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Cloud-management stacks have become an increasingly important element in cloud computing, serving as the resource manager of cloud platforms. While the functionality of this emerging layer has been constantly expanding, its fault resilience remains under-studied. This paper presents a systematic study of the fault resilience of OpenStack-a popular open source cloud-management stack. We have built a prototype fault-injection framework targeting service communications during the processing of external requests, both among OpenStack services and between OpenStack and external services, and have thus far uncovered 23 bugs in two versions of OpenStack. Our findings shed light on defects in the design and implementation of state-of-the-art cloudmanagement stacks from a fault-resilience perspective.

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Abstract-As use of location-based services (LBSs) is becoming increasingly prevalent, mobile users are more and more enticed to reveal their locations, which may be exploited by attackers to infer the points of interest (POIs) the users visit and then their privacy information. We propose a novel approach to the protection of a user's location privacy based on unobservability, preventing the attackers from relating any particular POI to the user's current location. We design, implement, and evaluate a privacyprotection system, called the Location Information ScrAmbler (LISA) which protects the user's location privacy by adjusting the location noise and hence, the uncertainty of associating his location with any POI, while conserving resources (especially battery energy) on mobile devices. By protecting location privacy locally on each mobile user's device, LISA eliminates the reliance on the trusted third-party servers required by most existing approaches. Therefore, it not only avoids the vulnerability of a single point of failure, but also facilitates the deployment of LBSs. Our evaluation of LISA using real-world users' traces demonstrates its efficacy and efficiency.