RESEARCH DIRECTIONS IN GRID COMPUTING Dr G Sudha Sadasivam Professor CSE Department, PSG College of Technology

Works to be presented Task Scheduling in Computational Grids using Swarm Intelligence DLS using Hadoop data grid

Optimisation of communication bandwidth and grid utilisation Introduction to PSO Proposed approach Experimental results OVERVIEW

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.

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

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

GroupedGridlet 2 /200 GroupedGridlet 1 /104 GroupedGridlet 0 / 98 GroupedGridlet 5 / 104 GroupedGridlet 3 / 93 GroupedGridlet 4 /203 Gridlet 0/20 Gridlet 1/21 Gridlet 2/20 Gridlet 3/22 Gridlet 4/15 Gridlet 5/19 Gridlet 6/18 Gridlet 7/19 Gridlet 8/22 Gridlet 9/26 …………… Gridlet 50/29 Gridlet 51 /35 Gridlet 52/ 29 …………… Gridlet 96/28 Gridlet 97/22 Gridlet 98/30 Gridlet 99/24 R1 / 33 MI : 99 R2 / 35 MI : 105 R3 / 70 MI : 210

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.

Simulator Grid simulation toolkit Bricks Microgrid Globus SimGrid

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.

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 entity’s 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.

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

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

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)

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

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.

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

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.

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

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

Basic Idea: Cognitive Behavior ~ An individual remembers its past knowledge Food : 100Food : 80Food : 50 Where should I move to?

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

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?

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;

w is a parameter controlling the dynamics of flying; fast  slow ( w=w*β) 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.

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.

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.

PSO is adaptive when compared to GA Cognitive / experiential behavior Social sharing of information No operators simple PSO ties GA and evolutionary programming

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.

EXAMPLE Let n= 10 and R = 4 Dimension XSR

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

Simulation Time Processing Cost

GT Resources R1R2R3R4R5R6R7R8R Load of resources in PSO

GT Resources R1R2R3R4R5R6R7R8R Load Random job grouping

Processing Time Processing Cost

LoadLoad RESOURCE UILISATION

PERCENTAGE UTILISATION

Conclusion Communication overhead is minimised using Job grouping Efficient scheduling is achieved using swarm intelligence Overall grid application performance is enhanced