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An example that shows the request-level scheduling process. There are 3 switches(s 1 , s 2 , s 3 ), 2 controllers(c 1 , c 2 ), and 1 global scheduler. Each switch or controller maintains a queue that buffers requests. During each time slot, each controller can serve 2 requests while each switch can serve only 1 request. There is a communication cost per request if switches upload requests to controllers, and a computational cost (2 per request on each switch) of locally processing by switches themselves. At the beginning of time slot t, s 1 , s 2 , and s 3 generates 3, 2, and 2 requests, respectively. The scheduler then collects system dynamics and decides a switch-controller association (could be (b) or (c)), aiming at minimizing the sum of communication cost (could be the number of hops, RTTs, etc.) and computational cost, as well as request queueing delay. Each switch chooses to either locally process its requests or send them to controllers according to the scheduling decision. 

An example that shows the request-level scheduling process. There are 3 switches(s 1 , s 2 , s 3 ), 2 controllers(c 1 , c 2 ), and 1 global scheduler. Each switch or controller maintains a queue that buffers requests. During each time slot, each controller can serve 2 requests while each switch can serve only 1 request. There is a communication cost per request if switches upload requests to controllers, and a computational cost (2 per request on each switch) of locally processing by switches themselves. At the beginning of time slot t, s 1 , s 2 , and s 3 generates 3, 2, and 2 requests, respectively. The scheduler then collects system dynamics and decides a switch-controller association (could be (b) or (c)), aiming at minimizing the sum of communication cost (could be the number of hops, RTTs, etc.) and computational cost, as well as request queueing delay. Each switch chooses to either locally process its requests or send them to controllers according to the scheduling decision. 

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In software-defined networking (SDN), as data plane scale expands, scalability and reliability of the control plane has become major concerns. To mitigate such concerns, two kinds of solutions have been proposed separately. One is multi-controller architecture, i.e., a logically centralized control plane with physically distributed controllers. The...

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... example of dynamic association and devolution is shown in Fig. ...
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... we focus on the behavior of s 3 . In Fig. 1 (b), s 3 chooses to process its requests locally, and that incurs a computational cost of 2 per request. In Fig. 1 (c), s 3 decides to upload requests to c 2 and that incurs a communication cost of 3 per request. Although the computational cost is less than communication cost, the decision of local processing leaves one request not ...
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... we focus on the behavior of s 3 . In Fig. 1 (b), s 3 chooses to process its requests locally, and that incurs a computational cost of 2 per request. In Fig. 1 (c), s 3 decides to upload requests to c 2 and that incurs a communication cost of 3 per request. Although the computational cost is less than communication cost, the decision of local processing leaves one request not processed yet at the end of the time slot. Hence, it is not necessarily a smart decision for a switch to perform control ...
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... we focus on the behavior of associations. Fig. 1 (b) and (c) show two different associations. Fig. 1 (b) shows the switch-controller association with (s 1 , c 1 ) and (s 2 , c 1 ) (s 3 processes requests locally), denoted by X 1 . X 1 incurs the total cost of communication and computation by 9 but results in uneven queue backlogs, leaving four requests unfinished after time slot t. Fig. ...
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... we focus on the behavior of associations. Fig. 1 (b) and (c) show two different associations. Fig. 1 (b) shows the switch-controller association with (s 1 , c 1 ) and (s 2 , c 1 ) (s 3 processes requests locally), denoted by X 1 . X 1 incurs the total cost of communication and computation by 9 but results in uneven queue backlogs, leaving four requests unfinished after time slot t. Fig. 1 (c) shows another association with (s 1 , c 1 ) ...
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... Fig. 1 (b) and (c) show two different associations. Fig. 1 (b) shows the switch-controller association with (s 1 , c 1 ) and (s 2 , c 1 ) (s 3 processes requests locally), denoted by X 1 . X 1 incurs the total cost of communication and computation by 9 but results in uneven queue backlogs, leaving four requests unfinished after time slot t. Fig. 1 (c) shows another association with (s 1 , c 1 ) and (s 3 , c 2 ) (s 2 processes requests locally), denoted by X 2 . X 2 incurs the total cost by 13 but does better in balancing queue backlogs. Thus there is a non-trivial trade-off between minimizing the total cost of communication and computation and maintaining small queue backlogs in ...
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... processes. They two are widely adopted in traffic analysis. We only show the simulation results under Fat-tree topology, because the simulation results in 3-Tiered topology is qualitatively similar to that in Fat-tree topology. Fig. 9 shows the communication cost comparison when the flow arrival process follows Poisson and Pareto, respectively. Fig. 10 shows the average queue backlog size comparison when the flow arrival process follows Poisson and Pareto processes, respectively. We can see from these figures that the scheduling policies perform qualitatively consistent under different arrival processes. In summary, among four schemes, Static is on the one end of performance ...

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... They also reformulate DCAP as an online optimization to minimize the total cost [27]. Huang et al. [28] propose a novel scheme to jointly consider both static and dynamic switch-controller association and devolution. ProgrammabilityGuardian [29] improves the path programmability of offline flows and maintains low communication overhead by using a middle layer to establish the fine-grained flow-controller mappings. ...
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