Joint data placement and flow control for cost-efficient data center networks

We study the problem of cost-efficient operation of data center networks used to deliver heterogenous online services. We split their aggregate cost into server-load-related and link-load-related segments. Thus, we formulate the problem of interest as that of joint data placement and flow control and use mixed integer-linear programming to compute the optimal solution. The high complexity of the latter motivated us to design two additional sets of strategies, based on data coding and heuristics, respectively. In our simulation experiments, carried out based on actual data center information, network topology and link cost, and electricity prices, we examine the advantages of data coding, in particular in the context of multicast, and the impact of different factors such as the network topology and service popularity, on the total cost incured by all strategies we consider. We show that network coding with multicast provides cost savings on the order of 30-80%, depending on the specific context under consideration, relative to the other optimization strategies and heuristic methods that we examine.

[1]  Tracey Ho,et al.  Optimal content delivery with network coding , 2009, 2009 43rd Annual Conference on Information Sciences and Systems.

[2]  Laurent Massoulié,et al.  Greening the internet with nano data centers , 2009, CoNEXT '09.

[3]  Abdallah Khreishah,et al.  Collaborative caching for multicell-coordinated systems , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[4]  Amir-Hamed Mohsenian-Rad,et al.  Energy-Information Transmission Tradeoff in Green Cloud Computing , 2010 .

[5]  Lachlan L. H. Andrew,et al.  Greening Geographical Load Balancing , 2015, IEEE/ACM Transactions on Networking.

[6]  Biswanath Mukherjee,et al.  On Routing and Transmission-Range Determination of Multi-Bit-Rate Signals Over Mixed-Line-Rate WDM Optical Networks for Carrier Ethernet , 2011, IEEE/ACM Transactions on Networking.

[7]  Xue Liu,et al.  Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment , 2010, 2010 Proceedings IEEE INFOCOM.

[8]  Yin Zhang,et al.  Optimizing cost and performance for multihoming , 2004, SIGCOMM '04.

[9]  Muriel Médard,et al.  Minimum Cost Mirror Sites Using Network Coding: Replication versus Coding at the Source Nodes , 2009, IEEE Transactions on Information Theory.

[10]  Bruce M. Maggs,et al.  Cutting the electric bill for internet-scale systems , 2009, SIGCOMM '09.

[11]  Mikkel Thorup,et al.  Traffic engineering with estimated traffic matrices , 2003, IMC '03.

[12]  Tracey Ho,et al.  A Random Linear Network Coding Approach to Multicast , 2006, IEEE Transactions on Information Theory.

[13]  Anees Shaikh,et al.  A comparison of overlay routing and multihoming route control , 2004, SIGCOMM '04.

[14]  Issa M. Khalil,et al.  Joint Caching and Routing for Greening Computer Networks with Renewable Energy Sources , 2014, 2014 International Conference on Future Internet of Things and Cloud.

[15]  Jie Wu,et al.  Asymptotically-Optimal Incentive-Based En-Route Caching Scheme , 2014, 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems.

[16]  Jie Wu,et al.  The Benefits of Cooperation Between the Cloud and Private Data Centers for Multi-rate Video Streaming , 2014 .

[17]  Jie Wu,et al.  Cache content placement using triangular network coding , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).