1. 2018/4/23
Dynamic Load-balanced Path Optimization in SDN-based Data Center Networks
Author: Yuan-Liang Lan , Kuochen Wang and Yi-Huai Hsu
Presenter: Yi-Hsien Wu
Conference: th International Symposium on Communication Systems, Networks and Digital Signal Processing Networking (Cloud net)
Date: 2017/2/7
Department of Computer Science and Information Engineering
National Cheng Kung University, Taiwan R.O.C.
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2. Outline Introduction Related Work Algorithm Performance Evaluation
Conclusion
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3. Introduction DLPO (Dynamic Load-balanced Path Optimization)
2018/4/23
Introduction
DLPO (Dynamic Load-balanced Path Optimization)
It is an algorithm which can suit for different SDN-based data center network topologies .
The proposed DLPO changes paths of flows during flow transmissions,
achieves load balancing among different links, and efficiently resolves the network congestion problem in SDN-based data center networks.
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4. 2018/4/23
Related Work
SDN-based path optimization can be classified into static path optimization and dynamic path optimization. Static path optimization : means that the path of a flow cannot be changed during flow transmissions Dynamic path optimization : It can change the path of a flow during flow transmissions
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5. Related Work Static path optimization DFS worst-fit :
2018/4/23
Related Work
Static path optimization
Tosmate et al. proposed a depth-first search worst-fit multipath routing algorithm (DFS worst-fit) to find static paths for new-coming flows based on the DFS search algorithm .
DFS worst-fit :
It selects the one with the largest amount of remaining bandwidth.
Its time complexity is O(1) by trying only the link with the largest amount of available bandwidth.
It also has a faster speed to find a path than first-fit and best-fit by avoiding excessive backtracking.
Since the available bandwidths of links in data center networks may change during flow transmission, static routing cannot efficiently balance link loads and may increase queuing delay in data center networks
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6. Related Work Dynamic path optimization
2018/4/23
Related Work
Dynamic path optimization
LABERIO (LoAd-BalancEd Routing wIth OpenFlow) is the first work to introduce an SDN-based dynamic path optimization algorithm to balance network traffic during flow transmissions.
At the initialization step of the path selection for a new coming flow, LABERIO chooses a temporary path that the amount of available bandwidth of its bottleneck link is the maximum among all possible paths according to current available bandwidths of links in data center networks.
The bottleneck link is defined as the link which has the minimal available bandwidth in a path.
LABERIO is highly dependent on network topology and hence LABERIO must
be modified if the network topology changes
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7. Related Work
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8. 2018/4/23
Related Work
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9. Algorithm Path initialization stage
2018/4/23
Algorithm
The proposed DLPO is composed of two stages, the path initialization stage and the dynamic path optimization stage.
Path initialization stage
Find a temporary path according to the available bandwidth of each path's bottleneck link. Among all possible paths between the source host and the destination host, the path whose bottleneck link has the largest available bandwidth will be chosen as a temporary path.
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10. Algorithm Dynamic path optimization stage
2018/4/23
Algorithm
Dynamic path optimization stage
DLPO will change paths of flows during flow transmissions to balance link loads and resolve the congestion problem in data center networks.
The proposed DLPO uses the OpenFlow protocol to retrieve load statistics from switches and detect the load-balance status.
If link loads in the data center network are imbalanced, the path optimization algorithm will be triggered to balance link loads.
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11. 2018/4/23
Algorithm
We use the simple moving average (SMA) of the variance of link loads as our load-balance detection parameter, which is defined in Eq. (1), to compute the average value of the variance of link loads during a period of time to smooth out short-term fluctuations.
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12. 2018/4/23
Algorithm
Besides, we will derive a path optimization trigger threshold δ*. If δ(t) > δ*, the proposed DLPO will be triggered to balance link loads in the date center network.
The value of δ* is network topology-dependent and can be determined by experiments under a fixed traffic scenario.
The dynamic path optimization stage of the proposed DLPO is composed of two algorithms, which are multi-link DLPO algorithm and single-link DLPO algorithm.
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13. 2018/4/23
Algorithm
When the imbalance of link loads in a data center network is detected , the multi-link DLPO algorithm will be triggered first to balance link loads. It will find the top 10% busiest links in the data center network and then change the path of the flow which covers most of the top 10% busiest links to a path whose bottleneck link has the largest available bandwidth. If the link loads are still imbalanced after all the top 10% busiest links have already been handled by the multi-link DLPO algorithm, the single-link DLPO algorithm will be triggered to resolve the congested paths that the multi-link DLPO algorithm cannot handle. The single-link DLPO algorithm will reroute the largest flow on the busiest link to a path whose bottleneck link has the largest available bandwidth so as to resolve the congested path problem efficiently.
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14. 2018/4/23
Algorithm
Because flows in a data center network may vary at any time, it is not feasible to change paths of all flows at one time. Thus, the proposed DLPO will only handle one flow at a time and sleep for a sleeping period Ps after changing the path of the flow that is most likely to cause congestion. The proposed DLPO balances the link loads efficiently and resolves the congestion problem, the queuing delay of packets can be reduced and hence the throughput of flows can be improved as well.
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15. Performance Evaluation
2018/4/23
Performance Evaluation
Setup :
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16. Performance Evaluation
2018/4/23
Performance Evaluation
We evaluate the performance of the proposed DLPO, LABERIO, and Floodlight in a fully populated topology, as shown in Figure 2. The traffic pattern for simulation is a hotspot traffic pattern , which is prone to cause congestion in certain links.
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17. Performance Evaluation
2018/4/23
Performance Evaluation
The relationship between average throughput per flow and the value of δ* is
shown in Figure 3. We observed that throughput reaches the maximum value when δ* is set to be 20× 109 byte2.
Therefore, we set the triggering threshold δ* to 20× 109 byte2 for the fully populated topology.
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18. Performance Evaluation
2018/4/23
Performance Evaluation
The average link bandwidth utilization of the proposed DLPO, LABERIO, and Floodlight are shown in Figure 4. It is obvious that although LABERIO reacts to imbalance quicker during the first 20 seconds, the proposed DLPO can eventually has higher overall bandwidth utilization. Simulation results show that the average bandwidth utilization of the proposed DLPO is 8.3% higher than that of LABERIO.
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19. Performance Evaluation
2018/4/23
Performance Evaluation
Figure 5 shows that DLPO can increase 24.8% of average throughput per flow compared to LABERIO in the fully populated topology. A fully populated topology is very suitable for dynamic path optimization because it provides many alternative links between switches.
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20. 2018/4/23
Conclusion
We have presented a Dynamic Load balanced Path Optimization (DLPO) algorithm which is suited for different data center network topologies. The proposed DLPO may change paths of flows during flow transmissions, achieve load balancing among different links, and efficiently resolve the congestion problem in data center networks.
近期因為ruleset size快速成長，因此ruleset複雜度造成一般封包分類方法memory表現很差
Swintop是一種將ruleset去分類的方式
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