EMPIRICAL ANALYSIS OF MACHINE

LEARNING-BASED ENERGY EFFICIENT

CLOUD LOAD BALANCING ARCHITECTURES:

A QUANTITATIVE PERSPECTIVE

K.R.Singh

Yeshwantrao Chavan College of Engineering,Nagpur, Maharashtra, (India).

Indira Gandhi Delhi Technical University for Women, New Delhi, Delhi, (India).

A.D.Gaikwad

B Yeshwantrao Chavan College of Engineering,Nagpur, Maharashtra, (India).

Indira Gandhi Delhi Technical University for Women, New Delhi, Delhi, (India).

S.D.Kamble

Yeshwantrao Chavan College of Engineering,Nagpur, Maharashtra, (India).

Indira Gandhi Delhi Technical University for Women, New Delhi, Delhi, (India).

Reception: 20/11/2022 Acceptance: 05/12/2022 Publication: 29/12/2022

Suggested citation:

Singh, K. R., Gaikwad, A. D., y Kamble, S. D. (2022). Empirical analysis of machine learning-based energy

efficient cloud load balancing architectures: a quantitative perspective. 3C Empresa. Investigación y pensamiento

crítico, 11(2), 232-248. https://doi.org/10.17993/3cemp.2022.110250.232-248

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ABSTRACT

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of

the cloud architecture and nature of requests served by the cloud. Depending upon these parameters,

machine learning models are designed which aim at assigning best possible resource combination to

serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include

task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to

perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of

these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic

Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before

task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing

might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult

for researchers to select the best possible algorithms for their cloud deployment. In order to reduce

this ambiguity, the underlying text compares different energy efficient cloud load balancing

algorithms; and evaluates their performance in terms of computational complexity, and relative

energy efficiency. This performance evaluation is further extended via inter architecture comparison;

in order to evaluate the most optimum load balancer implementation for a given energy efficient

application. Thus, after referring this text, researchers and cloud system designers will be able to

select optimum algorithmic implementations for their given deployment. This will assist in reducing

cloud deployment delay, and improving application specific load balancer performance.

KEYWORDS

Cloud, Load, Balancing, Machine, Learning, Task, Deadline, Energy.

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1. INTRODUCTION

Due to the current CoVID-19 pandemic, most businesses are forced to adopt the work-from home

(WFH) model. This model has increased dependency of users on cloud-based services, thereby

requiring cloud service providers to optimize their load-balancing models. These models are broadly

categorized into 2 types; which are hardware load balancing, and elastic load balancing. The former

type consists of optimizing performance of hardware components like virtual machines, servers,

memory utilization, task optimization, and central processing unit (CPU) optimization.

While, the later type consists of network load balancers, application load balancers and hybrid load

balancers. Hierarchical categorization of these algorithms can be observed from figure 1, from where

it can be observed that network load balancing depends on VM and Server load balancing; application

load balancing depends on memory, task and CPU load balancing; while classic (or hybrid) load

balancing depends on all the hardware-based load balancing models. VM load balancing models aim

at optimizing virtual machine performance by assigning tasks in such a manner that most VM

resources are utilized, thereby improving hardware utilization efficiency.

This model does not take into consideration deadline constraints, memory constraints, etc. while

modelling the load balancer. In contrast, memory-based load balancer models only take into

consideration memory utilization; and aim at optimizing task storage without considering resource or

CPU load values. Server load balancing algorithms assist in optimization server utilization while

performing load balancing, while CPU load balancers aim at optimizing CPU utilization while load

balancing. Moreover, task load balancing models aim at executing tasks under a given deadline

without considering CPU load, memory or virtual machine efficiency values.

Figure1. Hierarchical categorization of load balancing models.

Elastic load balancers aim at optimizing a group of parameters during load balancing. For instance,

network load balancers optimize virtual machine and server parameters; while application load

balancers aim at optimization of memory, CPU load and task parameters. A combination of these

parameters is optimized by classic load balancers, wherein depending upon the application; one or

more cloud task parameters are optimized. An in-depth survey of these optimization models can be

referred from the next section; wherein various machine learning models for load balancing are

described. This is followed by statistical analysis of these models; their comparative evaluation. The

evaluation assists in identification of best suited for models for any given application; which will assist

researchers and system designers for high speed and high efficiency system design. Finally, this text

concludes with some interesting observations about the reviewed algorithms; and recommends

methods to improve them.

2. LITERATURE REVIEW

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A wide variety of energy efficient cloud load balancing models have been proposed by researchers and

cloud designers. These algorithms aim at improving energy efficiency via application of different

machine learning models including but not limited to bio-inspired models, swarm-optimization

models, neural networks, etc. The work in (Panda, Moharana, Das, and Mishra, 2019)[1] introduces

such an energy efficient model that utilizes virtual machine consolidation in cloud environments.

The model aims at minimizing energy consumption during virtual machine (VM) migration process

via threshold-based sleep scheduling. Here, virtual machines with lower load levels are put to sleep for

specified clock cycles, thereby assisting in energy reduction. Due to this sleep scheduling, VMs with

high loads are easily identified. This identification assists in assigning tasks to the sleep mode VMs,

thereby reducing the probability of load imbalance. Description of the model can be observed from

figure 2, wherein load balancing and VM migration processes are described.

Figure2. Energy efficient VM migration and load balancing.

Figure 2. Energy efficient VM migration and load balancing (Panda et al., 2019)[1] Due to efficient

migration of VMs, and proper load division, the model is able to reduce load imbalance levels by 15%

energy consumption by 20% when compared with non-energy aware models. A similar model that

uses dynamic energy efficient resource (DEER) allocation can be observed from (Rehman, Ahmad,

Jehangiri, Ala’Anzy, Othman, Umar, and Ahmad, 2020)[2], wherein resource with minimum

utilization is used for load balancing. This model also performs intelligent sleep scheduling, due to

which an energy efficiency of 15% is achieved when compared with Dynamic Resource Allocation

Strategy (DRAM) model. This model also reduces computational cost by 10% when compared with

DRAM, thereby making is useful for real time load balancing applications. A load-based model that

uses self-organizing maps (SOMs) can be observed from (Malshetty and Mathapati, 2019)[3], wherein

cluster heads are created, and all load requests are handled by them.

The selected cluster heads are able to reduce computational load on centralized server, and select

machines with minimum power consumption, thereby improving overall energy efficiency of the

system. It is observed that the proposed model is able to reduce energy consumption by 18% and delay

of processing by 10% when compared with Low-energy adaptive clustering hierarchy (LEACH)

model. This approach can be compared with other models, a survey of these models can be observed

from (Ala’anzy and Othman, 2019)[4], wherein effects load balancing & server consolidation are

studied. The work compares algorithms like load-aware Global resource affinity management,

advanced prediction-based minimization of load migration, multidimensional hierarchical VM

migration, extended first fit decreasing algorithm, locusts inspired scheduling algorithm, etc.

It is observed that soft computing models outperform linear models in terms of energy efficiency &

load balancing performance. An example of such a soft computing model can be observed from

(Salem Alatawi and Abdullah Sharaf, 2020)[5], wherein Honey Bee optimization is combied with

fuzzy logic for improved energy efficiency during load balancing. The model uses a fuzzy approach

for host & VM selection, and then deploys a Honey Bee optimization model for load scheduling

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between these components. Due to incorporation of VM energy levels, and task length in the fitness

function (described in equation 1), the model is able to schedule tasks with good energy efficiency.

Where, T length T deadline are task length, and task deadline; while E vm is the per task execution

energy of the VM. The model aims are reducing this fitness value in order to improve the energy

efficiency, and execute tasks of the given length under the given deadline. The model is able to reduce

energy consumption by up to 18% when compared with only fuzzy model, and up to 15% when

compared with only the Honey Bee optimization model, thereby making it applicable for real time use.

Execution of tasks with high energy efficiency must be accompanied with effective placement of VM

services. This placement allows schedulers to select nearby VMs in order to execute tasks with high

efficiency and low energy consumption. Example of such an architecture can be observed from

(Alharbi, El-Gorashi, and Elmirghani, 2019)[6], wherein researchers have showcased the use of cloud-

to-fog load balancing reduces energy consumption by 75% when compared with only-cloud load

balancing architecture. This architecture can be observed from figure 3, wherein data from cloud is

offloaded to fog nodes for efficient balancing.

Figure3. Offloading data from single cloud to fog nodes for energy efficient load balancing [6].

Figure 3. Offloading data from single cloud to fog nodes for energy efficient load balancing [6] Due to

offloading of tasks on fog nodes, the computational load is shared between different nodes, thereby

assisting in faster task execution, and better energy efficiency. This efficiency can be further improved

via addition of soft computing models. Such a model is defined in (Dong, Xu, Ding, Meng, and Zhao,

2019)[7], wherein glow worm swarm optimization (GSO) is used. The model combines clustering for

extraction of large task resources, with sine cosine analysis (SCA) in order to modify the step size of

GSO for energy adaptive scheduling. This modification in step size allows the GSO model to select

load & energy optimized cloud resources for the given task.

The clustered input tasks are divided into edge group & cloud group; wherein each group has task

elements based on CPU utilization and memory consumption. For instance, the edge group retains

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tasks with high CPU utilization but low memory consumption, while the cloud group retains tasks

with high memory consumption. This task division is governed using the following equation,

Where, L group is resource requirement for the given group, L cpu is resource requirement w.r.t CPU

utilization, L memory is resource requirement w.r.t. memory utilization, while and are cloud and edge

constants. The model is compared with First Fit Decreasing (FFD) & OTS models and it is observed

that the GSO model has 25% better energy efficiency, 15% better throughput, and 18% better load

balancing degree when compared with these algorithms. This efficiency can be further improved by

performing computations on fog devices as observed from (Bhuvaneswari and Akila, 2019)[8],

wherein comparison of different fog-based load balancing algorithms w.r.t. their energy performance is

studied. Algorithms like ant colony optimization (ACO), max-min algorithm, active monitoring for

load balancing (AMLB), and round robin (RR) are compared. It is observed that the ACO based soft

computing model has 15% better efficiency when compared with RR, while AMLB when combined

with ACO provides 25% better energy efficiency than individual algorithms. Another hybrid

combination that uses ACO with support vector machines (SVM) can be observed from (Junaid,

Sohail, Ahmed, Baz, Khan, and Alhakami, 2020)[9], wherein file type formatting (FTF) is used for

load classification. Input requests are given to SVM model and depending upon file type at input, the

model classifies load types into low power, medium power and high power. After this, the classified

load is given to ACO, wherein VM to load mapping is performed using greedy heuristics. Flow of the

model can be observed from figure 4, wherein training phases and testing phases can be seen with the

final load balancing process. It is observed that the model performs SVM training on file formats, and

provides the classified results to ACO. The ACO model internally maps suitable VMs to tasks for high

energy efficiency. Due to this, the proposed SVMFTF model provides 8% better energy efficiency

than random forest (RF), 6% than Na¨ıve Bayes, 9% than k-nearest neighbours (kNN), and 4% than

convolutional neural network (CNN) models.

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Figure4.SVMFTF model for energy efficient load balancing [9].

Figure 4. SVMFTF model for energy efficient load balancing [9] The model also showcases better

throughput, and migration performance when compared with RF, Na¨ıve Bayes, kNN, CNN models.

Specifically, the model provides 8% better throughput than RF, 3% better than Na¨ıve Bayes, 15%

better than kNN, and 5% better than CNN; while it provides 10% better migration performance than

RF, 4% better than Na¨ıve Bayes, 8% better than kNN, and 5% better than CNN models when

compared on the same task set and cloud configurations . Comparison of this model can be done with

other standard load balancing models as suggested in (Dey and Gunasekhar, 2019)[10], wherein

models like first in first out (FIFO), fair scheduling, capacity scheduling, hybrid scheduling, longest

approximate time to end scheduling, self-adaptive mapreduce scheduling, and context-aware

scheduling for Hadoop are discussed. Each of these models are compared on parameters like Job

Characteristics, Responsiveness, Resource Pool Configuration, Queue Characteristics, Parallelization

of Tasks, Queue Responsiveness, Dynamic Priority, Locality Management, Remaining Burst Time,

Task Priority, Context and Energy Efficiency. All these parameters are evaluated on the Planet Lab

dataset, wherein it is observed that Robust Local Regression (RLR) methods like self-adaptive map-

reduce scheduling, and context-aware scheduling outperform other methods by over 10% in terms of

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energy efficiency. The same trend is observed for other parameters, due to the sophistication of the

RLR methods, and in-depth analysis for the given tasks.

A similar study like [10] can be observed in (Kulshrestha and Patel, 2019)[11], wherein models like

ALB, transport layer load balancer (TLLB), network layer load balancer (NLLB), VM provisioning on

host, consolidation of VMs on host, and VM-level task scheduling are described. Out of these

algorithm ALB outperforms other models in terms of energy efficiency by providing 8% better

performance than TLLB, 5% better performance than VM provisioning, and 15% better performance

than VM consolidation. An example the ALB scheme can be observed in (Zhang, Jia, Gu, and Guo,

2019)[12], wherein Matrix sparseness with normalizedWater-Filling (MSNWF) is described. This

model is compared with Heterogeneous Network (HETNET), and OPT models, and it is observed that

MSNWF provides 15% better energy efficiency than HETNET and 5% better energy efficiency than

OPT. Thus, the MSNWF model can be used for high performance cloud load balancing applications,

wherein along with efficiency of task scheduling, energy efficiency is also improved. This model

performance can be further improved by integration of broker service policy for software as a service

(SaaS) application. Modelling of such architectures requires high efficiency broker design, wherein

any incoming task is first given to a broker for estimation of approximate processing site. This

estimation allows the cloud VMs to pre-allocate resources for the task, thereby improving the task

execution efficiency. In order to model such brokers, architectures like shortest job scheduling, Min-

min, Max-min, Two-phase (OLB + LBMM), Modified active monitoring, Throttled Load Balancer,

Genetic Algorithm, Honey Bee foraging algorithm, ACO, etc. are available (Jyoti, Shrimali, Tiwari,

and Singh, 2020)[13]. It is observed that ACO and other soft computing models when utilizing fog and

cloud computing, outperform other models in terms of energy efficiency. An example of such a model

can be observed from (Lin, Peng, Bian, Xu, Chang, and Li, 2019)[14], wherein the soft computing

models are deployed on cloud. The results of these models are VM-to-task mapping, which are

executed either on the cloud infrastructure or offloaded to the fog device for better load balancing

capabilities. The model for this architecture can be observed from figure 5, wherein offloading process

is performed using different wireless standards.

Figure5. Fog-cloud load balancing model with soft computing for efficient task mapping [14].

Figure 5. Fog-cloud load balancing model with soft computing for efficient task mapping [14] The

model utilizes total amount of resource in host, total amount of resource in VM, estimated execution

time of task, normalization of task average demand for resource, normal resource load of task, relative

load of task for resource upon VM, task length, and task priority for scheduling. It is observed that the

proposed soft computing model outperforms first come first serve by 15%, random assignment by

25%, trade-off by 8%, main resource load & time balancing by 16%, and main resource task balance

by 9% in terms of energy efficiency. This is due to design of energy & task length aware fitness

function design, which suggests that soft computing must be used for any kind of load balancing

models. A similar model is proposed in (Mandal, Mondal, Banerjee, and Biswas, 2020)[15], wherein

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service level agreement (SLA) is used for detection of task overload at different hosts. It uses a

mapping ratio that consists of VM utilization and allocated resource characteristics in order to assign

tasks to non-overloaded VMs. The value of this mapping ratio (Mapping r) can be observed from

equation 3, wherein both the parameters are split into task & resource related characteristics.

where in, VM mips, VM RAM & VM BW are VM specific capacity, available RAM & bandwidth

while Tlength & Tdeadline are task related length & deadline parameters. It is observed that the

proposed model outperforms minimum migration time MMT by 45%, maximum correlation MaxCorr

by 35%, minimum utilization (MU) by 46%, and random selection RS by 34%, thereby making it

highly useful for real time cloud deployments. Context-aware load balancing models have better

efficiency than energy-aware, or task-length aware models because these models adaptively modify

their internal rules depending upon the context of given task and condition of the VMs. Such a model

that utilizes context information for energy efficient load balancing is described in (Royaee, Mirvaziri,

and Khatibi Bardsiri, 2021)16], wherein automata ant colony based multiple recursive routing protocol

(AMRRPL) is used. The model solves issues like bottlenecking, efficient parameter selection, effect of

upstream nodes, and congestion which are inherent with load balancing. The model uses destination

oriented directed acyclic graph (DODAG) in order to perform load balancing via laying out all

possible VM-to-Task combinations on an acyclic graph. Due to use of DODAG the model is able to

achieve an energy efficiency of 8% when compared with ERPL (enhanced RPL), and 45% when

compared with HECRPL (hybrid energy efficient RPL) and its configurations. This model can be

applied to various applications including software defined network (SDN), content delivery network

(CDN), cost-based distribution (CBD) networks, etc. for highly energy efficient load balancing. An

example of this application for CDN can be observed in (Gupta, Goyal, and Gupta, 2015)[17], wherein

a reliability aware load balancer model is applied.

The model uses a modified version of Genetic Algorithm (GA) for task scheduling, and is able to

obtain 15% better energy efficiency when compared with queue length-based load balancing

(QLBLB) model. Another low power model that uses first of maximum loss scheduling algorithm

(FOML) is described in (Liang, Dong, Wang, and Zhang, 2020)[18], wherein relationship between

energy utilization & average completion time is used. The model selects VM with maximum energy

utilization and assigns it to a task that has average completion time (when compared to all tasks in

queue). This task is then deleted, and a new average completion task is evaluated and assigned to the

next maximum energy utilization VM. This process makes sure that all the high energy consuming

VMs are assigned to moderate sized tasks, while other VMs are assigned to large & small sized tasks.

Flow of this model can be observed from figure 6, wherein ETC (extended time of completion) and

ACT (average completion time) matrices are evaluated for the given set of tasks.

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Figure6.Maximum energy utilization model for efficient task scheduling [18].

Figure 6. Maximum energy utilization model for efficient task scheduling [18] The FOML model is

able to obtain 10% better energy utilization when compared with min-min model, 8% better utilization

when compared with max-min model, 12% when compared with suffrage model, and 15% better than

E-HEFT model, thereby making it highly effective for real time deployments.

Similar models are described in (Hadikhani, Eslaminejad, Yari, and Ashoor Mahani, 2020)[19], and

(Sadeghi and Avokh, 2020)[20], wherein geographic information and Two-hop Routing Tree with

Cuckoo search (CCTRT) models are defined. These models able to achieve 5% and 12% better energy

efficiency when compared with E-HEFT model, thereby suggesting that the use of soft computing

models is fundamental to design of energy efficient load balancing algorithms. The efficiency of these

models can be further improved via use of predictive workload balancing, wherein the system is able

to predict workloads depending upon task patterns, and pre allocate cloud & fog VMs for efficient

execution. Architecture for such a system can be observed in (Jodayree, Abaza, and Tan, 2019)[21],

wherein rule-based workload prediction is defined. It uses a combination of historical data analysis

and random workload assignment in order to speed up workload balancing. Due to predictive analysis,

the model is able perform host reduction and thereby reduce energy consumption by 10% when

compared with random assignment algorithm.

This model can be further extended via use of deadline constrained task scheduling as suggested in

(Ben Alla, Ben Alla, Touhafi, and Ezzati, 2019)[22], wherein a dynamic classifier is used to divide

incoming tasks into priority queues, and each of these queues is processed using Fuzzy Logic and

Particle Swarm Optimization model (FLPSO). The FLPSO model used in this approach is able to

reduce energy consumption by 60% when compared with FCFS (first come first serve), 25% when

compared with EDF (earliest deadline first), and 15% when compared with Differential Evolution

(DE) with Multiple Criteria Decision Making (MCDM) algorithms. This comparison appears to be

true for different VM and task combinations, thereby assisting in deploying the FLPSO model for a

wide variety of cloud infrastructures.

This approach must be compared with other models like the ones mentioned in (Pourghebleh and

Hayyolalam, 2020)[23] in order to evaluate its real time applicability and deployment capabilities.

Similar energy efficient models are discussed in (Rashid, Tripathi, Prakash, and Tripathi, 2019)[24],

(Singh and Kumar, 2019a)[25] and (Kansal and Chana, 2018)[26] wherein load based energy

efficiency, security aware energy efficiency, and migration aware energy efficiency models are

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described. Each of these models utilize soft computing techniques like ACO, PSO, GA, and GSO in

order to achieve high energy efficiency. A resource aware load balancing model can be observed in

(Ahmed, Aleem, Noman Khalid, Arshad Islam, and Azhar Iqbal, 2021)[27], wherein heterogenous

clustering is used in order to perform resource-based task mapping. The model performs job to

resource mapping depending upon resource availability, and resource aware load balancing for

obtaining higher utilization ratio. It uses a predictive model for classification and forecasting job

device suitability & job time estimation matrix as observed from figure 7, wherein the overall model is

described. The model extracts features including front end clang (percentage of tasks remaining),

kernel features ratio of task length to current machine configuration), and static features (initial

performance of machines and number of tasks) from tasks and provides them to Resource aware load

balancing and hierarchical clustering (RALBHC) model for improvement of resource utilization.

The model is able to achieve an energy efficiency of 25% when compared with Max-min algorithm,

15% when compared with Minimum Completion Time, 8% when compared with Resource-Aware

Scheduling Algorithm (RASA), and 10% when compared with Task-Aware Scheduling Algorithm

(TASA). Another energy efficient model that uses equal load distribution for fog-to-cloud & cloudto-

fog migration (EDCW) is described in (Kaur and Aron, 2020)[28], wherein linear programming (LP)

is used. The use of LP results into equal distribution of tasks between fog node and cloud node,

thereby assisting in improved load balancer performance. The model is able to achieve 15% better

energy efficiency when compared with Round Robin model, and 8% better efficiency when compared

with throttled model, thereby making it useful for low energy load balancing applications.

Similar energy-efficient models are proposed in (Escobar, Ortega, D´ıaz, Gonz´alez, and Damas,

2019)[29], (?, ?)[30], (Taboada, Aalto, Lassila, and Liberal, 2017)[31],(Kumar, Singh, and Mohan,

2021)[32], and (Singh and Kumar, 2019b)[33], where in parallel evolutionary algorithms, context-

based load balancing, energy-aware load balancing, resource-efficient load-balancing, and secure load

balancing models are described. These models make use of different soft computing methods in order

to perform task-based & resource-based load balancing. The underlying models are able to reduce

energy consumption via optimization of the fitness function, wherein resource energy, task length, task

deadline, and resource performance parameters are used. A quantitative analysis of these models is

described in the next section, wherein the underlying models are compared in terms of relative energy

efficiency values, thereby assisting cloud system designers to identify energy-efficient load-balancing

models for their deployment.

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Figure7.Maximum energy utilization model for efficient task scheduling [18].

3. QUANTITATIVE ANALYSIS

From the literature, it is observed that the energy efficiency of different cloud load balancing

algorithms is estimated using their relative percentages. This limits the quantitative comparison

capability of these algorithms. In order to resolve this drawback, this section evaluates absolute

percentage energy efficiency when compared with the basic first come first serve (FCFS) algorithm.

This process will allow researchers to estimate energy performance of reviewed algorithms, along with

their computational complexity. The computational complexity is divided into fuzzy ranges of low (L),

medium (M), high (H) and very high (VH). Maintaining a balance between energy efficiency and

computational complexity is a must while designing load balancing models for cloud. The quantitative

results are tabulated in table 1, wherein the aforementioned parameters are compared across different

algorithms.Based on this analysis it can be observed that the FLPSO (Ben Alla et al., 2019)[22],

AMRRPL (Royaee et al., 2021)[16], MU (Mandal et al., 2020)[15], SLA based model (Mandal et al.,

2020)[15], EDF(?, ?)[22], and GSO SCA(Dong et al., 2019)[7] model outperform other models in

terms of relative energy efficiency.This performance evaluation can also be observed from the

visualization in figure 8, wherein different algorithms and their accuracies are compared. It can also be

observed that CNN and other deep learning models are not used for energy efficient load balancing,

because training of these models for dynamic loads is resource intensive, thereby requires large

amount of power.

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Moreover, standard CNN models are also not available for this purpose, therefore it is a necessity that

researchers should develop such models that aim towards energy efficiency. These models can then be

extended via transfer learning or recurrent networks in order to incrementally tune their performance.

Neural network models have very low energy consumption during evaluation, thus pre-training of

models is further recommended.

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Figure8.Energy efficiency of different load balancing models.

4. CONCLUSION AND FUTURE SCOPE

The comparative quantitative analysis indicates that FLPSO (Ben Alla et al., 2019)[22], SLA based

model (Mandal et al., 2020)[15], AMRRPL (Royaee et al., 2021)[16], EDF(Ben Alla et al., 2019)[22],

MU (Mandal et al., 2020)[15], MMT [15], RALBHC (Ahmed et al., 2021)[27], Cloud to fog migration

(Alharbi et al., 2019)[6], GSO SCA (Dong et al., 2019)[7], RS (Mandal et al., 2020)[15], MCDM (Ben

Alla et al., 2019)[22], and Max Corr. (Mandal et al., 2020)[15] outperform linear models like kNN

(Junaid et al., 2020)[9], HECRPL (Royaee et al., 2021)[16], CNN (Junaid et al., 2020)[9], and Rule

based prediction (Jodayree et al., 2019)[21] in terms of energy efficiency.

Energy efficient models utilize soft computing techniques like PSO, ACO, GA, GSO, and Honey Bee

Optimization in order to achieve this task via energy aware fitness function design. Deep learning

models are not used for this purpose due to their energy intensive training process.

This limitation can be removed via using pre-trained CNN models that are optimized for energy

efficient load balancing. Furthermore, existing models like SLA based model [15], AMRRPL (Royaee

et al., 2021)16], EDF (Ben Alla et al., 2019)[22], MU (Mandal et al., 2020)[15], MMT [15], RALBHC

(Ahmed et al., 2021)[27], etc. can be further improved via addition of soft computing for optimization

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of energy consumption. These additions will enhance system performance and help the models to be

tuned with high energy efficiency.

REFERENCES

[1] Ahmed, U., Aleem, M., Noman Khalid, Y., Arshad Islam, M., and Azhar Iqbal, M. 2021. RALB-

HC: A resource-aware load balancer for heterogeneous cluster. Concurr. Comput. 33, 14 (July).

[2] Ala’anzy, M. and Othman, M. 2019. Load balancing and server consolidation in cloud computing

environments: A meta-study. IEEE Access 7, 141868–141887.

[3] Alharbi, H. A., El-Gorashi, T. E. H., and Elmirghani, J. M. H. 2019. Energy efficient virtual

machine services placement in cloud-fog architecture. In 2019 21st International Conference on

Transparent Optical Networks (ICTON). IEEE.

[4] Ben Alla, S., Ben Alla, H., Touhafi, A., and Ezzati, A. 2019. An efficient energy-aware tasks

scheduling with deadline-constrained in cloud computing. Computers 8, 2 (June), 46.

[5] Bhuvaneswari, D. and Akila, A. 2019. An energy efficient management in various fog platforms. In

2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing

(COMITCon). IEEE.

[6] Dey, N. S. and Gunasekhar, T. 2019. A comprehensive survey of load balancing strategies using

hadoop queue scheduling and virtual machine migration. IEEE Access 7, 92259– 92284.

[7] Dong, Y., Xu, G., Ding, Y., Meng, X., and Zhao, J. 2019. A ‘joint-me’ task deployment strategy for

load balancing in edge computing. IEEE Access 7, 99658–99669.

[8] Escobar, J. J., Ortega, J., D´ıaz, A. F., Gonza´lez, J., and Damas, M. 2019. Energyaware load

balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous

CPU-GPU architectures. Concurr. Comput. 31, 6 (Mar.), e4688.

[9] Gupta, P., Goyal, M. K., and Gupta, N. 2015. Reliability aware load balancing algorithm for

content delivery network. In Advances in Intelligent Systems and Computing. Advances in

intelligent systems and computing. Springer International Publishing, Cham, 427–434.

[10] Hadikhani, P., Eslaminejad, M., Yari, M., and Ashoor Mahani, E. 2020. An energyaware and load

balanced distributed geographic routing algorithm for wireless sensor networks with dynamic

hole. Wirel. netw. 26, 1 (Jan.), 507–519.

[11] Jodayree, M., Abaza, M., and Tan, Q. 2019. A predictive workload balancing algorithm in cloud

services. Procedia Comput. Sci. 159, 902–912.

[12] Junaid, M., Sohail, A., Ahmed, A., Baz, A., Khan, I. A., and Alhakami, H. 2020. A hybrid model

for load balancing in cloud using file type formatting. IEEE Access 8, 118135–118155.

[13] Jyoti, A., Shrimali, M., Tiwari, S., and Singh, H. P. 2020. Cloud computing using load balancing

and service broker policy for IT service: a taxonomy and survey. J. Ambient Intell. Humaniz.

Comput. 11, 11 (Nov.), 4785–4814.

[14] Kansal, N. J. and Chana, I. 2018. An empirical evaluation of energy-aware load balancing

technique for cloud data center. Cluster Comput. 21, 2 (June), 1311–1329.

[15] Kaur, M. and Aron, R. 2020. Equal distribution based load balancing technique for fog-based

cloud computing. In Algorithms for Intelligent Systems. Springer Singapore, Singapore, 189–

198.

[16] Kulshrestha, S. and Patel, S. 2019. A study on energy efficient resource allocation for cloud data

center. In 2019 Twelfth International Conference on Contemporary Computing (IC3). IEEE.

[17] Kumar, J., Singh, A. K., and Mohan, A. 2021. Resource-efficient load-balancing framework for

cloud data center networks. ETRI J. 43, 1 (Feb.), 53–63.

[18] Liang, B., Dong, X.,Wang, Y., and Zhang, X. 2020. A low-power task scheduling algorithm for

heterogeneous cloud computing. J. Supercomput. 76, 9 (Sept.), 7290–7314.

[19] Lin, W., Peng, G., Bian, X., Xu, S., Chang, V., and Li, Y. 2019. Scheduling algorithms for

heterogeneous cloud environment: Main resource load balancing algorithm and time balancing

algorithm. J. Grid Comput. 17, 4 (Dec.), 699–726.

https://doi.org/10.17993/3cemp.2022.110250.232-248

3C Empresa. Investigación y pensamiento crítico. ISSN: 2254-3376

Ed. 50 Vol. 11 N.º 2 August - December 2022

246

[20] Malshetty, G. and Mathapati, B. 2019. Efficient clustering in WSN-Cloud using LBSO (load

based Self-Organized) technique. In 2019 3rd International Conference on Trends in Electronics

and Informatics (ICOEI). IEEE.

[21] Mandal, R., Mondal, M. K., Banerjee, S., and Biswas, U. 2020. An approach toward design and

development of an energy-aware VM selection policy with improved SLA violation in the

domain of green cloud computing. J. Supercomput. 76, 9 (Sept.), 7374–7393.

[22] Panda, B., Moharana, S. C., Das, H., and Mishra, M. K. 2019. Energy aware virtual machine

consolidation for load balancing in virtualized environment. In 2019 International Conference

on Communication and Electronics Systems (ICCES). IEEE.

[23] Pourghebleh, B. and Hayyolalam, V. 2020. A comprehensive and systematic review of the load

balancing mechanisms in the internet of things. Cluster Comput. 23, 2 (June), 641–661.

[24] Rashid, A., Tripathi, Y., Prakash, A., and Tripathi, R. 2019. Load aware energy-balanced data

gathering approach in CRSNs. IET Wirel. Sens. Syst. 9, 3 (June), 143–150.

[25] Rehman, A. U., Ahmad, Z., Jehangiri, A. I., Ala’Anzy, M. A., Othman, M., Umar,

[26] A. I., and Ahmad, J. 2020. Dynamic energy efficient resource allocation strategy for load

balancing in fog environment. IEEE Access 8, 199829–199839.

[27] Royaee, Z., Mirvaziri, H., and Khatibi Bardsiri, A. 2021. Designing a context-aware model for

RPL load balancing of low power and lossy networks in the internet of things. J. Ambient Intell.

Humaniz. Comput. 12, 2 (Feb.), 2449–2468.

[28] Sadeghi, F. and Avokh, A. 2020. Load-balanced data gathering in internet of things using an

energy-aware cuckoo-search algorithm. Int. J. Commun. Syst. 33, 9 (June), e4385.

[29] Salem Alatawi, H. and Abdullah Sharaf, S. 2020. Toward efficient cloud services: An energy-

aware hybrid load balancing approach. In 2020 International Conference on Computing and

Information Technology (ICCIT-1441). IEEE.

[30] Singh, A. K. and Kumar, J. 2019a. Secure and energy aware load balancing framework for cloud

data centre networks. Electron. Lett. 55, 9 (May), 540–541.

[31] Singh, A. K. and Kumar, J. 2019b. Secure and energy aware load balancing framework for cloud

data centre networks. Electron. Lett. 55, 9 (May), 540–541.

[32] Taboada, I., Aalto, S., Lassila, P., and Liberal, F. 2017. Delay- and energy-aware load balancing in

ultra-dense heterogeneous 5G networks. Trans. emerg. telecommun. technol. 28, 9 (Sept.),

e3170.

[33] Zhang, X., Jia, M., Gu, X., and Guo, Q. 2019. An energy efficient resource allocation scheme

based on cloud-computing in H-CRAN. IEEE Internet Things J. 6, 3 (June), 4968–4976.

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AUTORS BIOGRAPHY

Dr. Kavita Singh has completed her doctoral degree from SVNIT, Surat in 2014

in biometrics. Her areas of expertise are Machine Learning, soft computing,

computer vision, latex and Image Pro-cessing. She has more than 50

international paper publications in reputed conferences, book chapters and

journals. She worked as reviewer, session chair for many international journals

and conferences. She has delivered 20 talks on different topics in-cluding her

expertise area. She worked as Principal Investigator for projects funded by

AICTE under RPS Scheme. She has also a strong association with industries like

IBM, Nvidia etc. She is currently working as Associate Prof. at Department of

Computer Technology, YCCE, Nagpur.

Mr. Amol D Gaikwad has received his B.Tech degree in Computer Science

and Engineering from RTMNU Nagpur University, Nag-pur and MTech.

degree in Computer Science and Engineering Master of Technology degree

from Yeshwantrao Chavan College of Engineering, Engineering (An

Autonomous Institution Affili-ated to Rashtrasant Tukadoji Maharaj

Nagpur University), Nag-pur, India. He is currently pursuing Ph.D. from

from Yeshwan-trao Chavan College of Engineering, (An Autonomous

Institu-tion Affiliated to Rashtrasant Tukadoji Maharaj Nagpur Univer-

sity), Nagpur, India. His predominant research areas include High

Performance Computing, Cloud Computing. He has pub-lished research

papers in various reputed international confer-ences and journals. He is the

life member of ISTE, India, IE, India.

Shailesh D. Kamble received Bachelor of Engineering degree in Computer

Technology from Yeshwantrao Chavan College of Engineering, Nagpur,

India under the Rashtrasant Tukadoji Ma-haraj Nagpur University, Nagpur,

India. He received Master of Engineering degree from Prof Ram Meghe

Institute of Technolo-gy and Research, formerly known as College of

Engineering, Bandera under Sant Gadge Baba Amravati University,

Amravati, India. He completed his doctoral degree from G.H. Raisoni Col-

lege of Engineering (An Autonomous Institution Affiliated to Rashtrasant

Tukadoji Maharaj Nagpur University), Nagpur, In-dia. He is working as the

Associate Professor in Department of Computer Technology at Yeshwantrao

Chavan College of Engi-neering, Nagpur, India. He is having 20 years of

teaching and re-search experience. His current research interests include image processing, video

processing and language processing. He is the author or co-author of more than 20 scientific

publications in International Journal, International Conferences, and National Conferences. He is the

life member of ISTE, India.

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