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49
Poweraware QoS Management in Web Servers
 IN PROCEEDINGS OF THE 24 TH IEEE REALTIME SYSTEMS SYMPOSIUM (RTSS’03), CANCUN
, 2003
"... Power management in data centers has become an increasingly important concern. Large server installations are designed to handle peak load, which may be significantly larger than in offpeak conditions. The increasing cost of energy consumption and cooling incurred in farms of highperformance web se ..."
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Cited by 72 (4 self)
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Power management in data centers has become an increasingly important concern. Large server installations are designed to handle peak load, which may be significantly larger than in offpeak conditions. The increasing cost of energy consumption and cooling incurred in farms of highperformance web servers make lowpower operation during offpeak hours desirable. This paper investigates adaptive algorithms for dynamic voltage scaling in QoSenabled web servers to minimize energy consumption subject to service delay constraints. We implement these algorithms inside the Linux kernel. The instrumented kernel supports multiple client classes with perclass deadlines. Energy consumption is minimized by using a feedback loop that regulates frequency and voltage levels to keep the synthetic utilization around the aperiodic schedulability bound derived in an earlier publication. Enforcing the bound ensures that deadlines are met. Our evaluation of an Apache server running on the modified Linux kernel shows that nontrivial offpeak energy savings are possible without sacrificing timeliness.
Multiversion Scheduling in Rechargeable Energyaware Realtime Systems
 Journal of Embedded Computing
, 2003
"... In the context of batterypowered realtime systems three constraints need to be addressed: energy, deadlines and task rewards. Many future realtime systems will count on different software versions, each with different rewards, time and energy requirements, to achieve a variety of QoSaware tradeo ..."
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Cited by 35 (1 self)
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In the context of batterypowered realtime systems three constraints need to be addressed: energy, deadlines and task rewards. Many future realtime systems will count on different software versions, each with different rewards, time and energy requirements, to achieve a variety of QoSaware tradeoffs. We propose a solution that allows the device to run the most valuable task versions while still meeting all deadlines and without depleting the energy. Assuming that the battery is rechargeable, we also propose (a) a static solution that maximizes the system value assuming a worstcase scenario (i.e., worstcase task execution times); and (b) a dynamic scheme that takes advantage of the extra energy in the system when worstcase scenarios do not happen. Three dynamic policies are shown to make better use of the recharging energy while improving the system value. 1
Maximizing Rewards for RealTime Applications with Energy Constraints
 IN ACM TRANSACTIONS ON EMBEDDED COMPUTER SYSTEMS, ACCEPTED
, 2003
"... ... this paper we propose a solution to this problem; to our knowledge, this is the first solution that combines the three constraints mentioned above. We devise two algorithms, an optimal algorithm for homogeneous applications (with respect to power consumption) and a heuristic iterative algorithm ..."
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Cited by 30 (3 self)
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... this paper we propose a solution to this problem; to our knowledge, this is the first solution that combines the three constraints mentioned above. We devise two algorithms, an optimal algorithm for homogeneous applications (with respect to power consumption) and a heuristic iterative algorithm that can also accommodate heterogeneous applications (that is, those with different power consumption functions). We show by simulation that our iterative algorithm is fast and within 1% of the optimal.
An integrated approach for applying dynamic voltage scaling to hard realtime systems
 In Proceedings of the ninth IEEE RealTime and Embedded Technology and Applications Symposium
, 2003
"... Wireless and portable devices depend on the limited power supplied by the battery. Dynamic Voltage Scaling (DVS) is an effective method to reduce CPU power consumption by adjusting CPU voltage. For realtime systems, DVS algorithms must also guarantee that no job misses its deadline. In this paper, ..."
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Cited by 22 (1 self)
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Wireless and portable devices depend on the limited power supplied by the battery. Dynamic Voltage Scaling (DVS) is an effective method to reduce CPU power consumption by adjusting CPU voltage. For realtime systems, DVS algorithms must also guarantee that no job misses its deadline. In this paper, we propose an integrated approach for applying DVS to realtime systems. We define two functions, the available cycle function (ACF) and the required cycle function (RCF), to capture the CPU workload of the realtime tasks under a scheduling policy. We then formulate the DVS scheduling problem for realtime systems as a nonlinear optimization problem and propose an optimal offline algorithm to solve this problem. We also propose a novel online algorithm with time complexityÇ to further reduce power consumption when a job uses fewer execution cycles than the worstcase budget. The algorithms in this paper are based solely on ACF and RCF. When ACF and RCF are defined, the algorithms can be applied to any scheduling policy. We illustrate the generality of our approach over previous research by applying our method to several scheduling policies, including FIFO, EDF and RM. Our simulation results show significant improvement over previous work. 1
EnergyEfficient, Utility Accrual RealTime Scheduling Under the Unimodal Arbitrary Arrival Model
 in ACM Design, Automation, and Test in Europe (DATE
, 2005
"... We present an energyefficient realtime scheduling algorithm called EUA∗, for the unimodal arbitrary arrival model (or UAM). UAM embodies a “stronger ” adversary than most arrival models. The algorithm considers application activities that are subject to time/utility function time constraints, UAM, ..."
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Cited by 19 (5 self)
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We present an energyefficient realtime scheduling algorithm called EUA∗, for the unimodal arbitrary arrival model (or UAM). UAM embodies a “stronger ” adversary than most arrival models. The algorithm considers application activities that are subject to time/utility function time constraints, UAM, and the multicriteria scheduling objective of probabilistically satisfying utility lower bounds, and maximizing systemlevel energy efficiency. Since the scheduling problem is intractable, EUA ∗ allocates CPU cycles, scales clock frequency, and heuristically computes schedules using statistical estimates of cycle demands, in polynomialtime. We establish that EUA ∗ achieves optimal timeliness during underloads, and identify the conditions under which timeliness assurances hold. Our simulation experiments illustrate EUA∗’s superiority. 1.
Procrastination Scheduling in Fixed Priority RealTime Systems
 In Proceedings of the Language Compilers and Tools for Embedded Systems
, 2004
"... Procrastination scheduling has gained importance for energy efficiency due to the rapid increase in the leakage power consumption. Under procrastination scheduling, task executions are delayed to extend processor shutdown intervals, thereby reducing the idle energy consumption. We propose algorithms ..."
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Cited by 17 (2 self)
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Procrastination scheduling has gained importance for energy efficiency due to the rapid increase in the leakage power consumption. Under procrastination scheduling, task executions are delayed to extend processor shutdown intervals, thereby reducing the idle energy consumption. We propose algorithms to compute the maximum procrastination intervals for tasks scheduled by either the fixed priority or the dual priority scheduling policy. We show that dual priority scheduling always guarantees longer shutdown intervals than fixed priority scheduling. We further combine procrastination scheduling with dynamic voltage scaling to minimize the total static and dynamic energy consumption of the system. Our simulation experiments show that the proposed algorithms can extend the sleep intervals up to 5 times while meeting the timing requirements. The results show up to 18% energy gains over dynamic voltage scaling.
Optimized Slowdown in RealTime Task Systems,” CECS
 Univ. of California Irvine
, 2004
"... Abstract—Slowdown factors determine the extent of slowdown a computing system can experience based on functional and performance requirements. Dynamic Voltage Scaling (DVS) of a processor based on slowdown factors can lead to considerable energy savings. We address the problem of computing slowdown ..."
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Cited by 14 (4 self)
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Abstract—Slowdown factors determine the extent of slowdown a computing system can experience based on functional and performance requirements. Dynamic Voltage Scaling (DVS) of a processor based on slowdown factors can lead to considerable energy savings. We address the problem of computing slowdown factors for dynamically scheduled tasks with specified deadlines. We present an algorithm to compute a near optimal constant slowdown factor based on the bisection method. As a further generalization, for the case of tasks with varying power characteristics, we present the computation of near optimal slowdown factors as a solution to convex optimization problem using the ellipsoid method. The algorithms are practically fast and have the same time complexity as the algorithms to compute the feasibility of a task set. Our simulation results show an average 20 percent energy gain over known slowdown techniques using static slowdown factors and 40 percent gain with dynamic slowdown. Index Terms—EDF scheduling, realtime systems, low power scheduling, dynamic voltage scaling, slowdown factors, convex optimization.
GRACE1: CrossLayer Adaptation for Multimedia Quality and Battery Energy
, 2006
"... Mobile devices primarily processing multimedia data need to support multimedia quality with limited battery energy. To address this challenging problem, researchers have introduced adaptation into multiple system layers, ranging from hardware to applications. Given these adaptive layers, a new chal ..."
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Cited by 8 (0 self)
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Mobile devices primarily processing multimedia data need to support multimedia quality with limited battery energy. To address this challenging problem, researchers have introduced adaptation into multiple system layers, ranging from hardware to applications. Given these adaptive layers, a new challenge is how to coordinate them to fully exploit the adaptation benefits. This paper presents a novel crosslayer adaptation framework, called GRACE1, that coordinates the adaptation of the CPU hardware, OS scheduling, and multimedia quality based on users ’ preferences. To balance the benefits and overhead of crosslayer adaptation, GRACE1 takes a hierarchical approach: It globally adapts all three layers to large system changes, such as application entry or exit, and internally adapts individual layers to small changes in the processed multimedia data. We have implemented GRACE1 on an HP laptop with the adaptive Athlon CPU, Linuxbased OS, and video codecs. Our experimental results show that, compared to schemes that adapt only some layers or adapt only to large changes, GRACE1 reduces the laptop’s energy consumption up to 31.4 percent while providing better or the same video quality.
EnergyAware Implementation of HardRealTime Systems Upon Multiprocessor Platforms
 In Proceedings of the ISCA 16th International Conference on Parallel and Distributed Computing Systems
, 2002
"... Multiprocessor implementations of realtime systems tend to be more energyecient than uniprocessor implementations: since the power consumed by a CMOS processor is approximately proportional to the cube of the speed or computing capacity at which the processor executes, the total power consumed ..."
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Cited by 8 (1 self)
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Multiprocessor implementations of realtime systems tend to be more energyecient than uniprocessor implementations: since the power consumed by a CMOS processor is approximately proportional to the cube of the speed or computing capacity at which the processor executes, the total power consumed by an mprocessor multiprocessor platform is approximately (1=m ) times the power consumed by a uniprocessor platform of the same computing capacity. However several factors, including the nonexistence of optimal multiprocessor scheduling algorithms, combine to prevent all the computing capacity of a multiprocessor platform from being guaranteed available for executing the realtime workload. In this paper, this tradeo  that while increasing the number of processors results in lower energy consumption for a given computing capacity, the fraction of the capacity of a multiprocessor platform that is guaranteed available for executing realtime work decreases as the number of processors increases  is explored in detail.
Overview of RealTime Scheduling Problems
 Euro Workshop on Project Management and Scheduling
, 2004
"... Introduction A computerized realtime system is required to complete its work on a timely basis. Typical applications are digital control, command and control, signal processing and communication systems. The aim of realtime scheduling is to buildup a sequence of jobs that meets hard timing const ..."
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Cited by 6 (0 self)
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Introduction A computerized realtime system is required to complete its work on a timely basis. Typical applications are digital control, command and control, signal processing and communication systems. The aim of realtime scheduling is to buildup a sequence of jobs that meets hard timing constraints at runtime. In opposition with the classical scheduling literature, realtime scheduling is not necessarily online scheduling. The main characteristic of realtime systems is the behavioral predictability. Timing constraints will be met whatever happens in the system. Realtime operating systems define recurring tasks. Realtime tasks are often periodically released. Basically, a periodic task gets its input data from sensors and command the controlled process by sending data to actuators. Every execution of a task is called a job. In the general model of periodic tasks, a task # i is defined by an o#set O i , that defines the release time of its first job; its worstcase execution