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LATEST RELEVANT TECHNIQUES AND APPLICATIONS (DISTRIBUTED PROCESS SCHEDULING) Chetan V. Kokate CSc8320 – Advanced Operating Systems FALL 2008

LATEST RELEVANT TECHNIQUES AND APPLICATIONS (DISTRIBUTED PROCESS SCHEDULING)

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LATEST RELEVANT TECHNIQUES AND APPLICATIONS (DISTRIBUTED PROCESS SCHEDULING). Chetan V. Kokate CSc8320 – Advanced Operating Systems FALL 2008. OVERVIEW. Dynamic Load Sharing and Balancing Process Migration Process Implementation Real Time Scheduling. - PowerPoint PPT Presentation

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MAJOR DESIGN ISSUES

LATEST RELEVANT TECHNIQUESAND APPLICATIONS (DISTRIBUTED PROCESS SCHEDULING)

Chetan V. Kokate

CSc8320 Advanced Operating Systems

FALL 2008

1 OVERVIEWDynamic Load Sharing and BalancingProcess MigrationProcess ImplementationReal Time Scheduling

2Stork and Data Aware Schedulers [1].

The unbounded increase in the computation and data requirements of scientific applications has necessitated the use of widely distributed computing and storage resources to meet the demand.

Traditional systems closely couple data placement and computation, and consider data placement as a side effect of computation.

The insufficiency of the traditional systems and existing CPU-oriented schedulers in dealing with the complex data handling problem has yielded a new emerging era: the data-aware schedulers.

One of the first examples of such schedulers is the Stork data placement scheduler. 3

The reason that we categorize the data placement jobs into different types is that all of these types can have different priorities and different optimization mechanisms.

Example : Register, Unregister, Transfer, Release , etc.

Data placement is handled by scientific applications using the traditional systems. This example is a well known bioinformatics application: Blast.

Blast aims to decode genetic information and map genomes of different species including humankind. Blast uses comparative analysis techniques while doing this and searches for sequence similarities in protein and DNA databases by comparing unknown genetic sequences (on the order of billions) to the known ones.

Primararily scientific application oriented.

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Stochastic Approach to Scheduling [2].Focus is on the multiple divisible tasks on a HeterogeneousDistributed Computing System.

The stochastic approach, which was previously applied to DAG scheduling, is employed for scheduling a group of multiple divisible as well as whole independent tasks.

It explicitly considers the standard deviations (temporal heterogeneity) in addition to the mean execution times in deriving a schedule, in order tomodel more closely what would actually happen on averageon a temporally heterogeneous system (instead of approximating the random weights by their means only as in other approaches).

Through an extensive computer simulation, it has been shown that the approach can improve schedules significantly over those by a scheme which uses the average weights only.

5A conventional scheduling scheme which considers only the means of task execution times is not able to find the best possible schedule in a heterogeneous environment.

A new approach to scheduling a group of independent divisible and non-divisible tasks is the stochastic approach.

The first implementation of the approach is based on the Max- Min and Min-Min algorithms, called the stochastic Max- Min and Min-Min.

The schedules derived by the proposed approach are significantly better in terms of the average parallel execution time than those by the static Max-Min and Min-Min which consider only the average execution times of tasks.

Also, the stochastic Max-Min and Min-Min are able to accurately predict the actual performance one can expect on a temporally heterogeneous distributed computing system, i.e. the schedule length obtained by the stochastic Max-Min and Min-Min is very close to the average parallel execution time.

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Long Term CPU Load Prediction System [3]. There exist distributed processing environments composed of many heterogeneous computers.

It is required to schedule distributed parallel processes in an appropriate manner.

For the scheduling, prediction of execution load of a process is effective to exploit resources of environments.

A prediction module selection for an appropriate prediction method according to a state of changing CPU load using a neural network has been recently proposed.

The selection is expected to improve prediction accuracy under circumstancesboth of steady and unsteady states because each prediction method can predict more accurately in each different condition.

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9The NN is a 3-layer perception and each layer is fully connected.

The number of cells in each layer is 6-6-3 (input-hidden-output), respectively.

The last three observed load values V (T ), V (T 1), V (T 2) and three of those differences V (T ), V (T 1), V (T 2) are fed to the input layer.

Each cell in output layer corresponds to each prediction module, which is LAST or process search method or runtime-prediction based method, and the cell that most strongly fires indicates the prediction module to be selected at that time.

From the future prospective , parameters can be refined to improve prediction accuracy and develop scheduling algorithms to exploit long-term CPU load predictions.10

Enhancing Job Scheduling on NOWs [4].Using Simulation, Historical and Hybrid Estimation Systems.

An estimation engine termed CISNE has been introduced into the job scheduling system.

Three different estimation methods have been proposed and implemented in the CISNE system: a simulation tool, a historical system and an integration of both (hybrid).

CISNE is a new scheduling environment.

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The main objective of the CISNE system is to manage parallel applications in a non-dedicated environment, ensuring benefits for the parallel applications, while it preserves the local task responsiveness.

When a parallel job is submitted to the CISNE system, the job waits in a queue until the Queues Manager decides to schedule it.

This decision is taken according to the computational requirements of each parallel job waiting in the queue together with the Node State received from each node. The Node State includes the local load and the amount of idle computational resources on each node.

Once a job is selected from the Jobs Queue, CISNE will select the best subset of nodes to execute it.

Job Selection Policy (JSP) is the policy for selecting the next job to run from the waiting queue. This could depend on the job's priorities (order in the queue), and the cluster state (intrusion level into the local workload, the Multiprogramming Level (MPL) of parallel applications, the memory and CPU usage and the available nodes).

Node Selection Policy (NSP) is the policy for distributing the parallel tasks among the nodes. This depends on the cluster state and the parallel job's characteristics.13

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15Dynamic Scheduling with Process Migration [5].The migration cost has been modeled to introduce an effective method to predict the cost of process migration.

The dynamic scheduling mechanism considers migration cost as well as other conventional influential factors for performance optimization in a shared, heterogeneous environment.

Experimental results show that the proposed dynamic scheduling system is feasible and improves the system performance considerably.

The design of a migration-based dynamic scheduling is fourfold: reschedule triggering, migration cost modeling, task scheduling, and parameter measurement.

Select the destination machine based on an estimate of the completion time of the migrated process. When an application consists of multiple processes running concurrently on different machines, we need to consider the overall application completion time as a selection criterion.16

Assumption: an application is located on machine, m0.

Objective: dynamically reallocate an application when an abnormality is noticed

BeginReceiving the triggering signalList a set of idle machines that are lightly loaded over an observed time period, M= {m0,m1,.,mq} ; p = 1;

For each machine mk (1 k q) ,Calculate the migration cost, C0k ;Calculate the mean of the remote task execution time, Calculate the application completion time, T0kIfT 0 p' > T0k , then p = k ;

End For

Migrate the application from m0 to mp ;

End17

A Real Time Scheduler Using Generic Neural Network [6].A generic neural network scheduler for scheduling a set of jobs with deadlines on a set of resources in critical real time applications, in which a schedule is to be obtained within a short time span has recently been proposed.

Based on GENET network model with progressive stochastic search scheme.

To cope with the bi criterion of deadlines and optimization, a heuristic policy which is modified from the earliest deadline first policy and an optimal mechanism are embedded into the proposed model.18

19The real time scheduling problem could be viewed as a constraint satisfaction problem (CSP).

Different constraints that need to be taken into account :Order ConstraintsOverlap ConstraintsDeadline Constraints

GENET is a local search approach with a neural network connectionist architecture for solving finite CSPs with binary constraints .

In the GENET network, a binary CSP (U, D, C) can be represented by a set of label nodes and weighed incompatible connections.

GENET neural network scheduler consists of 3 type of neurons:u-type neuronsv-type neurons f- type neurons

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Energy-Aware Soft Real-Time Scheduling for Multi-Radio Embedded Devices [7]. An energy-efficient scheduling algorithm for the data-communications of soft real-time periodic tasks on multi-radio embedded devices was recently proposed.

To cope with the dynamic fluctuation of channel conditions, a feedback mechanism that monitors radio throughput is introduced to guarantee real-time behaviors.

A formal analysis on the scheduling, exploring the relationship between tardiness bounds and energy savings and that between network stability and (m; k)-firm deadline guarantees has also been done.

This approach is applicable to real-time communication scheduling problems in which background interference and other environmental factors are not known a priori.

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22The method is local in that it does not require broad knowledge about the networking environment, and is therefore suited to scenarios where interference is unpredictable.

The method results in significant energy savings by allowing radios to be turned off in ways that still allow real-time guarantees to be made.

There are several interesting areas of future work.

23REFERENCES[1]A new paradigm in data intensive computing: Stork and the data-aware schedulers Kosar, T.; Challenges of Large Applications in Distributed Environment, 2006 IEEE 19 June 2006 Page(s):5 12.

[2] Stochastic Approach to Scheduling Multiple Divisible Tasks on a Heterogeneous Distributed Computing System Kamthe, A.; Lee, S.-Y.; Parallel and Distributed Processing Symposium, 2007. IPDPS 2007. IEEE International 26-30 March 2007 Page(s): 1-11. [3] Long-Term CPU Load Prediction System for Scheduling of Distributed Processes and Implementation Sugaya, Y.; Tatsumi, H.; Kobayashi, M.; Aso, H.; Advanced Information Networking and Applications, 2008. AINA 2008. 22nd International Conference on 25-28 March 2008 Page(s):971 - 977.

[4] Using Simulation, Historical and Hybrid Estimation Systems for Enhacing Job Scheduling on NOWs Hanzich, M.; Hernandez, P.; Luque, E.; Gine, F.; Solsona, F.; Lerida, J.L.; Cluster Computing, 2006 IEEE International Conference on 25-28 Sept. 2006 Page(s):1 - 12 .

24[5] Dynamic Scheduling with Process Migration Du, Cong; Sun, Xian-He; Wu, Ming; Cluster Computing and the Grid. 2007. CCGRID 2007. Seventh IEEE International Symposium on May 2007 Page(s):92 99.

[6] A Real Time Scheduler Using Generic Neural Network for Scheduling with Deadlines Xin Feng; Lixin Tang; Hofung Leung; Neural Networks and Brain, 2005. ICNN&B 05. International Conference on Volume 1, 13-15 Oct. 2005 Page(s):504 508

[7] Energy-Aware Soft Real-Time Scheduling for Multi-Radio Embedded Devices http://research.microsoft.com/research/pubs/view.aspx?type=Technical%20Report&id=148325

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