Результаты тестирования масштабируемости облачных сервисов

The focus of the work is on transaction processing (i.e., read and update work-loads), rather than analytics workloads. We used the TPC-W, a standardized benchmark simulating a Web-shop, as the baseline for our comparison. The TPC-W defines that users are simulated through emulated browsers (EB) and issue page requests, called web-interactions (WI), against the system. As a major modification to the benchmark, we constantly increase the load from 1 to 9000 simultaneous users to measure the scalability and cost variance of the system. Figure 1 shows an overview of the different combinations of services we tested in the benchmark.
The main results are shown in Figure 2 and Table 1 — 2 and are surprising in several ways. Most importantly, it seems that all major vendors have adopted a different architecture for their cloud services (e.g., master-slave replication, partitioning, distributed control and various combinations of it). As a result, the cost and performance of the services vary significantly depending on the workload. A detailed description of the architectures is provided in the paper. Furthermore, only two architectures, the one implemented on top of Amazon S3 and MS Azure using SQL Azure as the database, were able to scale and sustain our maximum workload of 9000 EBs, resulting in over 1200 Web-interactions per second (WIPS). MySQL installed on EC2 and Amazon RDS are able to sustain a maximum load of approximate 3500 EBs. MySQL Replication performed similar to MySQL standalone with EBS, so we left it off the picture. Figure 1 shows that the WIPS of Amazon’s SimpleDB grow up to about 3000 EBs and more than 200 WIPS. In fact, SimpleDB was already overloaded at about 1000 EBs and 128 WIPS in our experiments. At this point, all write requests to hot spots failed. Google AppEngine already dropped out at 500 emulated browsers with 49 WIPS. This is mainly due to Google’s transaction model not being built for such high write workloads. When implementing the benchmark, our policy was to always use the highest offered consistency guarantees, which come closest to the TPC-W requirements. Thus, in the case of AppEngine, we used the offered transaction model inside an entity group. However, it turned out, that this is a big slow-down for the whole performance. We are now in the process of re-running the experiment without transaction guarantees and curios about the new performance results.