C3PO: Computation Congestion Control (PrOactive)

There is an obvious trend that more and more data and computation are migrating into networks nowadays. Combining mature virtualization technologies with service-centric networking, which is deemed as a natural evolution of information-centric networking in 5G context, we are entering into an era where countless (mobile) services reside in an ISP network to provide low-latency access. Such services are often computation intensive and are dynamically created and destroyed on demand everywhere in the network to perform various tasks. Consequently, these ephemeral in-network services introduce a new type of congestion in the network which we refer to as "computation congestion". The service load need to be effectively distributed on different nodes in order to maintain the functionality and responsiveness of the network, which calls for a new design rather than reusing the centralised scheduler designed for cloud-based services. In this paper, we study both passive and proactive control strategies, based on the proactive control we further propose a fully distributed solution which is low complexity, adaptive, and responsive to network dynamics.

[1]  R. Srikant,et al.  Multi-Path TCP: A Joint Congestion Control and Routing Scheme to Exploit Path Diversity in the Internet , 2006, IEEE/ACM Transactions on Networking.

[2]  David J. Scott,et al.  Unikernels: the rise of the virtual library operating system , 2013, CACM.

[3]  Jon Crowcroft,et al.  SCANDEX: Service Centric Networking for Challenged Decentralised Networks , 2015, DIYNetworking@MobiSys.

[4]  Jussi Kangasharju,et al.  Neighborhood search and admission control in cooperative caching networks , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[5]  Jon Crowcroft,et al.  TCP-like congestion control for layered multicast data transfer , 1998, Proceedings. IEEE INFOCOM '98, the Conference on Computer Communications. Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies. Gateway to the 21st Century (Cat. No.98.

[6]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[7]  Van Jacobson,et al.  Networking named content , 2009, CoNEXT '09.

[8]  Jussi Kangasharju,et al.  Effects of Cooperation Policy and Network Topology on Performance of In-Network Caching , 2013, IEEE Communications Letters.

[9]  Jussi Kangasharju,et al.  Hybrid renewable energy routing for ISP networks , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[10]  BY anIL maDhaVaPeDDY,et al.  What if all the software layers in a virtual appliance were compiled within the same safe , high-level language framework ? , 2013 .

[11]  Steven Hand,et al.  Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters , 2014, TAAS.

[12]  Michael J. Freedman,et al.  Serval: An End-Host Stack for Service-Centric Networking , 2012, NSDI.

[13]  Torsten Braun,et al.  Service-centric networking extensions , 2013, SAC '13.

[14]  Jussi Kangasharju,et al.  FairCache: Introducing fairness to ICN caching , 2016, 2016 IEEE 24th International Conference on Network Protocols (ICNP).

[15]  Jörg Ott,et al.  Pro-Diluvian: Understanding Scoped-Flooding for Content Discovery in Information-Centric Networking , 2015, ICN.

[16]  Randy H. Katz,et al.  Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center , 2011, NSDI.