Abstract not found

. Active networks is a framework where network elements, primarily routers and switches, are programmable. Programs that are injected into the network are executed by the network elements to achieve higher flexibility and to present new capabilities. This work describes a novel active network architecture which primarily addresses the management challenges of modern complex networks. Its main component is an active engine that is attached to any IP router to form an active node. The active engine we designed and implemented executes programs that arrive from the network and monitors and controls the router actions. The design is based on standards (Java, SNMP, ANEP over UDP), and can be easily deployed in todays IP networks. The contribution of this paper is the introduction of novel architectural features such as: isolation of the active mechanism, the session concept, the ability of active sessions to control non-active packets, and blind addressing. Implementing these ideas, we buil...

Active Networking provides a framework in which executable code within data packets can execute upon intermediate network nodes. Active Virtual Network Management Prediction (AVNMP) provides a network prediction service that utilizes the capability of Active Networks to easily inject fine-grained models into the communication network to enhance network performance. The models injected into the network allow state to be predicted and propagated throughout an active network enabling the network to operate simultaneously in real time and in the future. State information such as load, security intrusion, mobile location, faults, and other state information found in typical Management Information Bases (MIB) is available for use by the management system both with current values and with values expected to exist in the future. Implementing a load prediction and CPU prediction application has experimentally validated AVNMP. AVNMP implements a distributed, active, and truly proactive network management system. Active Networking enables the implementation of new concepts utilized in AVNMP such as the ability to quickly and easily inject models into a network. In addition, Active Networking enables the ability of messages to refine their prediction as they travel through the network as well as several enhancements to the basic AVNMP algorithm, including migration of AVNMP components and reduction in overhead by means of message fusion.

Active network technology envisions deployment of virtual execution environments within network elements, such as switches and routers. As a result, inhomogeneous processing can be applied to network traffic. To use such technology safely and efficiently, individual nodes must provide mechanisms to enforce resource limits. This implies that each node must understand the varying resource requirements for specific network traffic. This paper presents an approach to model the CPU time requirements of active applications in a form that can be interpreted among heterogeneous nodes. Further, the paper demonstrates how this approach can be used successfully to control resources consumed at an active-network node and to predict load among nodes in an active network, when integrated within the Active Virtual Network Management Prediction system. 1.

Active Network technology envisions deployment of virtual execution environments within network elements, such as switches and routers. As a result, nonhomogeneous processing can be applied to network traffic associated with services, flows, or even individual packets. To use such a technology safely and efficiently, individual nodes must provide mechanisms to enforce resource limits. To provide effective enforcement mechanisms, each node must have a meaningful understanding of the resource requirements for specific network traffic. In Active Network nodes, resource requirements typically come in three categories: bandwidth, memory, and processing. Well-accepted metrics exist for expressing bandwidth (bits per second) and memory (bytes) in units independent of the capabilities of particular nodes. Unfortunately, no well-accepted metric exists for expressing processing (i.e., CPU time) requirements in a platformindependent form. This paper investigates a method to express the CPU time r...

l operating on a subset of network resources. We call the former broadband kernel the "parent kernel". The term `spawning' finds a parallel with an operating system spawning a child process. In this case, however, the spawned "child" kernel typically represents a new network architecture supporting alternative signalling protocols, communications services, QOS control and network management in comparison to the "parent kernel" architecture. Our research goals raise a large number of important questions. We can not hope to address them all in this proposal, however. We therefore propose a research agenda that comprises of items that we consider the most pressing in building a network architectures creation platform and plan to: (i) establish a methodology and create an environment for spawning virtual networks and their architectures, (ii) investigate trade-offs of location and degree of cooperation and competition among the controllers that define and characterize the architecture, (i

This paper describes an approach to detecting distributed denial of service (DDoS) attacks that is based on fundamentals of information theory, specifically Kolmogorov complexity. The algorithm is based on a concept of Kolmogorov complexity that states that the joint complexity measure of random strings is lower than the sum of the complexities of the individual strings if the strings exhibit some correlation. Furthermore, the joint complexity measure varies inversely with the amount of correlation. The proposed algorithm exploits this feature to correlate traffic flows in the network and detect possible denial-of-service attacks. One of the strengths of this algorithm is that it does not require special filtering rules and hence it can be used to detect any type of DDoS attack. This algorithm is shown to perform better than simple packet-counting or load-measuring approaches.

This paper introduces a novel algorithm, the Active Virtual Network Management Protocol (AVNMP), for predictive network management. It explains how the Active Virtual Network Management Protocol facilitates the management of an active network by allowing future predicted state information within an active network to be available to network management algorithms. This is accomplished by coupling ideas from optimistic discrete event simulation with active networking. The optimistic discrete event simulation method used is a form of self-adjusting Time Warp. It is self-adjusting because the system adjusts for predictions which are inaccurate beyond a given tolerance. The concept of a streptichron and autoanaplasis are introduced as mechanisms which take advantage of the enhanced flexibility and intelligence of active packets. Finally, it is demonstrated that the Active Virtual Network Management Protocol is a feasible concept. 1. Network Management and Active Networks The problem this pa...

Telecommunication service providers have recently begun to offer ubiquitous access to packetised data. As a result, the Internet is not limited to computers that are physically connected but is also available to users that are equipped with mobile devices. This ubiquitous access fuels the growth and the usage of the Internet even further, and thus the realisation of dynamic Internet. With the realisation of the dynamic Internet, increasing support is needed for Internet protocol (IP) and transmission control protocol (TCP) over wireless/mobile networks. Two areas

The way in which research groups evaluate router software (QoS and routing components, for example) seems to be restricted to methodologies using mathematical modelling and simulation techniques. We believe that an experimental methodology is rarely used as the deployment of custom routing software to a testbed comprising multiple routers is a non-trivial task that is beyond the scope of most network research projects. This project intends to make experimental methodologies more accessible to researchers by using programmable networking techniques and by building a management system for a network testbeds.