Social media services and applications enable billions of users to share an unprecedented amount of social information, which is further augmented by location and collocation information from mobile phones, and can be aggregated to provide an accurate digital representation of the social world. This dissertation argues that extracted social knowledge from this wealth of information can be embedded in the design of novel distributed, socially-aware applications and services, consequently improving system response time, availability and resilience to attacks, and reducing system overhead. To support this thesis, two research avenues are explored. First, this dissertation presents Prometheus, a socially-aware peer-to-peer service that collects social information from multiple sources, maintains it in a decentralized fashion on user-contributed nodes, and exposes it to applications through an interface that implements non-trivial social inferences. The system's socially-aware design leads to multiple system improvements: 1) it increases service availability by allowing users to manage their social information via socially-trusted peers, 2) it improves social inference performance and reduces message overhead by exploiting naturally-formed social groups, and 3) it reduces the opportunity of attackers to influence application requests. These performance improvements are assessed via simulations and a prototype deployment on a local cluster and on a worldwide testbed (PlanetLab) under emulated application workloads. Second, this dissertation defines the projection graph, the result of decentralizing a social graph onto a peer-to-peer system such as Prometheus, and studies the system's network properties and how they can be used to design more efficient socially-aware distributed applications and services. In particular: 1) it analytically formulates the relation between centrality metrics such as degree centrality, node betweenness centrality, and edge betweenness centrality in the social graph and in the emerging projection graph, 2) it experimentally demonstrates on real networks that for small groups of users mapped on peers, there is high association of social and projection graph properties, 3) it shows how these properties of the (dynamic) projection graph can be accurately inferred from the properties of the (slower changing) social graph, and 4) it demonstrates with two search application scenarios the usability of the projection graph in designing social search applications and unstructured P2P overlays. These research results lead to the formulation of lessons applicable to the design of socially-aware applications and distributed systems for improved application performance such as social search, data dissemination, data placement and caching, as well as for reduced system communication overhead and increased system resilience to attacks.