Secure and energy aware load balancing framework for cloud data centre networks

Electronics LettersVolume 55, Issue 9 p. 540-541 Information and communicationsFree Access Secure and energy aware load balancing framework for cloud data centre networks A.K. Singh, Corresponding Author A.K. Singh ashutosh@nitkkr.ac.in Department of Computer Applications, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this authorJ. Kumar, J. Kumar Department of Computer Applications, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this author A.K. Singh, Corresponding Author A.K. Singh ashutosh@nitkkr.ac.in Department of Computer Applications, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this authorJ. Kumar, J. Kumar Department of Computer Applications, National Institute of Technology, Kurukshetra, IndiaSearch for more papers by this author First published: 01 May 2019 https://doi.org/10.1049/el.2019.0022Citations: 28AboutSectionsPDF 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 In a cloud system, a malicious user initiates the side channel attacks to steal the private information of other users that share the computing resources through virtualisation. In this Letter, the authors propose a multi-objective load balancing framework to minimise the likelihood of these attacks by reducing the number of shared servers along with efficient use of computing resources and power. The proposed framework significantly improves the security by reducing the shared servers up to 55.83% and improves the resource and power usage up to 96.54 and 21.97%, respectively, over best fit, first fit and random heuristic schemes. Introduction The efficient dynamic load balancing in a large cloud system has always been a challenging task for service providers. Numerous load balancing schemes have been proposed that concentrate on the different dimensions of efficient usage of resources [1, 2] and power [3, 4]. However, the resource sharing among multiple users introduces the security threats such as side channel attack (SCA). These attacks often occur across the virtual machines (VMs) sharing the same server and observes the side channel events to steal the sensitive information [5]. This Letter proposes a secure and energy aware load balancing (SEA-LB) framework to prevent the SCA along with efficient usage of computing resources and power. We formulate a novel machine learning based VMs allocation scheme that also addresses the security aspect along with resource utilisation and energy consumption. Since the malicious and victim VMs of these attacks share the same infrastructure in a data centre, the idea of preventing the SCA is employed by minimising the number of shared servers among multiple users. A multiobjective approach is implemented for the proposed model that leverages computational efficient non-dominated sorting genetic algorithm II (NSGA-II) [6]. SEA-LB framework Let a data centre equipped with m servers () and hosts n VMs () owned by p distinct users (). In such a scenario, the service providers always seek for an optimal mapping of VMs and servers to maximise their fiscal gain. Assuming that and are the CPU and memory utilisation of ; and are the CPU and memory capacity of . If hosts one or more VMs, it is active () otherwise idle (). The shows the mapping of VM assignment in such a way that if is assigned to then otherwise . The data centre's resource utilisation () can be obtained using (1) that needs to be maximised, where and are the CPU and memory utilisation of and can be computed from (2), is the number of resources to be monitored. (1) (2) Let and correspond to the maximum and minimum amount of power consumed by while is the power consumption in the idle state. The power consumption of any server at any instant can be modelled using (3). The data centre's total power consumption () is obtained by summing up all and the aim of the framework is to minimise it. (3) A server is an ideal candidate for the SCA if it hosts the VMs of two or more users and we refer it a conflicting server (). Since a malicious machine always targets the co-resident machines to initiate a connection for SCA, the framework aims to minimise the number of conflicting servers in a data centre. Let represents the mapping between users and servers, i.e. if 's one or more VMs are hosted on then otherwise . The number of users whose VMs are hosted on is obtained by computing . The presence of in the system can be modeled using (4). (4) The formulated model has multiple conflicting objectives, i.e. the resource utilisation drops down and power consumption increases when conflicting servers are reduced in a large scale data centre. Therefore the framework employed one of the most computational efficient multiobjective algorithm to learn the best allocation that must be secure, energy aware, and balanced. The pseudocode for the proposed framework is given in Algorithm 1. It first generates random allocations that satisfy the constraint (5) for both resources CPU and memory. (5) The load balancing unit of the system consists of four key operations named initialisation, evaluation, recombination and selection as shown in Fig. 1. The represents the possible VM allocation () and encoding scheme can be seen in Fig. 2, where each gene corresponds to the mapping of individual VM to the server. A cost function () is applied on every individual to compute the cost values associated to each objective resource utilisation, power consumption, and security, respectively. Further, non-dominated sorting () is applied that sorts each solution according to their dominance level and adds the non-dominated solutions into Pareto front () (lines 5–13). The dominates if its cost values are better than on at least one objective and same or better on other objectives. The solution space is explored for a better allocation by recombining the individuals using crossover () and mutation () operations. The single point crossover is used as it generates less infeasible solutions. The mutation operator avoids the local optima and helps in exploring the entire solution space. Fig. 2 highlights the mutation operator mechanism that swaps the randomly selected two distinct VMs ( and , ). The recombined allocations might be infeasible by violating the allocation constraint that are corrected by migrating () the infeasible allocated VMs to the appropriate servers. The new allocations are again evaluated on objective functions and selection () operator replaces the least fit solutions by their fitter offspring counterparts obtained through crossover () and mutation (). Fig 1Open in figure viewerPowerPoint SEA-LB framework Fig 2Open in figure viewerPowerPoint Chromosome encoding and mutation operation For instance, four users initiated eight VMs that are allocated among four servers each with 100 units of CPU and Memory both and , . Considering that given is the random VM allocation , where means VM is owned by user and allocated to server j. Let us assume that CPU and memory utilisation of each VM are (15, 20), (25, 50), (35, 40), (20, 20), (25, 75), (30, 60), (50, 45) and (25, 25) respectively. Based on given allocation, the , and . The SEA-LB optimises the above mentioned allocations and placed VMs as () with , and . Note that in optimised allocation the presence of conflicting servers is reduced by 33.33% on the cost of 2.03% more power consumption and 0.89% low resource utilisation. However, the simulation results show that the proposed scheme simultaneously improves the resource utilisation and power consumption as well. Algorithm 1.SEA-LB framework operational summary 1: Initialise random allocations () 2: for each do 3: [) 4: end for 5: for each and do 6: 7: if () 8: end for 9: while do 10: for and do 11: ; if () 12: end for 13: end while 14: while Termination criteria do 15: ; 16: 17: [) 18: [) 19: 20: end while Performance evaluation We conducted a series of simulations on a machine equipped with Intel Xeon CPU E5-2630 along with 128 GB memory. CPU and memory utilisation is measured in millions instructions per second ‘mips’ and mega bytes ‘MB’ respectively. The simulation environment used three types of servers (HP and IBM) each with (2660, 4096), (3067, 8172), (3067, 16384) capacity of CPU and memory [7]. Four types of VMs having (500, 512), (1000, 1024), (1500, 2048) and (2000, 3072) CPU and memory utilisation are used. We experimented with different number of VMs and results are compared with best fit, first fit and random heuristics based allocation schemes. The non-dominated solutions obtained by the proposed framework for 100 VMs is depicted in Fig. 3 that shows the conflicting behaviour of optimisation variables. Table 1 lists out the resource utilisation obtained from the VM allocations from each approach. We observed a relative improvement up to 39.36, 96.54 and 88.81% over best fit, first fit and random allocation schemes, respectively. Fig 3Open in figure viewerPowerPoint Pareto Front of non-dominated solutions for 100 VMs Table 1. Resource utilisation Method 20 VMs 60 VMs 100 VMs 300 VMs 500 VMs SEA-LB 48.33 45.02 47.65 41.60 39.98 best fit 38.04 40.21 38.93 38.88 38.67 first fit 31.91 28.22 27.46 27.00 26.95 random 32.80 29.07 29.45 28.01 27.82 The amount of power consumed by different policies' allocation patterns is shown in Table 2. The proposed scheme reduced the amount of required power over best fit and random policies with maximum improvement up to 19.63 and 21.97%, respectively. However, first fit achieves better efficiency as the number of VMs increases. Table 2. Power consumption, W Method 20 VMs 60 VMs 100 VMs 300 VMs 500 VMs SEA-LB best fit first fit random Table 3 reports the share of conflicting servers observed in the generated allocations. The proposed framework has significantly improved the number of non-conflicting servers in the data centre. The SEA-LB considerably reduced the number of conflicting servers with an improvement up to 55.83, 33.08 and 30.40% over best fit, first fit and random allocation strategies. Table 3. Conflicting servers, % Approach 20 VMs 60 VMs 100 VMs 300 VMs 500 VMs SEA-LB 15.56 11.00 11.11 13.13 13.25 best fit 31.11 31.33 32.93 33.53 35.54 first fit 28.89 18.00 18.18 17.60 18.39 random 27.78 20.33 22.63 20.27 20.72 Conclusion The private information of cloud users is always at risk as they share the resources with other unknown users. The proposed VM allocation policy reduces the threat of SCAs along with efficient resource utilisation and power consumption. It substantially decreases the SCAs' possibilities and simultaneously improves the power and resource usage. These evident results support the superiority of the proposed scheme. Acknowledgment This work was financially supported by the Ministry of Electronics and Information Technology (MeitY), Government of India. References 1Ma, T., Wu, J., Hu, Y. et. al.,: ‘Optimal VM placement for traffic scalability using Markov chain in cloud data centre networks’, Electron. Lett., 2017, 53, (9), pp. 602– 604 (https://doi/org/10.1049/el.2016.3891) 2Chhabra, S., Singh, A.K.: ‘Dynamic hierarchical load balancing model for cloud data centre networks’, Electron. Lett., 2019, 55, (2), pp. 94– 96 (https://doi/org/10.1049/el.2018.5427) 3Dabbagh, M., Hamdaoui, B., Guizani, M. et. al.,: ‘Energy-efficient resource allocation and provisioning framework for cloud data centers’, Trans. Netw. Serv. Manage., 2015, 12, (3), pp. 377– 391 (https://doi/org/10.1109/TNSM.2015.2436408) 4Sharma, N., Guddeti, R.M.: ‘Multi-objective energy efficient virtual machines allocation at the cloud data center’, Trans. Serv. Comput., 2019, 12, (1), pp. 158– 171 (https://doi/org/10.1109/TSC.2016.2596289) 5Han, Y., Chan, J., Alpcan, T. et. al.,: ‘Using virtual machine allocation policies to defend against co-resident attacks in cloud computing’, Trans. Dependable Secur. Comput., 2017, 14, (1), pp. 95– 108 6Deb, K., Pratap, A., Agarwal, S. et. al.,: ‘A fast and elitist multiobjective genetic algorithm: NSGA-II’, Trans. Evol. Comput., 2002, 6, (2), pp. 182– 197 (https://doi/org/10.1109/4235.996017) 7Beloglazov, A., Buyya, R.: ‘Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers’, Concurr. Comput. Pract. Exp., 2012, 24, (13), pp. 1397– 1420 (https://doi/org/10.1002/cpe.1867) Citing Literature Volume55, Issue9May 2019Pages 540-541 FiguresReferencesRelatedInformation