Proceedings of IEEE CCIS2012

STUDY ON CLOUD RESOURCE ALLOCATIONSTRATEGY BASED ON PARTICLE SWARM ANT

COLONY OPTIMIZATION ALGORITHMZhengqiu Yang1, Meiling Liu2, Jiapeng Xiu1, Chen Liu1

1School Of Software Engineering, 2School Of Computer Science Beijing University of Posts and Telecommunications, Beijing 100876, China

zqyang@bupt.edu.cn, eryuan@bupt.edu.cn

Abstract: Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems.Aiming at this problem, this paper proposes anoptimized ant colony algorithm based on particle swarm to solve cloud computing environment resourcesallocation problem.

Keywords: Particle swarm optimization algorithm; Ant colony optimization algorithm; Cloud resource allocation

1 Introduction

Cloud computing is a large-scale distributed computing model driven by scale economy effect, which can provide the abstraction, virtualization, dynamic scalable, efficient management, storage, computing platform and service resource pools to outside users through the Internet based on their needs [1]. The mass data storage and processing based on cloud computing platform havebecome the hot issues. Cloud computing platform can run a variety of tasks which request various kinds of resources in cloud environment and visit cloud platform data, so the rationality of cloud environment resource allocation has a great influence on the cloud platform performance.

The cloud computing resource allocation strategies arestudied by many people. Reference [2] presents an algorithm of resource load balancing scheduling based on trust driven. The algorithm takes users needs to trustinto consideration, aims at achieving balance of loadingresources, and taking into account factors such as the time spans of different tasks. Reference [3] presents aresource scheduling algorithm in virtual environment based on the laws of the market. The algorithm in resource price and maximize workloads profit has usedthe particle swarm optimization algorithm, finally making the system stable state of equilibrium. Reference [4] presents two resource scheduling algorithms which

are named AC01 and AC02, and two- way ant mechanism is introduced into the two resource scheduling algorithm, aiming to improve the convergence of the algorithm through the direct communication.

Ant colony algorithm is firstly presented by Italy scholar Dorigo M, who inspired by ant colony foraging behavior.The algorithm simulates natural ants searching food behavior characteristics, and adopts the methods such as probability of selecting and iterative searching to solve the difficult problems of NP. The algorithm is a new heuristic optimization algorithm. If it could be applied in the cloud computing resources allocation, it can solve agreat many issues such as the difficulty in prediction ofreal-time dynamic change of users needs in cloud resources allocation and widely distributed resources in application platform. But ant colony algorithm also has its own problems, such as the slow convergence speed in the process finding the optimal solution and difficulty inselection of the local optimal solution and parameters.Aiming at these problems, this paper proposes an ant colony optimization algorithm based on particle swarm to solve a cloud computing environment resource allocation problem, so as to achieve better application of cloud computing resource allocation strategies.

2 Problem definition

2.1 Cloud computing resource allocation modelCloud computing is the virtual machine for cloud computing resource management. The virtual machine is special software, which can completely simulate the hardware implementation. The resource allocation framework in Cloud computing environment is shown in Figure 1.[5] When users tasks are running on a virtual machine, user has the demand for the quality of service provided by a virtual machine. In order to meet usersrequirements, the task agent communicate with the proxy of the underlying hardware resources, using resource allocation algorithm in virtual environments. In that way, the proxy of the underlying hardware resources ultimately benefits from that mode and improvesresource utilization, while task agent improve itsefficiency and meets the demand for quality of service ofuser's.

___________________________________ 978-1-4673-1857-0/12/$31.00 2012 IEEE

Proceedings of IEEE CCIS2012

user1 user1user1 ...

task agent

resource agent

CPU memory Externalmemory bandwidth

Figure 1 Cloud computing environment resource allocation framework

2.2 Description of the problem of the basic ant colony

There are the obstacles, other ants and pheromones in ants world. Ant shed pheromones after the completion of the task, while the surrounding environment will make pheromones disappear at a certain rate. In order to describe the mechanism of the ant colony search path more vividly, I use Figure 2 to further elaborate. The Figure can be described as: there are two roads around the obstacle, which can access the food source from the ant nest (nest-ABD-food and nest-the ACD-food) per unit length at 4 and 6, respectively. In the initial moment there is no any pheromones on all routes. It is assumed that the ants can move a unit of distance per unit time and release a unit of the pheromones. The detailed process of ants searching is as follows:

Nest

Food

B C

A

D

Obstacle

Figure 2 Natural ants searching for food

At time t = 0, 20 ants crawl from the nest to A, with the same probability selecting the left or right side of the road, at this time, there are 10 ants in each route. At time t = 4, the left group of ants reach the food source, and begin to return.

At time t = 5, two groups of ants meet in the point D, the number of pheromones on the DB and DC are the same (because each route has 10 ants), so there are five returned ants selecting the route BD and route CD respectively.

At time t = 8, the first 5 ants return to the nest, there are five ants on AC, DC, BD respectively.

At time t = 9, the first 5 ants return to A again and need to make a choice on the left or right.

At this moment, the amount of pheromones on the AB is 20 while on the AC is 15. Therefore, there will be a greater number of ants choosing the path on the left, which further enhances the pheromones in this path. As this process continuing, the gap between the amounts of pheromones on the two paths will be larger and larger until the vast majority of ants chose the shorter route, and eventually all the ants will choose the shorter path.

3 Ant colony optimization algorithm based on particle swarm algorithm (ACOA based on PS)

3.1 Initialization of ant pheromones According to the characteristics of the cloud resources and inspiration from reference [4], we can use the virtual machine's hardware resources to measure a node's pheromones, i.e. CPU processing power (p), memory capacity(r), external memory capacity (h) and the bandwidth (b). Resource node unit of pheromones with the right method of calculating:

ti(0) = a pp0 + b rr0 + c hh0 + d bb0 (1) As in (1), a + b + c + d = 1, ti (0) represents resource pheromones initialization value, 0

Proceedings of IEEE CCIS2012

be rewarded, while the other solutions will be punished, thus, we can enlarge the difference between an optimal solution and other solutions in terms of pheromones, in order to solve the slow convergence problem of the ant colony algorithm. Rank r nodes can update the pheromones in accordance with the formula (3).

ti(j)= t(j) + ()() (0 < < )t(j) + ()() (s < < ) 3 3.3 The prediction of execution speed of resource node in completing the task Resources in the cloud environment have heterogeneous characteristics, i.e. there are differences in each resource node structure and hardware and software environment, and its difficult to predict the load of any resources at any time, thus, the prediction of execution speed of the resources node in completing the task will be very difficult. But when we assign the tasks to the most efficient node in the cloud environment, the performance of the entire cloud will be improved to a great extent. Therefore, a prediction model of execution speed of next task through the accumulation of historical values was established,[7] such as formula (4).

EV(k + 1) = (1 e)EV(k) + eRV(k) (4) As in (4), EV(k) the k-th forecast execution speed of the m-number resources, the unit can be the MIPS, ak is the k-th degree of system load forecast, RV(k) refers to actual speed of execution of kth times in the mth calculation. e is an adjustable parameter used to adjust the proportion of empirical value and predictive value in different cloud environment to achieve the optimal prediction.

3.4 Setting of the next point of the ants According to next hop calculation formula in classical ant colony algorithm, at time t, the probability of the ant k heading to next point j is:

p(t) = [()][!" ()]

([#()]$!#"()%)#"''*,-. , j allowed0 , else (5) As in (5), p (t) is the probability that the ants choosing the next point, and allowed is the ant ks feasible node. and are the relative weights of pheromones and resource processing speed predictive values.

3.5 Combinatorial optimization of initial amount of pheromones weight parameters based on Particle swarm algorithm In the cloud computing environment, different tasks require for different resources. Some tasks require the higher CPU speed, and some tasks require the larger

storage space. Therefore the weight calculation method of the resource node unit pheromones value is an important parameter affecting the performance of the ant colony algorithm of the formula (1). According to formula (1), a+b+c+d=1, so this is an optimization problem in combination of parameters. This paper uses particle swarm algorithm to solve the problem. Particle swarm algorithm is especially suitable for solving the continuous optimization problems.

Ant colony optimization algorithm framework based on particle swarm algorithm is shown in Figure 3.

Particle SwarmAlgorithm

Ant ColonyOptimizationAlgorithms

based on Particle Swarm Ant Colony Optimization Algorithm

ProblemOptimized Initial

Pheromone Optimal Solution

Figure 3 ACOA based on PS framework

Particle swarm algorithm, also known as particle swarm optimization algorithm, is a new evolutionary algorithm designed by James Kennedy and Russel Eberhart. Similar to the genetic algorithm, it is also developed from the random solution by iteration to find the optimal solution.

It evaluates the quality of the solution based on the fitness by following the optimal value of the current search to find the global optimal.[8] The algorithm is simple and has less need to adjust the parameters, fast search speed, good global skilled capacity and robustness, besides, it can demonstrate its superiority in solving practical problems.

Algorithm randomly produces a group of N particles in the solution space (assuming as D-dimensional) particle xi update themselves by tracking the location of the two extreme values: one is the optimal solution found by the particle itself, i.e. individual extreme pbesti; the other is the global optimal solution, i.e. the global extreme gbesti.[7] v = w v + c r(pbest x) + c3 r3(gbest x) (6) x = x + v (7) As in (6) and (7), v is the update process of the particle rate, and x is the update process of the particle position. vk is the particle rate vector; xk is the seat of the current particle. c1, c2 are constants the called the learning factor. r1, r2 is random uniformly distributed functions in the range [0,1]. W is the inertia weight.

3.6 Algorithm processes In order to make the steps of the ACOA based on PSA algorithm more clearly, we draw the flow chart of the ACOA based on PSA algorithm, and it is shown in Figure 4.

Proceedings of IEEE CCIS2012

Start

End

Submit tasks to the task agent

Determine the initial weights ofpheromone by Particle Swarm

Algorithm

Forward ants choose the next node

Generate a feedback ants, andreturned along the same route

Modify the pheromone

Failed task will be assigned the othernode by task agent

effectivenode

YES

NO

Figure 4 The flow chart of the ACOA based PC

In the following, I will introduce the executing procedure of Algorithm processes.

Step1: User submits R tasks to the Task Agent.

Step2: Take all weights in the formula (1) as a particle, using modified particle swarm optimization algorithm and formula (6) and (7) after 20 iterations to get the optimum solution of the problem in order to determine the initial amount of pheromones in ant colony algorithm.

Step3: The task agent starts a timer, and sends nR proceeding ants, among which, n is a parameter, deciding on the multiple relationship between tasks and the ants population. The ants will choose the next hop node i according to the formula (5).

Step4: Whenever a forward ant Af enters into a virtual node i, i is set up to avoid node set Ns of Af. And according to the formula (4), it starts to determine whether it is an effective node. If yes, then it is marked as the effective node, and generates feedback ants BAf. , assigning the Ns of the Af to BAf. BAf will carry an effective node is pheromones, as well as a new task execution time on node is pheromones returned on the same route, and the pheromones are left on the passed node.

Step5: If no and no feedback from ants, it will continue to choose the next node according to the formula (5).

Step6: Each node passes by feedback ants will modify of effective node pheromones at regular intervals according to the formula (2).

Step7: Before the task agent starts the timer from zero, if task agent receives feedback ant, the task agent will consider the task can find an effective node, and assigns the tasks to the above, and modifies the pheromones according to the formula (2) t; If the timer is timeout, and the task agent still has not received feedback ant, then it will assume there is no suitable resources for the tasks, so no task is assigned.

Step8: After the implementation of the tasks, according to whether the tasks are successful or not, the pheromones of node are modified as formula (3).Failed task will be assigned the other nodes by task agent.

In order to make the steps of the ACOA based on PSA algorithm more clearly, we draw the flow chart of the ACOA based on PSA algorithm, and it is shown in Figure 4.

4 Conclusions This study came up with a new Ant colony optimization algorithm based on particle swarm algorithm. The randomness, rapidity and overall performance of allocation of resources in the cloud environment are enhanced, besides, proceeding ants can find more appropriate and effective virtual node more rapidly, making the task agent allocation of resources more quickly and more reasonable, which can ensure that the users tasks could be completed as soon as possible and provide the Service Level Agreement (SLA) that the company signed with the user.

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[4] Liu Yong, Wang Xin-hua, Xing Chang-ming. Resources Scheduling Stragegy Based on Ant Colony Optimization Algorithms in Cloud Computing,2011-09.

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[6] Xu Jing-ming. Polymorphic Ant Colony Algorithm. Computer Engineering and Applications,2010.

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