Transcending POSIX: The End of an Era?

In this article, we provide a holistic view of the Portable Operating System Interface (POSIX) abstractions by a systematic review of their historical evolution. We discuss some of the key factors that drove the evolution and identify the pitfalls that make them infeasible when building modern applications.Peer reviewe

Cut-and-paste file-systems: integrating simulators and file-systems

We have implemented an integrated and configurable file system called the PFS and a trace-driven file-system simulator called Patsy. Patsy is used for off-line analysis of file-system algorithms, PFS is used for on-line file-system data storage. Algorithms are first analyzed in Patsy and when we are satisfied\ud with the performance results, migrated into PFS for on-line usage. Since Patsy and PFS are derived from a common cut-and-paste file-system framework, this migration proceeds smoothly.\ud We have found this integration quite useful: algorithm bottlenecks have been found through Patsy that could have led to performance degradations in PFS. Off-line simulators are simpler to analyze compared to on-line file-systems because a work load can repeatedly be replayed on the same off-line simulator. This is almost impossible in on-line file-systems since it is hard to provide similar conditions for each experiment run. Since simulator and file-system are integrated (hence, use the same code), experiment results from the simulator have relevance in the real system. \ud This paper describes the cut-and-paste framework, the instantiation of the framework to PFS and Patsy and finally, some of the experiments we conducted in Patsy

Improving I/O performance through an in-kernel disk simulator

This paper presents two mechanisms that can significantly improve the I/O performance of both hard and solid-state drives for read operations: KDSim and REDCAP. KDSim is an in-kernel disk simulator that provides a framework for simultaneously simulating the performance obtained by different I/O system mechanisms and algorithms, and for dynamically turning them on and off, or selecting between different options or policies, to improve the overall system performance. REDCAP is a RAM-based disk cache that effectively enlarges the built-in cache present in disk drives. By using KDSim, this cache is dynamically activated/deactivated according to the throughput achieved. Results show that, by using KDSim and REDCAP together, a system can improve its I/O performance up to 88% for workloads with some spatial locality on both hard and solid-state drives, while it achieves the same performance as a ‘regular system’ for workloads with random or sequential access patterns.Peer ReviewedPostprint (author's final draft

EFFECTIVE GROUPING FOR ENERGY AND PERFORMANCE: CONSTRUCTION OF ADAPTIVE, SUSTAINABLE, AND MAINTAINABLE DATA STORAGE

The performance gap between processors and storage systems has been increasingly critical overthe years. Yet the performance disparity remains, and further, storage energy consumption israpidly becoming a new critical problem. While smarter caching and predictive techniques domuch to alleviate this disparity, the problem persists, and data storage remains a growing contributorto latency and energy consumption.Attempts have been made at data layout maintenance, or intelligent physical placement ofdata, yet in practice, basic heuristics remain predominant. Problems that early studies soughtto solve via layout strategies were proven to be NP-Hard, and data layout maintenance todayremains more art than science. With unknown potential and a domain inherently full of uncertainty,layout maintenance persists as an area largely untapped by modern systems. But uncertainty inworkloads does not imply randomness; access patterns have exhibited repeatable, stable behavior.Predictive information can be gathered, analyzed, and exploited to improve data layouts. Ourgoal is a dynamic, robust, sustainable predictive engine, aimed at improving existing layouts byreplicating data at the storage device level.We present a comprehensive discussion of the design and construction of such a predictive engine,including workload evaluation, where we present and evaluate classical workloads as well asour own highly detailed traces collected over an extended period. We demonstrate significant gainsthrough an initial static grouping mechanism, and compare against an optimal grouping method ofour own construction, and further show significant improvement over competing techniques. We also explore and illustrate the challenges faced when moving from static to dynamic (i.e. online)grouping, and provide motivation and solutions for addressing these challenges. These challengesinclude metadata storage, appropriate predictive collocation, online performance, and physicalplacement. We reduced the metadata needed by several orders of magnitude, reducing the requiredvolume from more than 14% of total storage down to less than 12%. We also demonstrate how ourcollocation strategies outperform competing techniques. Finally, we present our complete modeland evaluate a prototype implementation against real hardware. This model was demonstrated tobe capable of reducing device-level accesses by up to 65%

A Study of Dynamic Optimization Techniques: Lessons and Directions in Kernel Design

The Synthesis kernel [21,22,23,27,28] showed that dynamic code generation, software feedback, and fine-grain modular kernel organization are useful implementation techniques for improving the performance of operating system kernels. In addition, and perhaps more importantly, we discovered that there are strong interactions between the techniques. Hence, a careful and systematic combination of the techniques can be very powerful even though each one by itself may have serious limitations. By identifying these interactions we illustrate the problems of applying each technique in isolation to existing kernels. We also highlight the important common under-pinnings of the Synthesis experience and present our ideas on future operating system design and implementation. Finally, we outline a more uniform approach to dynamic optimizations called incremental partial evaluation

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Emoji Company GmbH v Schedule A Defendants

Declaration of Dean Eric Goldma

Emoji Company GmbH v Schedule A Defendants

Declaration of Dean Eric Goldma

GNOSIS: Global Network Operations Status Information System

Monitoring the global state of a network is a continuing challenge for network operators and users. It has become still harder with increases in scale and heterogeneity. Monitoring requires status information for each node and to construct the global picture at a monitoring point. GNOSIS, the Global Network Operations Status Information System, achieves a global view by careful extraction and presentation of locally available node data. The GNOSIS model improves on the traditional polling model of monitoring schemes by 1.) collecting accurate data 2.) decreasing the granularity with which network applications can detect change in the network and 3.) displaying status information in near real-time. We define the Network Snapshot as the basic unit of information capture and display in GNOSIS. A Network Snapshot is a visualization of locally available state collected during a common time interval. A sequence of these Network Snapshots over time represent the evolution of network state. In this paper, we motivate the need for a network monitoring system that can detect global problems, in spite of both scale and heterogeneity. We present three design criteria, Accuracy, Continuity and Timeliness for a global monitoring system. Finally, we present the GNOSIS architecture and demonstrate how it better detects network problems which are currently of concern. The goal of GNOSIS is to present a stream of consistent, accurate local data in a timely manner