Abstract
The virtualization of hardware resources like network, memory and storage are included in the core of cloud computing and are provided with the help of Virtual Machines (VM). The issues based on reliability and security reside in its acceptance in the cloud environment during the migration of VMs. VM migration highly enhanced the manageability, performance, and fault tolerance of cloud systems. Here, a set of tasks submitted by various users are arranged in the virtual cloud computing platform by using a set of VMs. Energy efficiency is effectively attained with the help of a loadbalancing strategy and it is a critical issue in the cloud environment. During the migration of VMs, providing high security is a very important task in the cloud environment. To resolve such challenges, an effective method is proposed using an optimal key-based encryption process. The main objective of this research work is to perform the VM migration and derive the multi-objective constraints with the help of hybrid heuristic improvement. The optimal VM migration is achieved by the hybrid algorithm as Improved Binary Battle Royale with Moth-flame Optimization (IBinBRMO). It can also be used to derive the multi-objective functions by some constraints like resource utilization, active servers, makespan, energy consumption, etc. After VM migration, the data transmission should take place securely between the source and destination. To secure the data, the HybridHomophorphic and Advanced Encryption Standard(HH-AES) Algorithm, where IBinBRMO optimizes the key. After optimizing the keys, the data are securely transformed along with multi-objective functions using parameters includingthe degree of modification, hiding failure rate and information preservation rate. Thus, the effectiveness is guaranteed and analyzed with other classical models. Hence, the results illustrate that the proposed work attains better performance.
Keywords
Introduction
In Information Technology (IT), cloud computing is the most powerful establishment in a financial institution. In small and large-scale organizations, virtual services are used to compute and store information [1]. A wide range of services are provided with the help of recently used cloud systems, and it provides several benefits in terms of avoiding additional server requirements and reducing the utilization of power resources [2]. In the cloud computing platform, commercial virtualization applications migrate VMs to allocate complimentary resources [3]. The overload of the networks is handled very effectively while allocating the complementary resources, and these resource values cannot be extended to the appropriate limit without violating the Quality of Service (QoS) and service level agreement [4]. Cloud services support the migration of VMs in order to accomplish energy management, less hardware maintenance, server consolidation, and traffic management [5]. Initially, the migration of VMs is done manually. But, this tends to increase the cost, poor choices, and downtime due to the migration process [6]. The migration of VM is done in cloud services to decrease the cost requirements and energy consumption [7]. In order to provide adaptable and precise benefits in the application environment, automated migration approaches are introduced [8].
The VM migration process is broadly classified into two types such as non-live migration and migration [9]. The current process has been held by the non-live migration approaches, and the copy of the entire source to destination pages is resumed when the migration process is completed [10]. In the live migration approach, the current process has not stopped, and it needs less downtime for transferring the VM from the source to the target [11]. Small-scale operations are only suitable for live VM migration, but transferring entire machines to all the processes is difficult and quite complex [12]. For single live migrations, the requirements related to QoS are not considered fully, and hence, the non-live migration is selected [13]. Instead of giving restricted services to the appropriate user, the downtime is compared with the live migration, and the non-live migration is preferred by the service providers with the utilization of less downtime [14].
Many of the VM migration algorithms are developed to identify the status of the utilization of VMs, and hence, the under-utilized VMs are moved to the destination systems while increasing the resource requirements [15]. Here, the selection of VMs is important to migrating it from source to destination otherwise, the performance of the systems is affected in terms of QoS [16]. By using the live migration methods, the VMs are migrated transparently and seamlessly from one machine to another physical machine. Load balancing provides significant benefits with reduced power consumption and expenses to migrate the VMs from overloaded servers to light-loaded servers [17]. Moreover, online maintenance and proactive fault tolerance are the significant factors to provide major benefits over the migration of VMs. The main bottleneck during VM migration is memory mitigation that occurs due to the adequate dirtying of VM memory pages. Hence, several optimization algorithms are developed to analyze the total movement time, energy consumption, and the utilization of memory space. Most of the VM placement and VM migration approaches only concentrate on energy utilization and memory requirements. But, the security of data packets using transmission is not provided in VM migration approaches. Furthermore, trust, authenticity, privacy preservation, as well as integrity are not considered in cloud-based systems. Hence, to provide better VM migration with higher security, a heuristic-adopted secure mechanism is developed in this research work.
The contribution of the designed security-aware VM placement and VM migration are given below.
To develop a new VM placement and migration approach with an advanced hybrid encryption algorithm to provide highly secured data transmission with reduced usage of memory and power requirements. To design an efficient VM placement model with the help of developedIBinBRMO by optimally selecting the number of VMs to minimize the consumption of network resources and the usage of active servers. To propose an effective VM migration scheme with the usage of the suggested IBinBRMO, where the number of servers and the VMs are optimally selected to decrease the consumption of energy and improve the makespan of the cloud system. To implement an IBinBRMO in the developed VM placement and VM migrations scheme to improve the performance by optimally selecting the number of VMs and servers, and the security of data transmission is attained with the help of optimal key generation by using this IBinBRMO. To propose a hybrid advanced encryption scheme HH-AES with optimal key generation using IBinBRMO to improve security in terms of hiding failure rate, degree of modification, and information preservation rate. The performance of the developed IBinBRMO- HH-AES-based VM placement and migration model is ensured through distinct heuristic algorithms concerning several objective functions.
The remaining sections used in the developed IBinBRMO- HH-AES-based VM placement and migration model are described as follows. The features and disadvantages of conventional VM placement and migration approaches are illustrated in Section 2. The description of the dataset and the developed IBinBRMO are enumerated in Section 3. The VM placement and VM migration with its objective function are summarized in Section 4. The proposed HH-AES-based encryption scheme is illustrated in Section 5. The comparative analysis is explained in Section 6, and the conclusion of the proposed model is given in Section 7.
Related works
In 2022, Saxena et al. [18] have suggested a multi-objective-based VM placement approach in order to guarantee the energy-efficient distribution of physical resources with decreased intercommunication delay. This has emphasized timely execution and provided higher security over the transmitted data. Here, a Non-dominated genetic algorithm with whale optimization has been inspired to provide higher effectiveness over the suggested model. The effectiveness validation was made by comparing the dynamic and static approaches, but the suggested model attained less power consumption, intercommunication cost, and execution time than others.
In 2020, Inokuchi and Kourai [19] have demonstrated a VM management approach based on strong user bindings in semi-trusted cloud environments. The user bindings were strongly provided with the help of UVBond. In the trusted hypervisor, the UBBond booted the VM user by performing decryption over the encrypted disk. Here, hypercell automata have been utilized to provide a higher-level semantic gap between the low-level hypercells and the high-level management commands. By using this approach, the overhead of the network has been highly minimized.
In 2013, Li et al. [20] have introduced a Cyberlive approach for providing flexible collaboration for enabling desktop application sharing in a virtual environment on the basis of the virtualization infrastructure. Here, the view privileges were distinguished with the help of a secure access mechanism, and the events were tracked with the usage of a window filtering mechanism for delivering desktops for various users. The performance of the VM management systems was very high, and it was highly effective and useful.
In 2019, Pal et al. [21] have suggested a probabilistic resource migration algorithm for migrating VMs in the cross-cloud and public cloud. Here, the data has been privately and efficiently owned by third-party servers and cloud owners. This method was highly reliable and efficient. The important instances to be considered when sharing a huge volume of data were utilized using the memory, time and CPU. This method has also computed the VM memory flow of total memory during the movement of VMs.
In 2011, Danev et al. [22] have enabled secure VM-virtual Trusted Platform Module placement in private clouds. The classical platform processes should not impede the TPM virtualization. This method has provided strong security when compared to the previous solutions, and the feasibility of such systems was high when compared to other models. Here, the hypervisor was directly integrated by downloading the open-source software. The performance was analyzed to discuss the effectiveness of the developed model.
In 2021, Doyle et al. [23] have introduced a new lightweight framework named as blockchainBus for the secure migration of virtual machines using blockchain in cloud federation networks. This framework has been employed in the cloud environment of Microsoft Azure using the HyperLedger solution. After implementing the environment, the overhead of the network has been determined. The performance in regard to VM migration time has been considered for validating the efficiency of the BlockchainBus model. The obtained migration time was very shorter than the total movement time, and hence, this result demonstrated that the performance was higher in the BlockchainBus framework.
In 2019, Alkadi et al. [24] have introduced an anomaly detection framework for visualizing outsider attacks and insider attacks from the cloud environment. Here, the Gaussian mixture models and outlier localization-based mixture models have been utilized to fit the network, and the abnormal sequences in the network traffic were discovered with the help of the outlier factor function. The performance was analyzed concerning different anomaly detection approaches, and the suggested model provided a better outcome in detecting anomalies.
In 2020, Yu et al. [25] have designed a VM movement and management approach based on a physical server named a trust verification server for giving trust services based on the specification of the trust platform module. Here, the noise and interferences were effectively dealt with via non-interference trust measurement approach. This suggested model could solve complex cloud security issues very efficiently.
In 2020, Eirini et al. [31] have developed an effective algorithm for balancing the load in the 5G-VCC system. Here, the Modified Ant Colony Optimization (MACO) algorithm was adapted and has also been inspired by the ant’s natural behaviour. Moreover, the weight value has been assigned for each 5G-VCC. In the optimal route-selecting phase, the highest weight was selected with the help of the MACO algorithm. The result analysis has shown that the developed model has attained enhanced performance in the 5G-VCC systems.
In 2021, Eirini et al. [32] have implemented a novel network slicing scheme for tuning the performance of 5G vehicular networks. However, the LTE vehicle-to-everything (LTE-V2X) technology was implemented using the Road Side Unit (RSU) and Aerial Relay Nodes (ARN). While, the position of each RSU has been tuned using the fuzzy Multi-Attribute Decision-Making (fuzzy MADM) model. Often, the QoS and SINR factors have been monitored using the developed model. The performance has been validated regarding throughput, packet transfer delay, jitter and packet loss ratio using the developed method.
Features and disadvantages of existing VM Placement and VM Migration approaches
Features and disadvantages of existing VM Placement and VM Migration approaches
In 2021, Emmanouil et al. [33] have suggested a slicing scheme for 5G-VCC systems to enhance the performance in modern vehicular services. Here, the estimation of the user satisfactory grade was considered to evaluate the performance. The higher satisfactory grade has shown to allocate the user service with the help of Resource Blocks (RBs) and Point of Access (PoA). Moreover, the lower satisfactory grade has shown the Virtual Resource Pool (VRP), which was located at the Software Defined Network (SDN) and also it has been controlled by the required services. Thus, the outcome of the suggested model has been validated in terms of packet transfer delay, jitter, and throughput.
The secure allocation of VMs is important to facilitate elastic and cost-effective computing benefits for cloud users. The inefficient sharing of VM leads to increases the intercommunication costs, resource wastage, security breaches, and excessive power consumption. In order to provide higher security over the stored data concerning timely execution is performed with the help of several protocols and algorithms. The recently developed VM migration approaches with their features and demerits are illustrated in Table 1. SM-VMP [18] highly decreases the intercommunication delay. In addition, it provides a highly energy-efficient distribution of physical resources. But, it does not provide proper security for the transmitted data. Furthermore, it has a poor selection of VMs to be migrated from the source to the destination, and hence, the effectiveness is slightly reduced. AES-NI [19] provides negligible overhead in remote VM management. On the other hand, it effectively prevents the data from inside attackers. Yet, it does not prevent the lago attack. Moreover, the external commands are invoked by several management commands. CyberliveApp [20] computes the hidden areas of windows by tracking the window operation events in real-time. Furthermore, it provides live application sharing and migration between VMs. However, it does not provide flexible collaborations for cloud-based computing environments. In addition, it requires virtualization-based software platforms to provide effective performance. PRMA [21] achieves VM migration with less downtime. In addition, it effectively reduces the workload during migration. But, it is inapplicable to migrate between various subnet IPs. Moreover, it does not provide proper security over the transmission medium. TPM module [22] provides stronger security over the transmitted data. On the other hand, it provides greater feasibility. However, it does not have the possibility to secure live VM-vTPM migration. For instance, it does not support realistic virtualized environments. Blockchain [23] improves the performance and resilience of VM migration. Furthermore, it highly prevents vendor lock-in. But, it needs to reduce the system overhead. Nonetheless, it does not consider the system’s robustness. MLO [24] needs to reduce the system overhead and also, it does not consider the system’s robustness. Yet, it does not calculate the service level agreement evaluation metrics. Moreover, it does not support real cloud systems. TPM [25] effectively guarantees its own security. And also effectively solves complex cloud security issues. But, it does not have the ability to ensure reliable network transmissions via the cloud platforms. Furthermore, it does not provide the most trust features for many specific services. To address these issues, optimal VM placement and VM migration approaches are developed with encryption standards to provide higher security.
Block-wise schematic representation of vm migration with secure data transmission in the cloud environment
Proposed VM migration and VM placement in cloud environment
Virtualization is mainly adopted in mobile computing, resource sharing, portability requirements, and independent platforms to maximize utilization. During migration, availability, load balancing, and power management are the significant factors to be considered. To provide residual dependencies among the host machines are a significant issue in conventional VM migration schemes. The utilization of memory and resource requirements are needed to be highly minimized in these VM migration approaches. Moreover, the system overload and the cost requirements are high in the existing VM migration approaches. Live migration schemes solve these issues related to maintenance, overloading, load balancing, utilization of resources and time, and computational complexities. But, the live migration techniques do not focus on security because it is susceptible to several attacks like denial of service, man-in-the-middle attack, and stack overflow attack. During VM migration, the data can be tampered or sniffed, and hence, encryption algorithms are introduced to provide higher security during data transmission. But, analyzing the security threats in the network is slightly different. This has been accomplished by the newly developed optimization strategy-based VM placement and VM migration with a hybrid encryption scheme, and the architectural representation of the system is given in Fig. 1.
A new VM placement and VM migration approach with a hybrid encryption standard is introduced to perform the VM placement and migration very effectively with the help of a heuristic algorithm to reduce the utilization of memory resources, computational time, and overload with high security over the transmitted data. The required data are garnered from various online links. Initially, the developed IBinBRMO algorithm is used to optimally select the number of VMs to perform VM placement to decrease the number of active servers and the consumption of resources. Then, the same IBinBRMO algorithm is used for the migration of VMs in order to decrease the power utilization and makespan. After, the hybrid encryption scheme named HH-AES is introduced to provide higher security over the transmitted data, where the objectives related to high data hiding rate and high data modification rate are achieved with the usage of developed IBinBRMO. Finally, the efficacy of the suggested model is compared over various optimization strategies to examine the effectiveness of the suggested VM placement and VM migration with a hybrid encryption scheme.
Structural illustration of the developed Security aware VM Placement and VM Migration approach.
The implemented IBinBRMO is used in the developed security-aware VM placement and VM migration approach to optimally select the VMs for the placement to reduce the utilization of CPU resources and active server, to decrease the energy utilization and makespan by optimally selecting the number of VMs to improve the performance over VM migration. Moreover, the optimal key is generated with the usage of the proposed IBinBRMO for providing higher security over data transmission. The BinBRO algorithm is used in the approach because it converges faster than the other algorithms, and the MOA provides a better balancing ability between exploitation and exploration. But, these algorithms are struggled to solve local optima and global optima problems. Hence, to solve the global optima problems and improve the generalization effects, the IBinBRMO algorithm is developed. In this suggested IBinBRMO, the position of the candidate solutions is upgraded using the newly created concept, which is given in Eq. (1).
Here, the term
Here, the term
MOA: The MOA algorithm is initialized by assigning the problem variables as the moths’ position in the predefined solution space, and the population of moths is assumed as the candidate solution. In the problem space, the moths are flying either in a hyperdimensional plane of 1D, 2D and 3D space. The number of problem variables is indicated by
Here, the random population of the method is denoted by the term
The term
If the termination criteria are met if the
Moreover, the upper and the lower bound attributes also initialized in the solution space given in Eqs (7) and (8), respectively.
The upper bound value for
The lower bound value for
After, initializing all the parameters, the function
Here, the
The logarithmic function of spiral updating behavior is given in Eq. (10).
The distance parameter of the
Based on the value of
BinBRO: It is a population-based approach, where all the solutions are known as the soldier. The soldier tries to defeat the adjacent neighbour. To defeat the nearest neighbours, the soldier is placed in the best position. The BRO is initiated with a random population, and that is uniformly spread throughout the solution space. The soldiers defeat the nearest neighbours by hurting others and damaging others by finding a better position. The damage level of the soldiers at
The random attribute
The upper bounds and the lower bounds in the problem dimension space are denoted by the terms
The upper and lower bound values are given in Eq. (15) and Eq. (16), respectively.
The standard deviation of the whole population is indicated by
The dataset required for efficient data transmission among the migrated VMs is collected from five different datasets are summarized as below.
Dataset 1: The name of the dataset is “Air Quality,” that is available in the online link of “
Dataset 2: The name of the dataset is “Superconductivity dataset,” which is available on the link of “
Dataset 3: It includes the heart disease data available on the source of “
Dataset 4: This dataset is known as the “Concrete Compressive Strength Dataset” that is taken from the online source of “
Dataset 5: The name of the dataset is “Whole customer’s dataset,” that is taken from the online source of “
The term
Multi-objective constraints-based optimal VM placement and VM migration using improved binary battle royale with moth-flame optimization
Structural description of IBinBRMO-based VM placement in the cloud environment.
In the cloud platform, VM placement improves the performance of the system. Target tenants and the malicious are avoided by using this VM placement approach. The factors like availability, locality, energy consumption and prevention of congestion, security constraints and the const constraints are highly reduced while using this VM placement scheme. In addition, the maintenance cost of the networks is also minimized by using this VM placement and a better physical machine is discovered to host the VMs in the cloud systems. VM allocation is effectively done using this VM placement scheme, and the future availability of the resources is enhanced. Further improvement has been obtained in system overload, and the lifetime of the systems also increased. The suitable servers are effectively selected to host the VM based on several multi-objective constraints in the cloud environment with the support of IBinBRMO.The important aim of the VM placement using IBinBRMO is to decrease the consumption of energy, utilization of resources and active drivers.
In the system model, the total number of VMs is represented as
Schematic representation of IBinBRMO-based VM Migration in a cloud environment.
The main aim of the developed VM placement model is to decrease the number of active servers, energy consumption and the utilization of network resources. This has been accomplished by the selection of servers and VMs using developed IBinBRMO. The diagrammatic description of the suggested IBinBRMO-based VM placement model is given in Fig. 2.
The migration of VM is a significant function of virtualization in the cloud environment, and that has been accomplished from source to destination. The VM movement can be performed with the support of multi-objective constraints like resource utilization, active servers, makespan, and energy consumption. The effectiveness of the VM migration process is improved with respect to the attributes like QoS, downtime, overload and utilization of memory and storage requirements. The open connections are made alive for the live movement. Here, the developed IBinBRMO is used to decrease energy consumption and make a span of the cloud environment. The structural illustration of the developed IBinBRMO-based VM migration is showcased in Fig. 3.
The developed IBinBRMO algorithm is utilized for both the VM placement and VM migration process to optimally select the VMs and servers to maximize the efficacy of the cloud services. During VM placement, the consumption of resources and the active servers are effectively decreased with the help of the developed IBinBRMO that is given in Eq. (18).
Here, the term
Here, the term
The conditions that relate to the number of active server’s selection is discussed below.
The utilization of resources RE is estimated using Eq. (25).
Then, the developed VM placement model provides extensive performance by minimizing the active servers and utilizing energy sources.
During VM migration, the developed is utilized to find the destination of the physical machine by minimizing the energy consumption and making the span of the system that the objective fitness function is given in Eq. (26).
Here, the term
The amount of memory required is indicated by
The CPU utilization is denoted by
The CPU assumption or utilization of the specific server is denoted by
Homomorphic encryption scheme
The preservation of confidentiality between the provider and client is very important. This can be achieved by using an encryption algorithm [26], where the encrypted data is only stored in the cloud. The homomorphic encryption algorithm accepts only the encrypted inputs, and the blind processing can be performed in the cloud. The aim of this encryption function is to ensure data privacy in the data storage processes and communication process. The four functions of homomorphic encryption are key generation, encryption, decryption and evaluation. The four functions are represented as in the format of
Key Generation: It represents the key generation function and is generates keys based on the parameters
Here, the term
Here, the secret key is indicated by
Encryption: It represents the encryption process, where the my key is taken to encrypt the message MS to give the encrypted outcome, as D in the public key-based systems.
Here, the term MS represents the plain text and
Evaluation: It represents the evaluation function, and it is applied to the ciphertext. In symmetric key-based systems
Here, the cipher text is indicated by
It utilizes the small size symmetric keys to make it applicable for many data centric implications. It uses different key lengths for providing the block ciphertext. In this algorithm, the AES [26] uses 128-bit key length, and it is implemented to be effective in both software and hardware platforms. Moreover, it supports parallel computations and the nature of algebraic computations. It translates operations according to integers in a ring
Key generation process: It is incorporated into the public key and the symmetric key systems. The plaintext has been changed into ciphertext by using the encryption algorithm. In the symmetric scheme, the plaintext
Evaluation function: The general evaluation function Fn is used to translate the basic operations into integers. To execute subtraction, addition, division and multiplication of two integers are encrypted homomorphically, and their ciphertexts are subtracted, added, divided and multiplicated based on two matrices. It does not need any evaluation key, and all the functions are executed in the ring
Optimal key generation in HH-AES-based encryption for secured data transmission
The developed IBinBRMO algorithm is used to generate the optimal key for securing data transmission in the cloud. The key
The key
The terms
The information hiding failure rate HgRt is estimated using Eq. (37).
The length of non-zero indexes
The information preservation rate INpr is estimated using Eq. (38).
Diagrammatic representation of the developed HH-AES-based encryption and decryption mechanism with optimal key generation.
The total number of zero indexes is indicated by ZIn, and the total number of preserved data indexes is denoted as PdIn.
The degree of modification rate DoM is evaluated using Eq. (39).
It is the ratio of distance Dst to the number of bits
The data is encrypted using a homomorphic encryption algorithm, and AES is decrypted based on the generated key.
The decryption process performed in homomorphic encryption with AES is explained as follows.
Initialized parameters in the cloud environment
Initialized parameters in the cloud environment
Convergence validation of the proposed VM placement among various algorithms when “(a) Number of servers as 100 and VMs as 500 (b) Number of servers as 160 and VMs as 800 and (c) Number of servers as 200 and VMs as1000”.
Features and challenges of existing VM Placement and VM Migration approaches
Convergence validation of the proposed VM migration among various algorithms in regards with “(a) Number of servers as 100 and VMs as 500, (b) Number of servers as 160 and VMs as 800, and (c) Number of servers as 200 and VMs as 1000”.
Convergence validation of the proposed adaptive encryption-based security aware VM migration among various algorithms in regards to “(a) Dataset 1 (b) Dataset 2 (c) Dataset 3 (d) Dataset 4 and (e) Dataset 5”.
Statistical analysis on developed VM placement and VM migration approach among various heuristic algorithms in accordance with (a) Average Memory Utilization (b) Average CPU Utilization (c) Power Consumption and (d) MakeSpan.
Performance validation of the developed hybrid encryption scheme among various algorithms with respect to (a) KPA attack (b) CPA attack (c) Decryption time and (d) Decryption time.
Decryption process in AES: The new ciphertext
Decryption in Homomorphic encryption algorithm: It represents the decryption process, where the output of the evaluation function and the key ny are utilized to recover the original plaintext MS in the public key-based systems, which is denoted in Eq. (40).
But, the decryption process takes evaluation output and the secret key
In this way, the original plaintext is encrypted and to get the ciphertext, and in the decryption process, the ciphertext is given as the input and get the plaintext is the output. The generated key
Experimental setup
The implemented security aware VM placement and VM migration model was evaluated in MATLAB 2020a software. Here, the performance analysis has been performed to validate the effectiveness of the developed VM placement and VM migration with secure transmission of the data model. Various optimization strategies were considered for the validation purpose, that as the African Vultures Optimization Algorithm (AVOA) [27], Harris Hawks Optimization (HHO) [28], BinBRO [29] and MOA [30]. Here, the number of population counts to be taken as 10 and the maximum count of iterations to be taken as 100 for this experimentation. The parameters to be initialized are given in Table 2.
Convergence analysis on VM placement
The convergence evaluation of the VM placement process using the IBinBRMO algorithm among distinct heuristic strategiesisillustrated in Fig. 5. This validation has been carried out by varying the number of iterations, and the measure cost function is considered. The cost function validation of the recommended VM migration process using the IBinBRMO algorithm among distinctstrategies is depicted in Fig. 6. Here, the number of servers and the number VMs are changed to analyze the cost function. The developed VM placement approach attained 10.25% than AVOA, 22.22% than HHO, 36.36% than BinBROand 39.55% than MOA while taking at 40
Statistical analysis on VM placement and VM migration
The statistical analysis in regards to mean, best, standard deviation, worst and median values on the developed, optimized VM placement and VM migration approach among various heuristic strategies are indicated in Fig. 8. The objective constraints like average memory utilization, power consumption, average CPU utilization, and Makespan. The developed model accomplished with impoverished average CPU utilization value of 27.27% than AVOA, 30.43% than HHO, 30.43% than BinBRO and 36% than MOA with respect to the median value. The performance of the recommended VM migration and VM placement model is highly improved.
Performance analysis on developed hybrid encryption scheme
The performance validation of the developed hybrid encryption scheme that provides higher security in the cloud environment is given in Fig. 9. This analysis has been carried out over the performance measures like decryption time, encryption time, CPA attack and KPA attack. The suggested model obtained with maximized CPA attack of 4.25% than AVOA, 3.15% than HHO, 4.25% than BinBRO and 4.25% than MOA while taking the key of 20. The security of the data transmission is highly enhanced in the developed hybrid encryption algorithm than the other optimization strategies.
Execution time analysis
Statistical validation of the recommended security aware VM placement and migration model among various algorithms
Statistical validation of the recommended security aware VM placement and migration model among various algorithms
The time complexity analysis of the developed IBinBRMO-HH-AES-based security aware VM placement and VM migration approach among various algorithms are given in Table 3. The execution time of the investigated model is enhanced with 20.36% than AVOA, 7.60% than HHO, 30.6% than BinBRO and 21.11% than MOA while taking the number of VMs as 1400 and the number of servers as 280. The total execution time is highly reduced in the suggested approach to the other optimization strategies.
The performance of the suggested IBinBRMO-HH-AES-based security-aware VM placement and VM migration approach among various algorithms for all datasets are given in Table 4. From these results, the investigated approach attained with an improved mean value of 2.06% than AVOA, 1.02% than HHO, 3.62% than BinBRO and 9.5% than MOA for dataset 2. The efficacy of the recommended scheme is superior to the other strategies.
Conclusion
Cloud services was encountered with the new concept of virtualization technology. Though, the VM was integrated in the server to provide the enhanced service for the user request in an optimal manner. Several existing approaches have emerged for providing better services of VM in a cloud environment. But, running time is high for evaluating the larger amount of data using the existing models. For storing a large amount of data, the memory utilization becomes higher in the existing models. Thus, it is necessary to develop a new model for VM migration in the cloud environment. A new VM placement and migration approach was developed to provide secure transmission during the migration of VMs in the cloud environment. The required data were collected from various links, and the developed IBinBRMO was used for optimally select the VMs for providing better effectiveness over VM placement and migration to decrease the resources utilization and power consumption and utilization. Then, the HH-AES algorithm was introduced for providing higher security over the transmitted data using IBinBRMO by optimally generating the keys. The result analysis demonstrated that the suggested model accomplished with enhanced encryption time of 53.84% than AVOA, 20% than HHO, 33.33% than BinBRO and 47.82% than MOA for the data size of 400 Mb. The performance of the developed model was highly improved over the placement of VMs, migration of VMs and the security over the data transmission. The study is mainly useful for policy makers, researchers, and business executives. Here, the policy maker decides the best use of cloud services based on the involvement of cost saving and analyzing the exact risk in the cloud. Thus, the study is widely helpful for upcoming researchers to develop a new technology for providing better cloud services for users.
