This is due to the impact of error margins between simulated and real requests on the stress test’s structure, and the potential pollution of backend stores of data. Relatively easy, query simulations can be produced through open source or commercial tools like Apache Bench, Webbench, http_load, Apache Jmeter, and LoadRunner, and are better performed on unlaunched or low-traffic systems. Each of these methods fit the needs of specific scenarios, but are also accompanied by certain drawbacks. Per-system machine stress testing can be achieved in four ways: request simulation, request replication, request forwarding, and load balancing adjustment.

Traffic control - test-running thresholds to divert traffic and prevent shutdowns Per-system Machine Capacity Estimation.

Full-scale stress testing - site capability testing through high-volume query simulation.

Per-system machine capacity estimation - preliminary calculation of machines allocated per system.

After obtaining the projected traffic volume for the event, the remaining three stages are:

In summary, having honed capacity planning skills for Double 11 down to the minutest of details, Alibaba developed a four-stage capacity planning process in which the full-scale stress test is key to ensuring smooth operations even during high-traffic events.

Since traffic can still fluctuate unexpectedly during Double 11, the tech team also developed traffic control mechanisms to mitigate the problems that arise at peak capacity. This key step simulates the same business scenario and traffic volume as Double 11 across the whole platform, painting the team a more realistic picture of capacity requirements. To address this problem, the Alibaba tech team introduced “full-scale stress testing” as an additional stage in their capacity planning process. Since the calculation assumes that each machine functions in isolation, it neglects to consider how the system, as a whole, responds to increased traffic during Double 11. Years of experience with Double 11 events have shown Alibaba that this approach falls short of accurately predicting the number of machines required. In practice, the answer is more problematic. Adding further machines to this figure allows for a capacity buffer, based on the assumption that the figures used for the calculation are ultimately best guesses. In principle, the second question is also simple to answer - a case of determining the per-system machine capacity and then dividing the projected traffic volume by this figure to calculate the minimum number of machines required. Capacity Planning in PRINCIPLEĬapacity planning for Double 11 aims to answer two key questions: What is the projected traffic volume during the event, and how many machines will be needed to support that volume? Answering the first question is a straightforward case of using prediction algorithms and looking at historical data. Maintaining site availability in such situations requires careful planning and perseverance in problem-solving. This challenge is biggest during promotional events such as the Double 11 Global Shopping Festival, when Alibaba’s systems experience sudden and huge spikes in traffic. Spanning numerous business sectors and supported by a multitude of service systems, the scale of Alibaba’s operations poses a big challenge for the technical team when conducting capacity planning.

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