T DC = 1 and T DC = 10. It decreases slightly when T DC = 20 as shown in Figure
4.9(c) but is still comparable to the bandwidth of the baseline electrical network. This is because with high diversity traffic, there are plenty of requests for new connections and each request is delayed by the RTT of the control packet. The bandwidth of the interconnection is wasted during this RTT which results in decreasing overall network throughput in the presence of high diversity traffic. This behaviour is also observed in other optical interconnects[54,72]
4.5
Performance of the Control Plane
In order to assess the performance of the routing and scheduling algorithm of the con- trol plane, the algorithm was run on an Intel host with a Core i7, 2.17 GHz processor and 16 GB RAM. The results were obtained for several combinations of parameters. For statistical significance, the results of 1000 runs were averaged and the results are shown in Table 4.3. Table 4.3 shows the execution time for various numbers of racks N , for various values of topological degree of communication T DC, and various number of slow and fast optical switches. Although these execution times are implementation dependent, their variations illustrate the scalability properties of the algorithms in the control plane.
When a control packet arrives at the controller, the controller implements the rout- ing and scheduling operation of Algorithm 4. The complexity of the routing and scheduling algorithm is O(2(2K + L) + µ), where K is the number of ports of the ToR switch dedicated for the slow switch paths, L is the number of ports of the ToR switch assigned for the fast switch paths andµ represents the aggregate processing time of all other instructions. This is assumed to be a constant of negligibly low value. We mea-
sure the algorithm execution time in a 4:1 oversubscribed network when{K, L} = 5,
in a 2:1 oversubscribed network when{K, L} = 10 and in a fully subscribed network
when{K, L} = 20 using 40 servers per rack as shown in first three rows of Table 5.3. Fourth row of Table 5.3 shows its execution in a fully subscribed networking using 80 servers per rack. It can be inferred that the processing time of the control packet
4.5. PERFORMANCE OF THE CONTROL PLANE
Table 4.3. Performance of the Algorithms in the Control Plane
Al g o r i t hm
Rack s
(N)
T DC
K
L
E x ec.T
Routing and scheduling
∀ N
∀ T DC
5
5
< 0.1 µs
10
10
< 0.1 µs
20
20
< 0.5 µs
40
40
1.1
µs
Traffic Matrix Scheduling
102
1
∀ K
∀ L
5.6
µs
10
23.6µs
20
42.9µs
101
204.9µs
512
1
27.7µs
10
116µs
20
211.9µs
511
7.3 ms
1024
1
51.8µs
10
229.9µs
20
426.9µs
50
1.1 ms
100
2.1 ms
1023
29.2 ms
is independent of the network size and the T DC values. The execution time of the routing and scheduling algorithm is very low in 4:1 and 2:1 oversubscribed networks while it increases slightly because of the increased number of ports of ToR switches in a fully subscribed network.
The traffic demand scheduling is used to measure traffic statistics to configure the
slow optical switches. The complexity of this algorithm is O(N × (N − 1) + µ). The
performance of this algorithm depends upon the network size and T DC parameter. It is independent of the network over-subscription as shown in Table 5.3. This algorithm runs periodically to predict the new traffic matrix. It can be seen that the execution time is proportional to network size and T DC. In a very large network in a worst case
4.6. CONCLUSION
scenario, with N = 1024 and T DC = 1023, the execution time around 30 ms was
observed that is understandably high, but in a real network scenario, the TDC would not be too high because different studies on data centre traffic[102, 119, 121] have shown that traffic within data centres is bounded in degrees and racks communicate with only few other racks over a given period of time.
4.6
Conclusion
In this chapter, the performance of the original design HOSA is improved using traf- fic demand scheduling. In this technique, the controller maintains a traffic demand matrix which updates traffic demand periodically for each ToR pair and assigns slow paths to the ToR pairs that send high volume of traffic over a certain period of time. A resource allocation algorithm in the controller is proposed that ensures minimum latency and high throughput.
The network-level simulation investigating various traffic scenarios for stability and workload diversity, and considering various capacities of slow and fast optical switches is used to validate the proposed design. The results that the performance of HOSA has been improved by introducing HOSA with TDS scheme. Low latency and high throughput has been achieved with various workload communication patterns and that performance is comparable to that of electrical data centre networks for low and medium traffic loads.
In the next chapter, a new design for DCNs is proposed that is based on faster switching technologies that are now available[28, 29, 59, 64]. Instead of using OPS with fast optical switches, OBS with two-way reservation protocol is considered.
CHAPTER 5
PERFORMANCE ANALYSIS OF OBS
OVER FAST OPTICAL SWITCH
ARCHITECTURE FOR DCN
5.1
Introduction
The performance of optical network is directly related to the type of the optical switch- ing technique used. These switching techniques are OCS, OPS and OBS. The MEMS OXC or OCS switch has been used in the backbone optical network for many years. Hybrid designs for data centre networks that use OCS in conjunction with other tech-
nologies have been proposed[23,50–54]. Helios and cThrough [23,50] propose using
OCS in conjunction with traditional electrical packet switching while the LIGHTNESS
project[51,52] employs OCS together with optical packet switching. The Hydra, OSA
and Reconfigurable designs[53,54,72] augment OCS with a multi-hopping technique.
A major issue with these interconnects has been their slow reconfiguration time due to the limitation of 3D-MEMS technology[26]. This reconfiguration time is influenced by