In a data center, an IO from an application to distributed storage traverses not only the network but also several software stages with diverse functionality. This set of ordered stages is known as the storage or IO stack. Stages include caches, hypervisors, IO schedulers, file systems, and device drivers. Indeed, in a typical data center, the number of these stages is often larger than the number of network hops to the destination. Yet, while packet routing is fundamental to networks, no notion of IO routing exists on the storage stack. The path of an IO to an endpoint is predetermined and hard coded. This forces IO with different needs (e.g., requiring different caching or replica selection) to flow through a one-size-fits-all IO stack structure, resulting in an ossified IO stack. This article proposes sRoute, an architecture that provides a routing abstraction for the storage stack. sRoute comprises a centralized control plane and “sSwitches” on the data plane. The control plane sets the forwarding rules in each sSwitch to route IO requests at runtime based on application-specific policies. A key strength of our architecture is that it works with unmodified applications and Virtual Machines (VMs). This article shows significant benefits of customized IO routing to data center tenants: for example, a factor of 10 for tail IO latency, more than 60% better throughput for a customized replication protocol, a factor of 2 in throughput for customized caching, and enabling live performance debugging in a running system.

PCIe-based Flash is commonly deployed to provide datacenter applications with high IO rates. However, its capacity and bandwidth are often underutilized as it is difficult to design servers with the right balance of CPU, memory and Flash resources over time and for multiple applications. This work examines Flash disaggregation as a way to deal with Flash overprovisioning. We tune remote access to Flash over commodity networks and analyze its impact on workloads sampled from real datacenter applications. We show that, while remote Flash access introduces a 20% throughput drop at the application level, disaggregation allows us to make up for these overheads through resource-efficient scale-out. Hence, we show that Flash disaggregation allows scaling CPU and Flash resources independently in a cost effective manner. We use our analysis to draw conclusions about data and control plane issues in remote storage.

In data centers, caches work both to provide low IO latencies and to reduce the load on the back-end network and storage. But they are not designed for multi-tenancy; system-level caches today cannot be configured to match tenant or provider objectives. Exacerbating the problem is the increasing number of un-coordinated caches on the IO data plane. The lack of global visibility on the control plane to coordinate this distributed set of caches leads to inefficiencies, increasing cloud provider cost.

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For over 40 years the notion of the file, as devised by pioneers in the field of computing, has been the subject of much contention. Some have wanted to abandon the term altogether on the grounds that metaphors about files can confuse users and designers alike. More recently, the emergence of the 'cloud' has led some to suggest that the term is simply obsolescent. In this paper we want to suggest that, despite all these conceptual debates and changes in technology, the term file still remains central to systems architectures and to the concerns of users. Notwithstanding profound changes in what users do and technologies afford, we suggest that files continue to act as a cohering concept, something like a 'boundary object' between computer engineers and users. However, the effectiveness of this boundary object is now waning. There are increasing signs of slippage and muddle. Instead of throwing away the notion altogether, we propose that the definition of and use of files as a boundary object be reconstituted. New abstractions are needed, ones which reflect what users seek to do with their digital data, and which allow engineers to solve the networking, storage and data management problems that ensue when files move from the PC on to the networked world of today.

The purpose of the study is to explore how chronically ill patients and their specialized care network have viewed their personal medical information privacy and how it has impacted their perspectives of sharing their records with their network of healthcare providers and secondary use organizations. Diabetes patients and specialized diabetes medical care providers in Eastern England were interviewed about their sharing of medical information and their privacy concerns to inform a descriptive qualitative and exploratory thematic analysis. From the interview data, we see that diabetes patients shift their perceived privacy concerns and needs throughout their lifetime due to persistence of health data, changes in health, technology advances, and experience with technology that affect one's consent decisions. From these findings, we begin to take a translational research approach in critically examining current privacy enhancing technologies for secondary use consent management and motivate the further exploration of both temporally-sensitive privacy perspectives and new options in consent management that support shifting privacy concerns over one's lifetime.

In data centers, the IO path to storage is long and complex. It comprises many layers or "stages" with opaque interfaces between them. This makes it hard to enforce end-to-end policies that dictate a storage IO flow's performance (e.g., guarantee a tenant's IO bandwidth) and routing (e.g., route an untrusted VM's traffic through a sanitization middlebox). These policies require IO differentiation along the flow path and global visibility at the control plane. We design IOFlow, an architecture that uses a logically centralized control plane to enable high-level flow policies. IOFlow adds a queuing abstraction at data-plane stages and exposes this to the controller. The controller can then translate policies into queuing rules at individual stages. It can also choose among multiple stages for policy enforcement.

Power consumption imposes a significant cost for data centers implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of low load. This paper investigates how much can be saved by dynamically “right-sizing” the data center by turning off servers during such periods and how to achieve that saving via an online algorithm. We propose a very general model and prove that the optimal offline algorithm for dynamic right-sizing has a simple structure when viewed in reverse time, and this structure is exploited to develop a new “lazy” online algorithm, which is proven to be 3-competitive. We validate the algorithm using traces from two real data-center workloads and show that significant cost savings are possible. Additionally, we contrast this new algorithm with the more traditional approach of receding horizon control.

People are amassing larger and more diverse collections of digital things. The emergence of Cloud computing has enabled people to move their personal files to online places, and create new digital things through online services. However, little is known about how this shift might shape people's orientations toward their digital things. To investigate, we conducted in depth interviews with 13 people comparing and contrasting how they think about their possessions, moving from physical ones, to locally kept digital materials, to the online world. Findings are interpreted to detail design and research opportunities in this emerging space.

Online services hosted in data centers show significant diurnal variation in load levels. Thus, there is significant potential for saving power by powering down excess servers during the troughs. However, while techniques like VM migration can consolidate computational load, storage state has always been the elephant in the room preventing this powering down. Migrating storage is not a practical way to consolidate I/O load.

Power consumption imposes a significant cost for data centers implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of predictably low load. This paper investigates how much can be saved by dynamically `right-sizing' the data center by turning off servers during such periods, and how to achieve that saving via an online algorithm. We prove that the optimal offline algorithm for dynamic right-sizing has a simple structure when viewed in reverse time, and this structure is exploited to develop a new `lazy' online algorithm, which is proven to be 3-competitive. We validate the algorithm using traces from two real data center workloads and show that significant cost-savings are possible.

Perhaps surprisingly, no practical performance models exist for popular (and complex) client applications such as Adobe's Creative Suite, Microsoft's Office and Visual Studio, Mozilla, Halo 3, etc. There is currently no tool that automatically answers program developers', IT administrators' and end-users' simple what-if questions like "what happens to the performance of my favorite application X if I upgrade from Windows Vista to Windows 7?". This paper describes our approach towards constructing practical, versatile performance models to address this problem. The goal is to have these models be useful for application developers to help expand application testing coverage and for IT administrators to assist with understanding the performance consequences of a software, hardware or configuration change.

Recently, flash-based solid-state drives (SSDs) have become standard options for laptop and desktop storage, but their impact on enterprise server storage has not been studied. Provisioning server storage is challenging. It requires optimizing for the performance, capacity, power and reliability needs of the expected workload, all while minimizing financial costs. In this paper we analyze a number of workload traces from servers in both large and small data centers, to decide whether and how SSDs should be used to support each. We analyze both complete replacement of disks by SSDs, as well as use of SSDs as an intermediate tier between disks and DRAM. We describe an automated tool that, given device models and a block-level trace of a workload, determines the least-cost storage configuration that will support the workload's performance, capacity, and fault-tolerance requirements. We found that replacing disks by SSDs is not a costeffective option for any of our workloads, due to the low capacity per dollar of SSDs. Depending on the workload, the capacity per dollar of SSDs needs to increase by a factor of 3-3000 for an SSD-based solution to break even with a diskbased solution. Thus, without a large increase in SSD capacity per dollar, only the smallest volumes, such as system boot volumes, can be cost-effectively migrated to SSDs. The benefit of using SSDs as an intermediate caching tier is also limited: fewer than 10% of our workloads can reduce provisioning costs by using an SSD tier at today's capacity per dollar, and fewer than 20% can do so at any SSD capacity per dollar. Although SSDs are much more energy-efficient than enterprise disks, the energy savings are outweighed by the hardware costs, and comparable energy savings are achievable with low-power SATA disks.

Traditional performance models are too brittle to be relied on for continuous capacity planning and performance debugging in many computer systems. Simply put, a brittle model is often inaccurate and incorrect. We find two types of reasons why a model's prediction might diverge from the reality: (1) the underlying system might be misconfigured or buggy or (2) the model's assumptions might be incorrect. The extra effort of manually finding and fixing the source of these discrepancies, continuously, in both the system and model, is one reason why many system designers and administrators avoid using mathematical models altogether. Instead, they opt for simple, but often inaccurate, rules-of-thumb.This paper describes IRONModel, a robust performance modeling architecture. Through studying performance anomalies encountered in an experimental cluster-based storage system, we analyze why and how models and actual system implementations get out-of-sync. Lessons learned from that study are incorporated into IRONModel. IRONModel leverages the redundancy of high-level system specifications described through models and low-level system implementation to localize many types of system-model inconsistencies. IRONModel can guide designers to the potential source of the discrepancy, and, if appropriate, can semi-automatically evolve the models to handle unanticipated inputs.

Administration tasks increasingly dominate the total cost of ownership of database management systems. A key task, and a very difficult one for an administrator, is to justify upgrades of CPU, memory and storage resources with quantitative predictions of the expected improvement in workload performance. Current database systems are not designed with such prediction in mind and hence offer only limited help to the administrator. This paper proposes changes to database system design that enable a Resource Advisor to answer "what-if" questions about resource upgrades. A prototype Resource Advisor built to work with a commercial DBMS shows the efficacy of our approach in predicting the effect of upgrading a key resource-buffer pool size-on OLTP workloads in a highly concurrent system.