NOVEL DYNAMIC SCALING (NDS) ALGORITHM TO BALANCE ENERGY LEVEL IN EIoT FOR EFFICIENT CLOUD COMPUTING PROCESS

: The cloud computing is very efficient platform for storing and processing huge data applications. IoT, Edge computing models helps the cloud infrastructure for processing data with less network latencies, efficient data processing with minimum energy consumption for better infrastructure. More researches on cloud computing process makes them matured in providing best services to the users. Also IT services on cloud have developed rapidly. The different cloud services have different energy level with is memory, processing speed, bandwidth and system capability. The users have different data and requirements to process their job. Process of finding the best resources with less energy consumption is main goal of our proposed article. The edge devices, IoT devices first focused to help cloud architecture. Edge and IoT are data application. The speed processor scaling is a methodology used for consuming less energy and gaining less rates. if processing speed is high then energy consumption will be higher vise versa if processing speed is low then energy consumption will be less. This ideology is developed using NDS algorithm in edge cloud devices to compute data using less energy. The proposed algorithm is compared with existing energy saving algorithms and its efficiency is better than other algorithms is evaluated.


INTRODUCTION:
Usage of IoT devices widely for every information computing like sharing, storing, processing etc makes edge devices more important. Data Storing and processing of huge applications uses cloud computing as a best infrastructure. While more and more devices are being connectedwe are talking about billions of unitsimagine the amount of energy they consume. Moving AI/ML to the edge means that the focus is on custom designs that must successfully meet the critical requirements of low power use with high performance. Energy efficient ML algorithms take less processing power, run fewer CPU cycles and consume less memory; this enables us to run our algorithms on tinier devices that never before had the capacity to run machine learning on the edge. A slightly more energy efficient ML algorithm saves battery, CPU cycles, etc., which means we can run on smaller things. While it saves energy it also contributes to a development of more sustainable devicesand less CO2 emissions.
The smart devices like mobile phones, Bluetooth's, wearable smart devices perform internetworking using emerging IoT devices [1,2]. At present, sensors, RFID embedded with smart devices and connected to network forms mobile computing more popular. Health care applications, businesses, hotel management uses IoT more determinately [3,4]. Measuring of noise over the network is very difficult task. So nowadays high mobile applications improves it service quality by measuring energy, time, location, image capturing [5]. The real time data are sensed using the devices and processed by IoT and mobile based applications [6,7 ]. Further efficiency of devices are calculated using how less energy require to process for computing the data. If the devices have more computation capacity it consume more energy, time and memory for processing [8,9]. When the mobile devices consume more energy, it will abbreviate battery life and leads to emit greenhouse gases.
Cloud computing is considered as burgeoning method to overcome the limitation of the resources for mobile devices. This computing infrastructure helps mobile based applications to offload the data in cloud centralized server [10,11]. A cloud manager executes the on demand resources for requested mobile applications. Above process helps to reduce the time of execution and energy consumption of smart IoT devices. This helps to increase life of battery and reduce time. Another problem addressed is, due to offloading data continuously, produce congestion in the network. Further WAN is used to connect the mobile devices leads to high data latency. so it is important to address the cloud offloading time of data in the network especially in intensive networks [12,13]. Edge computing makes small servers ( data centers) to transmit data with minimum resources , access points and base station using edge based data radio network [14]. The process of communicating in cloudlets to local mobile devices via LAN reduces the data latency, bandwidth and offloading time efficiently [15]. Sometime Edge computing offloads data from both clouds as well as cloudlet server.

MOTIVATION:
Energy consumption is the major problem in every environment. Not only in cloud computing, it also big issues in small data processing in CPU. More energy consumption makes more power consumption, less data loading in more time and more bandwidth. The battery life is very less while consuming more power during processing. This makes huge data processing very difficult in maintenance of resources in cloud computing infrastructure. Our proposed article uses NDS algorithm to track the performance of CPU, processor based on input data. Our output is evaluated with existing algorithms.

paper contributions :
Our research mainly focused on energy saving concept in EIoT for efficient cloud computing process:  First we focus on where the energy is consumed more in edge nodes in the mobile devices.  Cloud-edge process with IoT is termed as multi objective problems.  Here we check every edge node where the energy is wasted and where to process the next data.  NDS algorithm is implemented to for scheduling the data process by consuming less energy.  Then the results are compared with varies energy saving algorithms for comprehensive evaluation process.

Related Work:
Present information development makes big data age for every human being. Due to demand s of intelligent data processing, intelligent data terminals are required to process the data efficiently [6]. IoT have increased its usage and attracted more due to inventions of smart devices information process [7]. Problem of reducing the IoT loss in every life cycle is achieved through analyzing the data efficiently by mining rules and characteristics on IoT [8]. The advanced precision mathematical technique has to be enhanced in the present cloud IoT model to upgrade the traditional IoT methods [9]. Application limitations are high knowledge in technical field and low precision. Recently IoT has more perfect data collection techniques in huge areas [10].
Messages which are undiscovered will be collected by IoT databases. But it has concerns, when using traditional IoT analyzing methodology. Traditional IoT techniques are very difficult to summarize the IoT content and messages [11]. This background uses knowledge discovery process, machine learning algorithms, data management and mining techniques for data management. Large capacity of data are handled using data mining techniques. This technique also capable of handling potential data and essential messages [12].
Expert knowledge on the domain are not necessary for data mining applications to identify the necessary information. This mining process proves it achievement in various fields [13]. In depth study on data mining process to build the analysis model mainly based on grouping and clustering data. The clustering data in IoT applications is main method in data analysis strategy. Next, association based rule mining method in data mining technology used to analyze a efficiency and effectiveness of IoT. Some software technologies are used to analyze theoretically about IoT data through mining strategy [14]. Various communications like wired, wireless uses IoT to transmit and collect data from sensors by online process, early warning system, businesses by mobile terminals [15].
Markov decision making process used in making decision regarding the offloading of the data in the cloud [16]. The performance of the system and usage of powers are machine in cloud is applied with numerous queuing models in [17,18], for computing efficiently. Data offloading can be done on incoming tasks based on decision algorithms to offload data in local cloud or remote cloud through WLAN or cellular connectivity.
Total cost of Amazon server maintenance is estimated as 52% in which energy related estimation is 41% for consuming the power, cooling infrastructure etc [19]. Advisor on global policy at Sweden of WWF suggested various IT solutions for reducing the green house gas emissions [20] like CO2. Smart buildings, smart infrastructure, smart communications on businesses, commerce etc are some IT based implementations for various solutions. In report [21] of energy based forecast results as 25% of energy saving by improving the cooling needs of server machine. Most IT networks is need of less energy consumption programme's. Mainly in cloud computing infrastructure is widely used application in 20 th centaury for data communication and storage. Energy efficient virtualization method used in paper [22] remotely computes the cloud and improves the scalability of the resources.
The task dependency, transmission process, transfers condition like time, cost is considered to model the energy optimization problem using genetic algorithms. The performance of the system is evaluated for efficiency calculation [23]. A cloud software toolbox with libraries and service components used to develop a energy efficient infrastructure in stack of three layers. In this model reduce the consumption of power by deploying software in cloud [24]. Energy efficient methodology for Mobile device is proposed in [25], it improves the time of execution of mobile based applications cloudlet devices. Edge cloud based framework using three layers for wireless resource scheduling of data offloading service computation is performed. This algorithm can save 80% of energy [26]. Cloud resources status is monitored by analyzing data obtained from tasks. At the right time target migration of data is consolidated before placement of data into the resources. It saves energy up to 21% on heavy load [27].
The energy and power efficiency of cloud physical and virtual machines is optimized in [28].
Here we study about different workloads affects the energy efficiency of the cloud computing system. Security of mobile cloud is proposed in novel offloading methodology. It combines techniques like SHA, AES and diffie helman key transfer exchange technique. It provides high security to cloud when comparing with other types of algorithms [29]. Offloading algorithm provides less energy computation. The complex infrastructure like multi load parallel process in the area of cloud computing uses decoupling strategy in the virtualization [30]. Energy based resources allocation in distributed network improves the cloud computing process more efficiently. Consumption of energy and delay process in network is handled using NOVEL system design algorithm. It shows how better novel algorithm works to provide various simulation results [31]. Placing the virtual machines in the network is addressed using swarm modified salp algorithm. this algorithm computes the virtual machine placement in the network with less energy consumption and maximum utilization of resources. It also reduces cost of migration and violations [32].
The virtual machines allocated are directly proportional to used physical system. This will provide less energy consumption [33]. Randomly incoming tasks which are heterogeneous in mobile cloud architecture satisfies the QoS for reducing the consumption of energy [34]. Heterogeneous cloud computing infrastructure uses power aware scheduling methodology to consume less power [35]. Swarm intelligence based artificial bee colony strategy [36] is used in power consumption of cloud computing platform. Failed machines also will be under same requests. This means QoS is higher for this operation.

Problem Formulation :
Edge computing is an emerging trend to bring computing and system monitoring activities that are related to the IoT devices. To address the issues in IoT based applications like network latencies, data processing and energy consumption, edge computing is the key to solve the issues. Edge computing focuses many advantages over the critical challenges on IoT environment such as edge devices security,agility of deployment, low latency and efficiency of cost and performance. AI based machine learning algorithms used to predict the future needs on IoT, to concentrate the possibilities to integrate the machine learning with IoT and edge computing in terms of energy saving.
In day today life, more IoT devices are in use. While more and more devices are connected, the energy consume by the devices are day by day increasing. To focus on low power usage with high performance, the machine learning algorithms are combined with edge computing that will focus on custom designs to meet the requirements successfully. Energy consumption algorithms on IoT with edge take less processing power in terms of processor utilization and also consumes less memory. Due to the processor device scaling based on edge, our algorithms can be run on smaller devices.
The proposed framework is capable to process the data in edge node of the network. With the extension of the statements, the paper presents a novel dynamic scaling on edge node algorithm in edge computing frameworks for IoT environment. This paper also compared the proposed methodology with the existing energy consumption algorithms such as deep learning, Hoeffding Tree algorithm (HTA), dynamic Speed controller, MBFD and LOADto analyze the performance of the proposed work. The architecture for the proposed work is shown in fig 1.

ARCHITECTURE
In edge computing, the edge node is present in between of the end user (IoT devices) and cloud. It is also termed as fog node. In our proposed work, the algorithm has been applied to utilize the cloud processor in less energy in terms of the edge node. As faster the workload, the energy will be saved. To balance the workload based on the proposed criteria, the speed of the cloud processor is changed according to the algorithm applied on the edge node. The proposed architecture is shown in Figure 1. In general, high processing will consume more energy while less processing job consume less energy. Hence, the proposed algorithm maintains two flags to monitor the cloud processor utilization. The flag1 to monitor the cloud processor utilization is high at the edge node and flag2 is to monitor the cloud processor utilization is low. Based on the flags, the cloud processor speed is increased or decreased. According to the workload, the speed of the cloud processor executes the instructions based on the edge node, so that the speed is altered. The workload is considered as areas set under the surveillance and monitoring of the possibilities. In this proposed work, the workload area is between the ranges 1 to 16.

Proposed Working process and Algorithm:
The proposed work flow stated in following steps, i) Analyze the energy model of the algorithms -where energy is being utilized ii) Updating the flag to check the change of cloud processor speed. iii) Proposed article is implemented in terms of edge node in the cloud environment iv) Testing is done for types of possible workload areas; lower and higher. v) The proposed algorithms is compared with the existing energy efficient algorithms to analyze the performance.
To keep track of the utilization level of cloud processor is high or low, the amount of workload assigned to the EIoT application is monitored at each iteration. The flag value indicates the need to switch the cloud processor speed from high to low or low to high at the edge node. The maximum value of each flag is set to two iterations, which means if the amount of a workload is reached 2 iterations then the cloud processor speed is changed. The importance of flag is to avoid the consequent cloud processor speed alterations. Here, the workload categories are limited to higher and lower in terms of area. The areas ranged from 1-8 are considered as lower and areas ranges from 9-16 are considered as higher. The lower workload area is allotted to flag1 and higher workload area is allotted to flag2.
According to the received flag value at the second iteration, the speed of the cloud processor will be altered and reaches final stage in terms of edge node. Here, if flag1 function receives value 2 as signal for functioning iteration 2, then the speed of cloud processor at the edge device decreases. In case the signal received indicates the flag2 then the speed of cloud processor at edge device would be increased. The algorithm for the proposed methodology Novel Dynamic Scaling on Edge Node (NDSEN) is, In Figure 2, the flow of our proposed NDSEN algorithm is depicted. The designing of proposed methodology of dynamic scaling on edge node can consume energy less than any other traditional IoT applications used by the authors in reference paper [ 1,2,3,4]. In the proposed methodology, the cloud processor edge node speed is increased or decreased based on the workload area. So that the energy will be saved according to the speed of the cloud processor. Eventhough for maximum workload needs more energy our algorithm effectively performing the computation so that the speed increased to complete the task on time to save the energy.

Existing energy saving algorithm:
(i) Hoeffding Trees: [1] Hoeffding Trees is a decision tree algorithm which use static hyper parameters for data to avoid the repetitive tasks since the repetitive tasks need more energy consumption. They proposed the solution that based on the characteristics of the data, the hyper parameters value will be changed. The proposed trees splitting criteria is based the energy used the splitting nodes. The reasonable accuracy node used the increasing energy whereas the rest of the nodes in the tree ran on less energy environment. These solutions of the proposed hoeffding trees reduce the energy level by only 20-40%. So there is a need to develop better algorithm in terms of energy consumption.
(ii) Deep learning algorithms: Deep learning algorithms [1] are based on neural network to solve complex problems. Due to the power of this networks to solve the complex problems like object recognition, it consumes major amount of energy. The existing method saves energy in terms of contributing methods like pruning and quantization. To remove the redundant connection in the neural networks, pruning used. This will reduce the memory access and computations. Quantization was used to reduce the energy by reducing the weights of the network with lower precision.
(iii) Dynamic speed controller algorithm:The processor speed controller [2] is a recent methodology implemented by more research scholars for energy consumption. In this work, the energy saved based on the CPU speed. Based on the workload areas like higher, moderate and lower, the speed can be increased or decreased in terms of the Counter variable. The workload on EIoT can be tracked by using dynamic speed controller which is placed on EIoT device by the IoT applications, in order to change the speed of the CPU.
(iv) MBFD: The term "Modified Best Fit Decreasing (MBFD)" [3] uses SLA (Service Level Agreement) to assure the reduction of energy consumption at data centers. VM placement at initial stage is solved using bin packing strategy. Tasks have uncertainty in this process. This problem became main motivation for our proposed algorithm. Also uncertainty in task makes few VM to host high provisions of applications whereas other VM use fewer resources to run. If cloud servers have unbalanced work, then it results in resources wastage and less performance.
(V) LOAD: The algorithm in [4] -LOAD used to learn energy utilization automatically. It also considers SLA at VM's in cloud servers. This algorithm counts demands of users on resources and predicts the overload of hosts. It helps to reduce overloading at early stage. It stops idle hosts in demanding to save energy consumption at servers. The process of automatic learning detects the overloading and improves VM allocation to hosts by consolidating. It also monitors the hosts usage of CPU resources.

RESULT AND ANALYSIS
iFogSim in [5] used to produce simulation for implementation of proposed methodology. It is considered as a dynamic platform for IoT based applications. In the cloud and edge computing platform iFogSim used to produce resource handling simulation and policy of scheduling applications. IoT networks receives data from sensors on network and processed by edge computing devices. This processed applications transfer the actuators sense of process to iFogSim. The CloudSim functionalities are combined with iFogSim architecture for basic simulations of events. Figure 3 is used to depict energy consumption system. The consumption of energy increases by running system with high speed.

Figure 3: Simulation of Energy Consumption System
The inputs of proposed methodology are workload areas in the range of A1-A16. The corresponding cloud processor speed is either increased or decreased based on the flag function, which is shown in table 1. Here the processor speed is changed after two iteration of each flag. Because constant change of the processor speed causes system inefficiency.
To analyze proposed NDSEN algorithm performance, the measures like Energy consumption in terms of Mega Joules, Network Usage in terms of Kilo Bytes, Execution Time in terms of Seconds and Loop Delay in terms of Seconds are compared with the existing Energy Saving algorithms such as Hoeffding Tree algorithm (EEA), dynamic Speed controller, MBFD and LOAD. The first measure energy consumption is calculated based on the computation complexity.
Computation complexity is one of the important metrics, which help the determination of the extent of effectiveness of the power consumption. In this research work, the energy of the proposed algorithm is determined using the power utilisationof the cloud processor in terms of joulesand time utilized for particular area as follows.
Energy (MegaJoules) = power utilization * time Table 1 represents the energy consumption of the energy efficiency algorithms. The equivalent chart is depicted in     scaling algorithm DSC is best. Hence, in all the cases in terms of consuming energy, using networks, Time of execution and delay at loop, our Proposed Novel Dynamic Scaling on Edge Node algorithm gives better result compared to the existing energy saving algorithms.

CONCLUSION AND FUTURE WORK:
Future ICT mainly depends on energy consumption and power consumption. This process mainly saves the CO2 emissions. The proposed strategy of novel dynamic scaling algorithms is implemented in this article. This research works mainly focus on the cpu and processor of the virtual machine. It identifies main energy consumption machine in the cloud computing network. then the flags are used to monitor the energy of the respective system and what data to be processed based on its capacity. Large scale computer communications can achieve good energy saving plan by integrating the communication services based on needs. We have seen that large scale energy consumption networks have been proposed by this article. The output of the system is compared with various existing algorithms. The result obtained proves that novel dynamic scaling approach saves energy higher than older algorithms.
In future energy consumption algorithm with various services must be implemented. The Metaheuristic algorithm with AI can be also implemented for less energy consumption. Swarm intelligence and machine learning combine to give better results in future implementations.

Availability of data and materials
The data sets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. To balance the workload based on the proposed criteria, the speed of the cloud processor is changed according to the algorithm applied on the edge node. The proposed architecture is shown in Figure 1. In general, high processing will consume more energy while less processing job consume less energy. Hence, the proposed algorithm maintains two flags to monitor the cloud processor utilization. The flag1 to monitor the cloud processor utilization is high at the edge node and flag2 is to monitor the cloud processor utilization is low. Based on the flags, the cloud processor speed is increased or decreased. According to the workload, the speed of the cloud processor executes the instructions based on the edge node, so that the speed is altered. The workload is considered as areas set under the surveillance and monitoring of the possibilities. In this proposed work, the workload area is between the ranges 1 to 16.  Figure 2, the flow of our proposed NDSEN algorithm is depicted. The designing of proposed methodology of dynamic scaling on edge node can consume energy less than any other traditional IoT applications used by the authors in reference paper [ 1,2,3,4]. In the proposed methodology, the cloud processor edge node speed is increased or decreased based on the workload area. So that the energy will be saved according to the speed of the cloud processor.     From figure 7, one can understand that our proposed algorithm loop delay time is 2.5s compared to the existing algorithms loop delay time such as Hoeffding Tress (9.4s), DL (5s), dynamic speed controller (4.6s), MBFD (7.5s)and LOAD (6.8s). Next to the proposed novel dynamic scaling algorithm DSC is best. Hence, in all the cases in terms of consuming energy, using networks, Time of execution and delay at loop, our Proposed Novel Dynamic Scaling on Edge Node algorithm gives better result compared to the existing energy saving algorithms.