LAZO86 Lazowska, E.D., Zahorian, J., Graham, S.G., and Sevcik, K.C., Quantitative System Performance, Computer System Analysis Using Queueing Network Models, Prentice-Hall, Englewood Cliffs, USA, 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....to worker processes. Of course, our presentation of placeholder scheduling is in the context of job scheduling and not task scheduling. Nonetheless, the basic strategies are identical. Of course, there is a large body of research in the area of job scheduling and queuing theory (for example, [5, 11, 13]) This paper has taken a more systems oriented approach to scheduling. Our scheduling discipline at the metaqueue (i.e. command line server) is currently simple: rst come rst served. In the future, we hope to investigate more sophisticated scheduling algorithms that understand the ....

E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik. Quantitative System Performance. Computer Systems Analysis Using Queueing Network Models. Prentice Hall, Inc., 1984.

....of tasks etc. and the assignment of program parts (tasks that execute concurrently and cooperatively) to resources. Neither the approach of resource oriented performance evaluation of parallel processing systems, where only the system resources are modeled to some extent of detail [Lazo 84] nor the program or process oriented approach, where exclusively software aspects are subject to performance modeling and evaluation [Vern 85] Vern 87] Gele 86] Chim 88] seem adequate to characterize the performance of parallel systems. The actual performance of such systems is always ....

E. D. Lazowska, J. Zahorjan, G. Scott Graham, and K. Sevcik. Quantitative System Performance. Computer System Analysis using Queueing Network Models. Prentice-Hall, Englewood Cliffs, New Jersey, 1984.

....to worker processes. Of course, our presentation of placeholder scheduling is in the context of job scheduling and not task scheduling. Nonetheless, the basic strategies are identical. Of course, there is a large body of research in the area of job scheduling and queuing theory (for example, [4, 7, 9]) This paper has taken a more systemsoriented approach to scheduling. Our scheduling discipline at the metaqueue (i.e. command line server) is currently simple: first come first served. In the future, we hope to investigate more sophisticated scheduling algorithms that understand the ....

E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik. Quantitative System Performance. Computer Systems Analysis Using Queueing Network Models. Prentice Hall, Inc., 1984.

....in [26] requires the mean service time for each class to be identical for service centers that have First Come First Serve (FCFS) service policy. Because this will be restrictive for our multi class model, we adapt the MVA algorithm modification given by Lazowska, Zahorjan, Graham and Sevcik [28] that allows different mean service times for different classes of requests, even when the service centers have FCFS policy. 2.1.3 Striped File Systems Striping distributions are foundational to RAID (Redundant Array of Independent Disks) systems [29] and striping file systems (e.g. Zebra [30] ....

....service time needed by the customers waiting currently in the queue. This type of the MVA technique assumes all the job classes exhibit the same service time distribution for this recurrence relation to hold. The standard MVA technique can be modified to allow class dependent mean service times [28]. By expending Equation 3.18 to include different service times and mean queue lengths for each class we get R c ( Gamma N ) S c C X i=1 S i Q i ( Gamma Gamma Gamma Gamma N Gamma 1 c ) 3.19) The MVA algorithm iterates over each possible Gamma N vector and computes the mean ....

E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik, Quantitative System Performance, Computer Systems Analysis Using Queueing Network Models. Prentice Hall, Inc., 1984. 164

....within a very small number of iterations. The high degree of accuracy is largely due to heuristic extensions for representing a number of important system features such as priority queueing disciplines, simultaneous resource possession, and FCFS queues with class dependent mean service times [14]. The work in this paper is motivated by a recent highly e# cient heuristic AMVA model for evaluating shared memory architectures that contain complex modern processors [24] In that architecture model, each processor is modeled by a FCFS queue. Service times at the processor represent the time ....

....that were modeled. For several of the benchmarks, Figure 1 shows the throughput (in units of instructions per cycle, IPC) obtained in [24] by (1) a detailed architecture simulator called RSIM, 2) the AMVA model with the standard AMVA approximation for FCFS centers with high service time CV [14], 3) the AMVA model with a new simple heuristic interpolation ( simple interp ) for estimating the mean residual service time of the customer in service at an arrival instant at any of the processors. Note that the standard AMVA model provides system throughput estimates that have large error ....

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E. Lazowska, J. Zahorjan, G. Graham, and K. Sevcik. Quantitative System Performance, Computer System Analysis Using Queueing Network Models. Prentice-Hall, Englewood Cli#s, NJ, 1984.

....clustering. This paper presents the results of using GAs to characterize the workload of a computer system. 1. Introduction Queueing network models are often used to model computer systems. Their advantages include the fact that they are well understood and they have good predictive power [3]. When using a queueing network model as a system model, the workload is characterized by the demands that are placed on system resources by each customer and by the number of customers. Customers that exhibit roughly the same behavior are aggregated into customer classes. Such aggregation reduces ....

....clustering, a manageable set of workload classes is identified, each class with an associated average feature vector. From the analytical modelling perspective, stochastic queueing networks are often the models of choice. The mathematical basis of these models is well studied and largely accepted [3]. In a queueing network model, the workload is typically represented by a specified number of workload classes. Each class has a specified number of customers and each class has a specified demand that is placed upon each service center (i.e. device) The (per class) service center demands are ....

E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik, Quantitative System Performance. Computer System Analysis Using Queueing Network Models, Prentice-Hall, 1984.

....formal and intuitive understanding of the modelled systems. Since performance is the only reason to use parallel systems, it is vital that programmers gain such understanding. Mean Value Analysis is a valuable modelling technique for computer system performance studies, but conventional techniques [LaZaGrSe84] for evaluating these models produce only numerical solutions. In this paper we present a closed form approximate solution to a Mean Value Analysis model of a busbased multiprocessor with coherent caches. The resulting solution can be used to relate the performance of a system directly to the ....

Lazowska E.D., Zahorjan J., Graham G.S., and Sevcik, K.C. Quantitative System Performance, Computer System Analysis Using Queueing Network Models, Prentice-Hall, Inc., Englwood Cliffs, N.J., 1984

.... effect on estimated throughput (less than 4 reduction in throughput for all applications validated in [8] except FFTopt which has a 10 reduction) and they are not discussed in [8] 2 The estimated mean residual life for random arrivals equals the second moment of service time divided by 2 [7]. upgrade memory system transactions. For any of these memory requests, the customer leaves the processor and visits the appropriate memory system resources. Once throughput is computed from the weighted average of the value at each M , synchronization effects are accounted for as described in ....

E. Lazowska, J. Zahorjan, G. Graham, and K. Sevcik. Quantitative System Performance, Computer System Analysis Using Queueing Network Models. Prentice-Hall, Englewood Cliffs, NJ, May 1984.

....by Martin Marietta Energy Systems, Inc. for the U.S. Department of Energy under contract no. DE AC05 84OR21400. 1. Introduction Queueing network models are often used to model computer systems. Their advantages include the fact that they are well understood and they have good predictive power [11]. When using a queueing network model as a system model, the workload is characterized by the demands that are placed on system resources by each customer and by the number of customers. Customers that exhibit roughly the same behavior are aggregated into customer classes. The demands are ....

....clustering, a manageable set of workload classes is identified, each class with an associated average feature vector. From the analytical modelling perspective, stochastic queueing networks are often the models of choice. The mathematical basis of these models is well studied and largely accepted [11]. In a queueing network model, the workload is typically represented by a specified number of workload classes. Each class has a specified number of customers and each class has a specified demand that is placed upon each service center (i.e. device) The (per class) service center demands are ....

E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik, Quantitative System Performance. Computer System Analysis Using Queueing Network Models, Prentice-Hall, 1984.

.... are: T x = 1 1 U rv NU U rv ######### ) T a T xo = 1 U rv 1 NU ####### T a T xo ; x = r, v T w = 1 1 U NU U ######## ) T a T wo = 1 U 1 NU ####### T a T wo Waiting Time Equations Using the mean value technique for queuing network models [40], The waiting time of an arriving request is decomposed into three components based on the types of the requests that delay the service of the new request. For a blocking request: W rv = K rv r K rv v K rv w where K rv x = N 1) # # (Q ## x B x ) T x B x Re x # # ; x = r, v ....

.... W rv = K rv r K rv v K rv w where K rv x = N 1) # # (Q ## x B x ) T x B x Re x # # ; x = r, v K rv w = N # # (Q ## w B w ) T w B w Re w # # The residual service time for the request that is being serviced when a new read or invalidation request arrives is [40]: Re x = 2 T x ### ; x = r, v, w The probabilities that the bus is busy servicing the request from a particular cache when a new read or invalidation request arrives can be approximated as: B x = 1 U rv U x ####### ; x = r, v, w where U x = R P x T x ##### ; x = r, v U w = R P w P ....

E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik, Quantitative System Performance, Computer System Analysis Using Queueing Network Models. Englewood Cliffs, New Jersey: Prentice Hall, 1984.

....rather than contributing directly to processor stall time. 4 FastILP underpredicts for Water because rollbacks of misspeculated loads, triggered by disambiguating stores, are not yet accurately modeled. 5 The estimated mean residual life equals the second moment of service time divided by 2 [17]. 6 Note that the standard formula for mean residual life is assumed at all other queues in the model. Since the variance in service time at the bus, memory modules, and other memory system resources is low, the standard MVA approximation can be expected to perform well. model RSIM benchmark ....

E. Lazowska, J. Zahorjan, G. Graham, and K. Sevcik. Quantitative System Performance, Computer System Analysis Using Queueing Network Models. Prentice-Hall, Englewood Cliffs, NJ, May 1984.

....of tasks etc. and the assignment of program parts (tasks that execute concurrently and cooperatively) to resources. Neither the approach of resource oriented performance evaluation of parallel processing systems, where only the system resources are modeled to some extent of detail [Lazo 84] nor the program or process oriented approach, where exclusively software aspects are subject to performance modeling and evaluation [Vern 85] Vern 87] Gele 86] Chim 88] seem adequate to characterize the performance of parallel systems. The actual performance of such systems is always ....

E. D. Lazowska, J. Zahorjan, G. Scott Graham, and K. Sevcik. Quantitative System Performance. Computer System Analysis using Queueing Network Models. Prentice-Hall, Englewood Cliffs, New Jersey, 1984.

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LAZO86 Lazowska, E.D., Zahorian, J., Graham, S.G., and Sevcik, K.C., Quantitative System Performance, Computer System Analysis Using Queueing Network Models, Prentice-Hall, Englewood Cliffs, USA, 1986.

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E. Lazowska, J. Zahorjan, G. Graham, and K. Sevcik, Quantitative System Performance, Computer System Analysis Using Queueing Network Models. Prentice Hall, May 1984.

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