A virtual machine migration Algorithm Based on Network flow balance YangYu 1, a, ZhouHua 2,b, LiuJunHui 3,c and FengYun 4,d*

Transcription

1 Advanced Materials Research Submitted: ISSN: , Vols , pp Accepted: doi: / Online: Trans Tech Publications, Switzerland A virtual machine migration Algorithm Based on Network flow balance YangYu 1, a, ZhouHua 2,b, LiuJunHui 3,c and FengYun 4,d* 1,2,3 School of Software, Yunnan University, Kunming, China 4 Department of Computer and Information Science, Zunyi Normal College, Zunyi, China a @qq.com, b dr.hua.zhou@qq.com, c hankslau@foxmail.com, d fyun3ok@163.com Keywords: Cloud Computing, Workload Balance, Network Traffic, Network Balance Abstract. Current research of virtual machine migration strategy mainly focuses on how to reduce the delay of virtual machine migration process but does not pay much attention to the network flow problem caused by the virtual machine migration. Because of the difference caused by Infrastructure Operator's network location makes a different virtual machine migration strategy, which will result in large differences in network traffic. Infrastructure operator's network resources are scarce resources. Therefore, how to reduce the network flow of virtual machine migration is a problem to be studied. In order to reduce network traffic virtual machine migration, this paper proposes a virtual machine migration algorithm (NFBA) based on network flow balance to obtain the minimum scheduling cost. Experimental results show the migration strategy can effectively reduce the communication traffic between the virtual machine clusters within the system and reduce the burden of network and consider workload balance at the same time. INTRODUCATION In a traditional data center, administrators usually configure a server for each of the services. Because the yearly increase in the number of services, the number of servers will also increases year by year. So the effective operation and management of servers may not get full play and full guarantee which lead to the server resources utilization decline noticeably [1]. In this case, people put forward the use of virtual machine. The use of virtual machine could make full and effective use of remaining resources of the server, so the server's resources can be fully utilized and played. Therefore, how to maintain the stability and performance of virtual machines has become a hot topic. Live migration of virtual machines can run without stopping the service, and in a very short period of time, the virtual machine host migrated from the source node to the target node, in order to achieve load balancing resources between nodes. The method is to keep the virtual machine performance and service to improve the availability of effective methods. Virtual machine migration technique allows a virtual machine running on a physical machine to be migrated to another physical machine. Migration can be classified into two types, offline migration and real-time migration. For offline migration, the current user's state must be suspended or shut down before the migration can be performed, and users will not be able to take any action. For real-time migration, in contrast, it is not necessary to shut down the original virtual machine and the task can be migrated at the user unaware situation. The advantages of the real-time migration include load balancing, power efficiency, and convenient maintenance. Some mainstream virtualization software providers have proposed their own virtual machine live migration technology, such as VMware's VMotion [2] and Xen's Live Migration [3] and so on. There are 3 aspects of the problem that scheduling technology of virtual machine migration mainly focus on: 1selection of virtual machine migration. It means which virtual machine need migrate from source node that triggers the migration condition. 2selection of timing. It means what time to start migrating. 3selection of migration target node. It means which target node could receive this virtual machine that migrates from source node. The existing migration strategies usually determine whether a node overload or under load according to resource utilization of each physical server in cluster system. If resource utilization of All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (ID: , Pennsylvania State University, University Park, USA-21/02/16,05:09:55)

2 Advanced Materials Research Vols a physical server exceeds a default threshold, it will migrate virtual machine that run on it to other physical server. The resource utilization of target physical server is usually not high or migration virtual machine is compatible with the central processor. However, the existing technology for virtual machine migration may increase the traffic flow between virtual machine in cluster system, thus increasing the burden on network. In order to solve the above problem, this paper presents a virtual machine migration scheduling algorithm based on network flow balance. When in the process of migrating virtual machine to the corresponding target service node, the algorithm try to deploy these virtual machines that exist data interaction relationship between each other in a same business node and it is in consideration of load balance at the same time, thus reduce data transmission that through physical network card between virtual machine in cluster system, which can effectively reduce the communication traffic between virtual machines within a cluster system and reduce the burden of network. RELATED TECHNIQUE Xen [4] is an open source virtual machine monitor developed by the University of Cambridge. It can divide hardware resources of host node such as CPU, memory, I/O and so on by a virtual machine monitor named Hypervisor and use pre-copy technology to achieve virtual machine live migration. According to given cloud resources dynamic scheduling model, literature [5] proposes a virtual machine dynamic scheduling algorithm on migration technology. The algorithm combines with the physical node load evaluation and the virtual machine migration loss evaluation, multiple trigger control, the target node localization, in order to achieve an efficient dynamic load balance of a cloud computing data center. Literature [6] presents a load balancing method which based on migration of virtual machine. Based on threshold, this method predicts load tendency of host machine in the future, ensures that a small transient spike does not trigger needless virtual machine migration. After triggering migration, the method uses the probability-weighted approach to select the target node. Literature [7] presents an algorithm which dynamically allocates resources based on the need and distributes the load across the servers and conducts the experiment on Xen Cloud Platform. Literature uses response time as a metric. Literature [8] proposes a load balancing algorithm based virtual machine dynamic migration scheme for datacenter application with optical networks. The combination of the virtual machine dynamic migration scheme and the load balancing algorithm can help the virtual machines' migration with a dynamic adjustment on the basis of the current data center's situation in real-time. And in this way, it can reach the minimum average load among data center's occupation of service resource. Literature [9] concentrates on developing a multiple VMs migration scheduling algorithm such that the performance of migration is maximized. It evaluates its proposed algorithm through simulation. VIRTUAL MACHINE MIGRATION SCHEDULING STRATEGY 1. Definition of migration scheduling strategy Definition1 :Let all host node as a set PM={PM 1, PM 2, PM m }, the set of source node as S={S 1,S 2, S n }.Each source node can be seen as a set of virtual machine Si={v i1, v i2, v ik }, the set of target node as T={ t 1, t 2, t u }, PM =S D Definition2: The formalized description of physical machine could be defined as a twelve tuple. PM {PId, IP, CPU, RCPU, UCPU, Mem, RMem, UMem, Bw, RBw, UBw, UIO}. PId is the number of physical machine.ip is IP address of physical machine. CPU indicates physical computing power of processors. RCPU indicates reversed physical computing power of processors. UCPU indicates processor utilization of physical machine. Mem indicates the size of physical machine memory. RMem indicates reserved size of physical machine memory. UMem indicates

3 1030 Frontiers of Energy, Materials and Information Engineering memory utilization of physical machine. Bw indicates size of physical machine network bandwidth. RBw indicates reserved size of physical machine network bandwidth. UBw indicates network bandwidth utilization of physical machine. UIO indicates disk I/O utilization of physical machine. Definition3: The formalized description of physical machine could be defined as a nine tuple. VM {VId, IP, CPU, UCPU, Mem, UMem, Bw, UBw, UIO}. VId indicates the number of Virtual machine. IP is IP address of virtual machine. CPU indicates computing power of virtual machine processors. RCPU indicates reversed computing power of virtual machine processors. UCPU indicates processor utilization of virtual machine. Mem indicates the size of virtual machine memory. UMem indicates memory utilization of virtual machine. Bw indicates size of virtual machine network bandwidth. UBw indicates network bandwidth utilization of virtual machine. UIO indicates disk I/O utilization of virtual machine. Definition4: The formalized description of network flow between virtual machines could be defined as a triple Network Flow{VM ik, VM ju, Networkflow,UnitComCost}. VM ik indicates virtual machine k run on physical machine i. VM ju indicates virtual machine u run on physical machine j. Establish a virtual machine network flow table for each virtual machine when new a virtual machine to record the interaction of network flow between virtual machines. UnitComCost indicates unit communication costs. If VM ik and VM ju run on same physical machine, UnitComCost = 0, otherwise UnitComCost =1. Definition5: The formalized description of mgration match could be defined as a triple. Q={<s i,v ij,t u > s i S,v ij s i,t k T}. si indicates source node, v ij indicates virtual machine run on s i that need to migrate. t k indicates the most suitable target node that receive v ij. 2. Timing of migration strategy We should prevent occurrence of virtual machine migration event caused by a peak of physical node workload. Based on a workload peak of physical node to determine whether this node overload or not may easily lead to frequent occurrence of virtual machine migration events even increase overhead cost of cloud computing center especially consumption of network bandwidth. Therefore when comprehensive load of a physical node is greater than Threshold max or less than Threshold min did not immediately trigger a migration, but turn to the analysis of historical load information (set load manager record physical node load information in every period of time interval T). When the comprehensive load value is greater than Threshold max or less than Threshold min in a given period for 3 times, load manager could determine whether the node is an overloaded node or a low load node. 3. Virtual machine migration selection strategy When load manager judges S i is overloaded or under loaded. Load manager takes this algorithm to select suitable virtual machines to migrate from physical machine. First, compute all the priority of virtual machines on overloaded or under loaded physical machines according to the description model of virtual machine and physical machine. Second, construct a scheduling queue according to the priority sequence from big to small. The time complexity of this algorithm is O (n 2 ). Priority (VM ij ) =α* VM ij.ucpu+β* VM ij.umem +γ* VM ij.ubw +δ* VM ij.uio (1) VM ij indicates virtual machine j run on physical machine j. Priority (VM ij ) indicates the scheduling priority of VM ij. VM ij.ucpu indicates processor utilization of VM ij. VM ij.umem indicates memory utilization of VM ij. VM ij.ubw indicates network bandwidth utilization of VM ij. VM ij.uio indicates disk I/O utilization of VM ij. α is the weight parameter of CPU utilization. β is the weight parameters of the memory utilization. γ is the weight parameters of the bandwidth utilization. δ is the weight parameters of disk I/O utilization. α+β+γ+δ=1,0 α 1, 0 β 1, 0 γ 1, 0 δ 1, α,β,γ,δ which represent the degree of emphasis on compute intensive virtual machine, memory intensive virtual machine, network throughput intensive virtual machine and disk I/O intensive virtual machine. The algorithm constructs a migration queue according to Eq.1 on source host according to the priority from large to small descending.

4 Advanced Materials Research Vols Target node selection strategy Select resources suitable host t u for VM ij according to VM ij load and network flow, which satisfies the following conditions: Minimize: µ*load(vm ij,t u )+(1-µ)* NFR(VM ij,t u ) µ [0,1] (2) Subject to: Load(VM ij,t u ) = (L(VM ij, t u )-L min )/(L max -L min ) (3) NFR(VM ij,t u )=(NF(VM ij,t u )-NF min )/( NF max -NF min ) (4) The time complexity of algorithm is O (n 2 ). µ is used to reflect the equilibrium relationship between load and network flow. If the value of µ is increasing, it means we pay more attention to the balance of workload. If the value of µ is decreasing, it means we pay more attention to the balance of network flow balance. L (VM ij,t u ) indicates comprehensive load value of t u if VM ij migrate from Source host s i to target host t u. L min indicates the minimum comprehensive load value if VM ij migrate from source host s i to a target node that belongs to target node Set T. L max indicates the maximum comprehensive load value if VM ij migrate from source host si to a target node that belongs to target node Set T. Comprehensive load L of physical machine can be calculated by the following formula: L = (5) r ij indicates resource feature vector j of physical node u, l indicates resources dimension of physical node, in this paper we just considers there are 4 type of resource, they are CPU, memory, bandwidth and throughput of IO, so we set l=4, let Uu= [qcpu, qmem, qbw, qio] to record CPU performance vector, memory performance vector, network performance vector and disk I/O throughput vector of physical node u. =1. (6).. t u.cpu indicates target node u s physical computing power of processors. t u.rcpu indicates target node u s reserved physical computing power of processors.. indicates total computing power of virtual machine processors(these virtual machines run on target node u). = 1. (7).. t u.mem indicates target node u s the size of memory. t u.rmem indicates target node u s the size of reserved memory.. indicates total size of virtual machine memory(these virtual machines run on target node u). =1. (8).. t u.bw indicates target node u s the size of network bandwidth. t u.rbw indicates target node u s the size of reserved network bandwidth.. indicates total size of virtual machine network bandwidth (these virtual machines run on target node u). =1 (9) MINIO(t u ) indicates the minimum IO throughput of virtual machine that run on target node u. MAXIO(t u ) indicates the maximum IO throughput of virtual machine that run on target node u. NF (VM ij,t u ) indicates target node u's ration of network flow that through physical network card and not through physical network card if VM ij migrate from s i to t u. NF max indicates the maximum ration value ration of network flow that through physical network card and not through physical network card if VM ij migrate from s i to t u. NF min indicates the minimum ration value of network flow that through physical network card and not through physical network card if VM ij migrate from s i to t u. NF, =,.. (10),..

5 1032 Frontiers of Energy, Materials and Information Engineering For example: There are two virtual machine vm 1, vm 2 run on physical node 1. There are two virtual machine vm 3, vm 3 run on physical node 2. The network flow between each virtual machine in a period of T could read from network flow table. If the network flow tables are shown as follow: (vm11, vm12, 0.21, 0) (vm11, vm23, 0.22, 1)(vm11, vm24, 0.41, 1) (vm12, vm11, 0.80, 0) (vm12, vm23, 0.25, 1) (vm12, vm24, 0.21, 1)(vm23, vm11, 0.37, 1) (vm23, vm12, 0.42, 1) (vm23, vm24, 0.89, 0) (vm24, vm11, 0.32, 1)(vm24, vm12, 0.30, 1) (vm24, vm23, 0.91, 0) Now physical node 1 is overloaded, we need to migrate vm1 from physical node 1 to physical node 2. The network flow tables will change as follow if we migrate vm1 from physical node 1 to physical node 2: (vm21, vm12, 0.21, 1) (vm21, vm23, 0.22, 0)(vm21, vm24, 0.41, 0) (vm12, vm21, 0.80, 1) (vm12, vm23, 0.25, 1) (vm12, vm24, 0.21, 1)(vm23, vm21, 0.37, 0) (vm23, vm12, 0.42, 1) (vm23, vm24, 0.89, 0) (vm24, vm21, 0.32, 0)(vm24, vm12, 0.30, 1) (vm24, vm23, 0.91, 0) NF(vm11,t2) = ( )/( ) = 70.2% 5. Pseudo code of migration algorithm Pseudo Code of Network Flow & Load Balance Algorithm(NFBA) Input: S: the set of Source Physical Machine T: the set of Target Physical Machine Output: Q: the set of Matching Queue 1. Compute priority for all VMs VM ij S i ; (according to given Eq.1) 2. Sort VM ij in the descending order by its priority value; 3. for each VM ij S i do 4. for each t u T do 5. Compute µ*load(vm ij,t u )+(1-µ)* NFR(VM ij,t u ); (according to Eq.2,3,4,5,6,7,8,9,10) 6. end 7. end 8. end 9. for each VM ij in ready queue do 10. Assign and output VM ij to the t u that minimizes the objective 11. end ANALYSIS OF EXPERIMENTAL RESULTS 1. Related Experimental Environment 1.1 Dynamic Voltage Frequency Scaling (DVFS) Dynamic voltage frequency scaling [10] is a hardware technology that can dynamically adjust the voltage and frequency of the processor in execution time. By applying DVFS technology without having to restart the power supply, system voltage and frequency can be adjusted in accordance with the specification of the original CPU design into a different working voltage. While CPU works in lower voltage, the energy consumption can effectively be saved. 1.2 CloudSim CloudSim [11] is a cloud computing simulation software developed by Melbourne University in Australia. It can be used to evaluate the performance of the proposed mechanism. CloudSim can support the construction of the large-scale cloud computing. 2. Design and analysis of experiment In order to verify effectiveness of this algorithm, we use CloudSim with no migration strategy and DVFS strategy and NFBA algorithm to simulate experiment. We use CloudSim to simulate a data center that owns three kinds of different performance servers. The total number of physical servers is 30. The total number of virtual machine is 50.We deploy three different performance of virtual machine at beginning. The simulation parameters as shown below: The detail configuration of virtual machine 1 is: 300 MI/s CPU Computing Capacity, 1GB memory, 10Mb/s network bandwidth. The detail configuration of virtual machine 2 is: 500 MI/s CPU Computing Capacity, 1GB memory, 10Mb/s network bandwidth. The detail configuration of virtual machine 3 is: 800 MI/s CPU Computing Capacity, 1GB memory, 10Mb/s network bandwidth.

6 Advanced Materials Research Vols The detail configuration of physical machine 1 is:3000 MI/s CPU Computing Capacity, 8GB memory, 100Mb/s network bandwidth. The detail configuration of physical machine 2 is: 4000 MI/s CPU Computing Capacity, 8GB memory, 100Mb/s network bandwidth.the detail configuration of physical machine 3 is: 5000 MI/s CPU Computing Capacity, 8GB memory, 100Mb/s network bandwidth. In order to simulate the load of Cloud Computing center, we need to modify the DatacenterBroker class and add a load method in the class to randomly generate the number of rand()%5+3 tasks to virtual machines in every 10 seconds. we make virtual machines interact with each other. So the number of tasks and the allocation of tasks are random. The number of virtual machine varies with the number of task loading. The load of virtual machine is different from the load of physical machine at the same time. In this way we can simulate the load balance of Cloud Computing Center. Output the number of migration, the average SLA violation rate, network congestion rate and the number of physical node started in different time. Parameter α=0. 5, β=0.25, γ=0.15, δ=0.1, µ=0.6, Threshold max =0.7, Threshold min =0.3. According to above parameters, experiment results are shown as blow: Fig.1 The number of migration diagram Fig.2 The average SLA violation rate diagram From Fig.1 we can know NFBA s the number of migration is less than DVFS s. The lower the number of migration, the number of migration less, the impact on cloud computing performance center is smaller and more stable performance of cloud computing center. From Fig.2 we can know the average rate of SLA violate among Non-migration, DVFS, NWBA is essentially the same. The lower the average rates of SLA violate the impact on performance of application service smaller. Fig.3 Network congestion rate diagram Fig.4 The number of physical node started in different time diagram From Fig.3 we can know Network congestion rate of NFBA is far less than DVFS and Non-migration. The lower the network congestion rates the impact on communication of cloud computing center smaller. From Fig.4 we can know NFBA is less than no migration and DVFS at the number of starting physical node in different time. The number of physical nodes to start is smaller, energy-saving effect is better. According to above analysis, we can conclude that NFBA algorithm could greatly reduce the number of virtual machine migration on the basis of ensuring the application service performance. It also could alleviate the impact on overall performance of the cloud computing center caused by migration and greatly improve effective server resource utilization and reduce the number of physical server started and reduces the burden of network at the same time to achieve a more energy-saving effect. Summary This paper proposes an algorithm for virtual machine migration from three aspects: 1selection of virtual machine migration.2selection of timing.3selection of migration target node. This

7 1034 Frontiers of Energy, Materials and Information Engineering algorithm can solve the problem of the amount of data transmission through physical card between virtual machines is too large and solve the problem of Frequent migration of virtual machine. The experiment results show this algorithm could reduce the migration times and network congestion and improve the efficiency of management of cloud computing data center. But this paper still exists some problems that is worthy of further research and exploration, the following will summarize and give the ideas to solve these problems. Acknowledgements The corresponding author of this paper is FengYun. This paper is supported by Yunnan Province high-tech industry development project - key technology research & application demonstration based on cloud content management platform, NO:Yunnan province development and Reform Commission high-tech[2012]1957. References [1] Sharma S.,Maulana Azad Nat,Chawla: A Technical Review for Efficient Virtual Machine Migration.International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (CUBE),Vol.1(2012),p [2] Information on [3] Information on [4] Information on [5] Liu-JinJun,Chen-GuiLin,Hu-ChengXiang:Virtual Machine Migration Scheduling Strategy Based on Load Characteristic.Computing Engineering,Vol.37(2011),p [6] Gong-SuWen,Ai-HaoJun,Yuan-YuanMing:Cloud Resource Fynamic Scheduling Strategy Based on migration technology.compter Engineering and Application,Vol.5(2014),p [7] Achar R.,Karnataka,Surathkal,Vikyath:Load balancing in cloud based on live migration of virtual machines. India Conference (INDICON) 2013 Annual IEEE, Vol1.(2013),p.1-5 [8] Xinyu Zhang,Yongli Zhao,Xin Su,Ruiying He: Load balancing algorithm based virtual machine dynamic migration scheme for datacenter application with optical networks th International ICST Conference on Communications and Networking in China (CHINACOM),Vol.1(2012),p [9] Sarker T.K.,Maolin Tang:Performance-driven live migration of multiple virtual machines in datacenters.2013 IEEE International Conference ongranular Computing (GrC),Vol.2(2013),p [10] Spiliopoulos Bagdia:Introducing DVFS-Management in a Full-System Simulator.2013 IEEE 21st International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS).Vol.5(2013),p [11] Buyya R.,Ranjan:Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit Challenges and opportunities.international Conference on High Performance Computing & Simulation, Vol.7(2009),p.1-11

8 Frontiers of Energy, Materials and Information Engineering / A Virtual Machine Migration Algorithm Based on Network Flow Balance / DOI References [9] Sarker T.K., Maolin Tang: Performance-driven live migration of multiple virtual machines in datacenters IEEE International Conference ongranular Computing (GrC), Vol. 2(2013), pp /GrC