Research on Virtual Machine Resources Dynamic Allocation Method Based on Revenue in Cloud Computing

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1 Journal of Computational Information Systems 9: 22 (2013) Available at Research on Virtual Machine Resources Dynamic Allocation Method Based on Revenue in Cloud Computing Jun GUO, Junkui WU, Qiang LIU, Yongming YAN, Bin ZHANG College of Information Science and Engineering, Northeastern University, Shenyang , China Abstract In order to improve the utilization of the computing resources, to allocate resource to the virtual machine effectively and dynamically, and at the same time, to maximize the revenue that the cloud resources provider obtained. A dynamic allocation process of the virtual machine in cloud environment based on revenue is presented. This method can improve the utilization of resources that allocated to the client service which running on the virtual machine, meanwhile, meet the terms of the cloud resources provider s biggest benefit. For the resource allocation method, the thesis adopts basis cloud computing platform hadoop to do the experience to verify the method is effective and feasible. Keywords: Cloud Computing; Allocate Resource Dynamically; Maximize the Revenue 1 Introduction Cloud computing is based on the virtualization technology, from [1] we can see, virtualization technology can reduce the number of physic servers in the cloud data center or large scale resource pool, [2, 3] tell us that virtualization technology can allow multiple virtual machines with different operating systems running parallel and independent, so we think this technology can be used to the problem of resource allocation. As we know, there are also some people applied it into the resource allocation. Resource allocation refers to the process of adjusting the resource between different computing tasks in a given environment. The author in [4] proposed the resource allocation in Market-Based compute grids, but the process is not dynamic,and they have not proposed a new method, it just adopts a not mature method, so the conclusion is restrained. [5, 6] talks about the dynamic resource allocation method, and they thought the method is considered as the process of scheduling the resource dynamically when the services run on the virtual machine, and their method is indeed effective in that field. Although the allocation of resources to the virtual Project supported by the Key Technologies R&D Program of Shenyang City (F ), and the Fundamental Research Funds for the Central Universities (No. N ), and the Fundamental Research Funds for the Central Universities (No. N ). Corresponding author. address: guojun@mail.neu.edu.cn (Jun GUO) / Copyright 2013 Binary Information Press DOI: /jcis8520 November 15, 2013

2 9236 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) machine have certain research at home and abroad,and the related articles are not so much, like [7, 8], they indeed introduced the method which can allocate resources dynamically in cloud, but they all have not talk about the cloud resource provider s revenue. It means that they don t talk about the resource allocation from the viewpoint of the cloud resource provider s revenue. So it is of great significance to put forward a feasible and effective method which is a dynamic allocation process of the virtual machine in cloud environment based on revenue, this method can improve the utilization of the computing resources, allocates resource to the virtual machine effectively and dynamically, and ensures the quality of user services. So firstly, this paper introduces some related concepts about the method to make the readers understand the method better; secondly, analyses the method in detail; thirdly, determines the equilibrium point and gives the algorithm description, at last, adopts basis cloud computing platform hadoop to do the experience to verify the method is effective and feasible. 2 Related Concepts about the Method The dynamic resource allocation method which is based on revenue has three key concepts: the cloud resource provider s returns (shorthand for R), the cloud resource provider s incomes (shorthand for I) and the cloud resource provider s costs (shorthand for C). The R refers to the I (the hardware resources income which is provided for the user in the virtual machine) minus the cost of the hardware resources. The I refers to the benefit which is gained because of the cloud distributes a certain number of hardware resources for the user s virtual machine in accordance with the budget and the quality of service (QoS). The I is evaluated mainly by the user s budget functions and services average response time. The C refers to the cost brought about by deploying the resources. Based on the definition of R, we can get the formula: R = I C, so we can adjust resource in the virtual machine dynamically according to calculating maximum the R. 3 The Resources Dynamic Allocation Method Based on Revenue The virtual machine resources dynamic allocation process mainly divided into three stages: firstly, get the user service request in a virtual machine; secondly, determine the user s service level [9], lastly, send the cloud resources budget (B i (t) refers to the user i s budget when the time is t), the average response time user expected (T i ), and the demand for the resources to the algorithm. So the resource allocation actuators can calculate the amount of resources allocated to the virtual machine respectively. 3.1 Analyze the method and determine the equilibrium point Cloud computing is a multi-user environment, the resources provided by the cloud resource providers are fairly and sharing to every running virtual machine (V M). It is not directly deal with between the users. But once a V M running the user service gain the resources provided by the cloud resource providers, it will affect the service response time on other users V M. The resource allocation method (take a V M i running on user i for an example) is implemented

3 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) by calculating the maximum of R through the formula: R = I C. The process as follows: 1) Get the user service request, mainly refers to the budget the user proposed, the expectations of the average response time and the demand for each kind of resource; 2) Calculate the R s interval; 3) Increase the amount of a certain resource according to the index or linear form, meanwhile, calculate the marginal revenue (shorthand for M R); 4) Determine whether the marginal revenue (MR) and the marginal cost (MC) is equal or not; 5) Repeat 1) 4), until the R is maximize. In order to meet the R is maximum, we must make the MR = MC, it means the R in the equilibrium point attains the maximum. According to the above, we can know the point which MR = MC is the equilibrium point, at this time, the resource allocated to V M is assumed to be P units. When the resource allocated to V M i is more than P, allocating one more unit of the resource to V M i, then MC > MR, it means the R = I C is reduced; When the resource allocated to V M i is less than P, allocating one more unit of the resource to V M i, then MR > MC, it means the R = I C is increased. So when the resource is P, the R = I C is maximum. 3.2 The resources dynamic allocation method based on revenue In this part, I describe the dynamic allocation method among the multiple V M, it means how to control resource allocation proportion, and make each user willing to pay the corresponding amount. This method is fair for every user, in order to show the strategy s fair, there are some specifications as following: [specif ication1] Each kind of resource s unit costs are fixed, defined as K [specification2] If a V M use p units of resources, then its average response time is: Q T (p) = e p 1 p + 1. (1) Q stands for the time which is the user on the V M takes in the unit of resource. The formula (1) indicates: With the increase of resource allocation, the average response time would reach a saturation point, and after that, the influence degree that the increase of resources on the average response time will be small. [specif ication3] Each user s budget function is: stands for each user i s constant, t is the response time. B i (t) = t. (2) Through the [specification1], the cost of each unit of a certain resource is fixed, according to the definition of MC, we can get the value of MC: MC = K (3) So we can also get the formula: MR = K (4) According to the (1) and (2), we can calculate the B i (t): Q B i (t) = B i (T (p)) = B i ( e p 1 p + 1 ) (5)

4 9238 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) B i (t) = Q (ep 1 p + 1) (6) According to the condition of R = I C : MR = MC and the above, we can get the follows: dc(p) = dr(p) (7) dp dp So we get the value of p: K = dr d( dp = Q (ep 1 p + 1)) dp = Q (ep 1 1) (8) p = ln( KQ + 1) + 1 (9) So as for n users, each user has its own budget function (2), the allocation for certain types of resources can also be calculated as above. The Table 1 gives the algorithm: the virtual machine resources dynamic allocation based on revenue in cloud. This algorithm can mainly get the certain resources according to the different budget. Table 1: Algorithm: The virtual machine resources dynamic allocation based on revenue in cloud Input: B i (t), MC Output: Allocated resources to each V M Algorithm description: Q 1. T (p) = e p 1 //Get the service response time of user used v units of resource p + 1 Q 2. B i (t) = B i (T (p)) = B i ( e p 1 p + 1 ) 3. B i (t) = Q (ep 1 p + 1) //Calculate the income of user i 4. R = I C = B i (t) C = Q (ep 1 p + 1) C //Calculate the R 5. dc(p) = dr(p) //According to the definition of the equilibrium point dp dp 6. K = dr d( dp = Q (ep 1 p + 1)) = dp Q (ep 1 1) //Deduce the relationship K and p 7. p = ln( KQ + 1) + 1 //Calculate the value of p 8. Return the value of resources allocated to each V M 4 The Experience In this part, we adopt basis cloud computing platform hadoop to do the experience to verify the method is effective and feasible.

5 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) The data of experience The data which is needed by this experience mainly are: The average response time of each user in each time interval, the historical demand of the resources of each user. Taking the user 1 as an example to analyze and verify. 1) The response time The Table 2 shows the response time of user 1 Table 2: The average response time for each time interval of user 1 T ime interval The response time (sec 0.1) [0, 5] 6.67 [5, 10] [10, 15] [15, 20] 7.16 [20, 25] [25, 30] 8.69 [30, 35] [35, 40] [40, 45] 5.43 [45, 50] ) The resources demanded in history The Table 3 shows all types of resources demanded by user 1 in history Table 3: All types of resources demanded by user 1 in history Historical demand number Resources in equilibrium point (CP U/M em/bandw/swap/disk) 1 8(time slices)/512(m)/1(m)/64(m)/20(g) 2 2.5(time slices)/128(m)/0.5(m)/128(m)/10(g) 3 4(time slice)/100(m)/0(m)/200(m)/5(g) 4 0.5(time slice)/300(m)/2.5(m)/512(m)/30(g) 5 5(time slice)/256(m)/1(m)/100(m)/25(g) 6 10(time slice)/1024(m)/2(m)/256(m)/8(g) 7 6(time slice)/500(m)/1(m)/0(m)/15(g) 4.2 The experiences for verifying the allocation method In order to verify the allocation method, we should do the following experiences: The experience for verifying all types of resources demanded by V M, the experience for verifying the process of calculating the MR = MC, the experience for verifying the allocation method.

6 9240 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) According to all types of resources demanded by user 1 in history, we adopt the grey forecasting method [10], we do the experience for Verifying All Types of Resources Demanded By V M, and get all types of resources demanded by V M. As shown in Table 4. Table 4: All types of resources demanded by V M The sequence All types of resources (CP U/M em/bandw/swap/disk) (time slices)/1972(m)/4.32(m)/534.6(m)/52.64(g) (time slices)1092(m)/2.89(m)/685.2(m)/43.35(g) (time slice)/945.3(m)/2.53(m)/760.6(m)/34.07(g) (time slice)/1385.3(m)/5.75(m)/911.6(m)/61.92(g) (time slice)/1238.7(m)/3.25(m)/609.9(m)/57.28(g) (time slice)/2078(m)/4.67(m)/835.9(m)/38.71(g) (time slice)/1532(m)/3.96(m)/459.2(m)/48(g) Because MR is changing dynamically, so in a time interval, it changes as the allocated resources and the budget function change. Therefor, we do the experience for Verifying the Process of Calculating the MR = MC. Allocating a certain amount of resources, then calculate the MR, until MR = MC. According to the Table 2 and the Table 5, we calculate the changes of CP U. The data is shown in Table 6. Table 5: Parameter settings of the virtual machine resources The types of resources The original value CP U(original/max/unit) 5/20/5$ M emory(original/max/unit) 256/2G/0.15$ Bandwidth(original/max/unit) 1/10/1.2$ Swap(original/max/unit) Disk(original/max/unit) 2G/4G/0.3$ 30G/200G/0.3$ Table 6: He margin revenue of CP U at different time interval Time interval MR [0, 5] [5, 10] [5, 10] [10, 15] [10, 15] [15, 20] [15, 20] [20, 25] [20, 25] [25, 30] [25, 30] [30, 35] [30, 35] [35, 40]

7 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) According to the data in Table 6, we can get the Fig. 1. The point (MR = MC) is the maximum revenue point. As is shown in Fig. 1, we get the first equilibrium point in [0, 10], in this process, dynamic allocation method increases the amount of resources in index form firstly, calculating the M R constantly. When the resources increased is more than the required, using the previous increment as the initial increment, converted to a linear form. All the equilibrium points after this interval will be gotten as above. According to the above analysis, we do the Experience for Verifying the Allocation Method, and get the CP U allocation shared, shown as Fig. 2. Fig. 1: Determine the equilibrium point Fig. 2: The CPU assignment According to the process of resources allocation in user 1, we adopt the Algorithm and get all the users (2, 3, 4, 5, 6, 7, 8, 9) process of resources allocation. This experience verifies the method (dynamic allocation process of the virtual machine in cloud environment based on revenue) is effective and feasible. 5 Conclusion In my paper, a dynamic allocation process of the virtual machine in cloud environment based on revenue is presented. Aiming to discuss and analyze the dynamic resource allocation and there source providers revenue in cloud. We adopt the gray prediction method to predict the pre-demand of resources. And then, according to the relationship of the resources allocated to user s V M and the providers revenue, we confirm that the revenue is maximal at equilibrium point. So we can allocate the resource according to the budget and the quality of Service, and at the same time, every V M running on the PM can get reasonable resource. At last, we adopt basis cloud computing platform hadoop to do the experience to verify the method is effective and feasible. Acknowledgement This work was supported by the Key Technologies R&D Program of Shenyang City (F ), and the Fundamental Research Funds for the Central Universities (No. N ), and

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