RESEARCH DIRECTIONS IN GRID COMPUTING Dr G Sudha Sadasivam
Professor CSE Department, PSG College of Technology
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Works to be presented Task Scheduling in Computational Grids
using Swarm Intelligence DLS using Hadoop data grid
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Optimisation of communication bandwidth and grid utilisation
Introduction to PSO Proposed approach Experimental results
OVERVIEW
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Computational Grid Computational grids provide a new platform
for executing large-scale resource intensive applications on a
number of heterogeneous computing resources across political and
administrative domains A grid coordinates resources that are not
subject to centralized control. The goal of the GRID system is that
the utility of the combined system is significantly greater than
that of the sum of its parts.
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Current Scenario In a grid environment, the jobs are processed
at the grid resources in a fine-grained form Sending, processing,
and receiving the jobs one at a time, increases the total amount of
time needed to execute all the jobs from a user. Total execution
time = Transmission time + Processing time. Objective : to minimise
total processing time. Minimise Communication time, Processing
time; Improve overall grid utilisation in the application
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Improvements in Communication Time Processing a small job with
low processing capabilities in a capable resource leads to poor
utilization of that particular resource due to Overhead time Job
transmission time Efficient job grouping-based scheduling system
dynamically assembles the individual fine-grained jobs of an
application into a group of jobs, and sends these coarse-grained
jobs to the grid resources. Objective is achieved through good
scheduling strategy
The scheduling strategy takes into account (i) the processing
requirements and priority for each job, (ii) the grouping jobs
according to the processing capabilities of available resources,
and (iii) transmitting of the job grouping to the appropriate
resources. The job grouping is done based on a particular
granularity size. It measures the total amount of jobs that can be
completed within a specified time in a particular resource.
Processing Elements (PEs) with different speeds (measured in
either MIPS) are created One or more PEs can be put together to
create a machine. One or more machines can be put together to
create a grid resource. Each grid resource is described in terms of
their various characteristics, such as resource ID, name, total
number machines in each resource, total processing elements (PE) in
each machine, MIPS of each PE, and bandwidth speed.
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Once GridSim starts, the resource entities register themselves
with the Grid Information Service (GIS) entity. The broker entity
queries GIS entity for resource discovery, based on the user
entitys request. The GIS entity returns a list of registered
resources, and their contact details. The broker entity queries the
resources for resource configuration, and properties. They respond
with resources cost, capability, availability, load etc. Broker
entity selects the appropriate resources, and sends user jobs
(gridlets) to those resources for execution. The resources send
back the processed gridlets to the I/O queue of the broker entity.
Finally, the user will collect the processed gridlets from the I/O
queue.
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GIS Broker Resources Users 2. Request 1. Register 3.Query 4.
Resource list 5.Query load 6.returns load 7. Send jobs Queue 8.
Store results ] 2. Request has gridlets, average MI, granularity
time, resource details (MI) anf granulaity time, overhead time
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SHEDULER ARCHITECTURE Register resources to GIS. Accept fine
grained job details from the user. Grid scheduler queries GIS to
get grid resource characteristics from the resource file specified
by the user. Priority is assigned to jobs according to the given
average MI and MI deviation percentage. Select a resource and
multiply the resource MIPS with the given granularity size. Group
the jobs based on the total MI of the resource This group is
associated with a resource ID. Submit the job groups to their
corresponding resources for job computation using a dispatcher. Get
the results and record statistics
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Total number of jobs Average MI rate of job MI deviation
Percentage Overhead processing time Granularity time Grid Resource
Grid resource 0 Grid resource 1 Grid resource N Grid Resource File
User Input Gridlets Grid resources characteristics Gridlet MI
Resource MIPSGranularity time Total MIPS Grid resource 0 Gridlet
group 0 Grid resource 1 Gridlet group 1 Grid resource 2 Gridlet
group 2 Gridlet groupsResource IDs .. Gridlet Scheduler (1) (3) (4)
(5) (6) (7) (2)
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Drawback In the above algorithm the load at the resources may
not be balanced. To achieve load balancing and to improve the
efficiency of the entire grid application, a Particle Swarm
Optimization based job grouping is proposed
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Particle Swarm Optimisation People solve problems by
interacting with others. the individuals may move towards one
another in a sociocognitive space. Social influence and social
learning enable a person to maintain cognitive consistency Social
influencesocial learningcognitive consistency Swarm intelligence is
based on social-psychological principles and provides insights into
social behaviorwarm intelligencesocial-psychologicalsocial behavior
It applies the concept of social interaction to problem solving. It
is based on the movement of swarms (with a velocity) in search
space looking for the optimum solution based on its own experience
and experience of its neighbours.
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Particles in the swarm move through the solution space, and are
evaluated according to some fitness criterion after each
timestep.fitness Over time, particles are accelerated towards those
particles within their communication grouping which have better
fitness values A swarm is a set of (mobile) agents which which
communicate directly or indirectly with each other, and which
collectively solve a problem in a distributed fashion Eg body swarm
of swarms Bee swarms
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The swarm is typically modelled by particles in
multidimensional space that have a position (X i ) and a velocity
(V i ). These particles fly through hyperspacemodelled
multidimensional space velocity Particles havetwo reasoning
capabilities their memory of their own best position (pbest)and
knowledge of the global or their neighborhood's best (gbest).
Members of a swarm communicate good positions to each other and
adjust their own position and velocity based on these good
positions.
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Inertia Term: -This term forces the particle to move in the
same direction - Audacious tendency, following own way using old
velocity VELOCITY UPDATING 3 terms that create new velocity: 1.
Inertia Term 2. Cognitive Term 3. Social Learning Term
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Cognitive Term: (Personal Best) This term forces the particle
to go back to the previous best position: Conservative tendency
Velocity Updating 3 terms that create new velocity: 1. Inertia Term
2. Cognitive Term 3. Social Learning Term
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Basic Idea: Cognitive Behavior ~ An individual remembers its
past knowledge Food : 100Food : 80Food : 50 Where should I move
to?
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Social Term: This term forces the particle to move to the best
previous position of its neighbors - Sheep like tendency, be a
follower Velocity Updating 3 terms that create new velocity: 1.
Inertia Term 2. Cognitive Term 3. Social Learning Term
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Basic Idea: Social Behavior ~An individual gains knowledge from
other population member Bird 2 Food : 100 Bird 3 Food : 100 Bird 1
Food : 150 Bird 4 Food : 400 Where should I move to?
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PSO BASIC ALGORITHM Step 1: The velocity and position of all
particles are randomly set within a range Step 2: Velocity updating
At each iteration, the velocities of all particles are updated
according to, where p i and v i are the position and velocity of
particle i, p i,best and g i,best is the position with the best
objective value found so far by particle i and the entire
population respectively;
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w is a parameter controlling the dynamics of flying; fast slow
( w=w*) R 1 and R 2 are random variables in the range [0,1]; -
stochastic exploration. c 1 and c 2 are factors controlling the
related weighting of corresponding terms Step 3: Position updating
The positions of all particles are updated according to, After
updating, p i should be checked and limited to the allowed
range.
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Step 4: Memory updating Update p i,best and g i,best when
condition is met, where f(x) is the objective function to be
optimised. Step 5: Stopping Condition The algorithm repeats steps 2
to 4 until convergence. Once stopped, the algorithm reports the
values of g best and f(g best ) as its solution.
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PSO algorithm Initialize particles with random position and
zero velocity Evaluate fitness value Compare & update fitness
value with pbest and gbest Stop? Update velocity and position Start
End YES NO pbest = the best solution (fitness) a particle has
achieved so far. gbest = the global best solution of all
particles.
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PSO is adaptive when compared to GA Cognitive / experiential
behavior Social sharing of information No operators simple PSO ties
GA and evolutionary programming
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SPV rule The Smallest Position Value (SPV) rule is used find a
permutation corresponding to the continuous position. Consider n
tasks and m resource problem, The position vector has a continuous
set of values. Based on the SPV rule, the continuous position
vector is transformed to dispersed value permutation for task set.
The operation vector defines resource to which the task is to be
allotted.
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EXAMPLE Let n= 10 and R = 4 Dimension XSR 01.0330 13.8190
20.1100 30.3911 43.1571 53.4182 62.6452 73.0063 80.8922
91.5241
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Results The MIPS of each resource is computed as follows:
Resource MIPS = Total_PE * PE_MIPS, where Total_PE = Total number
of PE at the resource, PE_MIPS = MIPS of PE Process_Cost = T * C,
where T = Total CPU Time for Gridlet execution, and C = Cost per
second of the resources
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Simulation Time Processing Cost
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GT Resources R1R2R3R4R5R6R7R8R9
2545.3944.0544.7846.5544.1345.3143.2927.6828.66
5092.0086.0289.6390.9086.2370.5873.6777.6875.88
75133.27135.94117.33134.79117.37121.29117.42120.77120.10
100183.43159.78159.36161.74162.74163.13170.29165.64169.94 Load of
resources in PSO
Conclusion Communication overhead is minimised using Job
grouping Efficient scheduling is achieved using swarm intelligence
Overall grid application performance is enhanced