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Network cost services

A generic Network Cost provision framework is described, accompanied with a preliminary proofof-concept implementation. Deployment considerations and open research issues are also discussed.

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Network cost services

  2. 2. • Promising aspect within the FI scene • On-demand signaling of specific network-layer metrics to querying applications aiming in optimization of application behavior and performance • Within the generic “Resource Awareness” FI design principle.2
  3. 3. • Present model: implicit derivation of network status (e.g. TCP) • Applications assume network conditions by observing their own performance • Implicit network awareness has survived during the years, and works well for most of the applications • It is also scalable3
  4. 4. • Gradual paradigm shift from host-centric to content-centric communication • Existing implicit mechanisms may be not as efficient due to new communication architectures, beyond the traditional client-server: – peer-to-peer – CDNs/Content-centric networks – in-network clouds etc. • Content is distributed into the network, rather than concentrated in a single server -> simultaneous communication with tens, hundreds, or even thousands of peers. • Assessing the communication with each of these peers at application level via the usual in-band methods would pose significant overhead in the network, besides requiring a considerable amount of time.4
  5. 5. • A scalable and efficient way to provide explicit Network Awareness • Offered by Network Operators (or third-parties) • “Network Cost” (NC) assessment for any given pair or group of network endpoints. • NC could be instantly exploited at application level in order to optimize operations such as in-network content placement, peer selection, prioritization and load balancing. • A similar approach is promoted by the Application Layer Traffic Optimisation (ALTO) WG of the IETF5
  6. 6. • peer-to-peer file sharing and streaming - for optimizing p2p communication via selection of “best peers” i.e. peers with lower NC • one-to-one and one-to-many real-time audiovisual communication – for a priori optimizing stream format and rate according to NC • distributed caching, Content-Delivery Networks (CDN) and Information Centric Networking (ICN) – for choosing optimal locations for caching content within the network • in-network clouds – for efficient load balancing, traffic distribution and resource transfer to optimal locations, including Virtual Machine (VM) migration.6
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  8. 8. • Network Cost Query: pair or group of endpoints’ addresses, optionally application type (for application-aware networks) • Response: Network Cost vector where ci(a,b), denotes a certain network metric between endpoints a and b (e.g. hop count, one-way delay, average packet loss, jitter/delay variation etc.) • Can be transformed to a scalar value, taking into account the sensitivity wi of the specific application against each of the aforementioned metrics.8
  9. 9. • Customised SNMP agents on Linux-based routers • On-demand execution of active end-to-end measurements according to the OWAMP protocol (RFC 4656) • Configuration of measurements and collection of results over SNMP • Network Cost provided to applications over SOAP/XML protocol • NC elements: – Number of hops traversed – Average One-Way Delay (OWD) – Average packet loss – Average jitter – Percentage of duplicates • Also tested on DiffServ-enabled configurations (Network Cost varies upon different DSCP values)9
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  11. 11. • OpenSource implementation of the SAM framework is available at • Technical information available in the work: G. Gardikis, K. Sarsembagieva, G. Xilouris, A. Kourtis, “An SNMP Agent for Active In- Network Measurements”, in Proc. ICUMT 2012, October 3-5, 2012, St. Petersburg, Russia (to appear).1
  12. 12. • Security and Network Operator Privacy; authentication of the querying entity, anti-(D)DoS protection etc. • Inter-domain operation – “assembling” NCs across a chain of network domains • Inclusion of the access network; separate assessment of the access link and combining results with edge/core network statistics so as to form end-to-end cost in a scalable manner1
  13. 13. xilouris@iit.demokritos.gr1