On Using Reliable Network RAM in Networks of Workstations
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Abstract
File systems and databases usually make several synchronous disk write accesses in order to make sure that the disk always has a consistent view of their data, and that data can be recovered in the case of a system crash. Since synchronous disk operations are slow, some systems choose to employ asynchronous disk write operations, at the cost of low reliability: in case of a system crash all data that have not yet been written to disk are lost.
In this paper we describe a software-based approach into using the network memory in a workstation cluster as a layer of Non-Volatile memory (NVRAM). Our approach takes a set of volatile main memories residing in independent workstations and transforms it into a fault-tolerant memory—much like RAIDS do with magnetic disks. This layer of NVRAM allows us to create systems that combine the reliability of synchronous disk accesses with the cost of asynchronous disk accesses. We demonstrate the applicability of our approach by integrating it into existing database systems, and by developing novel systems from the ground up.
We use experimental evaluation using well-known characterize the performance of our systems. Our experiments suggest that our approach may improve performance by as much as two orders of magnitude.
In this paper we describe a software-based approach into using the network memory in a workstation cluster as a layer of Non-Volatile memory (NVRAM). Our approach takes a set of volatile main memories residing in independent workstations and transforms it into a fault-tolerant memory—much like RAIDS do with magnetic disks. This layer of NVRAM allows us to create systems that combine the reliability of synchronous disk accesses with the cost of asynchronous disk accesses. We demonstrate the applicability of our approach by integrating it into existing database systems, and by developing novel systems from the ground up.
We use experimental evaluation using well-known characterize the performance of our systems. Our experiments suggest that our approach may improve performance by as much as two orders of magnitude.
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Proposal for Special Issue Papers