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Current Project - ECS: Energy-Efficient
Computing Systems This research is supported in part by the National Science Foundation
(NSF) under Grant CCF-0702781, the New Mexico Institute of Mining and
Technology under Grant 103295, by Intel Corporation under Grant 2005-04-070,
and by Altera Corporation under an equipment grant. |
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With the advances of computing
technology, the demand on computing systems for high performance and low
energy consumption exponentially increases. Next-generation computing systems
require innovative energy-efficient scheduling techniques for resource
management. In this project, new job and packets scheduling algorithms are
designed and implemented for embedded systems, cluster computing platforms,
and wireless networks. The algorithms are aimed at achieving the best
tradeoffs between energy conservation and high performance for
energy-efficient computing systems. The algorithms are evaluated through
extensive experiments based on both synthetic benchmark/traces and real-world
applications. Our
approaches to conserving energy for energy-efficient computing systems
include the following: 1. Energy-Efficient
Duplication-Based Scheduling for Clusters 2. An Energy-Delay Tunable Task Allocation Strategy for Embedded
Systems 3. Energy-Efficient
Storage Systems 3.1 Power
Aware Disk Scheduling Algorithms 3.2 Energy-Efficient
Disk Buffer Disks 4. Energy-Efficient
Packet Transmissions in Real-Time Wireless Networks 1. Energy-Efficient Duplication-Based Scheduling
for Clusters. [Back to Top] Optimizing
energy consumption has become a major concern in designing economical and
environmentally friendly clusters for a wide variety of applications. In
recognition that network interconnects are a major part of todays clusters,
we propose in this study novel scheduling strategies aimed at reducing energy
consumption for the entire interconnect of a cluster. Scheduling precedence constrained parallel
tasks on clusters is technically challenging because of, in part, high
communication overhead in parallel tasks running on clusters. Therefore,
duplication heuristics are applied to schedule parallel tasks to minimize
communication overhead. However, most of the available duplication algorithms
are developed with consideration of schedule lengths, completely ignoring
energy consumption of clusters. In this regard, we design two energy-aware
duplication scheduling algorithms, called EADUS and TEBUS, to schedule
precedence constrained parallel tasks with a complexity of O(n2), where n is the number
of tasks in a parallel task set. Unlike existing duplication-based scheduling
algorithms that replicate all the possible predecessors of each task, the
proposed algorithms judiciously replicate predecessors of a task if the
duplication can help in conserving energy. Our energy-aware scheduling
strategies are conducive to balancing the scheduling length and energy
consumption of a set of precedence constrained parallel tasks. We conducted extensive experiments using synthetic
benchmarks and real-world applications to compare our algorithms with an
existing approach. Experimental results based on simulated clusters
demonstrate the effectiveness and
practicality of the proposed duplication scheduling strategies. 1.1 An Energy-Efficient Scheduling Algorithm
Using Dynamic Voltage Scaling for Parallel Applications on Clusters. [Abstract | PDF] X.-J. Ruan, X. Qin, M. Nijim, Z.-L. Zong, and K. Bellam, Proc. 16th IEEE Int'l Conference on Computer Communications and Networks (ICCCN),
Honolulu, Hawaii, Aug. 2007. 1.2 Energy-Efficient Scheduling for Parallel
Applications Running on Heterogeneous Clusters. Z.-L. Zong, X. Qin*, M. Nijim, X.-J. Ruan, K. Bellam,
and M. Alghamdi, Proc. 36th
International Conference on Parallel Processing (ICPP), Sept. 2007. 1.3 Energy-Efficient Scheduling for Parallel
Applications on Z.-L. Zong, M. Nijim, and X. Qin, Cluster Computing:
The Journal of Networks, Software Tools and Applications. Submitted May
2006; revised Jan. 2007; accepted March 2007. 1.4 Energy-Efficient Duplication Strategies for
Scheduling Precedence Constrained Parallel Tasks on Clusters. [ Abstract | PDF ] Z.-L.
Zong, A. Manzanares, B. Stinar, and X. Qin, Proc. IEEE 8th
International Conference on Cluster Computing (Cluster'06), Sept. 2006. 1.5 HAGEES: A High Availability Guaranteed
Energy-Efficient Scheduling Strategy for High-Performance Clusters. Z.-L.
Zong, M. Nijim, M. Alghamdi, and X. Qin, Proc. 2006 the 7th Symposium of the Los Alamos
Computer Science Institute, 2. An Energy-Delay Tunable Task Allocation
Strategy for Embedded Systems. [Back to Top] Applications
with energy and low-latency constraints are emerging in various networked
embedded systems like digital signal processing, vehicle tracking, and
infrastructure monitoring. However, conventional energy-driven task
allocation schemes for a cluster of embedded nodes only concentrate on
energy-saving when making allocation decisions. Consequently, the length of
the schedules could be very long, which is unfavourable or in some situations
even not tolerated. In this work, we address the issue of allocating a group
of tasks on a heterogeneous embedded system with an objective of
energy-saving and short-latency. A novel task allocation strategy, or BEATA
(Balanced Energy-Aware
Task Allocation), is developed to find an optimal allocation that minimizes
overall energy consumption while confining the length of schedule to an ideal
range. In addition, we proposed mathematical models to describe a
system framework, a set of tasks, energy consumption, and heterogeneity,
which are used by BEATA to measure energy dissipation caused by both computation and
communication activities. Experimental results show that BEATA significantly
improves the performance of embedded systems in terms of energy-saving and
schedule length over existing allocation
schemes. 2.1 J26. An Energy-Delay Tunable Task
Allocation Strategy for Collaborative Applications in Networked Embedded
Systems. T. Xie and X. Qin, IEEE Transactions on Computers. Submitted Nov. 2006; revised
April 2007; accepted July 2007. 2.2 Solving Energy-Latency Dilemma: Task Allocation for Parallel
Applications in Heterogeneous Embedded Systems. [ Abstract
| PDF ] T. Xie, X. Qin, and M. Nijim, Proc. 35th International Conference on Parallel Processing (ICPP),
Columbus, Ohio, Aug. 2006. Energy-Efficient Storage Systems [Back to Top] Improving
the power consumption attributes of embedded and real-time systems has become
an important area of interest as processors and other system components have
become increasingly powerful and demanding in their energy consumption. 3.1 Power Aware Disk Scheduling
Algorithms. [Back to Top] The focus of this project is on
examining the characteristics of real-time hard-disk scheduling algorithms,
as hard-disk power usage can amount to a substantial portion of the total
system power consumption. Conventional disk scheduling algorithms typically
either disregard power consumption completely, or at best apply a fairly
naïve power management policy. Given
the potential implications for improving the efficiency and longevity of
real-world disk systems, we investigate the problem of scheduling a set of independent real-time
disk requests such that the total power consumption is minimized, while the
efficacy of the disk system is not compromised. We define a power consumption model that
can reasonably approximate the performance characteristics of real-world
disks. Next, we discuss two power-aware power management
policies, I/O Burstiness for Energy Conservation (IBEC) and Speed-Aware Real-time Disk Scheduling for energy
conservation (SARDS), which integrate differing power management policies
into the disk scheduling algorithms for real-time I/O-intensive
applications. Furthermore, to evaluate
the performance of the proposed algorithms against existing solutions and
ensure that the efficacy of the system is not compromised, we incorporate the
earliest deadline first (EDF)
and least laxity first (LLF) scheduling policies into SARDS and IBEC to
implement power-aware real-time scheduling algorithms. Experimental results
from real-world traces and synthetically generated workloads show that the dynamic algorithms have
the potential to substantially reduce power consumption over existing
scheduling algorithms and power management policies without compromising the
overall performance of disk systems. 3.1.2 Power Management Policies for Real-Time Disk
Systems. A. Roth and X. Qin, IEEE Transactions on Computers. Submitted
Aug. 2006; revised Feb. 2007. 3.1.1 Energy Management for Real-Time Embedded
Storage Systems. A. Roth, T. Xie, M. Nijim, and X. Qin, ACM Transactions
on Embedded Computing Systems. Submitted Feb. 2006. 3.2 Energy-Efficient Disk Buffer
Disks. [Back to Top] Huge energy consumption has become a critical
bottleneck for further applying large-scale cluster systems to build new data
centers. Among various components of a data center, storage subsystems are
one of the biggest consumers of energy. In this project, we propose a novel
buffer-disk based framework for large-scale and energy-efficient parallel
storage systems. To validate the efficiency of the proposed framework, a
buffer-disk scheduling algorithm is designed and implemented. Our algorithm
can provide more opportunities for underlying disk power management schemes
to save energy by keeping a large number of idle data disks in sleeping mode
as long as possible. The trace-driven simulation results based on a revised
disksim simulator show that this new framework can significantly improves the
energy efficiency of large-scale parallel storage systems. 3.2.2 Design and Performance Analysis of
Energy-Efficient Parallel Storage Systems. Z.-L. Energy-Efficient Packet
Transmissions in Real-Time Wireless Networks [Back to Top]
Reducing
energy consumption has become a major goal in designing modern real-time
wireless networks. The focus of this study is to investigate the power and real-time
issues in wireless networks. The study aims to develop a rich variety of
scheduling schemes to reduce energy dissipation while
meeting timing
constraints of real-time applications in wireless networks. In what
follows, we describe our solutions to the energy problem in the context of
real-time wireless networks. 4.
Reducing Power Consumption in Real-Time Wireless Networks. In this work we addressed the
issue of scheduling real-time messages in wireless networks subject to timing
and power constraints. We developed a novel energy-aware message
scheduling scheme, or PARM (Power-aware Real-time Message), which generates
optimal schedules minimizing both power consumption and the probability of
missing deadlines for real-time messages. In addition, we extended a power
consumption model to calculate power consumption rates in accordance to
message transmission rates. Experimental results show that PARM significantly
improves the performance in terms of missed rate, energy efficiency, and
overall performance over four baseline message scheduling schemes. 4.4 Security-Aware Packet Scheduling in Real-Time
Wireless Networks. M. Alghamdi, Z.
Zong, K. Bellam, and X. Qin. Submitted. 4.3 Scheduling of Periodic Packets in Energy-Aware
Wireless Networks. [Abstract | PDF | Back to Top ] X.
Qin, M. Alghamdi, M. Nijim, Z.-L. Zong, and K.
Bellam, Proc. the 26th
IEEE Int'l Performance Computing and Communications Conf. (IPCCC'07), 4.2 Conserving Energy in Real-Time
Wireless Networks via Message Scheduling. X. Qin, M. Alghamdi, T. Xie, M. Nijim, and Z.-L. Zong, IEEE Transactions on Wireless Communications. Submitted Jan. 2006; revised Aug.
2006. 4.1 PARM: A Power-Aware Message Scheduling
Algorithm for Real-Time Wireless Networks. [ Abstract |
PDF ] M. Alghamdi, T. Xie, X. Qin, ACM Int'l Symp. Modeling,
Analysis and Simulation of Wireless and Updated
on 7/29/2007 End |