# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 7 Summary and Concluding Remarks  
An energy-aware data center network may save the energy utilized by adapting its topology and resources to the actual traffic load and demands while ensuring the end-to-end quality of service. We designed the mathematical model of the DCN network and, using it, defined three versions of the optimal energy-saving routing problem. The resulting mathematical programming tasks may be solved using an appropriate solver, i.e., mixed-integer linear for \(\mathit{SEUlm}\) and \(\mathit{DEUlm}\) versions and mixed-integer nonlinear for most complex \(\mathit{DEUnm}\) versions.  
Taking into account the limitations of formal analysis in this paper, we proposed activity control heuristics that may wake up and deactivate network nodes to adapt available bandwidth to offered traffic. Another two heuristics were designed to control pivot selection in Valiant routing based on the average port load of switches and the search depth limitation. The algorithm for selecting a pivot saves significant energy, on par with classic ECMP routing. The cost is increased devices load caused by traffic consolidation, which, in an unfortunate case, may lead to lowering QoS. However, this result may be partially mitigated by applying search depth limitation, which we recommend using in all scenarios. We verified, by simulation experiments, that activation heuristics provide solutions consistent with the exact ones calculated using the mathematical model and MIP solver. Moreover, the comparison of two selected topologies, i.e., fat-tree and butterfly, confirms the quite obvious fact of greater reliability and flexibility of the butterfly network resulting from redundant nodes. Again, the price for it is higher energy consumption, which the use of energy-aware routing may mitigate.  
As a conclusion based on the simulation study, our routing algorithm implementing proposed heuristics can be successfully used for energy-efficient dynamic management in large-scale data centers and for adapting to dynamically changing network loads. In general, our heuristics aim to determine at a given time such a set of active devices that allows for reducing energy consumption while ensuring the expected QoS. It is essential that they may provide the solution in a shorter time than solving the mathematical programming task, allowing fast adaptation to changing traffic matrix even for larger networks. Their computational complexity is low. They perform relatively simple arithmetic operations compared to the optimization problems defined in Section 4, which are solved by an expensive discrete optimization algorithm, e.g., the branch and bound method. They only monitor ports and, on this basis, decide to enable or disable the switch or choose the pivot. Their complexity is approximately linear; thus, they can be used for networks with hundreds of thousands of ports or tens of thousands of servers.  
For further research, we envisage the construction of a randomized energy-aware pivot selection heuristic based on the relation between port use and its selection probability to combine energy-conserving path consolidation with the load-balancing mechanism.  
Figure 10: Reliability indicators of Valiant routing with reduced pivot search depth.  
**Author Contributions:** Conceptualization, P.A., T.J. and E.N.-S.; methodology, P.A., T.J. and E.N.-S.; software, T.J.; validation, T.J. and P.A.; formal analysis, T.J. and P.A.; investigation, T.J., P.A. and E.N.-S.; writing--original draft preparation, P.A., E.N.-S. and T.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.