In this paper, we propose a two-phase methodology for systematically evaluating the performability (performance and availability) of cluster-based Internet services. In the first phase, evaluators use a fault-injection infrastructure to characterize the service's behavior in the presence of faults. In the second phase, evaluators use an analytical model to combine an expected fault load with measurements from the first phase to assess the service's performability. Using this model, evaluators can study the service's sensitivity to different design decisions, fault rates, and other environmental factors. To demonstrate our methodology, we study the performability of a multitier Internet service. In particular, we evaluate the performance and availability of three soft state maintenance strategies for an online bookstore service in the presence of seven classes of faults. Among other interesting results, we clearly isolate the effect of different faults, showing that the tier of Web servers is responsible for an often dominant fraction of the service unavailability. Our results also demonstrate that storing the soft state in a database achieves better performability than storing it in main memory (even when the state is efficiently replicated) when we weight performance and availability equally. Based on our results, we conclude that service designers may want an unbalanced system in which they heavily load highly available components and leave more spare capacity for components that are likely to fail more often.

In this paper, we evaluate the performance, availability, and combined performability of four soft state maintenance strategies in two multitier Internet services, an online book store and an auction service. To take soft state and service latency into account, we propose an extension of our previous quantification methodology, and novel availability and performability metrics. Our results demonstrate that storing the soft state in a database can achieve better performability than storing it in main memory, even when the state is efficiently replicated. Strategies that offload the handling of soft state from the database increase the load on other tiers and, consequently, increase the impact of faults in these tiers on service availability. Based on these results, we conclude that service designers need to provision the cluster and balance the load with availability and cost, as well as performance, in mind.

E-commerce services have become a promising and profitable application of the Internet. In order to keep them growing, solutions must be found to deal with unreliable connections and high latencies, among other problems. The best solutions to such problems tend to depend on the distribution of the service over the network, placing servers in multiple locations, closer to customers. If placement of servers is effective it tends to reduce delays and traffic-related costs. In this paper we discuss the distribution of e-commerce services by introducing a traffic-aware cost model and evaluating it using an actual log from an e-tailer. The results show that the model yields good placement solutions, which perform better than simpler ad-hoc solutions.