Sustainable and cost-effective long-term storage remains an unsolved problem. The most widely used storage technologies today are magnetic (hard disk drives and tape). They use media that degrades over time and has a limited lifetime, which leads to inefficient, wasteful, and costly solutions for long-lived data. This article presents Silica: the first cloud storage system for archival data underpinned by quartz glass, an extremely resilient media that allows data to be left in situ indefinitely. The hardware and software of Silica have been co-designed and co-optimized from the media up to the service level with sustainability as a primary objective. The design follows a cloud-first, data-driven methodology underpinned by principles derived from analyzing the archival workload of a large public cloud service. Silica can support a wide range of archival storage workloads and ushers in a new era of sustainable, cost-effective storage.

Sustainable and cost-effective long-term storage remains an unsolved problem. The most widely used storage technologies today are magnetic (hard disk drives and tape). They use media that degrades over time and has a limited lifetime, which leads to inefficient, wasteful, and costly solutions for long-lived data. This paper presents Silica: the first cloud storage system for archival data underpinned by quartz glass, an extremely resilient media that allows data to be left in situ indefinitely. The hardware and software of Silica have been co-designed and co-optimized from the media up to the service level with sustainability as a primary objective. The design follows a cloud-first, data-driven methodology underpinned by principles derived from analyzing the archival workload of a large public cloud service. Silica can support a wide range of archival storage workloads and ushers in a new era of sustainable, cost-effective storage.

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.

Large-scale network services can consist of tens of thousands of machines running thousands of unique software configurations spread across hundreds of physical networks. Testing such services for complex performance problems and configuration errors remains a difficult problem. Existing testing techniques, such as simulation or running smaller instances of a service, have limitations in predicting overall service behavior at such scales. Testing large services should ideally be done at the same scale and configuration as the target deployment, which can be technically and economically infeasible. We present DieCast , an approach to scaling network services in which we multiplex all of the nodes in a given service configuration as virtual machines across a much smaller number of physical machines in a test harness. We show how to accurately scale CPU, network, and disk to provide the illusion that each VM matches a machine in the original service in terms of both available computing resources and communication behavior. We present the architecture and evaluation of a system we built to support such experimentation and discuss its limitations. We show that for a variety of services---including a commercial high-performance cluster-based file system---and resource utilization levels, DieCast matches the behavior of the original service while using a fraction of the physical resources.

This work explores the viability and benefits of time dilation - providing the illusion to an operating system and its applications that time is passing at a rate different from real time. For example, we may wish to convince a system that for every 10 seconds of wall clock time, only one second of time passes in the host's dilated time frame. This enables external stimuli to appear to take place at higher rates than would be physically possible. For example, a host dilated by a factor of 10 receiving data from a network interface at a real rate of 1-Gbps believes it is receiving data at 10-Gbps.