crucial to meet the challenges of future networks, such as efficient resource usage, scalability, robustness, and adaptability. We discuss the potential of decentralized probabilistic management and its impact on management operations, and illustrate the paradigm by three example solutions for real-time monitoring and anomaly detection.

Ambient Networks Management Challenges and Approaches

System management addresses the provision of functions required for controlling, planning, allocating, monitoring, and deploying the resources of a network and of its services in order to optimize its efficiency and productivity and to safeguard its operation. It is also an enabler for the creation and sustenance of new business models and value chains, reflecting the different roles the service providers and users of a network can assume. Ambient Network represents a new networking approach and it aims to enable the cooperation of heterogeneous networks, on demand and transparently, to the potential users, without the need for pre-configuration or offline negotiation between network operators. To achieve these goals, ambient network management systems have to become dynamic, adaptive, autonomic and responsive to the network and its ambience. This paper discusses relationships between the concepts of autonomous and self-manageability and those of ambient networking, and the challenges and benefits that arise from their employment.

Adaptive real-time monitoring for large-scale networked systems

The focus of this thesis is continuous real-time monitoring, which is essential for the realization of adaptive management systems in large-scale dynamic environments. Real-time monitoring provides the necessary input to the decision-making process of network management. We have developed, implemented, and evaluated a design for real-time continuous monitoring of global metrics with performance objectives, such as monitoring overhead and estimation accuracy. Global metrics describe the state of the system as a whole, in contrast to local metrics, such as device counters or local protocol states, which capture the state of a local entity. Global metrics are computed from local metrics using aggregation functions, such as SUM, AVERAGE and MAX. A key part in the design is a model for the distributed monitoring process that relates performance metrics to parameters that tune the behavior of a monitoring protocol. The model has been instrumental in designing a monitoring protocol that is controllable and achieves given performance objectives. Our design has proved to be effective in meeting performance objectives, efficient, adaptive to changes in the networking conditions, controllable along different performance dimensions, and scalable. We have implemented a prototype on a testbed of commercial routers, which proves the feasibility of the design, and, more generally, the feasibility of effective and efficient real-time monitoring in large network environments.

Towards ambient networks management

Ambient Networks (AN) are under development and they are based on novel networking concepts and systems that will enable a wide range of user and business communication scenarios beyond todays fixed, 3rd generation mobile and IP standards. Central to this project is the concept of Ambient Control Space (ACS) and the Domain Manager control function, which manages the underlying data transfer capabilities and presents a set of interfaces towards the supported services and applications. Network Management Systems of Ambient Networks must work in an environment where heterogeneous networks compose and cooperate, on demand and transparently, without the need for manual (pre or re)-configuration or offline negotiations between network operators. To achieve these goals, ambient network management systems must become dynamic, distributed, self-managing and responsive to the network and its ambience. This paper describes the different management research challenges and four complementary solution approaches (i.e. Pattern-based Management, Peer-to-Peer Management, (Un)PnP Management, Traffic Engineering Management Application Approaches) that enable efficient management of ambient networks, and the relationships between them, and presents the main results achieved so far.

Policy-based congestion management for an SMS gateway

We present a policy-based approach to managing congestions in short message service (SMS) systems. Congestion situations typically occur on SMS gateways (SMSGs), which route SMS messages between different networks and domains. In our architecture, an SMS operator can dynamically define the maximum acceptable loss of messages of a nonguaranteed SMS service class, thereby controlling the trade-off between minimal message loss and maximum throughput in an SMS system. We present the functional architecture of a manageable SMSG and discuss the realization of the policy decision point (PDP), which applies the congestion policy on the SMSG. An implementation of our architecture on a commercial SMSG, the EMG, is underway.

Controlling performance trade-offs in adaptive network monitoring

A key requirement for autonomic (i.e., self-*) management systems is a short adaptation time to changes in the networking conditions. In this paper, we show that the adaptation time of a distributed monitoring protocol can be controlled. We show this for A-GAP, a protocol for continuous monitoring of global metrics with controllable accuracy. We demonstrate through simulations that, for the case of A-GAP, the choice of the topology of the aggregation tree controls the trade-off between adaptation time and protocol overhead in steady-state. Generally, allowing a larger adaptation time permits reducing the protocol overhead. Our results suggest that the adaptation time primarily depends on the height of the aggregation tree and that the protocol overhead is strongly influenced by the number of internal nodes. We outline how A-GAP can be extended to dynamically self-configure and to continuously adapt its configuration to changing conditions, in order to meet a set of performance objectives, including adaptation time, protocol overhead, and estimation accuracy.

Monitoring Flow Aggregates with Controllable Accuracy

In this paper, we show the feasibility of real-time flow monitoring with controllable accuracy in todays IP networks. Our approach is based on Netflow and A-GAP. A-GAP is a protocol for continuous monitoring of network state variables, which are computed from device metrics using aggregation functions, such as SUM, AVERAGE and MAX. A-GAP is designed to achieve a given monitoring accuracy with minimal overhead. A-GAP is decentralized and asynchronous to achieve robustness and scalability. The protocol incrementally computes aggregation functions inside the network and, based on a stochastic model, it dynamically configures local filters that control the overhead and accuracy. We evaluate a prototype in a testbed of 16 commercial routers and provide measurements from a scenario where the protocol continuously estimates the total number of FTP flows in the network. Local flow metrics are read out from Netflow buffers and aggregated in real-time. We evaluate the prototype for the following criteria. First, the ability to effectively control the trade off between monitoring accuracy and processing overhead; second, the ability to accurately predict the distribution of the estimation error; third, the impact of a sudden change in topology on the performance of the protocol. The testbed measurements are consistent with simulation studies we performed for different topologies and network sizes, which proves the feasibility of the protocol design, and, more generally, the feasibility of effective and efficient real-time flow monitoring in large network environments.