background transfers transfers of data that humans are not waiting for to improve availability, reliability, latency or consistency. However, given the rapid fluctuations of available network bandwidth and changing resource costs due to technology trends, hand tuning the aggressiveness of background transfers risks (1) complicating applications, (2) being too aggressive and interfering with other applications, and (3) being too timid and not gaining the benefits of background transfers. Our goal is for the operating system to manage network resources in order to provide a simple abstraction of near zero-cost background transfers. Our system, TCP Nice, can provably bound the interference inflicted by background flows on foreground flows in a restricted network model. And our microbenchmarks and case study applications suggest that in practice it interferes little with foreground flows, reaps a large fraction of spare network bandwidth, and simplifies application construction and deployment. For example, in our prefetching case study application, aggressive prefetching improves demand performance by a factor of three when Nice manages resources; but the same prefetching hurts demand performance by a factor of six under standard network congestion control.

The emerging edge services architecture promises to improve the availability and performance of web services by replicating servers at geographically distributed sites. A key challenge in such systems is data replication and consistency so that edge server code can manipulate shared data without incurring the availability and performance penalties that would be incurred by accessing a traditional centralized database. This paper explores using a distributed object architecture to build an edge service system for an e-commerce application, an online bookstore represented by the TPC-W benchmark. We take advantage of application specific semantics to design distributed objects to manage a specific subset of shared information using simple and effective consistency models. Our experimental results show that by slightly relaxing consistency within individual distributed objects, we can build an edge service system that is highly available and efficient. For example, in one experiment we find that our object-based edge server system provides a factor of five improvement in response time over a traditional centralized cluster architecture and a factor of nine improvement over an edge service system that distributes code but retains a centralized database.

The emerging edge services architecture promises to improve the availability and performance of web services by replicating servers at geographically distributed sites. A key challenge in such systems is data replication and consistency, so that edge server code can manipulate shared data without suffering the availability and performance penalties that would be incurred by accessing a traditional centralized database. This article explores using a distributed object architecture to build an edge service data replication system for an e-commerce application, the TPC-W benchmark, which simulates an online bookstore. We take advantage of application specific semantics to design distributed objects that each manages a specific subset of shared information using simple and effective consistency models. Our experimental results show that by slightly relaxing consistency within individual distributed objects, our application realizes both high availability and excellent performance. For example, in one experiment we find that our object-based edge server system provides five times better response time over a traditional centralized cluster architecture and a factor of nine improvement over an edge service system that distributes code but retains a centralized database.

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My research has focused on constructing robust large-scale distributed systems. The bulk of this work can be understood in the context of two intertwined efforts: constructing cooperative and peer-to-peer services and understanding the fundamental principles of large-scale data replication. Cooperative and peer-to-peer services Cooperative and peer-to-peer services both seek to provide a way to scale services beyond what can be provided by even a high-end server machine. Cooperative services do this by treating a service as a parallel program and then running the program across a cluster of machines. Key challenges include rearchitecting services not only to provide good performance by balancing parallelism and locality but also to ensure good reliability and simple management. Peer-to-peer services go further and enlist machines controlled by different users to collectively provide a service to each other. In addition to the problems of cooperative services, peer-to-peer services have to cope with new issues of trust that arise when a service runs across machines spanning multiple administrative domains with limited trust or competing interests. Some highlights of this stream of work include • Serverless file systems and cooperative caching. We constructed xFS [ADN + 96] to explore an extreme point in the design space of constructing file systems as parallel programs: xFS’s goals included