You are here

Anemone

Title: Anemone: An Adaptive Network Memory Engine.
35 views
7 downloads
Name(s): Hines, Michael R., author
Gopalan, Kartik, professor directing thesis
Duan, Zhenhai, committee member
Wang, An-i Andy, committee member
Department of Computer Science, degree granting department
Florida State University, degree granting institution
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2005
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Memory hungry applications consistently keep their memory requirement curves ahead of the growth of DRAM capacity in modern computer systems. Such applications quickly start paging to swap space on the local disk, which brings down their performance, an old and ongoing battle between the disk and RAM in the memory hierarchy. This thesis presents a practical low-cost solution to this important performance problem. We give the design, implementation and evaluation of Anemone - an Adaptive NEtwork MemOry engiNE. Anemone pools together the memory resources of many machines in a clustered network of computers. It then presents an interface to client machines in order to use the collective memory pool in a virtualized manner, providing potentially unlimited amounts of memory to memory-hungry high-performance applications. Using real applications like the ns-2 simulator, the ray-tracing program POV-ray, and quicksort, disk-based page-fault latencies average 6.5 milliseconds whereas Anemone provides an average of latency of 700.2 microseconds, 9.2 times faster than using the disk. In contrast to the disk-based paging, our results indicate that Anemone reduces the execution time of single memory-bound processes by half. Additionally, Anemone reduces the execution times of multiple, concurrent memory-bound processes by a factor of 10 on the average. Another key advantage of Anemone is that this performance improvement is achieved with no modifications to the client's operating system nor the memory-bound applications due to the use of a novel NFS-based low-latency remote paging mechanism.
Identifier: FSU_migr_etd-4038 (IID)
Submitted Note: A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science.
Degree Awarded: Spring Semester, 2005.
Date of Defense: April 7, 2005.
Keywords: Kernel Programming, Systems, Disk, Networks, Distributed Networks, Memory, Remote Memory, Kernel, NFS
Bibliography Note: Includes bibliographical references.
Advisory Committee: Kartik Gopalan, Professor Directing Thesis; Zhenhai Duan, Committee Member; An-i Andy Wang, Committee Member.
Subject(s): Computer science
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_migr_etd-4038
Owner Institution: FSU

Choose the citation style.
Hines, M. R. (2005). Anemone: An Adaptive Network Memory Engine. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-4038