This work is supported by the project of Shanghai Science and

Technology Innovation Action Plan (Grant No. 13511500400). Corresponding Author: XinLi Huang. E-mail: xlhuang@cs.ecnu.edu.cn

Scheduling Workflow in Cloud Computing Based on Ant Colony Optimization Algorithm

Yue Zhou School of Computer Science and technology

East China Normal University Shanghai, China

E-mail:zhouyue0101@126.com

XinLi Huang School of Computer Science and technology

East China Normal University Shanghai, China

E-mail: xlhuang@cs.ecnu.edu.cn

AbstractCloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. So how to schedule applications to cloud resources and the execution time should be taken into account. In this paper, we propose a scheduling strategy based on ant colony optimization (ACO) and two-way ants mechanism is introduced .Setting the pheromone threshold is to avoid the premature phenomenon, in addition, taking a two-tier search strategy and introducing pre-execution time is to avoid the local optimum so that tasks can be assigned to highest efficient computing resources. The simulation results show that the algorithm can greatly shorten the time to find the computing resource in cloud computing environment and significantly improve the efficiency.

Keywords: cloud computing; ACO ; workflow; two-way ants; two-tier search; pre-execution

I. INTRODUCTIONCloud computing does not have a common definition,

Vaquero et al. [1] have compared more than 20 different concepts of cloud computing in order to give a common definition. Cloud computing is further development of distributed computing, parallel processing and grid computing, and is internet-based computing [2]. Cloud computing is a new type of share infrastructure which can connect huge pools of systems for providing users with a variety of storage and computing resources via the Internet [3]. The business features of cloud computing require it to meet users application needs. These applications can usually be broken down into a number of tasks associated with each other [4]. Therefore an effective scheduling optimization is needed in order to achieve the goal that workflow tasks can complete an entire application.

Ant colony optimization algorithm is self-adaptive search algorithm, It simulates the process of ants looking for the food. Because of its property of parallel distributed, scalability, easy implementation, robustness, etc. and in a dynamic environment also showed a high flexibility and robustness. It has successfully solute many combinatorial optimization problems. The ant colony optimization algorithm is very appropriate way to solve resource scheduling problems in the cloud computing [5]. Efficient and accuracy of scheduling algorithm and directly affects the performance of the cloud. This article studied the mechanism of ant colony optimization algorithm, combined with the

characteristics of cloud computing, this paper did a greater improvement on ant colony optimization algorithm to improve the ability of optimization and speed of execution .

II. RELATED WORKThere are many papers about scheduling in grid

environment. For conventional applications with independent tasks, some simple methods such as Min-Min and Max-Min [6, 7] are used to meet the QoS. In paper [8], in order to reduce the time, Suraj Pandey used partical swarm optimization (PSO) to schedule the workflow. Time was also treated as optimization objective in literature [9] and [10]. Paper [11] proposed Multiobjective Differential Evolution(MODE). WeiNeng Chen and Jun Zhang [12] used ACO to solve workflow with various QoS requirements in grid computing. Genetic-based optimization techniques also have been used to solve scheduling problem in Grid environment, for review, readers are referred to [13, 14, 15]. Although these approaches worked effectively in grid environment, they couldnt be directly applied to solve scheduling problem in cloud computing, because cloud computing is more commercialized than grid computing.

In cloud computing, Meng Xu [16] introduced a Multiple QoS constraining scheduling strategy of Multi-Workflows (MQMW) to solve workflow scheduling problem. Zhang Jun Wu[18] used ACO, GA, and PSO to solve workflow scheduling problem, the experimental results show that the performance of ACO based scheduling algorithm is better than others.

III. SCHEDULING WORKFLOW STRATEGY BASED ON ANT COLONY ALGORITHM

A. Two-way ant mechanism The ants are divided into two categories: the Forward-

Ant and the Back-Ant. Forward-Ant is used to find the available virtual machine nodes in the cloud. Back-Ant isproduced when the Forward-Ant finds the available resources. The Back-Ant returns in the original way and leaves the pheromone of the available resource to the nodes. The structures of Forward-Ant and Back-Ant are shown in TableI and TableII .

TABLE I. FORWARD-ANT

Ant ID Nodes on the pathAnt ID Ns

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TABLE II. BACK-ANT

Ant ID Nodes on the path

pre-execution time

pheromone of the available resource

Ant ID NS ETe e

B. The definition and updating of pheromone 1) The definition of pheromone

We use virtual hardware resources as pheromones of a node. represents the number of CPU,

p stands for CPU

processing power (MPIS), represents the memory capacity, p is storage capacity and is broadband. Set the threshold

value for each parameter according to the formula (1), if the threshold is exceeded, the value of parameter should be set the value of threshold.

Firstly initialize the pheromone of the hardware: The pheromone of CPU:

The pheromone of memory:

The pheromone of storage:

The pheromone of broadband:

The pheromone of node i is calculated according the

formula (6) :

is the pheromone of the node . 2) Update the pheromone

The modification of pheromone is divided into two categories, one is to update the node which is an available resource. The other is the nodes which the Back-Ant comes back through.

a) Update the available node When a new task is assigned to the node, CPU utilization

will increase, the pheromone decreases. So the pheromone is updated according to formula (7)

Where is the pheromone of the node at ,represents the pheromone of the node at when the task is assigned, is the adjustment factor and .

When the task runs to completion or failure, the load on system reduces, in order to ensure to balance the load on the node, the pheromone is updated according to formula (8)

Where is the pheromone of the node at ,represents the pheromone of the node at when the task runs to completion or failure, is the adjustment factor and

. When the task runs to completion successfully on the

node of which the pheromone will increase, if the task runs to failure, the pheromone of the node will reduce. Therefore, on the basis of the above formula, adding the factor 2 to update the pheromone of the node, formula (9)

Where is the pheromone of the node at ,represents the pheromone of the node at .If the task runs to completion successfully, 0

tasks to the higher efficiency node, the performance of the cloud will be improved a lot, so if we can calculate the pre-execution time of a task, it will be a key role to select the next node. This paper the introduces the task pre-execution time according to the formula (12)

Where is the pre-execution time of a task. represents the number of the tasks which is predicted, p

represents the previous number of the tasks, stands for the real execution time, stands

for the previous pre-execution time, is the adjustment factor and

p.

D. Rules Forward-Ant choose next node 1) Description of the problem

In the ant colony algorithm, how the ant select the next node is based on the concentration of the pheromone. If it is only based on the concentration of the pheromone, at the beginning of the algorithm, the ant will select the path with high initial pheromone of the node which will lead the singularity of the solution. With the development of the time, it is easy to fall into local optimization. In order to solve the problem of singularity, this paper proposes a strategy to set the threshold. In order to solve the problem of local optimization, this paper proposes a two-tier search strategy.

2) Solution to the problem a) setting the threshold value

The ant always selects the node with higher concentration of pheromone due to the characteristics of the ant colony algorithm. When the higher concentration of pheromone is just on a few nodes, it is more possibility to be selected. That all the ants will focus on that few paths in the early research will lead the singularity of the solution. In order to solve it and make the ants find more effective nodes, this paper proposes the following solution, the value 0 of the pheromone is set on every node, if the pheromone on the node is less than 0, the ants will ignore the impact of the concentration of the pheromone and select the next node randomly, if the pheromone on the node is not less than 0,the ant will select the next node according the two-tier search strategy. That in the early research ants can select the multi-path ensures a diversity of solutions.

b) Two-tier search strategy In the ant colony algorithm, the ants select the next node

based on the concentration of the pheromone. That the concentration of pheromone on the path is too high or too low is so easy to fall into local optimization. In order to solve the problem, we use the two-tier search strategy to select the next node. When the ant k is in the search path (i j), both the pheromone of node j and the next node of j will be taken into account. This will avoid the algorithm falling into local optimization. The ants select the next node according to the formula (13)

Where is the probability the ant on the node selects the next node , represents the pre-execution time of the node j, represents the importance of the pheromone, represents the importance of the pre-execution time. is the set of nodes which is next to node , the node is one the ,

represents the set of nodes which is next to the node ,is the adjustment factor and .

IV. DESCRIPTION OF THE SCHEDULING WORKFLOW STRATEGY

Scheduling workflow algorithm as follows: (1) Initial the pheromone of each node. (2) Submit the batch of jobs to the Master node. (3) Master node selects the first job . Assume that the

size of job is and the job is divided into tasks of which the size is . The Master node starts a timer and sends forward-ants. is a parameterwhich is to decide the multiple relationship between the forward-ants and the tasks. Every forward-ant randomly selects the next node.

(4) When the forward-ant comes into the node , the node i will be set into the of Forward-Ant. The pre-execution time will be calculated according to the formula (11), if the pre-execution time is less than , the node i is available node, otherwise not.

(5) If the node is available node, a Back-Ant is produced which will get the of the Forward-Ant, the pheromone of the node and the pre-execution time. The pheromone of node in the back path is updated according to the formula (10).

(6) If the node is not available node and the pheromone of the node does not reach the threshold , the Forward-Ant selects the next node randomly.

(7) If the node is not available node but the pheromone of the node reaches the threshold ,the forward-ant selects the next node according to the formula (13).

(8) The pheromone of the node in the back path is updated according to the formula (11) from time to time.

(9) Before the timer of the Master node reaches zero, if the Master node receives the Back-Ant, Master node will assign the tasks to the available nodes which have the least pre-execution time. The pheromone of the available node is updated according to formula (7). If the Master node does

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receive the Back-Ant, that means no available node, the Master node does not assign tasks.

(10) When the tasks are completed or failed, the pheromone of the available node will be updated according to the formula (9), the tasks that are not completed will be assigned to another node by Master node.

(11) The Master node accepts the next job. Repeat steps (3) to (10).

V. EXPERIMENTS AND RESULTSIn order to verify the new algorithm, we use the

CloudSim to do the simulations. We compare ACON (Ant Colony Optimization New) in resource scheduling time with AS based on ant system, ACS based on ant colony system and other ant colony optimization (ACO).

A. Time Complexity

The time complexity of the algorithm is where k is the number of tasks.

g is the time complexity

of Forward-Ants finding nodes, is the time complexity of putting the tasks to the nodes. The algorithm that this paper introduces is to minimize the time of finding the available resources to improve the efficiency.

B. Experimental parameter setting

In the algorithm, , , , represents the importance of the CPU, memory, storage and the bandwidth of the virtual machine. Because the importance of the CPU is more, so set the value of ,

e

,he

, to 4, 2, 2, 2. ,U

represents the importance of pheromone and the pre-execution time, represents the number of ants. The value of , , will be set by the experiment. Adjustment factors ,

,,

,, are set

to 0.2. When the task is completed successfully, ,

is set to 0.2 or it will be set to -0.2.

p is set to 0.3.

y is set to 0.4.

The number of virtual machine node is 200. Masters timer is set to 5 seconds. The size of task is 500M. The frequency of CPU is 200MIPS~400MIPS, the size of memory is 512M~1G, the bandwidth is 1M/S~2M/S, the size of storage is 10G~20G.

C. The results and analysis The size of job is 2000 MB, the job is submitted 12 times.

We learn that when , j

, the time of scheduling is the least. We repeat the experiment above with different size of job, we get the same result. So the value of

, , is set to 1, 2, 2.5. Refer to TABLE III.

TABLE III. THE DIFFERENT SCHEDULING TIME WITH DIFFERENT VALUE OF N, , .

Experiments n Total time1 1 1 1.5 3.7562 1 2 1.5 3.7443 2 1 1.5 3.7304 1 1 2 3.7335 1 2 2 3.7206 2 1 2 3.711

7 1 1 2.5 3.6988 1 2 2.5 3.6719 2 1 2.5 3.69910 1 1 3 3.71511 1 2 3 3.70312 2 1 3 3.714

As can be seen from TABLE III, when , , , the time of scheduling is the least.

The size of the job is 2000M, the value of , , is set to 1, 2, 2.5, this table shows the different scheduling time of ACON, ACO, AS, ACS. We learn that the scheduling time of ACON is the least. Refer to TABLE IV

As can be seen from TABLE IV, the scheduling time of ACON is the least, which is due to less time for searching virtual machine.

The size of single job is 2000M, we submit 5~25 jobs, we repeat the experiment 10 times, we get the average time of all the results. Refer to Figure 1

As can be seen from Figure 1, the strategy this paper proposes is more efficiency than the ACO, AS and the ACS.

VI. SUMMARYIn this paper, considering the defects of ant colony

optimization and a series of characteristics of cloud computing, this paper propose a new scheduling strategy (ACON) based on ant colony optimization (ACO). Ant Colony Optimization New (ACON) contains two-way ants mechanism, Setting the pheromone threshold, taking a two-tier search strategy and introducing pre-execution time. The simulation results show that the algorithm can greatly shorten the time to find the computing resource in cloud computing environment and significantly improve the efficiency.

TABLE IV. THE DIFFERENT SCHEDULING TIME OF ACON, ACO, AS,ACS

algorithm N Total timeACON 1 2 2.5 3.671ACO 1 2 2.5 3.683AS 1 2 2.5 3.713

ACS 1 2 2.5 3.693

Figure 1. The time of ACON, ACS, AS, ACO

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