1. Omar Darwish

2.  Load balancing is the process of improving the performance of a parallel and distributed system through a redistribution of load among the processors.

3. Who Initialized the load balancing algorithm ?  Sender Initiated ◦ Initialized by the sender. ◦ Sender sends request messages till it finds a receiver that can accept the load.  Receiver Initiated ◦ Initiated by the receiver. ◦ Receiver sends request messages till it finds a sender that can get the load.

4.  The performance of the processors is determined at the beginning of execution.  Master processor and slave processors.  A task is always executed on the processor to which it is assigned.  Reduce the execution time, minimizing the communication delays

5.  Round Robin Algorithm ◦ Processor choosing is performed in series and will be back to the first processor if the last processor has been reached.  Randomized Algorithm ◦ Uses random numbers to choose slave processors based on statistics.

6.  Central Manager Algorithm ◦ The central processor is able to gather all slave processors load information ◦ The chosen slave processor is the processor having the least load  Threshold Algorithm ◦ The processes are assigned immediately upon creation to hosts. ◦ Under loaded, medium and overloaded.

7.  Dynamic algorithms allocate processes dynamically when one of the processors becomes under loaded.  Buffered in the queue  Allocated dynamically upon requests from remote hosts

8.  Central Queue ◦ Stores new activities and unfulfilled requests as a cyclic FIFO queue on the main host.  Local Queue  A parameter defines the minimal number of ready processes the load manager attempts to provide on each processor

9.  Adaptability ◦ Static No, Dynamic Yes  Predictability ◦ Static Yes, Dynamic No  Waiting Time (Queuing time )  Execution System

10.  Fitness Function ◦ The main objective of GA is to find a schedule with optimal cost while load-balancing.  Less execution time.  Less communication cost.  Higher processor utilization.  Maximum system throughput

11.  Selection  Processors permutation  Crossover  Exchange portions between strings.  Mutation  Change the genes in a chromosome (Processors set)

12.  NP complete problem.  Untractable with large N of tasks and P number of processors.

13. Actual execution cost If we consider communication time, the work load L is

14.  Objective function

16.  Processor 1  3 tasks/slot  Processor 2  6 tasks/slot  GCD =3  P1 P0 P1 P1 P0 P1 P1 P0 P1 O(n log log n/log n), GCD in parallel

17.  Static Algorithms  Round robin fashion  Heterogeneous processors  Weighting processors depends on capabilities  Fibonacci gives the high capabilities processors extra load.

18.  Rank processors depends on capabilities  Give each processor weight depends on its rank (linearly or Fibonacci)  While (process<>0) ◦ Assign tasks for each processor depends on its weight

19.  Linear approach  Ex ◦ Ordering weights (1,2…,7) ◦ 7 processors ◦ The highest capability takes weight 7 ◦ The lowest capability takes weight 1 ◦ Processor 7 will get 7 process each slot time ◦ Processor 1will get 1 process each slot time

20.  Fibonacci approach  Ex ◦ Ordering weights (1,1,2,3,5,8,13) ◦ 7 processors ◦ The highest capability takes weight 13 ◦ The lowest capability takes weight 1  Processor 7 will get 13 process each slot time  Processor 1will get 1 process each slot time

21. Load Balancer Tasks P1 P2 P3 Distributing Tasks among different processors

22. 1 t2 t3 t4 t5 t6 t7 t Round 1 1 t2 t3 t4 t5 t6 t7 t Round 2 Until No more tasks

23. 1 t 2 t3 t5 t8 t13 t Round 1 1 t 2 t3 t5 t8 t8 t13 t Round 2 Until No more tasks

24.  M tasks  K processors  How to distribute tasks among the processors  Less drops packets  Less time

25.  Number of processors N  6,7,8,9,10  Processors speed : ◦ High speeds (N/2) =0.10 * i  i is the processor # ◦ Low speeds (N/2) =0.03 * i  i is the processor #  For example processor with id 6 can process 6.0*0.10*number of tasks in the Queue  Processor with id 2 can process 2.0*0.3*number of tasks in the Queue.  Memory For High speed computers 20 * i locations For low speed computers 320*i locations  1000 tasks  Arrival packets =20, 33,….

28.  Fibonacci distribution guarantee the more utilization of higher capabilities processors and less load on the less capabilities processors.

29.  The presentation explore: ◦ Static vs. Dynamic load balancing technique. ◦ The formulization of task scheduling problem.

30.  Sharma, Sandeep, Sarabjit Singh, and Meenakshi Sharma. "Performance analysis of load balancing algorithms." World Academy of Science, Engineering and Technology 38 (2008): 269-272.  Rajguru, Abhijit A., and S. S. Apte. "A Comparative Performance Analysis of Load Balancing Algorithms in Distributed System using Qualitative Parameters." International Journal of Recent Technology and Engineering 1.3 (2012).  Shah, Purnima, and S. M. Shah. "Load Balancing in Distributed System Using Genetic Algorithm}." Special issues on IP Multimedia Communications}: 139-142.  Attiya, Gamal, and Yskandar Hamam. "Task allocation for minimizing programs completion time in multicomputer systems." Computational Science and Its Applications–ICCSA 2004. Springer Berlin Heidelberg, 2004. 97-106.  Chor, Benny, and Oded Goldreich. "An improved parallel algorithm for integer GCD." Algorithmica 5.1-4 (1990): 1-10.  http://kb.linuxvirtualserver.org/wiki/Weighted_Round-Robin_Scheduling

31. Questions ?