Dynamic hierarchical load balancing model for cloud data centre networks

Electronics LettersVolume 55, Issue 2 p. 94-96 Information and communicationsFree Access Dynamic hierarchical load balancing model for cloud data centre networks S. Chhabra, Corresponding Author S. Chhabra sakshichhabra555@gmail.com Department of Computer Applications, NIT Kurukshetra, Kurukshetra, Haryana, IndiaSearch for more papers by this authorA.K. Singh, A.K. Singh Department of Computer Applications, NIT Kurukshetra, Kurukshetra, Haryana, IndiaSearch for more papers by this author S. Chhabra, Corresponding Author S. Chhabra sakshichhabra555@gmail.com Department of Computer Applications, NIT Kurukshetra, Kurukshetra, Haryana, IndiaSearch for more papers by this authorA.K. Singh, A.K. Singh Department of Computer Applications, NIT Kurukshetra, Kurukshetra, Haryana, IndiaSearch for more papers by this author First published: 01 January 2019 https://doi.org/10.1049/el.2018.5427Citations: 9AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Today's data centres need efficient traffic management to improve resource utilisation in their networks. It helps to solve the load imbalance problem of each processor. This Letter presents a 'dynamic hierarchical load balancing' approach for higher traffic scalability. The method selects the most favourable host satisfying the multi-dimensional resource constraints in terms of computing capability and its performance. The proposed method achieves significant traffic scalability improvement upto 66% over sequential and random virtual machine placement heuristics. Introduction Cloud computing has been growing tremendously over the past few years and comes out to be of great success with many exceptional contributions. At present, traffic in data centre networks is rapidly increasing which needs to be managed efficiently to improve the resource utilisation of the data centres [1]. It is also crucial for the business to avoid the unnecessary cost of virtual machines (VMs), i.e. excessive computing power beyond its need. The scheduling and allocation of resources in compelling way is always a challenging task and still far away from getting 100% utilisation in any form of computation. Cloud traffic engineering solves the problem of load balancing by searching the optimal resources for the particular deployment [2]. In this Letter, we introduce a unified framework for traffic management by optimisation of VM placement which aims for the minimisation of energy consumption and improvement in the usage of resources in cloud data centres. The idea of selecting the most favourable host is employed by placing VMs optimally to access these significant reliable indices including the high execution efficiency, high resource utilisation and optimised the external service performance [3, 4]. Dynamic hierarchical load balancing (DHLB) inquires the favourable host among all which accomplishes and fulfil the requirements of deploying tasks. The traffic exchanged between different hosts in data centres with priority-based resource constraints of CPU, memory and energy consumption to be considered. Efficient VM placement has addressed the searching of an optimal host with considerations of hierarchical network architecture and multi-dimensional resource constraints. DHLB design The problem is formulated with n Clients , q job requests , t server set with v virtual machines . The clients are requesting jobs to m physical hosts residing in server . A mapping allocates each physical host from each client with specific task requests, iff job request jr of client n is allocated to optimal physical host} and 0 otherwise. It should be noted that a feasible VM placement decision should satisfy the following resource constraints and find favourable VMs as given in the following equation: (1)where , and represent the CPU, memory and energy consumption of . , and define the total CPU, memory and energy resource amount of , respectively. At the time of allocating tasks, first we check whether an available memory is greater or close to the requested ones then it can only deploy the tasks. It ensures that the total required consumption of processors resource amount should not be exceeded its total capacity [5]. The DHLB method improves the load balancing performance by offering best fit allocation policy as illustrated in Fig. 1. When users submit their task requests in the resource pool of the cloud data centres, and generally it chooses the physical host randomly for deploying the tasks. But it becomes optimal, if we can decide the most favourable machine for a particular task and VM migration has been applied to achieve system load balancing in a cloud environment. For avoiding the overflow condition of the node servers, we have the following load constraint: (2)where denotes the bandwidth of servers . The coefficient defines for the VM placement pair of respective servers. Therefore, we need to calculate the load twice using (2). It guarantees that each task should be allocated in one VM as respective physical host with specific requests. A hierarchical-structure data centre network consisting of multiple physical hosts assigns VM capacity X can be seen in Fig. 2. Fig 1Open in figure viewerPowerPoint DHLB architecture Fig 2Open in figure viewerPowerPoint DHLB hierarchical structure (3) We find the proportion between requested and capacity amount of the resources. is the served VM capacity and is the actual capacity as in (3). For , its expected traffic to physical host is defined as and served traffic to physical host is described as where is the real traffic rate from VM i to VM v. The available capacity (AC) of respective resources: CPU, Memory and Energy as in (4). Using (5), we can find unoccupied resources which is left till now (4) (5) (6) (7) (8)We consider multi-objective optimisation with these resources: CPU, Memory and Energy per VMs with priority-based strategy. This model tolerates the wastage of CPU, Memory and Energy is 20, 30 and 50%, respectively. We find optimal VMs for the task deployment using priority-based resource scheduling using (7) and finds wastage tolerance power and is the allowance parameter which only permits the wastage to this extent in (8). The operational summary of the process is mentioned in Algorithm 1. Algorithm 1.Operation summary of DHLB method 1: initialise , , and are total CPU, memory and energy resource amount of 2: for each client's request do 3: Evaluate each job request in on test data 4: 5: Check 6: end for 7: if and then 8: Compute served VM capacity/actual VMs 9: Check current AC of CPU, Memory and Energy 10: 11: end if 12: Evaluate 13: Update new available capacity 14: Update with 15: return optimal Performance evaluation The experiments are conducted using Java-Eclipse IDE on a machine equipped with IntelⓇ CoreTM I5 processor of 2.60 GHZ clock speed and 8 GB of main memory. The simulated cloud network is considered for achieving realistic results. The evaluation is conducted on 200 physical machines with different configurations which can efficiently fulfill the requirements of upcoming load simulation conditions. These machines have a different available computing resource amount of CPU, Memory and Energy. DHLB model is compared with other two VM placement heuristics: sequential placement and random placement. The sequential placement strategy assigns VMs sequentially on the host, and the random placement strategy randomly chooses available physical machines for VM placement request. Fig. 3 shows the performance of evaluated algorithm DHLB which produces the less traffic overflow than other two heuristics, as it considers both the current system state and the future VM placement request arrivals. It achieves the overload avoidance for systems with multi-resource constraints. The makespan time is evaluated for all heuristics under 100 VMs as per requested tasks. Table 1 presents the obtained results for experiments which conveys that DHLB takes less time for processing the tasks than other heuristics. DHLB improves the average processing time by 30.89 and 26.55% over random and sequential deployment policies, respectively. Fig 3Open in figure viewerPowerPoint Comparison of RND, SEQ and DHLB in proportion of overflow traffic During the scheduling and tasks deployment, the finding of these failure nodes in the dynamic environment is shown in Fig. 4. It can only be possible if the chosen physical machine cannot fulfill some of the demands of requested tasks. When a number of tasks increases, then the chances of the number of failures increases gradually because the ability to handle tasks will weaken slowly. The primary concern is to find the best utilisation schedule for a given resource in terms of profit obtained by utilising that resource, and the number of time slices during which the resource will be utilised. Table 2 depicts the percentage of resource utilisation and DHLB minimises the wastage of resources while maximising the performance and its usage of their data centres. DHLB improves the utilisation of resources up to 61.29.53 and 66.76% for random and sequential placement, respectively. Table 1. Makespan for requested tasks Number of VMs Requested 60 100 tasks RND SEQ DHLB RND SEQ DHLB 20 29.622 28.165 14.673 12.984 11.676 5.427 40 31.627 30.092 21.675 31.676 28.221 19.678 60 40.625 37.788 25.680 21.603 19.625 11.581 80 60.025 50.895 47.682 25.648 23.002 19.688 100 72.681 69.182 50.814 30.771 32.678 29.254 Fig 4Open in figure viewerPowerPoint Comparison of RND, SEQ and DHLB resource allocation in failures Table 2. Resource utilisation (in %) Allocation Number of VMs Average policies 40 80 120 160 200 wastage, % RND 76.42 71.56 56.14 51.67 50.68 38.71 SEQ 79.61 73.84 64.87 58.56 56.96 33.24 DHLB 89.62 83.27 76.98 70.01 70.21 21.98 Conclusion This Letter proposes an efficient solution for load balancing issues by optimisation of VM placement. Contrast to sequential and random placement, our solution avoids the traffic overflow, reduced the number of failures and increases the utilisation of resources. Performance evaluation demonstrates that proposed algorithm achieves satisfactory outputs which reduces the wastage of resources upto 78.18% approximately in all types of analysis for the experimental data when pointing out the observations of existing well-known algorithms. Acknowledgments The authors thank the DeitY (Department of Electronics and Information technology) for supporting this research project. References 1Rong, Y., Zhang, Y., Gjessing, S. et. al.,: 'Toward cloud-based vehicular networks with efficient resource management', Network, 2013, 27, (5), pp. 48– 55 2Pezaros, D.P., Fung Po, T.: 'Improving data center network utilization using near-optimal traffic engineering', Trans. Parallel Distrib. Syst., 2013, 24, (6), pp. 1139– 1148 (https://doi/org/10.1109/TPDS.2012.343) 3Teng, M., Wu, J., Hu, Y. et. al.,: 'Optimal VM placement for traffic scalability using Markov chain in cloud data centre networks', Electron. 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