Abstract-Content Centric Networking (CCN) is a recently proposed information-centric Internet architecture in which the main network abstraction is represented by location-agnostic content identifiers instead of node identifiers. In CCN each content object is divided into packet-size chunks. When a content object is transferred, routers on the path can cache single chunks which they can use to serve subsequent requests from other users.Since content chunks in CCN may be retrieved from a number of different nodes/caches, implicit-feedback transport protocols will not be able to work efficiently, because it is not possible to set an appropriate timeout value based on RTT estimations given that the data source may change frequently during a flow.In order to address this problem, we propose in this paper a scalable, implicit-feedback congestion control protocol, capable of coping with RTT unpredictability using a novel anticipated interests mechanism to predict the location of chunks before they are actually served. Our evaluation shows that our protocol outperforms similar receiver-driven protocols, in particular when content chunks are scattered across network paths due to reduced cache sizes, long-tail content popularity distribution or the adoption of specific caching policies.
Arguably, one of the most cumbersome tasks required to run a network simulation is the setup of a complete simulation scenario and its implementation in the target simulator. This process includes selecting a topology, provision it with all required parameters and, finally, configure traffic sources or generate traffic matrices.Many tools exist to address some of these tasks. However, most of them do not provide methods for configuring network and traffic parameters, while others only support a specific simulator. As a consequence, a user often needs to implement the desired features personally, which is both time-consuming and error-prone.To address these issues, we present the Fast Network Simulation Setup (FNSS) toolchain. It provides capabilities for parsing topologies from datasets or generating them synthetically, assign desired configuration parameters and generate traffic matrices or event schedules. It also provides APIs for a number of programming languages and network simulators to easily deploy the simulation scenario in the target simulator. Categories and Subject Descriptors General TermsDesign, Performance, Experimentation Keywords network topology, network simulation, link capacity, modeling, traffic matrix * Source code, binaries and documentation of the FNSS toolchain is available at
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