Now that you know that network management is a tough, complicated process, we’re going to take a look at one of the most recent advancements in networking, software defined networking.
Along the way, you’re going to complete two exciting projects in Mininet. In the first, you’ll write your own virtual switch. In the second, you’ll use a programming language designed for software-defined networking to create a firewall.
In this lesson we’ll be exploring consistent updates in SDN’s. As a reminder from the last lesson, we looked at how to update switch flow table entries using OpenFlow control commands from the control. The OpenFlow API, however, does not provide specific guarantees about the level of consistency that packets along an end-to-end path can experience. So for example, updates to multiple switches along a path in a network that occur at different times may result in problems such as forwarding loops. Additionally, if updates to the switches along an end-to-end path occur in the middle of a flow, packets from the same flow may be subjected to different network states. These two problems are known as consistency problems. The first problem is known as a packet level consistency problem, and the second problem is known as a flow level consistency problem. In this lesson, we will explore these problems in more detail and look at various approaches to guaranteeing consistent updates in SDNs. To think about consistency properly, we first need a notion of a high level programming model that sits on top of what we would call the southbound interface. We’ll talk about how to write applications that use the controller interface that we learned about in the last lesson that can rely on a better notion of consistency than existing controller platforms currently provide. Let’s first think about how we want to program these applications and what type of abstraction the applications would require from the underlying control interface.
Let’s consider a network of SDN switches, such as OpenFlow switches, and a controller that is controlling those switches, and let’s assume that we would like to write a program using this interface. We can think about this programming as proceeding in three steps. The first is that the controller needs to read or monitor network state, as well as various events that may be occurring in the network. These events may include failures, topology changes, security events, and so forth. The second step is to compute the policy based on the state that the controller sees from the network. This is effectively what we talked about last time, is the role of the decision plane, in deciding what the forwarding behavior of the network should be, in response to various states that it reads from the network switches. The third step is to write policy back to the switches by installing the appropriate flow table state into the switches. Consistency problems can arise in two steps. First, the controller may read state from the network switches at different times, resulting in an inconsistent view of the network-wide state, and second, the controller may be writing policy as traffic is actively flowing through the network, which can disrupt packets along an end-to-end path or packets that should be treated consistently because they’re part of the same flow. Both reading and writing networks state can be challenging because OpenFlow rules are simple match action predicates, so it can be very difficult to express complex logic with these rules. If we want to read state that requires multiple rules, expressing a policy that allows us to read such a state can be complicated without more sophisticated predicates.
For example, let’s suppose that when we are reading state, we’d like to see all web serving traffic except for source 22.214.171.124. Simple match action rules do not allow us to express such exceptions. As a solution to this problem, we need a language primitive that allows us to express predicates. Here is a simple statement that has several predicates, such as AND and NOT. A runtime system can then translate these predicates into low-level OpenFlow rules, ensuring that they are installed atomically and in the right order. Another problem that arises is that switches only have limited space for rules. It’s simply not possible to install all possible rule patterns for every set of flows that we’d like to monitor.
For example, if we’d like to count the number of bytes for every source IP address and generate a histogram with the resulting traffic, we would potentially need a flow table entry for every possible source IP address. It’s simply not possible to install all of these possible rules. The solution is to have the run time system dynamically unfold rules as traffic arrives. A programmer would specify something like a group by source IP address, and the run time system would dynamically add open flow rules to the switch as traffic arrives, thereby guaranteeing that there are only rules in the switch that correspond to active traffic.
Another problem that arises when reading state is that extra, unexpected events may introduce inconsistencies. A common programming idiom is that the first packet goes to the controller and once the controller figures out what policy to apply for that flow, the controller then installs rules in the switches, in the network, corresponding to that flow. What if more packets should arrive at the switch before the controller has a chance to install rules for that flow? At this point, multiple packets may reach the controller, but the application it is running on top of the controller may not need or want to see these additional packets. So, the solution is to have the programmer specify by a high level language a limit of one, indicating that the application should only see the first packet of the flow and that the subsequent packet should be suppressed. The runtime system then hides the extra events.
So to remind you where we are, we talked about problems with consistency when reading state from the network, and we talked about three approaches to helping guarantee consistency when reading state: predicates, rule unfolding, and suppression. And let’s now talk about primitives that can help maintain consistency when writing state.
There are many reasons that a controller might want to write policy to change the state and the network switches, including maintenance, unexpected failure, and traffic engineering. Any of these network tasks involve or require updating state in the network switches, and when that state transition happens, we want to make sure that forwarding remains correct and consistent. In particular, we would like to maintain the following invariance. There shouldn’t be any forwarding loops and there shouldn’t be any black holes whereby a router or switch receives a packet and doesn’t know what to do with it. There also shouldn’t be cases where traffic is going where it shouldn’t be allowed to go because of the network being in an inconsistent state.
Let’s now consider an example of what might happen when policies are written to the network if they’re written in an inconsistent fashion. Let’s consider a case where we have a network that is performing shortest routing to some destination, and the link weights are as shown here in the figure. Traffic in the network would flow along the path shown in green. Let’s suppose now that an operator wants to change the network state to shift traffic off of this link. He could do so by updating the link weight. In doing so, the new shortest path from this top router would be as follows. But, what if the state in the top switch occurred before the state in the bottom switch could be updated? In this case, we would have a potential forwarding loop. Traffic would proceed to the bottom switch. But the bottom switch would still have the old network state and would continue to forward traffic to the top switch, resulting in a forwarding loop. If rules are installed along a path out of order, packets may reach a switch before the new rules do. So, in this type of model we would have to think about all possible packet and event orderings to ensure that consistent behavior resulted. So we need atomic updates of the entire configuration.
The solution to this problem is to use a two phase commit so that packets are either subjected to the old configuration on all switches, or to the new configuration on all switches. But packets aren’t subjected to the new policy on some switches and the old policy on others. The idea is to tag the packet on ingress so that the switches maintain copies of both P1 and P2 for some time. When all switches have received rules corresponding to the new policy, then incoming packets can be tagged with P2. After some time, when we’re sure that no more packets with P1 are being forwarded through the network, we can only then remove the rules corresponding to policy P1. Now, the naive version of two-phase commit, requires doing this on all switches at once, which essentially doubles the rule space requirements since we have to store the rules for both P1 and P2. We can limit the scope of the two phase commit by only applying this mechanism on switches that involve the affected portions of the traffic or the affected portions of the topology.
So here’s a quick quiz. What types of problems can arise from inconsistent applications of writing policy? Inability to respond to failures, forwarding loops, a flood of traffic at the controller, or security policy violations?
Inconsistent writes can result in forwarding loops or security policy violations where traffic ends up going to parts of the network where it shouldn’t go as a result of inconsistent switch state. The ability to respond to failures is orthogonal to consistency. A flood of traffic at the controller technically involves problems with reading state in a consistent fashion. But since there also involves a step where the controller writes state to the switches while packets are still arriving at the controller, I would consider that answer to be correct as well.
What are some approaches to coping with inconsistency? Running different controllers for different switches? Keeping a “hot spare” replica that has a complete view of the network state? Keeping the old and new state on the routers and switches and switching over only when all of the switches have received the new state? Or relying on the routers themselves to resolve the conflict?
In this case, there is only one correct answer, which is keeping the old and new state on the routers and switches. This is the two-phase commit approach that we talked about. Running different controllers for different switches could obviously result in an inconsistent state, since each of those controllers may be making independent decisions. Keeping a “hot spare” replica does no good if the replica also writes state inconsistently to the network. And resolving conflicts on the routers also doesn’t work because no router has a complete view of the network state.
Let’s now talk about an application of software defined networking, which is network virtualization. So we’ll talk first about what network virtualization is, then we’ll talk about how it’s implemented, and then we’ll talk about some examples and applications, such as Mininet.
So network virtualization is simply an abstraction of the physical network, where multiple logical networks can be run on the same underlying shared physical substrate. For example, a logical network might map a particular network topology onto the underlying physical topology. And there might be multiple logical networks that map onto the same physical topology. And these logical networks might actually share nodes and links in the underlying physical typology, but each logical network has its own view as if it were running its own private version of the network. Now you can see from this picture that the nodes in the physical network need to be shared or sliced. So the nodes in the physical topology might be virtual machines. Similarly, a single link in the logical topology might map to multiple links in the physical topology. The mechanism to achieve these virtual links is typically through tunneling. So a packet that’s destined from A to B in the logical topology, might be encapsulated in a packet that’s destined for node X first, before the packet is decapsulated and ultimately sent to B.
It may also be easy to understand virtual networking as an analogy to virtual machines, which you may be familiar with already. So in a virtual machine environment, we have virtual machines where a hypervisor arbitrates access to the underlying physical resources, providing to each virtual machine the illusion that it’s operating on its own dedicated version of the hardware. Similarly, with virtual networking, a network hypervisor of sorts arbitrates access to the underlying physical network to multiple virtual networks, providing the illusion that each virtual network actually has its own dedicated physical network.
One of the main motivations for the rise of virtual networking was the “ossification” of the internet architecture. In particular because the internet protocol was so pervasive, it made it very difficult to make fundamental changes to the way the underlying internet architecture operated. There was a lot of work on overlay networks in the 2000’s, but one size fits all network architectures were very difficult to deploy. So rather than try to replace existing network architectures, network virtualization was intended to allow for easier evolution. In other words, network virtualization enables evolution because we didn’t have to pick a winner for a replacement for IP. We could instead let multiple architectures exist in parallel. Now, this was sort of a green field view of why virtual networking was potentially a good idea. In practice, network virtualization has really taken off in multi-tenant data centers where there may be multiple tenants or applications running on a shared cluster of servers. Well known examples of this include Amazon’s EC2, Rack Space, and things like Google App Engine. Large service providers such as Google, Yahoo, and so forth, also use network virtualization to adjust the resources that are devoted to any particular service at a given time.
So what are the motivations for network virtualization or virtual networks that we’ve discussed? Easier troubleshooting? Facilitating research and evolution by allowing coexistence of production networks with experimental ones? Better forwarding performance? And adjusting the resources of the network as demands change? Please check all that apply.
As we discussed, virtual networks can facilitate research in evolution by allowing experimental networks to coexist with production networks. Because the networks are virtual, they can be scaled up and down, adjusting the resources that are devoted to any one particular service as demands change. We discuss this in the context of production networks, such as Google and Yahoo. Virtual networks are not inherently easier to troubleshoot, nor do they necessarily provide better forwarding performance. In fact, forwarding performance may be worse, due to the additional level of indirection that has been added.
Some of the promised benefits of Network Virtualization are more rapid innovation since innovation can proceed at the rate at which software evolves rather than on hardware development cycles, allowing for new forms of network control and potentially simplifying programming. It is important to make a distinction between Network Virtualization and Software-Defined Networking. Network Virtualization is arguably one of the first killer applications for SDN. And in some sense, SDN is a tool for implementing Network virtualization. But the two are not one and the same. Remember the defining tenant of SDN is the separation of the data and control plane, whereas the defining tenant of Network virtualization is to separate the underlying physical network from the logical networks that lie on top of it. So SDN can be used to simplify many aspects of Network virtualization. But it does not inherently obstruct the details of the underlying physical network.
So which of the following are characteristics of network virtualization, but not necessarily characteristics of SDN? Be careful, the distinction between SDN and virtual networks was as we discussed in the previous part of this lesson. Allowing multiple tenants to share underlying physical infrastructure? Controlling behavior from a logically centralized controller? Separating logical and physical networks? Or separating data and control planes? Again, please feel free to check all that apply.
Network virtualization can allow multiple tenants to share the underlying physical infrastructure. And it also separates logical and physical networks. The other two options are defining characteristics of software defined networking, but not of network virtualization.
So virtual networks have various design goals. It should be flexible, able to support different topologies, routing, and forwarding architectures, and independent configurations. They should be manageable. In other words, they should separate the policy that a network operator is trying to specify from the mechanisms of how those policies are implemented. They should be scalable, maximizing the number or coexisting virtual networks. They should be secure by isolating the different logical networks from one another. They should be programmable, and they should be heterogeneous in the sense that they should support different technologies.
So virtual networks have two components, nodes and edges. The physical nodes themselves must be virtualized. One possible way of virtualizing a node is a virtual machine. A more lightweight way of virtualizing a node is using a virtual environment such as a VServer or a Jail. The hypervisor, or whatever technology is enabling the virtual environment, can effectively slice the underlying physical hardware to provide the illusion of multiple guest nodes or multiple virtual nodes. Examples of node virtualization include virtual machine environments such as Xen or VMware, or what’s called OS level virtualization or virtual environments, such as Linux Vservers. Now, in a virtual network, we need to connect these virtual machines. Each virtual machine or virtual environment has its own view of the network stack. And we may want to provide the appearance that these nodes are connected to one another over a Layer two topology, even if they are in fact separated by multiple IP hops. One possible way of doing that is to encapsulate the Ethernet packet as it leaves the VM on the left in an IP packet. The IP packet can then be destined for the IP address of the machine on the right, and when the packet arrives at this machine, the host can decapsulate the packet and pass the original Ethernet packet to the VM or the virtual environment that’s residing on that physical node. Each one of these physical hosts may, in fact, host multiple virtual machines or virtual environments, which creates the need for a virtual switch that resides on a physical host. This virtual switch provides the function of networking virtual machines together over a virtual layer two topology. The Linux bridge is an example of a software switch that can perform this type of function. Open Vswitch is another example of software that performs this type of glue function. You can see more information about Open Vswitch on the URL provided here.
The Mininet tool we have been using in the course is actually an example of network virtualization. We are in fact running an entire virtual network on your laptop. When you start Mininet using the MN script, each host in the virtual network is a bash process with its own network name space. A network name space is kind of like a virtual machine except it’s a lot more lightweight. It’s in fact called OS Level Virtualization. So, each one of these virtual nodes has its own view of the network stack as shown here with these interfaces. But it has a shared file system and it’s not, in fact, running its own independent virtual machine. The root namespace manages the communication between these distinct virtual nodes, as well as the switch that connects these nodes in the topology that you set up. Virtual Ethernet pairs are assigned two name spaces. For example, S1 eth1 is assigned to an interface in H2’s network name space. And S1 eth2 is assigned to a network name space in H3’s virtual network name space. The OpenFlow switch effectively performs forwarding between the interfaces in the root name space. But because the interfaces are paired, we get the illusion of sending traffic between h2 and h3. When we make modifications to the OpenFlow switch via the controller, we’re in fact doing that in the root name space.
In summary, virtual networks facilitate flexible, agile deployment, by enabling rapid innovation at the pace of software, vender independence, and scale. We talked about the distinction between SDN’s and virtual networks, as well as various technologies that enable virtual networks, such as virtual machines for creating virtual nodes and tunneling for creating virtual links.
In this lesson we’ll talk about the why and how of programming SDNs. Unfortunately, programming OpenFlow is not easy. It offers only a very low level of abstraction in the form of match action rules. The controller only sees events that switches don’t know how to handle, and there can be race conditions if switch level rules are not installed properly, as we’ve already seen in the lesson on consistent updates
The solution to this is to provide some kind of northbound API, which is a programming interface that allows applications and other kinds of orchestration systems to program the network. So where we have at the low-level the controller updating state in the switch using OpenFlow flow modification rules, we may have applications or orchestration systems that need to perform more sophisticated tasks, such as path computation, loop avoidance, and so forth. But we need a higher-level programming interface that allows these applications to talk to the controller so the application isn’t writing low-level OpenFlow rules, but rather is expressing what it wants to have happen in terms of higher-level behaviors without regard to such things as whether or not the rules are being installed in a consistent, and correct fashion. Various people may write these applications including network operators, service providers, researchers, and really anyone who wants to develop capabilities on top of OpenFlow. The benefits of such a northbound API are vendor independence, as well as the ability to quickly modify or customize control through various popular programming languages. The idea is that these applications might be written in high-level programming languages, such as Python, and wouldn’t actually have to perform low-level switch modifications, but rather could express policies in terms of much higher-level abstractions. Examples of such applications include the implementation of a large virtual switch abstraction, security applications, and services that may need to integrate traffic processing with middle boxes. This programmatic interface is called the northbound API and currently there’s no standard for the northbound API, as there is for the southbound API in OpenFlow. But we’ll look at various APIs in programming languages that each compile to OpenFlow rules that are installed on switches across the network.
One example of a programming language that sits on top of such a north-bound API is Frenetic, which is an SQL-like query language. For example, Frenetic would allow a programmer to count the number of bytes, grouped by destination Mac address, and report the updates to these counters every 60 seconds. The “group by” statement allows a grouping of counts by the destination mac address. “Where” allows restrictions to only count traffic coming from a web server coming in on a particular port, and” every” specifies that the results of this query should only be returned every 60 seconds. More information about Frenetic is available at frenetic- lang.org. And in the course, we’ll actually going to use a language called Pyretic that is based on the same underlying theory as Frenetic, except that it’s embedded in Python.
One issue with programming at this higher level of abstraction is that an operator might write multiple modules, each of which effects the same traffic. For example, an operator might write an application that monitors traffic. Another one that specifies how routing should take place, another that involves the specification of firewall rules And yet another that balances traffic load across the links in the network. Ultimately, all of these applications, or modules, must be combined into a single set of OpenFlow rules that together achieve the network operator’s overall goal. For this, we need composition operators, or ways that specify how these individual modules should be combined or composed into a single coherent application. Let’s now talk about two different ways to compose policies.
One way of composing policies is to perform both operations simultaneously. For example, one might want to forward traffic but also count how much traffic is being forwarded. Both of those operations can be performed in parallel. Another way of composing policies is in sequence. Sequential composition performs one operation then the next. For example, we might want to implement a firewall, and whatever traffic makes it though the firewall might then be subjected to the switching policy.
One example of sequential composition, might be a load balancer. In this example, a policy might take some traffic coming from half of the source IP addresses and rewrite that to one server replica, and take the other half of the traffic and rewrite it to the other replica. After the load balancer rewrites the destination IP address, we need a routing module to forward the traffic out the appropriate port on the switch. In this case, we’ve used sequential composition to first apply a load balance policy that rewrites the destination IP address based on the source IP address where the traffic is coming from and sequentially apply a routing policy that forwards the traffic out the appropriate port depending on the resulting destination IP address after that rewrite has taken place. Notice that we can use predicates to specify which traffic traverses which modules. Those predicates can apply specific actions based on things like the input port and the packet header fields. The ability to compose policies in this fashion allows each module to partially specify functionality without having to write the policy for the entire network. This leaves some flexibility so that one module can implement a small bit of the network function, leaving some functions for other modules. This also allows for module re-use, since a module need not be tied to a particular network setting. For example, in this particular example where we’ve applied that load balancer followed by routing, the load balancer spreads traffic across the replicas without regard to the underlying network paths that traffic takes once those destination IP addresses are rewritten.
In summary, we’ve covered two concepts. One is the notion of a Northbound API, which sits on top of an SDN controller and provides and exposes higher level abstractions that allows the operator or programmer to write policies without regard to how OpenFlow rules eventually get installed. We’ve also talked about two different composition operators. Parallel composition and sequential composition, which specify how individual simpler policies can be composed to implement more complex network applications, thus allowing different SDN control programs to independently perform tasks on the same traffic.
In this lesson we will look at Pyretic which is an SDN language and run time that implements some of the composition operators that we discussed in the last lesson. The language is a way of expressing these high level policies, and the run time provides the function of compiling these policies to the OpenFlow rules that eventually are installed on the switches. One key abstraction in Pyretic is the notion of located packets, the idea that we can apply a policy based on a packet and its location in the network, such as the switch at which that packet is located or the port on which that packet arrives.
Pyretic offers several features. The first is the ability to take as input a packet and then return packets at different locations in the network. This effectively allows the implementation of network policy as a function that simply takes packets and returns other packets at different locations. The second feature of Pyretic is the notion of Boolean predicates. Unlike OpenFlow rules which do not permit the expression of simple conjunctions such as AND and OR, or negations like NOT, Pyretic allows the expressions of policies in terms of these predicates. Pyretic also provides the notion of virtual package header fields. Which allows the programmer to refer to packet locations and also to tag packets so that specific functions can be applied at different portions of the program. Pyretic also provides composition operators, such as parallel and sequential composition, which we discussed in the last lesson. The notion of network policy as a function contrasts with the OpenFlow style of programming. In OpenFlow, policies are simply bit patterns, in other words, match statements for which matching packets are subject to a particular action. These types of policies can be particularly difficult to reason about. In contrast, in Pyretic, policies are functions that map packets to other packets. Some example functions in Pyretic include the identify function, which returns the original packet; none or drop, which returns the empty set; match, which returns the identity if the field f matches the value v and returns none or drop otherwise; mod, which returns the same packet with the field f set to v; forward, which is simply syntactic sugar on mod to say that the output port field in the packet should be modified to the parameter specified; and flood, which returns one packet for each port on the network spanning tree. In OpenFlow, packets either match on a rule or they simply fall through to the next rule. So, OR, NOT, etc, can be tough to reason about. In contrast, Pyretic’s match function outputs either the packet or nothing, depending on whether the predicate is satisfied. For example, we could apply a match statement that says match destination IP equals 10.0.0.3. and this function would take packets as input and only return packets that satisfy this particular predicate. In addition to the standard packet header fields, Pyretic offers the notion of virtual packet header fields, which is a unified way of representing packet metadata. In Pyretic, the packet is nothing more than a dictionary that maps a field name such as the destination IP address to a value. Now, these field names could correspond to fields in an actual packet header. But they can also be virtual. For example, we could provide a match statement based on a switch, indicating that we only want to return packets that are located at a particular switch or on the input port, indicating that we only want to see packets whose attributes match a particular input port. The match function matches on this packet meta-data and the mod function can modify this meta-data.
Pyretic enables the notion of both sequential and parallel composition as we’ve discussed in previous lessons. For example, we could match all packets for a particular destination IP address and send them or forward them out a particular output port. The double greater than sign shown here is the way of expressing sequential composition in Pyretic. Parallel composition allows two policies to be applied in parallel. In this example, we match on a particular destination IP address and subsequently forward the traffic out Output Port one. In Parallel, we apply a different set of policies that match on a different source IP address and output the packets on a different output port. In Pyretic, the plus operator performs parallel composition of policies.
Pyretic allows an operator to construct queries which allow the program to see packet streams. For example, the packets query allows the operator to see packets arriving at a particular switch with a particular source MAC address. The one parameter here indicates that we only want to see the first packet that arrives with a unique source MAC address and switch. We can then register callbacks for these packet streams that are invoked to handle each new packet that arrives for that query.
Dynamic policies are policies whose forwarding behavior can change. They are represented as a time series of static policies. The current value of the policy at any time is self dot policy. A common programming idiom in Pyretic is to set a default policy and then register a call back that updates that policy. In the assignment, you will create a similar topology that you created in the pox assignment, but we will now use pyretic to implement a simple switch and firewall. In pyretic every first packet with a new source MAC address at the switch is read by a query. The policy is updated with a new predicate every time a new mapping between a MAC address and an output port is learnt. In the assignment, you also create a dynamic firewall policy, register a callback to check the rules, and sequentially compose your firewall policy with a learning switch, thus provided as part of the pyritic distribution.
In summary, pyretic allows operators to write complex network policies as functions. It allows an operator to express predicates on packets including things such as AND, OR, and NOT. It provides the capability to specify and modify virtual packet headers as packet metadata, and it provides ways to compose complex network policies from simpler independent ones.
As a quiz, which of the following is the appropriate pyretic rule for sending traffic from source IP address 10.0.0.1 to destination at IP address 10.1.2.3, and traffic from source IP address 10.0.0.2 to destination IP address 10.2.3.4
The following policy matches the appropriate source IP addresses and forwards to the corresponding output destination IP address. Each of these matching and forwarding operations can happen in parallel.