Sai Susarla

CPU inheritance scheduling

Traditional processor scheduling mechanisms in operating systems are fairly rigid, often supportingonly one fixed scheduling policy, or, at most, a few “scheduling classes” whose implementations are closely tied together in the OS kernel. This paper presents CPU inheritance scheduling, a novel processor scheduling framework in which arbitrary threads can act as schedulers for other threads. Widely different scheduling policies can be implemented under the framework, and many different policies can coexist in a single system, providing much greater scheduling flexibility. Modular, hierarchical control can be provided over the processor utilization of arbitrary administrative domains, such as processes, jobs, users, and groups, and the CPU resources consumed can be accounted for and attributed accurately. Applications, as well as the OS, can implement customized local scheduling policies; the framework ensures that all the different policies work together logically and predictably. As a side effect, the framework also cleanly addresses priority inversion by providing a generalized form of priority inheritance that automatically works within and among diverse scheduling policies. CPU inheritance scheduling extends naturally to multiprocessors, and supports processor management techniques such as processor affinity[29] and scheduler activations[3]. We show that this flexibility can be provided with acceptable overhead in typical environments, depending on factors such as context switch speed and frequency.

Khazana: an infrastructure for building distributed services

Essentially all distributed systems, applications, and services at some level boil down to the problem of managing distributed shared state. Unfortunately, while the problem of managing distributed shared state is shared by many applications, there is no common means of managing the data-every application devises its own solution. We have developed Khazana, a distributed service exporting the abstraction of a distributed persistent globally shared store that applications can use to store their shared state. Khazana is responsible for performing many of the common operations needed by distributed applications, including replication, consistency management, fault recovery, access control and location management. Using Khazana as a form of middleware, distributed applications can be quickly developed from corresponding uniprocessor applications through the insertion of Khazana data access and synchronization operations.

Flexible Consistency for Wide Area Peer Replication

The lack of a flexible consistency management solution hinders P2P implementation of applications involving updates, such as read-write file sharing, directory services, online auctions and wide area collaboration. Managing mutable shared data in a P2P setting requires a consistency solution that can operate efficiently over variable-quality failure-prone networks, support pervasive replication for scaling, and give peers autonomy to tune consistency to their sharing needs and resource constraints. Existing solutions lack one or more of these features. In this paper, we described a new consistency model for P2P sharing of mutable data called composable consistency, and outline its implementation in a wide area middleware file service called Swarm. Composable consistency lets applications compose consistency semantics appropriate for their sharing needs by combining a small set of primitive options. Swarm implements these options efficiently to support scalable, pervasive, failure-resilient, wide-area replication behind a simple yet flexible interface. Two applications was presented to demonstrate the expressive power and effectiveness of composable consistency: a wide area file system that outperforms Coda in providing close-to-open consistency over WANs, and a replicated BerkeleyDB database that reaps order-of-magnitude performance gains by relaxing consistency for queries and updates

Discovering Rules from Disk Events for Predicting Hard Drive Failures

Detecting impending failure of hard disks is an important prediction task which might help computer systems to prevent loss of data and performance degradation. Currently most of the hard drive vendors support self-monitoring, analysis and reporting technology (SMART) which are often considered unreliable for such tasks. The problem of finding alternatives to SMART for predicting disk failure is an area of active research. In this paper, we consider events recorded from live disks and show that it is possible to construct decision support systems which can detect such failures. It is desired that any such prediction methodology should have high accuracy and ease of interpretability. Black box models can deliver highly accurate solutions but do not provide an understanding of events which explains the decision given by it. To this end we explore rule based classifiers for predicting hard disk failures from various disk events. We show that it is possible to learn easy to understand rules, from disk events, which have extremely low false alarm rates on real world data.