Quantifying the performability of cluster-based services

Abstract

Abstract-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.

Automatic configuration of internet services

“…performance, availability, performability [12], as the system evolves. In particular, common changes that are made to services over their lifetimes, such as increasing the amount of memory per node, adding new nodes, or replacing a pair of expensive database systems (a primary and a hot backup) with multiple cheaper computers, may cause the current configuration settings to behave poorly with respect to the metric.…”

Peer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks

“…This rating mechanism entirely decoupled the credibility assessment of the evaluations from the ranking itself, which makes it very robust against collusion attacks as well as random and biased raters. A twophase methodology was proposed in [15] for systematically evaluating the performance and availability of cluster-based Internet services. A service rating scheme that is robust against manipulations by malicious users and services was proposed in [16].…”

System-of-Systems to Support Mobile Safety Critical Applications: Open Challenges and Viable Solutions

“…An effective approach in facing dynamicity and evolution consists in combining modeling and experimentation (e.g., [11], [14]). We advocate the need of a dynamic model generation (and evaluation) process, capable to dynamically produce at run-time different models representing the current system state and conditions, and capable to feed the models' parameters with values coming from monitoring and experimental evaluation activities.…”