ICA-MMT: A Load Balancing Method in Cloud Computing Environment S.Yakhchi, S.M.Ghafari, M.Yakhchi Department of Computer Engineering Borujerd Branch, Islamic Azad University Borujerd, Iran
[email protected] [email protected] [email protected] Abstractʊenergy consumption has become a major challenge in cloud computing infrastructures. Cloud computing data centers consume enormous amount of electrical power resulting in high amount of carbon dioxide that affects the green environment as well as high operational costs for cloud providers. On the other hand, reducing the energy consumption would negatively impact the SLA (Service Level Agreement) that is a crucial concern in any resource allocation policy. In this paper, we propose a novel power aware load balancing method, named ICAMMT to manage power consumption in cloud computing data centers. We have exploited the Imperialism Competitive Algorithm (ICA) for detecting over utilized hosts and then we migrate one or several virtual machines of these hosts to the other hosts to decrease their utilization. Finally, we consider other hosts as underutilized host and if it is possible, we migrate all of their VMs to the other hosts and switch them to the sleep mode. The results indicate that our method as compared to the previously proposed resource allocation policies such as LR-MMT (local Regression-Minimum Migration Time), MAD-MMT (Median Absolute Deviation- Minimum Migration Time), Bee-MMT (Bee colony algorithm- Minimum Migration Time) and non-Power aware policy offers least power consumption and SLA violation. Keywords—cloud computing; resource allocation; power consumption; ICA Algorithm
I. INTRODUCTION Generally, one of the major challenges in cloud computing environment is energy consumption. As energy consumption increased, the more carbon dioxide is generated which would has negative impact on green environment. In addition, consuming more energy would increase the electricity billing cost both for providing the required energy as well as the cooling process. Consequently, power awareness is a serious concern for a cloud computing environments. One effective way to reduce the power consumption in cloud computing data centers is to design an effective resource allocation policy 978-1-4799-8172-4/15/$31.00 ©2015 IEEE
M.Fazeli, A.Patooghy School of Computer Engineering, Iran University of Science and Technology
[email protected] [email protected]
to manage the utilization of the resources. Meanwhile, decreasing power consumption in cloud data centers could has negative impacts and may also cause SLA violation. In fact, these two objectives are conflicting. Therefore, an efficient resource allocation method should provide an attractive trade-off between these two objectives. In this work, we employ a swarm intelligence algorithm, named Imperialism Competitive Algorithm (ICA) for detecting over utilized hosts; following that we migrate one or several VMs from these hosts to the other hosts with respect to one condition: “the migration process should not result in more over utilized hosts”. The method which we have exploited for selecting VMs from over utilized hosts is The Minimum Migration Time (MMT) proposed in [5] that chooses VMs requiring least time to complete their migration process. Finally, we consider rest of the hosts as underutilized hosts and if it is possible, we repeat our migration process again to allocate all their VMs to other hosts. The rest of the paper is organized as follows. In section2, we present Imperialism Competitive algorithm; following that in selection3, we discuss about our proposed method. Section4 is about our system setup, our simulator and the results of simulations. Finally, we discuss about future work and conclusion of our method in section5. II. RELATED WORK Beloglazov et.al [7] have proposed an architecture for power management in cloud computing environment. They have assumed an overloading and an under loading thresholds to determine the utilization of resources. If we use a resource more than its capabilities, resource utilization will become greater than overload threshold. Consequently, the host may could not satisfy the requests or it may increases the response time of requests resulting in an SLA violation. To tackle this problem, they consider an overload threshold to detect over utilized hosts. In the
other hand, decreasing the load of a host may make the utilization of the host become very low leading to inefficient use of the hosts. To address this issue, they have considered an under-load threshold to detect underutilized hosts; however, as cloud computing environment is a dynamic environment using fix utilization threshold would not be an efficient approach in such environments. In [5], authors have proposed an online deterministic algorithm and adaptive heuristic for dynamic consolidation of VMs based on analysis of historical data from the resource usage by the VMs. In conclusion, instead of applying a fix threshold, they have considered the amount of the resources utilization in the past. They have claimed that this approach can reduce the energy consumption of data centers. In addition, authors applied some algorithms like Median Absolute Deviation (MAD), inter quartile Range (IQR), local Regression (LR) and Robust Local Regression (LRR) for detecting overload hosts. Having the over utilized host been found, they select one or several VMs to migrate from them to the other hosts. It is noticeable that they proposed three different VM selection policy to select VMs from over utilize or underutilize hosts: The random choice policy, the maximum correlation policy and the minimum migration time policy. Finally, after all over utilized hosts are detected and some of the VMs are migrated, their approach investigates underutilized hosts. To do this end, it considers all the hosts except the over utilized ones as underutilized hosts. Hence, it tries to migrate VMs from underutilized hosts to the other hosts considering that migration process should not make the other hosts over loaded. When all the migrations of underutilized hosts have been complete, the hosts are switched to the sleep mode. Obviously, if we cannot migrate all the VMs in an under-utilized host, the host remains active. Interestingly our approach is similar to theirs in that we both use the Minimum Migration Time method for the selection policy; however, our host overload detection method is different, as we have applied ICA for detecting over utilized hosts. It is noticeable that, our simulation results illustrates that ICA-MMT has less power consumption and SLA violation compares to their methods. Kim et al. [9] have proposed a method for reducing power consumption in VM allocation process, based on DVFS algorithm. They have claimed that their method can reduce the power consumption, which in turn decrease the electricity cost. However, contrary to us, they have not considered SLA violation. Soni G et al. [12] proposed a novel load balancing method they claim it is more efficient policy compared to previous ones. They have established a central load balancer which characterizes VMs based on their CPU speed and memory. When a request coming, this balancer allocate it to the most suitable VM that have enough CPU speed and memory. Their simulation results illustrates
that their proposed method has the least response time among the other methods. However, there may be more parameters that must be considered in a load balancing method like energy consumption, number of VM migration and SLA violation which in contrary to us, they have not considered them. Arthi T et al. [3] have proposed a trigger engine that uses fixed threshold method: the hosts with the utilization under 25 percent, the hosts with the utilization between 25 percent and 75 percent and the hosts with the utilization over 75 percent. If a host has utilization under the 25 percent, they will migrate all of its VMs and switch it to the sleep mode. If a host has over 75 percent utilization, only one more VM could be allocate to it. Finally, if a host has utilization between 25 percent and 75 percent, we could migrate VMs of the other hosts to this host. There is significant different between our propose method and their method. Contrary to us, they apply and fix threshold method for load balancing. Because of the fact that cloud computing environment is a dynamic environment, this method may not suitable for cloud computing datacenters. Unlike us, they also did not considered SLA violation as a metric for evaluation of their method. Liao et al. [11] have proposed a resource provisioning methods that considers SLA violation. They have suggested that if a host has sufficient resources to satisfy a VM need, it should add to a list. In the other word, this list consist of the hosts that could run the VM. Finally, they select host which is more suitable to run the VM. Meanwhile, the hosts which are not in the list and the hosts that complete the run of their VMs must switch to the sleep mode. In our previous work [10], we presented a load balancing method for power consumption management in cloud computing environment. We applied Artificial Bee Colony (ABC) algorithm for detecting over utilized hosts. Our VM selection policy in both methods are similar in that both use Minimum Migration Time policy and both methods also have a same underutilized host detection policy; however, they use different over utilize detection methods. As we compared our new method with BeeMMT, Simulation results indicated that ICA-MMT is more appropriate method than Bee-MMT. III. IMPERIALISTIC COMPETITIVE ALGORITHM ICA (Imperialism Competitive Algorithm) is a novel global heuristic search method that uses imperialism and imperialistic competition process as a source of inspiration. Table 1 shows the pseudo code for this algorithm. This algorithm starts with some initial countries. Some of the best countries are selected to be the imperialist states and all other countries form the colonies of these imperialists. The colonies are divided among the mentioned imperialists based on their power.
TABLE I.
IMPERIALISM COMPETITVE ALGORITHM
1) Initializing the empires and selecting some random points. 2) Move the colonies toward most relevant imperialist 3) If a colony be found that has a lower cost than its imperialist, exchange the positions of that colony and the imperialist. 4) Calculating the total cost of the empires. 5) Select the weakest colony (colonies) from the weakest empires and consider it (them) as the colony (s) of the most powerful imperialist which has competition with weakest imperialist. 6) All the weakest empires eliminate. 7) Finally, if there is one empire left, stop the process. In the other hand, go to 2.
A. Creating Initial Empires We form an array of variable values that must be optimized. In GA terminology, this array is called “chromosome”, but here the term “country” is used for this array. In an Nvar-dimentional optimization problem, a country is a 1×Nvar array. This array is defined by [11]. Whit the help of this formula, we established initial countries in our proposed method. country = [ p1 , p2 , p3 ,..., pNvar ]
pn =
(4)
Cn Nimp
¦C
i
i =1
As we mentioned earlier, the initial colonies would be divided among the empires based on the power of empires. At that point, the algorithm calculates the initial number of colonies of the nth empire [11]. Consequently, we applied this formula to distribute the colonies (hosts) among imperialists (hosts with the high load). (5) Where N.C.n is the initial number of colonies of the nth empire and Ncol is the number of initial colonies. In this stage, N.C.n of the colonies are randomly chosen and with the nth imperialist, will form the nth empire. Fig. 1 shows the initial empires. N .C .n = round { p n .( N col )}
B. Movements of Colonies toward the Imperialists Having created the initial empires, the colonies of each empires is moving toward its imperialist position. Fig. 2 [11] shows this process. In this movement, ș and x are random numbers with uniform distribution and d is the distance between colony and the imperialist [1] x U (0, ȕ × d ),ș U (íȖ , Ȗ ) (6) Where ȕ and Ȗ are arbitrary numbers that modify the area that colonies randomly search around the imperialist. In this paper ȕ and Ȗ are 2 and 0.5 (rad), respectively.
(1)
The variable values defined as floating point numbers. We could calculate the cost of a country by evaluation of the cost function f at variables ( p1 , p2 , p3 ,..., p N var ) [1]: costi = f (countryi ) = f ( p1 , p2 , p3 ,..., pNvar )
(2)
At the beginning, the algorithm initial countries of size Npop are created. Selecting the Nimp of the most powerful countries as initial empires is the next step of algorithm. The remaining countries (Ncol) will be considered as the colonies. Then the algorithm distributes them between empires. To create the initial empires, based on the power of each empires we divide the colonies between empires. As a result, the algorithm defines normalized cost of an imperialist [11]. We applied this formula for calculating the costs of hosts in the cloud computing environment. Cn = max{ci } − cn i
(3)
Where cn is the cost of the nth imperialist and Cn is its normalized cost. After the calculating of normalized cost of all imperialists, we could compute the normalized power of each imperialist [11]. Based on this formula, we calculated the normalized power of the imperialists (hosts with the high load).
FIGURE 1. Generating the initial empires Imperialist
X
New position of colony
ș
d
Colony
FIGURE 2. Motion of colonies toward their relevant imperialist
C. Revolution A colony might find a position which has the lower cost that the imperialist position. Consequently, the imperialist should move toward this position. Following that, the position of colony and imperialist, be exchanged. At that point, the algorithm will continue by the new
imperialist in the new position and other colonies of the empire start moving toward this new position Fig.3 [1]. Best Colony Imperialist
(10) P = ª« p p1 , p p2 , p p3 ,..., p pN º» imp ¼ ¬ Then a vector with the same size as P whose elements are uniformly distributed random numbers is created [1]. (11) R = ª r1 , r2 , r3 ,..., rNimp º r1 , r2 , r3 ,..., rN U (0,1) ¬ ¼ imp
Then vector D is formed by subtracting R from P [1]. D = P - R = ª D1 , D 2 , D 3 , ..., D N imp º ¬ ¼ = ª« p p1 − r1 , p p2 − r2 , p p3 − r3 ,..., p p N − rN imp º» imp ¬ ¼
FIGURE 3. Position exchange Empire 1 Weakest Colony In the Weakest Empire
Referring to vector D, the mentioned colony (colonies) is handled by an empire whose relevant index in D is maximum.
The Weakest Empire
Imperialist 1
Empire N P2
PN
Empire 2
P3
Empire 3
F. The final stage When an empire loses all its colonies, it has no power and it will be collapse. At the final stage of the algorithm, all the empires, except the most powerful one, will be collapse and all the countries considered as the colonies of this empire. In this stage, the algorithm stops.
Imperialist N
Imperialist 3 Imperialist 4
FIGURE 4. Imperialistic competition.
D. Total Power of an Empire To compute the total power of the empires, the algorithm considers the total power of an empire as the power of its imperialist plus the mean power of its colonies [1]. In our method it means that we should calculate sum of the load of the imperialist host plus the (7) load of all its colonies hosts. T.Cn . = Cost ( imperialist ) +
ζ mean{Cost ( colonies of impiren)}
E. Imperialistic Competition In this algorithm, empires try to steal the colonies of each other. In this process the weaker empire became weaker and the powerful one, gains more power Fig.4 [1]. The competition process begins with the calculating the probability of each empire which is based on its total power. The algorithm could normalize the total cost as follow [11] N .T .C.n = max{T .C.i } − T .C.n i
(8)
Where T.C.n and N.T.Cn are the total cost and the normalized total cost of nth empire, respectively. At that point, the algorithm can calculate the possession probability of each empire by [11] p pn =
(9)
N .T .C.n
(12)
Nimp
¦ N .T .C.
i
i =1
The algorithm defines the vector P to divides the colonies among the empires based on their possession probability [1]
IV. PROPOSED METHOD Our proposed method consist of three different Stages. First of all, to find the over-utilized hosts, we applied ICA. Following that, in terms of decreasing the utilization of the over-utilized hosts, we selected some of the VMs from these hosts to migrate to the other hosts. In the final stage, we managed underutilized host. We considered all the hosts except over-utilized ones as underutilized hosts and attempted to migrate all their VMs to the other host and switch them to the sleep mode. It is noticeable that, if this process could not be completed, the underutilized host kept active. A. Detecting over utilized hosts Generally, an over-utilized host cannot service all the requests which be allocated to it. In this situation, the response time of the requests are dramatically increased and it would cause SLA violation. Hence, in any cloud service provider, it is essential to deal with the overutilized hosts. We proposed an algorithm based on Imperialism Competitive Algorithm (ICA) to find over utilized hosts. In this algorithm we consider hosts as countries and hosts with the upper load have more power compared to the others. At the beginning, the algorithm select some of the most powerful countries (hosts) as imperators and save their properties in initial imperators list. Following that, each imperator, based on its power, takes some of the hosts as its colonies. In the other word, the imperator with the more power owns more colonies; at that point the empires would build. In each empire when a colony becomes more powerful than its imperator, a revaluation could be occur; therefore, if load of a host in one of the empires be increased, it is
possible that this colony gains more power than its imperator. Making a revaluation and becoming the imperator may be a result of this process. Moreover, the empires compete to steal the colonies of each other. In this competition the most powerful imperator and the weakest imperator are determined. After that, the imperator of most powerful empire would try to take away the colonies of the weakest one. When a colony is exchanged between two empires, the algorithm recalculates the power of empires and updates the list of colonies of two empires. This competition process iterates until one empire remains and all imperators and colonies of other empires become colonies of this last empire. Finally, the algorithm considers the imperator of the last empire as the best solution and the host with the most utilization. B. Selection policy When a host is marked as over utilized host we must choose one or more Virtual Machines (VM) to migrate from these hosts to the others with one condition: this process cannot take the other hosts to the over utilized mode. As we mentioned in the last section, there are different types of selection algorithms and we chose the Minimum Migration Time (MMT) policy which presented in [5] to select VMs from over utilized hosts. This policy migrates VMs which requires the minimum time to complete migration process. The migration time be estimated as amount of RAM utilized by the VM divided by the spare network bandwidth available for the host j. If VM v has following conditions, The MMT policy finds it [5].
v ∈ V j | ∀a ∈ V j RAmu (v) RAM u (a) ≤ NET j NET j
(13)
D. SLA Violation Metrics As mentioned earlier, we must consider SLA violation as an important metric to satisfy QOS that we guaranteed to the customers; as a result, in the next section we compare our method with the other methods from the energy consumption and SLA violation points of view. We use a method that represented in [5] to evaluate SLA violation. They defined SLAs are delivered when 100% of performance requested by applications inside a VM is provided at any time bounded only by the parameters of the VM. Authors proposed two metrics for measuring the SLA violation. The first metric is SLA Violation Time per Active Host (SLATAH) which is percentage of time that active hosts experienced CPU utilization of 100% (14); and the second metric is Performance Degradation due to Migrations (PDM) (15). The reason that they considered SLATAH is that if host utilization is 100%, the performance of applications is bounded by host capacity and VMs are not provided with the required performance level.
SLATAH =
PDM =
M
Csi
¦C j =1
(14)
(15)
ai
Where N is the number of hosts and M is the number of VMs; Tsi is the total time during that host i has experienced the utilization of 100%. Tai is the time during which host I being in active state; Csi is the estimate of performance degradation of VM j caused by migrations; Cai is the total CPU capacity requested by VM j during its lifetime. They also proposed a combined metric that encompassed both SLATAH and PDM metrics. It is called SLAV and it is the main metric to measure SLA violation. It is calculated as (8).
Whereas Vj is a set of VMs currently allocated to host j. NETj the spare network bandwidth available for the host j; and RAMu (a) is the amount of RAM currently utilized by the VM a. C. Host Under loading Detection Finally, we must manage the underutilized hosts. We applied a policy to deal with underutilized hosts which presented in [5]. Having detected the over utilized hosts and migrate some of their VMs, we need to find hosts with the minimum utilization and if it is possible, try to migrate all VMs which allocated to these hosts to the other hosts while keep them not overloaded and when all the migration have been complete, switch host to the sleep mode. If this cannot be accomplished, the host is kept active. This process is iteratively repeated for all host except the overloaded ones. The main steps of our method are shown in Table 2.
1 M
1 N Tsi ¦ N i =1 Tai
SLAV = SLATAH .PDM TABLE II.
PROPOSAL METHOD
Input: hostList Foreach host in the hostList do If host is over loaded then
// Part A
Get VMs to migrate from this host //Part B Foreach host in hostList do If host is under loaded then //Part C If it is possible to migrate all VMs which allocated to his host to the other hosts then Migrate all VMs Else Keep the host active
(16)
V. EXPRIMENT SETUP In this paper simulation have been chosen as a way to evaluate the power consumption and SLA violation of the proposed algorithm and CloudSim toolkit [6] has been chosen as a simulator. We have simulated a data center that consist of 800 heterogeneous physical nodes with the dual-core CPUs. 50 percent of hosts are HP ProLiant ML110 G4 servers(Intel Xeon 3040, 2 cores × 1860 MHz, 4GB), and the others are HP ProLiant ML110 G5 (Intel Xeon 3075, 2 cores × 2660 MHz, 4GB). Each server modeled to have 1GB/s network bandwidth. The characteristics of our VMs type are similar to Amazon EC2 Instances types; however, because of the fact that our workload data that comes from single-core VMs, all the VMs are single-core. We also divide the amount of RAM based on number of cores for each VMs types: High-CPU Medium Instance (2500MIPS, 0.85 GB); Extra Large Instance (2000 MIPS, 3.75 GB); Small Instance (1000 MIPS, 1.7 GB); and Micro Instance (500 MIPS, 613 MB). The frequency of the servers’ CPUs are mapped to MIPS rating: 1860 MIPS for each core of HP ProLiant ML110 G4 servers, and 2660 MIPS for each core of HP ProLiant ML110 G5 servers. We have used workload trace from a real system and data provided as a part of the CoMon project, a monitoring infrastructure for PlanetLab [4]. The interval of utilization measurement is 5 minutes. The characteristics of the data for our experiments shown in Table 3. We compared our method with the five other methods as follows. LR-MMT (local regression-The minimum Migration Time) as an optimal algorithm which has been suggested in [5]. It applies Local Regression as a host over loading detection approach and the minimum Migration Time for the VM selection policy; and non-power aware policy which does not consider any power aware policy. In this policy, all hosts run at 100% CPU utilization and consume maximum power all the time [7]; and IQR-MMT (Interquartile Range-MMT) which it is based on an adaptive Utilization threshold for detecting over loaded hosts (IQR) and MMT for the VM selection policy; and MAD-MMT (Median Absolute Deviation) which it is using MAD as an adaptive Utilization threshold for detecting over loaded hosts and MMT for the VM selection policy [5]; and Bee-MMT that applies ABC (Artificial Bee Colony) algorithm for detecting over utilized hosts and MMT for the VM selection. VI. SIMULATION RESULTS We compared our method with other methods in three different areas. Energy consumption, SLA violation and the number of VMs migration. Meanwhile, SLA violation consists of 3 metrics: SLAV, SLATAH and PDM which SLAV is the main metric to measure SLA violation. The results of simulations demonstrates in Table3. The results indicates that ICA-MMT has the least Energy consumption among the other methods. Our proposal
method has nearly 29 times less energy consumption than non-power aware policy. It also has 29.16% and 27.65% less energy consumption than LR-MMT and MAD-MMT, respectively. It may because the fact that this method have few active hosts. Meanwhile, the VMs migration process may spend some extra times. As a result, the respond times of the requests may increase and more energy consume to serve the requests. Since ICA-MMT has the minimum number of VMs migrations, it effects on energy consumption of datacenter too. However, because of the fact that there is a trade-off between energy consumption and SLA violation, decreasing in power consumption could have negative impact on the SLA violation. The results indicate that ICA-MMT has the least SLAV compared to the other methods except non-power aware policy. It has 2.42% SLAV more than non-power aware policy and 0.01% and 2.76% SLAV less than LR-MMT and MAD-MMT, respectively. As we mentioned earlier, we also have considered number of VMs migrations as a metric to compare the efficiency of these methods. VMs migration process is an essential process to manage the resource of cloud computing datacenters. However, increasing in number of VMs migration could decline the system performance and increase the response time of requests. It may because of the fact that this process consist of following stages: stop the running VM, save the VM stat, find new host for this VM, reallocate the VM to the new host and resume the VM. Table3 indicates that the ICA-MMT has the least number of VMs migrations among the other methods except non-power aware policy. Whereas ICA-MMT has number of VM migrations with nearly 15.84 times less than LR-MMT and almost 22.93 times less than MAD-MMT and IQR-MMT. It also has the minimum PDM among other methods. By this we means that it has the least performance degradation due to the migrations. Additionally, ICA-MMT has bigger SLATAH compared to other methods except Bee-MMT; therefor the percentage of time that active hosts experienced CPU utilization of 100% in ICA-MMT method, is more than other methods except Bee-MMT. VII. CONCLUSION AND FUTURE WORK Cloud computing datacenters consume large amount of electrical energy. Hence, they generate a lot of carbon dioxide and need more operational costs. In this situation, we need to manage the cloud computing resources and decline the power consumption. In this paper, we proposed a load balancing method, named ICA-MMT. It detect over-utilized hosts and migrates some of their VMs to the other hosts to reduce their utilization. Following that ICA-MMT detect the Underutilized hosts and if it is possible, migrates all of their VMs to the other hosts and switch them to the sleep mode. However there is a tradeoff between energy consumption and SLA violation in cloud computing environments. In the other word, decreasing in power consumption may increase the SLA violation. As a result, in any load balancing solution, SLA violation must be considered. We consider SLA Violation
as three different metrics: SLAV, SLATAH (SLA Violation Time per Active Host) and PDM (Performance Degradation due to Migrations) which SLAV was the more important metric. Another metric that we consider was the number of VM migrations. As the simulation results indicate, ICA-MMT has the least power consumption compared to the other methods except nonpower aware policy. Meanwhile, it has the least SLAV among the load balancing methods. ICAMMT has also the least number of VMs migrations compared to the other methods. According to results of simulations ICAMMT can decline the power consumption; hence, it can be a green solution and reducing production of carbon dioxide and operational cost. However, our method may cause SLA violation compared to the non-Power aware policy but it also has the least SLA Violation (in the PDM TABLE III.
and SLAV metrics) compared to the other methods. ICAMMT also has much less number of VM migrations compared to LR-MMT, MAD-MMT, IQR-MMT and Bee-MMT. In our future works, first of all, we tries to propose a much effective underutilized method to manage under load hosts; following that we should consider other metrics like response time as a requirement factor to guaranty a high quality of service (QOS) to satisfy the customers.
SIMULATION RESULTS
ENERGY CONSUMPTION (KWH)
SLAV (× 10-5)
SLATAH
PDM
VM MIGRATION (×103)
NON-POWER AWARE
2410.80
0
0%
0%
0
IQR-MMT-1.5
117.08
5.14%
5.08%
0.10%
26.42
MAD-MMT-2.5
114.27
5.18%
5.24%
0.10%
25.92
LR-MMT-1.2
116.71
2.52%
4.03%
0.06%
17.90
BEE-MMT
85.84
6.45%
80.85%
0.01%
2.00
ICA-MMT
82.68
2.42%
80.22%
0.00%
1.13
POLICY
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