Improving the performance of I/O-intensive applications on clusters of workstations |
| |
Authors: | Xiao Qin Hong Jiang Yifeng Zhu David R Swanson |
| |
Institution: | (1) Department of Computer Science, New Mexico Institute of Mining and Technology, 801 Leroy Place, Socorro, New Mexico 87801-4796, USA;(2) Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska 68588-0115, USA |
| |
Abstract: | Load balancing in a workstation-based cluster system has been investigated extensively, mainly focusing on the effective usage
of global CPU and memory resources. However, if a significant portion of applications running in the system is I/O-intensive,
traditional load balancing policies can cause system performance to decrease substantially. In this paper, two I/O-aware load-balancing
schemes, referred to as IOCM and WAL-PM, are presented to improve the overall performance of a cluster system with a general
and practical workload including I/O activities. The proposed schemes dynamically detect I/O load imbalance of nodes in a
cluster, and determine whether to migrate some I/O load from overloaded nodes to other less- or under-loaded nodes. The current
running jobs are eligible to be migrated in WAL-PM only if overall performance improves. Besides balancing I/O load, the scheme
judiciously takes into account both CPU and memory load sharing in the system, thereby maintaining the same level of performance
as existing schemes when I/O load is low or well balanced. Extensive trace-driven simulations for both synthetic and real
I/O-intensive applications show that: (1) Compared with existing schemes that only consider CPU and memory, the proposed schemes
improve the performance with respect to mean slowdown by up to a factor of 20; (2) When compared to the existing approaches
that only consider I/O with non-preemptive job migrations, the proposed schemes achieve improvements in mean slowdown by up
to a factor of 10; (3) Under CPU-memory intensive workloads, our scheme improves the performance over the existing approaches
that only consider I/O by up to 47.5%.
Xiao Qin received the BSc and MSc degrees in computer science from Huazhong University of Science and Technology in 1992 and 1999,
respectively. He received the PhD degree in computer science from the University of Nebraska-Lincoln in 2004. Currently, he
is an assistant professor in the department of computer science at the New Mexico Institute of Mining and Technology. His
research interests include parallel and distributed systems, storage systems, real-time computing, performance evaluation,
and fault-tolerance. He served on program committees of international conferences like CLUSTER, ICPP, and IPCCC. During 2000–2001,
he was on the editorial board of The IEEE Distributed System Online. He is a member of the IEEE.
Hong Jiang received the B.Sc. degree in Computer Engineering in 1982 from Huazhong University of Science and Technology, Wuhan, China;
the M.A.Sc. degree in Computer Engineering in 1987 from the University of Toronto, Toronto, Canada; and the PhD degree in
Computer Science in 1991 from the Texas A&M University, College Station, Texas, USA. Since August 1991 he has been at the
University of Nebraska-Lincoln, Lincoln, Nebraska, USA, where he is Associate Professor and Vice Chair in the Department of
Computer Science and Engineering. His present research interests are computer architecture, parallel/distributed computing,
computer storage systems and parallel I/O, performance evaluation, middleware, networking, and computational engineering.
He has over 70 publications in major journals and international Conferences in these areas and his research has been supported
by NSF, DOD and the State of Nebraska. Dr. Jiang is a Member of ACM, the IEEE Computer Society, and the ACM SIGARCH and ACM
SIGCOMM.
Yifeng Zhu received the B.E. degree in Electrical Engineering from Huazhong University of Science and Technology in 1998 and the M.S.
degree in computer science from University of Nebraska Lincoln (UNL) in 2002. Currently he is working towards his Ph.D. degree
in the department of computer science and engineering at UNL. His main fields of research interests are parallel I/O, networked
storage, parallel scheduling, and cluster computing. He is a student member of IEEE.
David Swanson received a Ph.D. in physical (computational) chemistry at the University of Nebraska-Lincoln (UNL) in 1995, after which he
worked as an NSF-NATO postdoctoral fellow at the Technical University of Wroclaw, Poland, in 1996, and subsequently as a National
Research Council Research Associate at the Naval Research Laboratory in Washington, DC, from 1997–1998. In early 1999 he returned
to UNL where he has coordinated the Research Computing Facility and currently serves as an Assistant Research Professor in
the Department of Computer Science and Engineering. The Office of Naval Research, the National Science Foundation, and the
State of Nebraska have supported his research in areas such as large-scale parallel simulation and distributed systems. |
| |
Keywords: | I/O intensive Clusters Slowdown Performance evaluation |
本文献已被 SpringerLink 等数据库收录! |
|