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chooses the average value of previous performance data as the controlled variable for our simple feedback mechanism. The average value is only the initial value for the future scheduling and it is a heuristic setting which can provide a minimum guarantee for QoS satisfactions. In the following scheduling period, each time when a job completes, the real time performance is sensed and collected by the feedback scheme and fed to the controller. And the following performance values are computed based on the initial value of performance threshold using moving average computation. Here are the pseudo codes of the single feedback mechanism. 1. When scheduling event occurs { 2. for each task in the task set 3. Compute estimated performance data of each task and its execution time 4. End for 5. Compute estimated power consumption of each job on specific processor through code profiling 6. Compute QoS gains of each job 7. for each task in the task set 8. Schedule the tasks with minimum QoS gains and power consumptions 9. Delete the task from the task set 10. Update job table 11. If cpu_queue of targeted processor is exceeded the maximum length 12. Insert the task into next scheduling tasks set 13. Update job table 14. End if 15. Else 16. Insert the task into next scheduling tasks set 17. Update job table 18. End if 19. End for 20. } III. Figure 1. The pseudo codes of SFM POWER AWARE JOB SCHEDULING WITH QOS GUARANTEES We use a periodic and independent task model in our framework. Let J = { Ji | i = 1, 2,3,..., n} denote jobs set, J = ( a , b, e , c , s , Q ) , M is set of processors, i i i i i i i M = { M j | j = 1,2,3,..., m} , M j = ( pj, f j, d j, BWj, PWj) . Where: a i is arrival time of job J i , b i is starting time of job J i , e i is the average execution time of job J i on all the processors, i.e. expected executed time on processor M j where there is no other running jobs except for 1 2 3 m J i , ei = ( ei, ei , ei ,..., ei ) , c ij is the expected completion time of job J i on processor M j , s i is the size of data needed by job J i (MB), Q i is the QoS of job J i . As for hosts set M , p j is the speed of processor M j (MHz), f j is the available memory capacity of host M j (MB) , d j is the available disk space on host M j (MB), BW j is the bandwidth of host M j (Mb/s), PW j is the level of power consumption of host M j , 1 2 3 u PW = ( PW , PW , PW ,..., PW ) , j j j j j ∀v∈ [1, u], 0 < PW v j < 1 ,where 0 stands for the lowest and 1 the highest. Let Q denote a set of Quality of Service constraints, Q = { Qi | i = 1, 2,..., n} , Qi = ( Ti, Ri, Si, Ai, Pi) , where: T i is timeliness requirement, R i is reliability requirement, S i is security requirement, A i is accuracy requirement, P i (Priority) is priority requirement. For simplicity, we use discrete values to modeling the Quality of Service constraints, i.e., the QoS constraint is presented by several levels like very low, low, medium, high, and very high, not a specific number like 10% or 90% because in real computing system with user interaction a user only cares the interactive experience, not the specific performance numbers. J Let ec = ( ec1, ec2,..., ec m ) denote the power consumption of m threads, and the matrix of n performance counters in m threads is C = [ ci, j](1 ≤i ≤ m,1 ≤ j ≤ n) . We define G j i is the gains of QoS of job J i on host M , i.e. j q j k k k i = i ⋅ i j k = 1 Where k i q k wi k = 1 G ∑ w g( Q , V ) (Eq.1) w is the weights of different QoS requirements k k of job J i , and ∑ = 1 ; g( Qi , V j ) is the kth gains of QoS requirements of job J i : k k k k ⎧ - , k k ⎪Qj Vi when Vj > Qi gQ ( i , Vj ) = ⎨ (Eq.2) k k ⎪⎩ 0 , when Vj < Qi k Where V j is the available QoS capacity of the corresponding host. We define D i as the available theoretical scheduling set j j of job J i with QoS satisfactions, ∃ Di = ( Di | Gi > 0) In order to avoid the QoS contention, the gains of QoS satisfactions must be minimized while still guarantying the QoS requirements. Assume that q j j ⎧ k k k ⎫ OPi = ( Di | Gi > 0) ∩min ⎨∑ wi ⋅g( Qi , V j ) ⎬ (Eq.3) ⎩k = 1 ⎭ Then the objective function for power and QoS constrained scheduling for job set J i is ⎧⎪ ⎫⎪ S = min ⎨∑ OPi⎬∩min(max{ T ( )}) i∈T ci (Eq.4) ⎪⎩Ji ∈J ⎪⎭ 198
Eq.4 is NP-hard and can be solved by heuristics scheduling. With the above task model, we use heuristics scheduling algorithm to solve this problem of power aware job scheduling with QoS constraints. IV. SIMULATION RESULTS AND PERFORMANCE ANALYSIS A. Simulation Setup and Parameters Settings Feedback control based scheduling algorithm is capable of modifying its own scheduling decision and program behavior through time, depending on the execution characteristics. Here, the objective of the feedback control is to schedule jobs to processors while guarantying the QoS requirements and minimizing the total power consumptions of the computing system, preserving its simplicity and low overhead. We test this algorithm and describe its behavior under a number of workloads. Simulations include an analysis of the performance sensibility with the variation of the control parameters and its application in a multi processor computing system. Although the processors of a Massively Parallel Processing system such as a computing cluster or a supercomputer may slightly differ in clock frequency and available memory, these differences usually have little influence on most applications in practice [6]. Hence, in the simulation we use an MPP with multiple machines as the testbed and it is feasible for job migration. B. Simulation Results and Analysis We use simulations and real workload experiments to study the performance of the proposed scheduling algorithm. Extensive sets of tests are executed to simulate the performance of our feedback scheduling power-aware framework under overload and under loaded conditions. In simulations, we use various matrix and vectors to modeling the controlling system, including task sets (i.e., arrival rate, task's period, deadline, actual execution time, worst case execution time, estimated execution time, etc), QoS requirements, power consumptions, and other system parameters. In a simulation setting, all tasks being scheduled are assumed to be periodic and each task's actual execution time is assumed to be known in advance and is indicated in matrix declared in the same .m program. Each task indicates its QoS needs quantitively to the scheduler, which in turn is able to know the global QoS requirements of the system to meet all task requirements. At the initial round when scheduling events occur (the threshold is reached and the scheduling is triggered), the scheduler make the task scheduling decision and the processor voltage/frequency scaling. After several scheduling periods, the scheduler makes the scheduling decision and adjusts the processor frequency and voltage level according to the feedback information collected from the sensor units. The common approach to study the performance of the scheduling algorithm is to compare it with a non-poweraware or non-QoS-aware scheduling algorithm. Thus we studied and compared the performance of the simple and frequently used heuristics such as EDF, Min-min [7], Max-min [7], QoS guided Min-Min [8], Sufferage [9], and MCT (Minimum Completion Time [10], with PFM by testing various scenarios. To evaluate the scheduling algorithm, we use the following metrics: Makespan, Average waiting time, Scheduling success rate, Power consumption, Average violating rate of QoS, Average migration rate, and Overall utilization. Due to space limitation, we only provide the makespan and the Power consumption results here. Makespan(sec) Power Consumption(kw.h) 7 6 5 4 3 2 1 0 70000000 60000000 50000000 40000000 30000000 20000000 10000000 0 Makespan Scheduling Algorithms (a) Makespan Power Consumption Scheduling Algorithms (b) Power consumption Figure 2. Relative performance EDF Min-Min Max-Min Qos-Min-min Sufferage MCT PFM EDF Min-Min Max-Min Qos-Min-min Sufferage In Figure 2(a), the makespan order of the scheduling algorithms from maximum to minimum is: (1) Max-Min, (2) QoS-Min-min, (3) Min-Min, (4) PFM, (5) Sufferage, (6) MCT, and (7) EDF. The makespan of EDF is the smallest because of its smallest computation consumption. PFM dynamically schedules jobs to computing sites according to the real time power consumption and QoS constraints. Thus the makespan of PFM is relatively large. MCT PFM 199
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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each sample belongs to each categor
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A. Data Preparing To generate our t
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oth α and β . determination of Qu
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Strong earthquake 0.1< M L
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indirect causes, and the logical re
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egression. Granger causality test i
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B. Evaluation We have evaluated thi
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used as a source and neighboring ce
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Figure 3. Drainage networks generat
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Combinational logic unit failures i
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Tabal.1 Combinational fault logic t
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under the endorsement of both the m
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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In order to reduce to the number of
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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Page 213 and 214: Where: G i is the selection field o
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input to the artificial neural netw
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(2) In a process (tokens from outsi
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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four layers.The far right of the gr
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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