Journal of Power and Energy Engineering, 2015, 3, 467-469
Published Online April 2015 in SciRes. http://www.scirp.org/journal/jpee
How to cite this paper: Shi, K. L. (2015) The Application of IHA in Grid Cloud Computing Task Decomposition and Scheduling
Based on Bionics. Journal of Power and Energy Engineering, 3, 467-469. http://dx.doi.org/10.4236/jpee.2015.34064
The Application of IHA in Grid Cloud
Computing Task Decomposition and
Scheduling Based on Bionics
North China Electric Power University, Beijing, China
Received April 2015
Based on the large amount and variations of the power grid task as well as its requirement of real-
time performance and economic benefit, we make a further improvement and expansion of IHA
(Improved Heuristic Algorithm) on the combination of bionics in genetic engineering and evolu-
tion to solve the decomposing and scheduling problems. Firstly, we transform those complex de-
composing problems into the operational optimal solution problem by IHA to decrease the rate of
running into the local optimal solution . In task scheduling, we classify the sub-tasks by the
emergency degree for resource allocation, which not only largely reduces the calculation and re-
source cost but also improves working efficiency and the speed of execution [ 2]. Finally, we select
optimal scheduling scheme by the Fitness function defined about time and cost.
Grid T ask , Cloud Computing, IHA, Bi onics, Cloudsim
With the development of modern science and technology, biological sciences and power system began to appear
increasingly close relationship. Like a large biological system, power system is carrying out complicated but
orderly work every moment, which distributes numerous power grid computing tasks in a wide range of re-
source pool constituted of many computers. Then corresponding solutions and scheduling mechanism plays in a
coordinating role to make the users obtain corresponding cloud resources on demand. In the same time, tasks
should be given the real-time and effective solution. Genetic engineering and evolution mechanism are com-
bined with cloud computing to solve the decomposing and scheduling problems . The close connection be-
tween modern biological science and power system is elaborated by simulation and results analysis.
At present, the characteristics of our power grid is large regional difference of distribution and complex rela-
tionship among each part. Based on powerful storage and computing capacity of intelligent cloud, we can dis-
tribute the sub-tasks of each node to many distributed computers to greatly increase the utilization rate of re-
K. L. Shi
source s, therefore, improves the economic benefit of power grid. However, the existing grid computing task de-
composition algorithm easily run into the problem of local optimal solution, which affects the normal manage-
ment of resource request from users shows a performance of power grid. The task scheduling under the cloud
computing is collecting all kinds of information from power grid and giving a feedback to the power cloud. The
computers will calculate the relevant parameters and indicators based on the information stored in cloud re-
source s, and give valid instructions by feasible judgment of operation state both in safety and economy to
achieve a relatively stable state by necessary adjustments.
Inappropriate task scheduling strategy can lead to waste of cloud resources and even influence the quality of
power grid, which may seriously affected the enterprise’s economic benefit.
3. The Basic Principle of IHA
The basic principle is to partition computing tasks. Namely, sub-tasks are stored in one node of the and/or tree,
and the user request task decomposition means choosing the right point of division and building structure of the
tree. We estimate overall consumption after using heuristic method to find a division of the task, when dividing
point selection is improper, sub-task will not be able to perform and be regard as infinity of consumption. Re-
peat these steps until all the sub-tasks couldn’t be further subdivided, then we think the task decomposition is
Task decomposition has two important steps: first, in order to ensure the optimal solution is for overall tasks,
every step must meet the constraint conditions of relevant task decomposition; Second, in order to improve the
efficiency of resource requesting and distributing and minimize the costs, we should carry on reasonable classi-
fication and allocation to these divided tasks.
4. The Combination of Bionics
Considering some related concept of genetic engineering in biological science, gene cutting, we need use a so-
called “scalpel” to do molecule specificity cutting for some specific nucleotide sequences on the DNA, whose
scientific name is restriction endonuclease. We regard the initial power grid computing tasks as a DNA analogy,
on which there are one or some nodes of operation, monitoring, protection, distribution, marketing, etc. The
constraint to divide the task can be regard as “scalpel” to do specific cutting for the computing task which con-
forms to the corresponding node cutting constr aint s. Then we take one of these nodes to do further division with
the IHA and look for task dividing point of a node. If the node has at least one task segmentation point and the
sub -tasks can be retrieved in the cloud after segmentation, then this point can be determined as the division point
of tasks. Do further division until all sub-task can’t be divided any more, task decomposition process is over. If
the divided sub-task can’t be retrieved in the cloud, rework following process above until all its tasks can be re-
trieved in the cloud. Finally, polish sub-tasks tree according to the consumption to perfect the decomposition
Based on the natural selection in biological science, we expand and perfect the original algorithm to make it
has a good performance both in reasonable decomposition but also for optimal resource allocation. We define
these alternative task scheduling schemes as a species. First of all, we encode the initialized population, then we
define a fitness function to evaluate whether the individual of population is competitive enough. The task sched-
uling scheme with highest fitness function value above all alternative schemes will give the most appropriate
5. Flow Chart of the Alg ori th m
Certain grid tasks and resources must correspond to different solutions. Because the degree of emergency and
importance is directly related to the real-time requirements of tasks, thus affecting the priority level of a task in
the process of solving . We first make a division of emergency degree for the sub-tasks which can’t be di-
vided any more, then a resource allocation scheme is presented. Next, to avoid the full arrangement for each task
as well as make a selection from the alternative schemes created by the full arrangement, therefore saving a lot
of time and computing resources to improve the efficiency and saving resources. Finally, we take further
screening of these relatively reasonable scheduling scheme by using the theory of natural selection and define
the fitness function considering time and cost to give a standard to screen out optimal solution as well as give
scheduling command . The process is shown in Fig ure 1.
K. L. Shi
& Cons traints
Nodes of operation, monitoring,
Find the task dividing
point of node
At least one dividing point
&sub-tasks can be retrieve
in the smart cloud
Any task dividing point?
Dividing finished, polish
the tree of sub-tasks
Present a resource
Give scheduling schemes
based on the allocation result
encode the initialized
Use the fitness function to
measure the competitiveness
of the scheduling schemes
Select the best
scheme&give command .
Figure 1. Flow chart of the algorithm.
This paper is based on grid computing task decomposing and scheduling problems, and gives a combination of
bionics to perfect IHA. In the scheduling process, We classify the sub-tasks by the emergency degree for re-
source allocation to reduce the resource consuming and improve the work efficiency. Then, we define the fitness
function of the time and cost to evaluate competitiveness of a individual scheduling scheme and select the most
optimal one. Finally, this algorithm is verified by simulation experiments in the process of grid task computing
can give a reasonable decomposition. In terms of resource allocation, we present a new method. In a word, the
task decomposition and reasonable allocation of resources in grid cloud environment can improve the stability
and efficiency of the power system operation, and improve the economic benefit of power enterprise, which laid
a good foundation for the application of intelligent cloud in power grid.
 Su, X.H. and Zhang, H.L. (2012) Improvement of Cloud Computing Task Decomposition Algorithm. Electronic De-
sign Engineering, 23, 47-49.
 Li , J., Li, P.-W., Li, Y.-K., Yu, J.-G. and Shao, Z.X. (2012) Cloud Computing in the Application of the Smart Grid
Research. Mathematics in Practice and Theory, 13, 123-129.
 Zhu, Y.H. (2013) Task Scheduling Research of Differential Evolution Algorithm and Its Application in Cloud Com-
puting. Lanzhou Jiaotong University.
 Yu, H.F. (2011) Scheduling Algorithm Research of Cloud Computing Based on Effect Function. Science and Tech-
nology Information, 3, 35+47.
 Kang, Y.M. and Hu, J. (1997) Task Decomposition, Task Scheduling and Parallel Algorithm Design. Computer De-
velopm ent & Applications, 3, 42-44.