Existing backup systems are unsatisfactory: commercial backup services are reliable but expensive while peer-to-peer systems are cheap but offer limited assurance of data reliability. This article introduces Friendstore, a system that provides inexpensive and reliable backup by giving users the choice to store backup data only on nodes they trust (typically those owned by friends and colleagues). Because it is built on trusted nodes, Friendstore is not burdened by the complexity required to cope with potentially malicious participants. Friendstore only needs to detect and repair accidental data loss and to ensure balanced storage exchange. The disadvantage of using only trusted nodes is that Friendstore cannot achieve perfect storage utilization. Friendstore is designed for a heterogeneous environment where nodes have very different access link speeds and available disk spaces. To ensure long-term data reliability, a node with limited upload bandwidth refrains from storing more data than its calculated maintainable capacity. A high bandwidth node might be limited by its available disk space. We introduce a simple coding scheme, called XOR(1,2), which doubles a node's ability to store backup information in the same amount of disk space at the cost of doubling the amount of data transferred during restore. Analysis and simulations using long-term node activity traces show that a node can reliably back up tens of gigabytes of data even with low upload bandwidth.

Most existing large-scale networked systems on the Internet such as peer-to-peer systems are vulnerable to Sybil attacks where a single adversary can introduce many bogus identities. One promising defense of Sybil attacks is to perform social-network based admission control to bound the number of Sybil identities admitted. SybilLimit, the best known Sybil admission control mechanism, can restrict the number of Sybil identities admitted per attack edge to O(log n) with high probability assuming O(n/ log n) attack edges. In this paper, we propose Gatekeeper, a decentralized Sybil-resilient admission control protocol that significantly improves over SybilLimit. Gatekeeper is optimal for the case of O(1) attack edges and admits only O(1) Sybil identities (with high probability) in a random expander social networks (real-world social networks exhibit expander properties). In the face of O(k) attack edges (for any k ∈ O(n/ log n)), Gatekeeper admits O(log k) Sybils per attack edge. This result provides a graceful continuum across the spectrum of attack edges. We demonstrate the effectiveness of Gatekeeper experimentally on real-world social networks and synthetic topologies.

We present Gatekeeper, a decentralized protocol that performs Sybil-resilient node admission control based on a social network. Gatekeeper can admit most honest nodes while limiting the number of Sybils admitted per attack edge to O(log k), where k is the number of attack edges. Our result improves over SybilLimit [3] by a factor of log n in the face of O(1) attack edges. Even when the number of attack edges reaches O(n/ log n), Gatekeeper only admits O(log n) Sybils per attack edge, similar to that achieved by SybilLimit.

Current businesses rely heavily on efficient access to their databases. Manual tuning of these database systems by performance experts is increasingly infeasible: For small companies, hiring an expert may be too expensive; for large enterprises, even an expert may not fully understand the interaction between a large system and its multiple changing workloads. This trend has led major vendors to offer tools that automatically and dynamically tune a database system. Many database tuning knobs concern the buffer pool for caching data and disk pages. Specifically, these knobs control the buffer allocation and thus the cache miss probability, which has direct impact on performance. Previous methods for automatic buffer tuning are based on simulation, black-box control, gradient descent, and empirical equations. This article presents a new approach, using calculations with an analytically-derived equation that relates miss probability to buffer allocation; this equation fits four buffer replacement policies, as well as twelve datasets from mainframes running commercial databases in large corporations. The equation identifies a buffer-size limit that is useful for buffer tuning and powering down idle buffers. It can also replace simulation in predicting I/O costs. Experiments with PostgreSQL illustrate how the equation can help optimize online buffer partitioning, ensure fairness in buffer reclamation, and dynamically retune the allocation when workloads change. It is also used, in conjunction with DB2's interface for retrieving miss data, for tuning DB2 buffer allocation to achieve targets for differentiated service.