Cloud Load Research Articles

Mitigation of DDoS Attack in Cloud Computing Domain by Integrating the DCLB Algorithm with Fuzzy Logic

Cloud computing would be an easy method to obtain services, resources and applications from any location on the internet. In the future of data generation, it is an unavoidable conclusion. Despite its many attractive properties, the cloud is vulnerable to a variety of attacks. One such well-known attack that emphasizes the availability of amenities is the Distributed Denial of Service (DDoS). A DDoS assault overwhelms the server with massive quantities of regular or intermittent traffic. It compromises with the cloud servers’ services and makes it harder to reply to legitimate users of the cloud. A monitoring system with correct resource scaling approach should be created to regulate and monitor the DDoS assault. The network is overwhelmed with excessive traffic of significant resource usage requests during the attack, resulting in the denial of needed services to genuine users. In this research, a unique way to the analyze resources used by the cloud users, lowering of the resources consumed is done when the network is overburdened with excessive traffic, and the dynamic cloud load balancing algorithm DCLB (Dynamic Cloud Load Balancing) is used to balance the overhead towards the server. The core premise is to monitor traffic using the fuzzy logic approach, which employs different traffic parameters in conjunction with various built in measured to recognize the DDoS attack traffic in the network. Finally, the proposed method shows a 93% of average detection rate when compared to the existing model. This method is a unique attempt to comprehend the importance of DDoS mitigation techniques as well as good resource management during an attack and analysis of the.

Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)

The computational cloud aims to move traditional computing from personal computers to cloud providers on the internet. Cloud security represents an important research area. Confidentiality, integrity, and availability are the main cloud security characteristics addressed. Cloud providers apply dynamic load balancing and reactive fault tolerance techniques to build secure cloud services to achieve high service availability. Dynamic cloud load balancing approaches distribute submitted tasks to virtual machines during tasks execution and the load of virtual machines is updated based on the system’s state. Reactive cloud fault tolerance is activated for system process failures after failure effectively happens. Reactive cloud fault tolerance handles failure after the fault has occurred. Despite the significance of dynamic load balancing and reactive fault tolerance techniques and mechanisms, few reviews focus on these approaches in a systematic, unbiased method focusing on integrating cloud dynamic load balancing and reactive fault tolerance techniques. This paper conducts a systematic literature review of the existing literature concerning reactive fault tolerance, dynamic load balancing, and their integration in their basic approaches, types, frameworks, and future directions.

Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

Novel load balancing approach based on ant colony optimization technique in cloud computing

The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consumption. The efficiency of the proposed approach simulated in terms of execution time, energy consumption and examined with CloudSim. The introduction is provided in this article with a detailed description of cloud computing and, in addition, green cloud computing with its models. This article also discussed the virtual machine (VM) in more depth in the introduction section, which allows cloud service providers to supervise cloud resources competently while dispensing with the need for human oversight. Then the article submitted and explained the related works with their discussion and then it explained the novel proposed load balancing based on ACO technique and concluded that the execution time and energy consumption of the proposed technique is better than the three-threshold energy saving algorithm (TESA) technique that is commonly used in cloud load balancing.

A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy

A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy

Joint Computation Offloading and Data Caching Based on Cooperation of Mobile-Edge-Computing-Enabled Base Stations

Mobile terminal applications with high computing complexity and high time delay sensitivity are developing quite fast today, which aggravates the load of mobile cloud computing and storage and further leads to network congestion and service quality decline. Mobile edge computing (MEC) is a way of breaking through the limits of computing and storage resources of mobile cloud and alleviating the load of mobile cloud. Computing time costs and transmission time costs are considered to be the main issues for the mobile cloud when carrying out computing offloading and data caching. Therefore, an efficient resource management strategy, which could minimize the system delay, is proposed in this paper. The new scheme offloads reasonably computing tasks and caches the tasks’ data from the mobile cloud to mobile edge computing-enabled base stations. An intelligence algorithm, genetic algorithm, is being used to solve the global optimization problem which would cause transmission delay and computing resources occupation, and to determine the computing offloading and data caching probability. The simulation of the system using MATLAB is conducted in 8 different scenarios with different parameters. The results show that our new scheme improves the system computing speed and optimizes the user experience in all scenarios, compared with the scheme without data caching and the scheme without computing offloading and data caching.

An Auto-Scaling Approach to Load Balance Dynamic Workloads for Cloud Systems

With The Advances In Modern Technology, Need For Cloud Services Is Ever Increasing. In Such Case On Public Real-Time Cloud Environments The Workload On The Respective Server Can Vary Depending On The User Needs. Cloud Computing Is The Latest Technology That Provides On-Demand Availability Of Computer Resources Without Direct Intervention Of The User. This Technology Combined With Cloud Load Balancing Enables Enterprises To Manage Workload Demands. The Cloud Load Balancer, With Auto Scaling Features, Further Increases The Efficiency And Optimized Utilisation Of The Computing Resources. This Paper Discusses How Amazon Web Services As The Cloud Medium Along With Mobaxterm For Remote Control, And Implement ARR Configurations For The Load Balancer To Route The Requests. AWS Gives Us Knowledge About Various Concepts Such As Auto-Scaling Groups, Target Groups, Load Balancers, Etc.

WITHDRAWN: Hybrid algorithm for performance improvement in Cloud load balancing

Abstract Cloud computing has cemented itself as a vital cog in technical world. Cloud computing is beneficial to all industries from manufacturing to healthcare. Cloud computing revolves around virtualization. All through the latest couple of years, conveyed figuring has emerged as quite possibly the most remarkable principles in the IT business and has pulled in noteworthy thought from both insightful world and industry. A bunch of cloud-based virtual machines measure all client demands. Burden adjusting is a method of spreading loads across different bodies, for example, CPUs, drives, workers or different PCs. The critical point of burden adjusting is to accomplish a vastly improved utilization of energy. This article has a half breed load offsetting calculation with the best round, push and equivalents proliferating qualities of the cooperative effort.

Optimization of cloud load balancing using fitness function and duopoly theory

PurposeCurrent industrial scenario is largely dependent on cloud computing paradigms. On-demand services provided by cloud data centre are paid as per use. Hence, it is very important to make use of the allocated resources to the maximum. The resource utilization is highly dependent on the allocation of resources to the incoming request. The allocation of requests is done with respect to the physical machines present in the datacenter. While allocating the tasks to these physical machines, it needs to be allocated in such a way that no physical machine is underutilized or over loaded. To make sure of this, optimal load balancing is very important.Design/methodology/approachThe paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks. The major focus of the proposed work is to optimize the load balancing in a datacenter. When optimization happens, none of the physical machine is neither overloaded nor under-utilized, hence resulting in efficient utilization of the resources.FindingsThe performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load (RR) ant colony optimization (ACO), artificial bee colony (ABC) with respect to the selected parameters response time, virtual machine migrations, host shut down and energy consumption. All the four parameters gave a positive result when the algorithm is simulated.Originality/valueThe contribution of this paper is towards the domain of cloud load balancing. The paper is proposing a novel approach to optimize the cloud load balancing process. The results obtained show that response time, virtual machine migrations, host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study. The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.

Cost-aware automatic scaling and workload-aware replica management for edge-cloud environment

As the mobile edge computing develops continually, many emerging and diversified edge applications, such as Internet of vehicles, virtual reality games and lightweight deep learning tasks, are emerged. These applications are latency sensitive and require a large number of network connections. As the data size and business requests processed per unit of time continue to increase, the pressure of edge cloud load increases so much that the edge cloud cannot provide timely and effective services for users. In order to meet the high requirement of delay sensitive application service, on the one hand, the energy consumption of edge devices should be reduced, and the modules with high computational load should be scheduled to the remote server for execution. On the other hand, the automatic scaling model is proposed to improve the total cost of the tenanted instances. In the dynamic replica management model, the response time and the energy consumption are reduced, moreover, the workload in the hosts are balanced. In the experiment of automatic scaling, the cumulative total cost and the power consumption of our proposed algorithm are lower than that of CAAS and MLC algorithm. Two hours after the experiment, the CPU utility of our proposed algorithm is higher than the CPU utilizations of CAAS and MLC. In the dynamic replica placement experiment, the average response time and load balancing value of the data migration algorithm in our paper are lower than that of BA and LA. The available storage space of our replica placement method is more balanced than both the DDD and WA.

An Accomplished Energy-Aware Approach for Server Load Balancing in Cloud Computing

Background:It is important to minimize bandwidth that improves battery life, system reliability and other environmental concerns and energy optimization. It also do everything within their power to reduce the amount of data that flows through their pipes.To increase resource exertion, task consolidation is an effective technique, greatly enabled by virtualization technologies, which facilitate the concurrent execution of several tasks and, in turn, reduce energy consumption. : MaxUtil, which aims to maximize resource exertion, and Energy Conscious Task Consolidation which explicitly takes into account both active and idle energy consumption.Materials and Methods:In this paper an Energy Aware Cloud Load Balancing Technique (EACLBT) is proposed for the performance improvement in terms of energy and run time. It predicts load of host after VM allocation and if according to prediction host become overloaded than VM will be created on different host. So it minimize the number of migrations due to host overloading conditions. This proposed technique results in minimize bandwidth and energy utilization.Results:The result shows that the energy efficient method has been proposed for monitor energy exhaustion and support static and dynamic system level optimization.The EACLBT can reduce the number of power-on physical machine and average power consumption compare to other deploy algorithms with power saving.Besides minimization in bandwidth along with energy exertion, reduction in the number of executed instructions is also achieved.Conclusion:This paper comprehensively describes the EACLBT (Energy Aware Cloud Load Balancing Technique) to deploy the virtual machines for power saving purpose. The average power consumption is used as performance metrics and the result of PALB is used as baseline. The EACLBT can reduce the number of power-on physical machine and average power consumption compare to other deploy algorithms with power saving. It shown that on average an idle server consumes approximately 70% of the power consumed by the server running at the full CPU speed.The performance holds better for Common sub utterance elimination. So, we can say the proposed Energy Aware Cloud Load Balancing Technique (EACLBT) is effective in bandwidth minimization and reduction of energy exertion.

CMODLB: an efficient load balancing approach in cloud computing environment

A hybrid of supervised (artificial neural network), unsupervised (clustering) machine learning, and soft computing (interval type 2 fuzzy logic system)-based load balancing algorithm, i.e., clustering-based multiple objective dynamic load balancing technique (CMODLB), is introduced to balance the cloud load in the present work. Initially, our previously introduced artificial neural network-based dynamic load balancing (ANN-LB) technique is implemented to cluster the virtual machines (VMs) into underloaded and overloaded VMs using Bayesian optimization-based enhanced K-means (BOEK-means) algorithm. In the second stage, the user tasks are scheduled for underloading VMs to improve load balance and resource utilization. Scheduling of tasks is supported by multi-objective-based technique of order preference by similarity to ideal solution with particle swarm optimization (TOPSIS-PSO) algorithm using different cloud criteria. To realize load balancing among PMs, the VM manager makes decisions for VM migration. VM migration decision is done based on the suitable conditions, if a PM is overloaded, and if another PM is minimum loaded. The former condition balances load, while the latter condition minimizes energy consumption in PMs. VM migration is achieved through interval type 2 fuzzy logic system (IT2FS) whose decisions are based on multiple significant parameters. Experimental results show that the CMODLB method takes 31.067% and 71.6% less completion time than TaPRA and BSO, respectively. It has maintained 65.54% and 68.26% less MakeSpan than MaxMin and R.R algorithms, respectively. The proposed method has achieved around 75% of resource utilization, which is highest compared to DHCI and CESCC. The use of novel and innovative hybridization of machine learning, multi-objective, and soft computing methods in the proposed algorithm offers optimum scheduling and migration processes to balance PMs and VMs.

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling.

Cloud computing resource elasticity scaling method based on neural network time series prediction

With the development of cloud computing, the requirements for dynamic distribution of cloud computing according to the real-time load are getting higher and higher. It is necessary to predict the load of cloud computing resources to achieve the goal of real-time elastic scaling. Cloud computing load will constantly change with time and actual demand. Therefore, lstm-attention model is used to collect data and model and predict cloud computing resource load for a long time, so as to achieve accurate prediction of cloud computing resource load in the future. It can be concluded that the lstm-attention model performs well in cloud computing resource load prediction.

An Efficient Load Balancing Method by Using Machine Learning-Based VM Distribution and Dynamic Resource Mapping

In cloud computing, balancing the load among VMs and resources is a major research area, which still needs attention. The primary aim of this research is to expand an effective cloud load balancing approach, enhance the reaction time, lessen the ready time, premiere utilization of sources as well lessen the activity rejection time. The proposed MLBL method is primarily based on the SVM and Ksuggest clustering method. In the proposed MLBL technique SVM classification method used to create activity businesses based totally at the size. Later Kmethod clustering technique is used to create the institution of Virtual machines based totally on their usage of CPU and number one memory (RAM). We are providing an MLBL method based totally on system learning-based VM distribution and dynamic aid mapping. In cloud computing, VMs are scheduled to hosts as in keeping with their utilization (a host which has better availability of memory) without thinking about common utilizations. The proposed technique divides the assets into various organizations as well as VMs and then follow dynamic aid mapping. The proposed MLBL technique creates VMs clusters to execute comparable task agencies that enhance the QoS and ideal utilization of sources in addition to lessen the process rejection time.

EFFICIENT VIRTUAL MACHINE LOAD BALANCING USING CLOUD COMPUTING ENVIRONMENT

Cloud computing is designed to provide a scalable and low cost way to delivering on-demand IT resource services over the internet. Over a period of time cloud computing experienced tremendous development . Nonethless, the economic model centered on hardware and software demand based on technological requirements (CPU utilization, memory...) or strongly contributed to productive use of computer resources. Load balancing is therefore a crucial aspect of cloud computing. Cloud computing requires optimizing the efficiency of the various services provided by the cloud providers to minimize SLA infringement, i.e,.high degree of security, availability and responsivity. As cloud computing is growing increasingly and customers are demanding better performance and more services, cloud resource scheduling and load balancing has become a very interesting and significant research field .Therefore SLAs are emerging as an important factor between consumers and providers.In the existing algorithms there are drawbacks due to their single objective. But in this work, we are considering multiple objectives and multiple parameters for balancing load across virtual machine and to achieve less power consumption and reduce SLA violation.

Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network

Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are in prediction accuracy higher than equivalent prediction algorithms.

A Conceptual Framework Towards Implementing a Cloud-Based Dynamic Load Balancer Using a Weighted Round-Robin Algorithm

Cloud load balancing has become one of the most vital aspects of Cloud computing that has captured the attention of IT organizations and business firms in recent years. Among the issues related to this particular aspect, one such issue which needs to be addressed is the issue of effectively serving the clients' requests among multiple servers using an appropriate load balancer. Previous survey papers discussed various issues of cloud load balancing and accordingly devised various methods and techniques to address those issues with the objectives of reduction of processing time and response time along with optimization of costs. In this article, we have discussed an effective load balancing technique using the weighted Round-Robin algorithm which can process the client requests among multiple servers with minimal response time. Considering all these aspects, a cloud-based dynamic load balancer is being used to solve the problem of load balancing in the cloud infrastructure.

Campus Edge Computing Network Based on IoT Street Lighting Nodes

This incredibly rapid adoption of Internet of Things (IoT) and e-learning technology, a smart campus provides many innovative applications, such as ubiquitous learning, smart energy, and security services to campus users via numerous IoT devices. However, as more and more IoT devices are integrated and imported, the inadequate campus network resource caused by the sensor data transport and video streaming is also a significant problem. This paper proposes a campus edge computing network in the hardware–software co-design process. The system employs street lighting as the IoT network communication node device. The campus platform integrates campus courses service, regulatory networks, mobile wireless networks, and other computing services. Neural network learning algorithms are employed to analyze the network and compute resource required by each network node operates as a whole network resource allocation service. Moreover, the learning algorithms will be adjusted as the bidirectional IoT communication to avoid inadequate resources with many IoTs service and data streams in the overall campus network service quality. The experimental results show that the proposed mechanism that the edge computing reduces the cloud loading and predicts and adjusts the distribution of the overall network can efficiently allocate resources and maintain load balance.

Load balancing mechanism in the cloud environment using preference alignments and an optimisation algorithm

Cloud computing platforms propose applied directional information technology services to its clients. It provides services according to demands by which shared resources, data, software, and other tools are to be made available based on the client's request. Load balancing is considered as one of the main issues in cloud computing. The quality of service (QoS) factors such as resources application, answering time, process time, scalability, throughput, the durability of the system, and energy consumption are improved by the balanced load. The cloud tasks scheduling and load balancing are considered as an NP-hard issue. Therefore, in this study, a new energy-aware cloud load balancing method is proposed by usage of preference alignments and ant colony optimisation algorithm. This approach aims at balancing the virtual machines workload and energy consumption of cloud data centres and also assures QoS for users. CloudSim simulator is used to confirm the performance of the method. It shows that the proposed method reduces waiting time and energy consumption on a virtual machine.