Modelling distributed stream processing topologies⌗
Distributed Stream Processing Systems (DSPS) take a stream of real time data (often arriving at high speed and large volume) and pass it through a series of operators. These operators perform tasks on the packets of data (often called “tuples” in the literature). These tasks can involve splitting large tuples into smaller ones, counting tuples with particular characteristics (counting hashtags in tweets for example) or combining them with other information from external systems (like databases). Operators can then create new tuples and pass them on to operators further along the topology (downstream). Figure 1 shows an example topology.
DSPS provide ways to spread these operators across several machines in a cluster. Each operator can be replicated multiple times in order to handle a large arrival workload. In the topology in Figure 1, operator B takes a long time to process tuples, so there are 3 copies of it to ensure that it does not become a bottle neck. In this way DSPS topologies can be tuned or scaled to ensure they meet throughput (tuples processed per second) or end-to-end latency (the time between a tuple entering the topology and the last tuple resulting from its entry leaving) targets. Conversely, if the input workload into the topology drops then copies of operators can be removed (scaled down) to save on cluster machine resources.
Figure 2 shows an example of how the various copies of the operators from the topology in Figure 1 could be laid out on the cluster. The process of deciding where to place each copy of the various operators is called scheduling (in Heron this is called packing) and the resulting plan of operators they produce is called a physical plan.
Most modern DSPS, such as Apache Storm and Apache Heron, have the ability to easily scale up or scale down the operators of a topology and re-schedule the physical plan. Many scheduling algorithms are available for these systems (each based on optimising for certain criteria) and even more are represented in the literature.
Most DSPS provide the ability to scale their topologies to meet varying demand. However, as far as I am aware, of all the mainstream DSPS only Heron provides a method to do this scaling automatically. This system, Dhalion, is available in the latest Heron version but has not yet been deployed in a production environment.
As well as Dhalion, there are many examples from the research community of attempts to create automatic scaling systems for DSPS. These are usually based on optimising schedulers that aim to minimise certain criteria, such as the network distance between operators that communicate large tuples or very high volumes of tuples or to ensure no worker node is overloaded by operators that require a lot of processing resources. While the new physical plans these schedulers produce may be optimal compared to the current cluster operator layout, none of these systems assess weather the physical plan they produce will actually result in a topology that is capable of meeting a performance target.
If a modelling system was available that could assess a proposed physical plan and predict its likely performance before it is deployed, an auto-scaling system could iterate to a resource and operator layout plan that is likely to meet a given performance target.
This would reduce the overhead from continually deploying a new physical plan (something which can take significant time for the system to complete and stabilise) and assessing its performance, only to have to re-schedule when it doesn’t meet the requirements.
What’s more, a modelling system would allow several different plans to be assessed in parallel. This means that schedulers optimised for different criteria could be compared simultaneously, which would remove the need for a DSPS user to know before hand which scheduler is best for their particular use case.
A further advantage of a modelling system for DSPS topologies is that of pre-emptive scaling. If the model can accept a prediction of future workload (number of tuples entering the topology) then the auto-scaling system, where paired with a future workload prediction technique, would be able to assess if a future workload will be an issue for the current cluster layout. The system could then use the future workload level to find a physical plan that is likely to meet the performance requirements and deploy this plan before the predicted workload arrives.
The aim of my PhD research is to create the modelling system, described above, for DSPS topologies. The test bed for this research is the Apache Storm DSPS.
I will use approaches from queuing theory, graph analysis and other areas to create a system capable of modelling the performance of a proposed physical plan from one or more scheduling algorithms.
The aim will be to create a topology model that can accept a predicted incoming workload and physical plan. The model will then use these, along with metrics from recorded from the topology in its current configuration, to estimate end-to-end latency, throughput and other attributes of the plan such as queue overloads for operators. I also hope to incorporate modelling of resource usage on the host cluster such that proposed plan can be rejected if they are likely to lead to overloading of worker nodes.
The final modelling system will be combined with Storm’s native rebalance function to automatically assess proposed physical plans and add resource to cluster if a plan is unlikely to meet a given performance target. In this way it is hoped that with only several minutes of running data this system could create a reasonable configuration to meet a given performance target without user intervention. Figure 3 show a flow diagram of proposed auto-scaling system.