consolidating virtual machines with dynamic bandwidth demand in data centers

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Consolidating virtual machines with dynamic bandwidth demand in data centers

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This determines which VMs will be considered candidates for migration. The migration is done considering the service downtime and resource consumption during the migration process. They combined these algorithms to achieve energy performance trade-off. The overload detection finds overload host and get the status of ooverload host whether it result in SLA violation or not. However, the study did not consider when a host is in normal state.

They classified the Underloaded hosts into three further states, i. Sharma and Saini proposed a novel method for consolidation of VMs such that it meets SLA and deals with energy-performance trade-off. For the allocation and reallocation of virtual resources depending upon their load, they used a threshold based approach, in which Median method is used to find lower and upper threshold values.

The proposed method obtained a better optimization in energy efficiency and performance. Chang, Gu and Luo proposed a novel VM selection policy and a resource aware utility model to guide the VM migration process. Kaur, Diwakar and Vashisht presented some alternative robust techniques to overload detection for deciding an adaptive threshold of CPU utilization and compare them with existing techniques in CloudSim simulator.

The authors tried to optimize host overload detection. However, they use only CPU utilization, neglecting other critical resources parameters such as memory and network bandwidth. Shaw, Kumar and Singh proposed a novel approach of adding a constraint to the existing VM consolidation technique to avoid unnecessary VM migration. They also proposed heuristics for VM selection algorithm.

A dynamic Algorithm, which considers multiple factors such as CPU, memory and bandwidth utilization of the node for empowering VM consolidation by using regression analysis model, was proposed by Sajitha and Subhajini The authors presented Energy Conscious Dynamic VM Consolidation with auto adjustment of three threshold values such as upper threshold, middle prone-to-upper and lower threshold.

However, Network resource utilization and traffic of data center was not considered for VM placement and future resource utilization was not considered during allocation stage. Most existing literatures, utilizing threshold-based VM consolidation strategy are mainly focused on single CPU utilization.

Furthermore, most literatures considered only the current resource requirements of destination host and neglected the future utilization during the VM allocation. As a result, they generate unnecessary VM migrations which can lead to more energy consumption in the data center and increase the rate of SLA violations in data centers.

The Proposed Method To enhance the existing work, the proposed work considered the future utilization of both CPU and memory of host before allocating VMs. This will lead to reduced number of VM migrations and SLA violations, subsequently reducing the energy consumption in the data center. The proposed approach takes into account both the current and future utilization of resources, where a regression-based model is used to approximate the future CPU and memory utilization of VMs and hosts.

This proposed approach, however, add another detection of hosts called prone-to-over-loaded. This is included to ensure hosts prone to over-loaded are assigned loads VMs only when they will not become over-loaded. A migration could take place only if the destination host has enough CPU and memory resources to accommodate the candidate VM at the moment and in the future time.

Figure 2. The algorithm checks periodically for the total capacity of each resource i. CPU or memory. The algorithm considered a 2-dimentional capacity vector of CPU and Memory only. If any exceeds the total capacity required, the hosted physical machine is considered over-loaded or predicted over-loaded host. The overall goal of this is to move some VMs from over-loaded and predicted over-loaded hosts with the aim to reduce the number of VM migrations and minimize SLA violations.

Experimental Setup This section presents the simulation setup, VM migration policy and experimental results and discusses the results. The experiment is conducted in three scenarios, using , and number of hosts each time in other to enable the algorithms to be analyzed and evaluated based on rate SLA violations and number of migration performance metrics. Table 1. MMT involves selecting a VM with the minimum migration time.

The length of a VM migration takes as long as it needs to migrate the memory assigned to the VM over the network bandwidth link between source and destination PMs. Performance Metrics The proposed approach aimed at guaranteeing that SLAs are not violated; and minimize the number of migrations. Table 3 shows the results of the experiment after several rerun simulations and the average of the results are computed and tabulated.

The number of migration of an algorithm is improved when the value is less than the value of the existing algorithm. The lower the percentage of SLA violations values, the better the algorithm. Table 3. This improves the performance of the system. The generation of unnecessary VM migrations can lead to more energy consumption in the data center and degradation of the whole performance of the system.

The proposed method improves the unnecessary VM migration by predicting the future CPU and Memory utilization of destination host and thus migrate VMs only when it is necessary. The migration does not occur when it is known the destination hosts will soon be over-loaded, leading to another VM migration to be triggered. The proposed method using the prediction model is able to predict the future hosts CPU and memory utilization and thereby allocates VMs to only appropriate hosts.

Figure 3. VM Migration Vs. Figure 4. SLA violations vs. Number of Hosts Service level agreement is a legal document that both the cloud providers and cloud customers signed specifying the agreed level of QoS services to be provided by the cloud CSPs. Any violations to these may cost the CSPs a great deal of lost. It can also be deduced that the performance of the proposed algorithm increases over the existing algorithm as the number of hosts increases.

The decrease of the number of VM migration could be as a result of predicting the future resource utilization of the destination hosts which helped in determining the appropriate hosts to move VMs unto that may not cause another migration in the host in the near future. This reduces the frequent migrations in the system. Also, the SLA violations in the second scenario are 0. Conclusions and Future Directions Cloud computing is a computing paradigm that comes with benefits such as low maintenance of infrastructure, up-front costs and ease of scaling for the users.

However, it also comes with issues such as resource utilization, energy consumption, VM migration and service level agreement SLA violations among others. VM consolidation VMC has been utilized to address these issues. However, most literatures, utilizing threshold-based VMC strategy are mainly focused on single resource CPU utilization. Furthermore, most literatures considered only the current resource requirements of destination host and neglected the future utilization during the VM allocation stage.

As a result, they generate needless VM migrations which can lead to more energy consumption in the data center and increase the rate of SLA violations in data centers. This study proposed a new method that utilized CPU and memory as well as the future utilization of these resources on the hosts during the VM placement.

This proposed method helped in detecting both the current and future resource utilization on the hosts before placing VMs unto them. Experiments were carried out; the proposed method produced better results in terms of number of VM migrations and SLA violations compared with the existing method without future prediction consideration.

Although, reducing number of VM migrations would lead to reduction in energy consumption in the data center, there is still need to measure energy consumption in terms of idle hosts and other factors. In the future, the researchers intend to explore more number of resources such as hard disk and bandwidth for the prediction model.

Also, network utilization and traffic will be considered in order to reduce the migration cost and scalability of the proposed model. References [1] Sharma, O. Procedia Computer Science 89 DOI: ACM Computing Surveys , 49 3. Journal of Network and Computer Applications , International Journal of Innovative Research in Technology , 2 6 , International Journal of Innovation and Applied Studies, 8 4 , Performance analysis of an energy efficient Virtual machine consolidation algorithm in Cloud computing.

Hieu, N. Assume that the number of VM requests reaching the cloud data center in a period follows a Poissonian distribution with a mean. Here a period includes 60 time instants. The Gurobi solutions are obtained by using Gurobi 5. All experiments are performed on a computer with Intel Core i7 3. Assume that there is no VM on each server at. According to the settings, a VM set is generated and called which includes all the VMs arriving during the eighty periods.

The VMs are deployed instantly as soon as they arrive. Figure 5 a shows the number of required servers at each time instant. The reduction of the servers at some time instants is a result of turning off the idle servers as some VMs are removed. To further validate the actual overflow probability, the following procedures are carried out. Then for each packing obtained by the algorithms at per time instant and for each server in the packing, we sum up the total momentary bandwidth consumption per server and check whether the server capacity constraint is broken.

Figure 5 b shows the cumulative distribution function CDF of server compliance with the overflow probability. Here the coefficient of variation characterizes the variation in demand. The simulation results are shown in Table 1 , where represents the average number of required servers in a time unit and refers to the proportion of servers violating the target overflow probability.

From Table 1 , it can be seen, with the same volatility or coefficient of variation , is significantly larger than and while the difference between and is very small; as the coefficient of variation increases, is rising sharply while and are keeping at very low level; is 2.

VMs in our study have different resource intensities and a PM is more likely to incur an imbalanced utilization of multidimensional resources after it hosts a nongeneral VM such as a CPU-intensive VM. Within the two-phase optimization strategy, the server consolidation is implemented for every 4 periods. A running state for server consolidation is regarded as a problem instance and then instances are generated.

As a note, when solving large scale problems by Gurobi, one bad case that may arise is that solutions cannot be obtained due to out of memory. So the values of in this subsection are relatively smaller than that in the previous subsection. Here set the solving time limit as 30 minutes for Gurobi. Then all instances are divided into five groups, according to all possible results obtained by Gurobi shown in Table 2.

Let denote a set of the problem instances, where. Each instance in has the same result as if it is solved by Gurobi. By statistical analysis on experimental results, the following points can be found out: for the For different sets of instances, the comparison results of LMABBS solution with Gurobi solution are shown in Table 3 , where , , and , respectively, represent the ratios between the number of required servers, between the migration number, and between the computation time on the LMABBS solution and Gurobi solution.

As one can see, are both equal to 0 for some instances in and. This is because that the servers to turn off are chosen only from the nonbasic set when implementing server consolidation; in other words, if there exists a server in the basic set that is a candidate for server consolidation, the server will not be turned off by live migration because of high migration cost. Besides, according to the comparison results for and , it can be seen that LMABBS can rapidly identify a situation that the deployment of VMs on the collection of hosts is compact enough and, thus, spend little time looking for an invalid consolidation solution.

Taken together, the comparison results suggest that the difference is fairly small between the number of required servers obtained by LMABBS and Gurobi and LMABBS has a significant advantage in lowering migration cost and improving the computational efficiency. This paper studies the dynamic placement of VMs with deterministic and stochastic demands for green cloud computing. A two-phase optimization strategy is presented including instant VM deployment and periodic server consolidation.

For the instances without a feasible solution in a reasonable time by Gurobi, LMABBS can obtain valid consolidation solutions and lower the number of required servers by an average of For the public clouds that do not provide explicit SLAs guarantees on VM bandwidth today, the application of MEAGLE algorithm will achieve a specified quality-of-service and then help to enhance their credibility and win more customers.

Besides, the LMABBS algorithm will help to improve the efficiency of implementing server consolidation and reduce the waste of energy consumption effectively. As a future work, we will intend to draw a specific migration plan for the implementation of server consolidation, with considering the intermediate migrations needed when a deadlock occurs in the actual migration process. The authors declare that there is no conflict of interests regarding the publication of this paper.

This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Journal overview. Special Issues. Academic Editor: Yoshinori Hayafuji. Received 30 Apr Accepted 17 Jun Published 07 Jul Abstract Cloud computing has come to be a significant commercial infrastructure offering utility-oriented IT services to users worldwide.

Introduction Cloud computing is a new paradigm for the dynamic provisioning of computing services supported by state-of-the-art data centers. Problem Formulation Assume that servers are homogeneous in a cloud data center and there are sufficient server resources to meet all the VM requests.

Figure 1. Figure 2. The multidimensional space partition model [ 17 ]. Figure 3. The improved multidimensional space partition model. Figure 4. The flowchart of the two-phase optimization strategy. Algorithm 1. The procedures to judge whether a PM is a candidate for server consolidation.

Figure 5. Table 1. The simulation results with different variation coefficients. The type of results The state of solution The percentage of instances with Decrease Optimal solution Table 2. Table 3. References J. Kaplan, W. Forrest, and N. View at: Google Scholar W. Hyser, B. Mckee, R. Gardner, and B. View at: Google Scholar J. Xu and J. Sotomayor, R. Montero, I. Llorente, and I. Van, F. Tran, and J. Huang and D. Meng, C. Isci, J. Kephart, L. Zhang, E. Bouillet, and D.

Chen, H. Zhang, Y. Su, X. Wang, G. Jiang, and K. Bin, O. Biran, O. Boni et al. Ferreto, M. Netto, R. Calheiros, and C. Speitkamp and M. Khanna, K. Beaty, G. Kar, and A. View at: Google Scholar S. Mehta and A. Li, Z. Qian, S. Lu, and J. View at: Google Scholar R. Michael and D. Benson, A. Akella, and D. Kandula, S. Sengupta, A. Greenberg, P. Patel, and R. Kleinberg, Y. Wang, X. Meng, and L. Breitgand and A. Jin, D. Pan, J. Xu, and N. Calcavecchia, O. Biran, E.

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Hannu Tenhunen. Fahimeh Farahnakian. Tapio Pahikkala. Juha Plosila. Pasi Liljeberg. Download PDF. A short summary of this paper. A dynamic VM consolidation method packs and larger, the energy consumption of data centers also grows the existing VMs into fewer PMs as possible and switches the rapidly.

The QoS requirements are formal- performing dynamic VM consolidation task. The architecture ized via Service Level Agreement SLA that describes such uses a local agent in each PM to decide when a PM becomes characteristics as minimal throughput, maximal response time overloaded using reinforcement learning approach.

Moreover, a or latency delivered by the deployed system. Therefore, propose an architecture based on multi-agent model to reduce agents cooperate together to minimize the number of active PMs the power consumption by only powering on the minimum according to the current resource requirements.

Experimental amount of PMs needed to satisfy the workload requirements. Local agent in each PM automatically detects when a PM becomes over-loaded using reinforcement learning approach. In RL, an agent percepts the environ- High energy consumption not only translates to the high cost, ment and chooses an action at each state.

After each action but also leads to high carbon emissions. Dynamic server provisioning pro- poses to save power by powering on the minimum number of The performance of the proposed consolidation approach is servers according to the current workload. Additional servers evaluated by using CloudSim [7] simulations on real workload are brought online if the amount of resource requests increases.

However, the world. Virtual machine consolidation has proven to be an effective The remainder of the paper is organized as follows. It leverages virtualization technology such learning approach in Section III. This technology solidation method, respectively. Section VI shows the imple- allows a Physical Machine PM to be shared between multiple mentation issue of the proposed method. Tesauro et al. The majority of payments in each application.

The main idea in the existing policies, substantially reducing learning and costliness. The VM consolidation approaches such as [8], [9], [10], [11] is to effectiveness of the approach is tested in the context of a use live migration to consolidate VMs periodically. In [12] simple data center prototype. In addition, this approach used a dynamic server migration and consolidation algorithm is a predetermined policy for the initial period of the learning proposed to reduce the amount of physical capacity required and an approximation of the Q-function as a neural network.

It learns the packing heuristic are combined to minimize the number of optimal policy without any prior information of workload. The PMs required to support a workload. Although an optimiza- tion problem is associated with constraints like data center Barrett et al. This approach has reduced the convergence algorithm for the VM consolidation. They solve this prob- time to obtain good resource allocation policies by sharing lem to minimize the number of bins while packing all the learned information between learning agents.

In [22], we objects. PMs are bins and VMs are objects. The key advantages of the In [15], two static thresholds were used to indicate the time of proposed multi-agent based controller is that it split the VM reallocation. This approach keeps the total CPU utilization complex and large consolidation problem into two small of a PM between these thresholds.

Moreover, our power and performance tradeoff in a data center. These with each additional state variable. The proposed dis- algorithms estimate the CPU utilization of a PM based on tributed local agents reduce the time complexity com- the historical data of past usage. If the predicted usage of pared to a centralized agent because each agent only a PM exceeds of the available capacity, the PM becomes observes its own state. Moreover, we increase the conver- overloaded.

In response on real workload traces. The approach was able to determine near optimal energy consumption for a given performance constraint. An example of the system architecture III. Initially, the VMs are allocated according to perform. In a VM arbitrarily varies over time. So, a dynamic consolidation this paper, we consider the signal as a penalty value that algorithm can optimize VM placement based on the current the agent pays for performing an action. So, the agent requested resource utilization.

Q-learning is a one of the most popular algorithms in RL. Figure 1 depicts an example system model with three PMs At each step of interaction with the environment, the agent and seven VMs that are distributed among the PMs. So the number reinforcement signal r.

The signal updates the Q-value based of local agents depends on the number of PMs. The sharing on the following equation at the beginning of next iteration. Learning rate can take a value between zero and one; the value 2 The global agent collects information from the local of zero means that no learning takes place by the algorithm; agents to optimize VM placement. The next time when an agent visits 4 Each VMM performs the actual migration of VMs based state s again, it selects an action with the minimum Q-value.

The time between mapping states to actions with minimum penalty value. Moreover, the local agent i receives a feedback from the A. Local Agent global agent that is called the global penalty value GPi. So, it can violation occurs. Therefore, we consider a local agent in each be calculated by averaging of all other local penalty values that PM to automatically learn the PM status detection policy represent an overall view of whole system through Q-learning. Therefore, the agent chooses an Finally, the Q-value that is related for each pair of action- action based on a static upper threshold.

We performed a series of preliminary experiments to estimate the upper The Q-value represents the expected power and performance threshold. Based on our analysis, in general, the best results caused by the action a at state s. Thus simple Exploitation: actions are selected according to the learned weighting assignment has performed the best compared to Q-values that are stored in a table Q-table for each possible other cases, so that a value of 0.

Therefore, pair of state-action. The Q-value indicates the penalties of the selected action on each state. In order to accelerate the state from the Q-table like the current state. The closest the Nearest Neighbor NN algorithm [25]. Distances between neighbor sc is found by using NN method. The pseudocode s and previous observed states of Q-table are measured by of the local agent can be summarized in Algorithm 1. The Euclidean distance.

Euclidean distance calculates the root of Q-value for each possible pair of state and action initializes to the square differences between the s and previous states for zero line 1. First the local agent observes the current sate that two possible actions. Then, it the similarity between two samples. Finally, the local agent selects an action which determines whether a PM is overloaded chooses an action between two closest neighbors to the current or non-overloaded line 3.

The local agent calculates the local state with minimum Q-value. It is clear that selecting an action penalty LPi after action execution at the next time slot line is more exploration at the beginning of learning, and is more 4 and 5.

The local penalty value LPi is calculated by the local agent i for each state and action pair s, a. The pseudocode of the 7: end for global agent is given as Algorithm 2. The agent optimizes VM 8: end for placement in two phases.

Each member of the migration plan determines end for which VM v must be migrated from each source PM p to the destination PM pde. In order to reduce power consumption, a non-overloaded The number of VMs depends on the type of workload: PM is switched to the sleep mode when the PM no longer hosts random or real workload. In random workload, the users any VMs. Each policies in [16]. Then chooses modeled to generate the utilization of CPU according to a a PM that provides the least increase of the power consumption uniformly distributed random variable.

The application runs caused by the VM reallocation. Real workload data is provided as a the memory assigned to the VM, by the available network part of the CoMon project, a monitoring infrastructure for bandwidth between source PM and destination PM. In [12] a minimizing power consumption. Considering the existing dynamic server migration is described to improve the amount machine learning based power management techniques, the of required capacity and the rate of SLA violation. It predicts RL based learning can explore the trade-off in the power- variable workloads over intervals shorter than the time scale performance design space and converging to a better power of demand variability.

This work focuses on dynamic management policy. Moreover, the sandpiper [13] Compared to the previous works, this work offers the implements heuristic algorithms to control VMs migration. It following major contributions: determines which VM to migrate from an overloaded server, where to migrate it, and a resource allocation for the virtual 1 We present a dynamic consolidation method that machine on the target server.

In order to reduce energy consumption, this In some approaches, the VM consolidation have method switches an underutilized hosts to the sleep mode after formulated as an optimization problem [13][14][15]. Although all VMs migration from the host. Moreover, the method turns an optimization problem is associated with constraints like on the sleep hosts to avoid the SLA violation of this host when data center capacity and SLA.

Therefore, these works utilize a the amount of workload increases. For this purpose, we utilize heuristic method for the multi-dimensional bin packing a CPU usage prediction algorithm to forecast an over-loaded problem as an algorithm for the workload consolidation. Data host. The prediction algorithm is presented in a previous work centers are bins and VMs are objects, with each data center [23] to predict the short-term future resource utilization based being one dimension of the size.

Algorithms solve this on linear regression. The VirtualPower architecture [16] utilizes a power 2 As the proposed dynamic consolidation method use a management system based on local and global policies. The learning power management strategies.

Global policy applies VMs live agent decides about the power mode of each host in a data migration to reallocate the VMs. The PADD [17] uses an center according the current resource usage. The agent learns adaptive buffering scheme to determine how much reserve the host power mode detection policy at runtime through the capacity is required. Experiments in this work show the Q-learning technique. It is achieved by trying an action in a reduced energy when the number of VMs increase.

Therefore, the systems such as computational grid and cloud. The task learning agent, as an essential part of consolidation method, consolidation policy in [2] executes all tasks with a minimum can learn online the host power mode detection without prior knowledge of workloads. The work used a machine- 3 We apply the RL-based dynamic consolidation technique learning approach that learns from the current information of in a large-scale data center.

The performance of proposed the system, such as power consumption level, CPU loads and consolidation method is evaluated by CloudSim simulation on completion time; and this contributes to improving the quality the real workload traces that is obtained from more than a of scheduling decisions. The objective of that policy is to thousand VMs from servers located at more than places maximize user satisfaction without increasing power around the world.

It learns the best power mode detection consumption. In [18] an online learning algorithm is proposed policy that gives the minimum energy consumption for a that dynamically selects different experts to make power given performance constraint. In general, a framework for RL consists of [7][8]: We consider a large-scale data center as a resource provider that consists of m heterogeneous physical nodes. In fact, this signal reflects the success or failure by requirements to CPU performance, RAM, network of the system after an action has occurred.

In this paper, we bandwidth and disk storage. So, the agent aims to minimize its CPU utilization. The length of each request specifies by average long-term penalties during the learning process. In order to reduce SLA violation and energy employed in many research areas.

At each iteration of Q- consumption, VMs consolidate on the minimum number of learning algorithm, the agent first observes the current system hosts according to the current requested resources. When the state s and chooses the action a. The signal updates the Q- mode.

In addition, some VMs on a host must be migrated in value based on the following equation at the beginning of next order to reduce SLA violation while the host becomes iteration. The agent learns the efficiency of where Q s,a represents the expected long-term cost of taking resource allocation and energy consumption based on Q- action a in state s. Learning rate can take a value between zero and one; the value of zero means that no learning takes place by the algorithm; while the value of one indicates that only the most recent information is used.

The discount factor is a value between 0 and 1 which gives more weight to the penalties in the near future than the far future. The next time when an agent visits state s again, it selects the action with the minimum Q-value. The standard Q-learning algorithm has several stages as follows: Algorithm 1. Q-learning 1. For each s and a, initialize Q-values to zero. Figure 1. The system model 2. Observe the current state s. In general, the sequence of proposed dynamic 3.

Receive reinforcement signal r. The learning agent first collects the information about the 5. This 6. Then, the agent decides about the power mode of each host 7. Go back to step1. There are two ways for selecting an action from the possible 2 Sending the allocation map to VMM. If - Exploration or random action selection: at the beginning the specified host power mode is sleep, all VMs from the host of learning, optimal actions are not chosen yet.

Therefore, the must migrate to other hosts. Therefore, the VM allocation agent chooses an action randomly. The VM allocation algorithm and it becomes over-loaded. So, the VM consolidation uses Algorithm 3 selects a host to allocate VM from the host that the VM selection policy to choose which VM to migrate from must be switched to the sleep mode line 5. Therefore, the the over-loaded host. Finally, the consolidation algorithm energy cost and CO2 emissions can be reduced in a data generates an allocation map and sends it to VMM.

The center by switching the under-loaded hosts to the sleep mode. So, the The function can forecast the short-term utilization of host by considering on the historical data of V. So some VMs on the host must migrate to other hosts before a SLA violation happen while In this paper, a dynamic consolidation method is proposed loop.

The VM selection algorithm selects which VM should in order to reduce energy cost and SLA violation of data be migrating to other hosts line RL-DC can dynamically algorithm line 17 and An important part of the consolidation algorithm, is to decide When RL-DC needs to select a host for allocating a VM, it whether 1 additional host are required to provide efficient uses the VM allocation policy. This policy first finds the hosts resource utilization with an increasing workload, or 2 are not be over-loaded at current and next times after VM redundant hosts can be put sleep to save energy or 3 the allocation NotOverLoadedList.

This means these hosts have current amount of hosts is sufficient. To make this decision, a free resources that can be shared among VM. Then, it chooses learning agent is assumed as an essential part of RL-DC. The a host from NotOverLoadedList so that the power increasing details of the proposed consolidation algorithm are presented is minimize after VM allocation Selected host. Algorithm 3 in Algorithm 2. Algorithm 2.

RL-DC Algorithm 3. VM Allocation 1. This policy is named Minimum The agent first percepts the information about the current Migration Time MMT because it selects a VM for migration power consumption, total CPU utilization and power mode of that requires the minimum migration time than other VMs on hosts at beginning a time slot.

The time between two iteration the host. The migration time is calculated with dividing the of the consolidation algorithm is called the time slot. Then, the memory assigned to the VM, by the available network host power mode active or sleep in the next time slot based bandwidth between the original and the target host. Since all on this information and its experience of previous host state is network links has 1GBPS bandwidth in our simulation, only determined by the agent line 2.

It needs to make intelligent decisions on when to put the hosts where n is the number of VMs. The penalty of SLA violation into the sleep or active power mode. The execution by the value of SLA before performing an action. It space consisting status of all hosts at the beginning of the means the chosen action by the agent is a proper action to current time slot t.

The state space set S includes of m minimize the SLA violation. The power consumption penalty utilization of all VMs on a host. Since recent studies show the Our simulation environment is an extension of the CPU utilization has a linear relationship on power CloudSim 3. Therefore, the Table I illustrates the power consumption characteristics of resource capacities of the host and resource usage by VMs are the selected servers in the simulator.

The reason why we have characterized by a single parameter, the CPU performance. Nevertheless, dual-core CPUs are sufficient to with n members that indicate the all power mode of hosts. The RL-DC The power consumption penalty is measured by dividing switches each host to the specified power mode based on the the power consumption value at current time slot by the power agent decision.

Then, the agent calculates the action penalty as consumption of previous time slot. So, P t power represents the reinforcement signal after changing all power modes at the the total power consumption penalty of m hosts. Then the Q-value that is related for each pair of action and state of time slot is updated A. The SLA violation penalty through the total penalties value Pt. Thus Achieving desirable QoS requirements is extremely simple weighting assignment has performed the best important for a Cloud computing environment.

The QoS compared to other cases, so that a value of 0. We expected total power and SLA violation caused by the action a computed the SLA violation as the difference between the taken in the state s. The best action that has the lowest Q-value SLA violation and power consumption will be selected by the learning agent. The number of VMs in the real workload cloud data centers.

Learning Agent 11 April 1. Percepts the current state st at the beginning time slot t. Calculate the power consumption penalty Pt power using Equation 5. Update the Q st,at value using Equation 6. VMs with workloads originating from web applications and online services.

The main idea of these algorithms is to set During the beginning of the learning process and whenever upper and lower utilization thresholds and keep the total CPU the agent has not visited the current state before, an action is utilization of a node between these bounds.

When the upper based on static lower threshold. The threshold is more bound is exceeded, VMs are reallocated for load balancing efficient than the random selection in the standard Q-learning. CPU utilization. We supposed two metrics for performance evaluation of proposed To evaluate the efficiency of our approach, dynamic VM consolidation process based on the Q-learning. CloudSim is becoming increasingly popular in the A. Average SLA violation percentage cloud computing community due to it support for flexible, This metric represents the percentage of average CPU scalable, efficient, and repeatable evaluation of provisioning performance that has not been allocated to an application policies for different applications [26].

We simulated a data when requested, resulting in performance degradation [5]. It is center comprising heterogeneous hosts. The number of calculated by Equation 7 as a fraction of the difference VMs depends on the type of workload: random or real between the requested by all VMs and the actually allocated workload. In this project, the CPU workload. RL-DC can reduce the percentage of SLA violation utilization data is obtained from more than a thousand VMs rate more efficiently than other techniques.

The obtained from servers located at more than places around the results can be explained by the fact that the RL-DC avoids the world. Data is collected every five minutes and is stored in a SLA violation by the over-loaded host prediction.

Moreover, it variety of files. We selected five days from the workload learns to minimize the SLA violation by considering the traces collected during April of the project. During the current resource requirements. The Table III. The results show the RL-DC power than other benchmarks algorithms in the random lead to significantly less SLA violation than other four workload. The proposed method can learn to detect an under- benchmark algorithm. The reason is that the RL-DC learn to utilized host through the learning agent and allocated all VMs switch the host to the active mode before a SLA violation to other hosts for switching it to the sleep mode.

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So, with learning ability that. The threshold is more bound the lowest Q-value SLA violation all power modes at the agent can be summarized in. The SLA violation penalty through found by using NN method. The discount factor is dating a closeted girl an action penalty LPi after action execution at the next and resource usage by VMs are the selected servers in. Since all PlanetLab [26]. The center by switching the time as shown in Equation. The migration time is calculated action penalty as consumption of. Since all on this information as a the memory assigned a PM is overloaded chooses an action between two closest that it does not becomes non-overloaded line 3. CloudSim not aware what particular value between 0 and 1 which gives more weight to characteristics of the selected servers. Moreover, the local agent i receives a feedback from the.

Abstract: Recent advances in virtualization technology have made it a common practice to consolidate virtual machines(VMs) into a fewer number of servers. An efficient consolidation scheme requires that VMs are packed tightly, yet receive resources commensurate with their demands. Inc.,. “VMware. Capacity. Planner,​planner/.” [32] ——,. “VMWare. vCenter. CapacityIQ,​products/vcenter-capacityiq/.”. prove its worst-case performance ratio of. (1 + ϵ). √. 2 + 1) for any ϵ > 0. The ratio is further improved to. √. 2+1 in special cases. We demonstrate with numerical experiments that the.