Server Load Balancing Research Articles

An Analysis of Load Balancing Algorithm Using Software- Defined Network

A data center is one of the secured space which serves as a house for many disks, switches, servers, routers, and several other hardware for a computer. It is a hardware solution for users who are present near the data center. Cloud Computing (CC) is the newer version of the data center which is capable of giving computer services to the users. Cloud service is the one which provides the users with data center set up when needed or preferred by the user themselves. In general cloud network service will be limited to a particular location or zone. In case if the target area of the user is near then the server will complete the action needed for the user. Only certain servers can provide service for the user in such cases some servers will remain idle. If the provided service by the servers is not used properly this may also lead to the issue of processing and managing a larger set of traffic which is one of the difficult tasks to perform. More network traffic may lead to giving more amount of pressure and complexity to the data center. In such a case Load Balancing (LB) is the preferred process for reducing the network failure and degradation process of the entire network. Software-defined networking is one the upcoming process which is capable of managing the entire network and also gives a general view about the network and its further configuration which needed to be upgraded. In this research paper, a software-defined network is developed for LB algorithms. In this paper, a Dynamic Server LB algorithm (Dserv- LB) is used for open-flow switches in the software-defined network system. This algorithm is one of the packet type LB algorithm. The request in the server is directly forwarded to the web-server with a higher level of server resources. From the result of the proposed algorithm, it was found that Dserv-LB is capable of improving the performance of the entire network and properly uses the server resource.

RETRACTED ARTICLE: Proactive Load Balancing Strategy Towards Intelligence-Enabled Software-Defined Network

Software-defined network provides a greater solution to many of the complex network management functionalities in a data center network (DCN). In a software-defined data center network, it is essential to manage heavy traffic, to avoid data loss, and to avoid server unavailability during heavy flows. One of the major tasks in a network is the load balancing in the available links. Due to the dynamic data traffic in the network, it is necessary to perform a deep learning of the long-term and the short-term data. Hence, this paper proposes an intelligent load balancing scenario that includes link load balancing, server load balancing, and traffic classification. An artificial neural network is needed because the data are uncertain all the time. Thus, our method provides improved throughput, minimized the flow completion time, and minimizes data loss.

Reliable scheduling and load balancing for requests in cloud-fog computing

Fog computing broadens the computing services to serve requests of Internet of Things (IoT) by resources at the edge of Cloud-Fog environments instead of serving these requests by resources at the environment’s core. The aim of fog computing is to reduce load of computing in data centers and reduce latency of requests, especially real-time ones. Load balancing and scheduling play essential roles and represent main key challenges to guarantee high throughput and reliability of services in Cloud-Fog environments. Therefore, this paper introduces a reliable scheduling approach for allocating customers’ requests to the resources of Cloud-Fog environments. The approach is called Load Balanced Service Scheduling Approach (LBSSA) and it considers load balancing among resources when assigning requests to them by classifying requests to real-time, important and time-tolerant. In addition, scheduling of requests in the proposed approach considers the failure rate of resources in order to provide high reliability for requested services. The approach has a set of algorithms for handling different types of requests. Simulation experiments using CloudSim are conducted to assess the LBSSA approach in terms of number of computing resources, utilization of resources, load balance variance and running time.

Energy Efficient Resource Migration Based Load Balance Mechanism for High Traffic Applications IoT

The biggest challenge for the network service providers is the day to day advancement of technologies which makes them difficult to manage the traditional networks. This day to day advancement has worked as a motivation to vendors for developing, deploying and migrating their services, installments of new hardware, trained people and up gradation of infrastructure which involves a huge cost and time. These frequent changes demand a new network architecture which supports future technologies and solves all these issues named as the proposal of networks defined by software. A large amount of data is being generated and through the internet, we interact with the world using our smart devices such as tablets, sensors, and smartphones using the concepts of Internet of Things (IoT). Along with continuous growth and development, there is a continuous heterogenous and ever-increasing demands of services. This leads to a cause of emerging challenge of load balancing of networks for meeting up with highly demanding requirements (e.g., high performance, lower latency, high throughput, and high availability) of IoT and 5G network applications. For meeting up highly increasing demands, various proposal of load balancing techniques comes forward, in which highly dedicated balancers of loads are being required for ever service in some of them, or for every new service, manual recognition of device is required. In the conventional network, on the basis of the local information in the network, load balancing is being established. However, the production of more optimized load balancers and a global view for the network is being contained by SDN controllers. So, these well-known techniques are quite time-consuming, expensive and impractical as well as service types aren’t being considered by various existing load balancing schemes. Through this paper, researchers focus on an SDN based load balancing (SBLB) service, in which minimized response time and maximized resource utilization are being considered for the user on cloud servers. The proposed scheme is being constituted by an application module which runs along with a SDN controller and server pools that connect to the controller through SDN enabled switches. The application module contains a dynamic load balancing module, a monitoring module and a service classification module. All messages are being handled in real time and host pool are being maintained by the Controller. The performance of the proposed scheme has been validated by experimental results. Through various experiments, results are being concluded that usage of SBLB results in significant decrease in average response and reply time.

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.

A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems.

This paper presents a solution to support service discovery for edge choreography based distributed embedded systems. The Internet of Things (IoT) edge architectural layer is composed of Raspberry Pi machines. Each machine hosts different services organized based on the choreography collaborative paradigm. The solution adds to the choreography middleware three messages passing models to be coherent and compatible with current IoT messaging protocols. It is aimed to support blind hot plugging of new machines and help with service load balance. The discovery mechanism is implemented as a broker service and supports regular expressions (Regex) in message scope to discern both publishing patterns offered by data providers and client services necessities. Results compare Control Process Unit (CPU) usage in a request–response and datacentric configuration and analyze both regex interpreter latency times compared with a traditional message structure as well as its impact on CPU and memory consumption.

Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as a job-assignment optimization problem and only consider the current VMHs' loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments. In this study, two genetic-based methods are integrated and presented. First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment. The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance. Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance. The performance of the proposed methods is evaluated in a real cloud-computing environment, Jnet, wherein these methods are implemented as a centralized load balancing mechanism. The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.

The network architecture optimization based on the server load balancing technology

The paper takes the authentication gateway system for internal office network as the research object, for the performance bottleneck and single point of failure problem of the single authentication gateway deployed in the existing primary and standby modes, chooses an authentication gateway integration method based on CA certificate. The practical results show that: through implementing the authentication gateway cluster design and introducing load balancing mechanism, this method solves the performance, stability and single point of failure of the authentication gateway, and improves the resource utilization of the authentication gateway device. This method introduced in this paper can be used for reference for the network architecture optimization based on the server load balancing technology.

Disaster-Aware Dynamic Routing for SDN-Based Multi-Site Data Center Networks

In recent years, cloud computing technology has been developing rapidly. As a result, the internal traffic of large-scale enterprises’ data centers has increased significantly. It has become important to improve the disaster tolerance capability of data centers to ensure user data security. However, the data center network relies on its physical infrastructure. Large-scale disasters may damage the infrastructure and cause huge data loss or connection interruption. Software-defined network (SDN) is an innovative network architecture that separates the control and forwarding layers of the network. Thus, SDN promotes network programmability and opens up new ways to design disaster-resistant networks. Based on SDN technology, we propose a disaster-aware dynamic routing (DADR) scheme. When a disaster signal is received, the SDN controller notifies the Global Server Load Balance (GSLB) device and stops declaring the IP of the disaster-stricken data center. At the same time, the SDN controller sends the server and client session information and the current traffic information of the disaster-stricken data center to other sites, where the optimal routing path is calculated for the data center based on the traffic characteristics by the proposed Lagrangian Relaxation based Bellman-Ford Parallel algorithm (LRBFP). Our results show that in the event of a disaster, based on the proposed DADR scheme and LRBFP algorithm, the packet loss rate and network delay can be greatly reduced.

ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

Due to the explosion of network data traffic and IoT devices, edge servers are overloaded and slow to respond to the massive volume of online requests. A large number of studies have shown that edge caching can solve this problem effectively. This paper proposes a distributed edge collaborative caching mechanism for Internet online request services scenario. It solves the problem of large average access delay caused by unbalanced load of edge servers, meets users’ differentiated service demands and improves user experience. In particular, the edge cache node selection algorithm is optimized, and a novel edge cache replacement strategy considering the differentiated user requests is proposed. This mechanism can shorten the response time to a large number of user requests. Experimental results show that, compared with the current advanced online edge caching algorithm, the proposed edge collaborative caching strategy in this paper can reduce the average response delay by 9%. It also increases the user utility by 4.5 times in differentiated service scenarios, and significantly reduces the time complexity of the edge caching algorithm.

A Secured and Intelligent Communication Scheme for IIoT-enabled Pervasive Edge Computing.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms.

Multi-objective resource allocation in mobile edge computing using PAES for Internet of Things

In recent years, mobile edge computing (MEC), as a powerful computing paradigm, provides sufficient computing resources for Internet of Things (IoT). Generally, the deployment of MEC servers closer to mobile users has effectively reduced access delays and the cost of using cloud services. However, the multi-objective resource allocation for IoT applications to meet service requirements (i.e., the shortest completion time of IoT applications, the load balance and lower energy consumption of MEC servers, etc.) still faces severe challenges. To address this challenge, a multi-objective resource allocation method, named MRAM, is proposed in this paper for IoT. Technically, the pareto archived evolution strategy is leveraged to optimize the time cost of IoT applications, load balance and energy consumption of MEC servers. Furthermore, the multiple criteria decision making and the technique for order preference by similarity to ideal solution are utilized to obtain the optimal multi-objective resource allocation strategy. Ultimately, the comprehensive analysis of MRAM is introduced in detail.

Impact analysis of SYN flood DDOS attack on HAPROXY and NLB cluster-base web servers

<p>In recent, the high available internet service is main demand of the most people. However, online services occasionally become inaccessible due to various threats and attacks. Synchronization (SYN) flood Distributed Denial of Service (DDoS) is the most used and has a serious effect on the public network services. Hence, the outcome of this attack on the commonly utilized cluster-based web servers is systematically illustrated in this paper. Moreover, performance of Internet Information Service 10.0 (IIS 10.0) on Windows server 2016 and Apache 2 on Linux Ubuntu 16.04 server is evaluated efficiently. The performance measuring process is done on both Network Load Balancing (NLB) and High Available Proxy (HAProxy) in Windows and Linux environments respectively as methods for web server load balancing. Furthermore, stability, efficiency and responsiveness of the web servers are depended as the study evaluation metrics. Additionally, average CPU usage and throughput of the both mechanisms are measured in the proposed system. The results show that the IIS 10.0 cluster-based web servers are more responsiveness, efficiency and stable with and without SYN flood DDoS attack. Also, the performance of IIS 10.0 web server is better than of the Apache 2 in term of the average CPU usage and throughput.</p>

Artificial Intelligence to Read Chest X-rays for Deduction of TB

The proposed system has the following working system that it is used to deduct TB with help of x-ray images along with various test reports of the patients. This system process the x-ray images and reports which are given as input to system with the help of the cloud server available then the report is generated for the details given. The copy of the reports is also stored for the future purpose. The existing system process only the x-ray images as their input so this can be improved by adding test reports also as input to have a best effective system. The proposed system uses deep learning conventional neural network algorithmic system to process the x-ray images. The system uses the cloud system to process everything so that this system can be accessed from anywhere. This system also has load balancing service so that there is no downtime in accessing and processing the data to produce the output. The CNN system process the images by considering the darker and lighter spots on the images. This takes only the lighter ruptured part for processing and gives the output. The is in the form of a result page that consist name of the patient and the level of TB which the patient undergoes along with his age is produced as an output to the user who inserts the x-ray images and test results in the form of documents for processing with the help of the mobile application. Hear the deep learning CNN frame work is used for providing a better processing system and the test results are also included in the processing system to provide an accurate result to the user. The cloud system of deployment is done to have the system that can be accessed from anywhere with the help of mobile app. This all features makes the processing system to work in a better way than the previous system available.

RETRACTED ARTICLE: Meta-heuristic firefly approach to multi-servers load balancing with independent and dependent server availability consideration

Load balancing is the foremost confront in a cloud environment. Load balancing is assisted to disseminate dynamic workload across many nodes to guarantee that not a single node gets overloaded. In the existing work Iterative Proximal Algorithm is introduced for the load balancing. This current work concentrates on request migration criteria between multiple servers for load balancing. It varies from common load balancing crisis, assume that it is under a disseminated, competitive environment, and is non-cooperative. For every server, its projected response time is taken to be a dis-utility function and its value is reduced. But load balancing with dependent and independent servers is a confronting errand. In order to resolve this challenge, Meta-heuristic scheme is carried out based on firefly algorithm to balance load on multiple servers. The main contribution of this research work is to perform the load balancing to improve the computational efficiency of the task submitted by the users. The anticipated multi-server load balancing is carried out based on dependent and independent tasks. The jobs comprise of various interdependent errands in which independent tasks might be processed in multiple cores of the VM or multiple VMs. The errands come through the server’s run-time in arbitrary time intervals for different loads. This crisis is resolved by using variation inequality (VI) theory and confirming that there prevails Nash equilibrium resolution set for the devised game. After this, a Nash equilibrium resolution for multi-server load balancing is calculated by using an Iterative Proximal algorithm (IP) is anticipated with Meta heuristic Firefly Optimization Algorithm. Convergence of IPA algorithm is analyzed so that it gets converged to Nash equilibrium. At last, many numerical computations are performed to confirm theoretical analysis.

Enhanced Performance and Privacy for TLS over TCP Fast Open

Abstract Small TCP flows make up the majority of web flows. For them, the TCP three-way handshake induces significant delay overhead. The TCP Fast Open (TFO) protocol can significantly decrease this delay via zero round-trip time (0-RTT) handshakes for all TCP handshakes that follow a full initial handshake to the same host. However, this comes at the cost of privacy limitations and also has some performance limitations. In this paper, we investigate the TFP deployment on popular websites and browsers. We found that a client revisiting a web site for the first time fails to use an abbreviated TFO handshake in 40% of all cases due to web server load-balancing using multiple IP addresses. Our analysis further reveals significant privacy problems of the protocol design and implementation. Network-based attackers and online trackers can exploit TFO to track the online activities of users. As a countermeasure, we introduce a novel protocol called TCP Fast Open Privacy (FOP). TCP FOP prevents tracking by network attackers and impedes third-party tracking, while still allowing 0-RTT handshakes as in TFO. As a proof-of-concept, we have implemented the proposed protocol for the Linux kernel and a TLS library. Our measurements indicate that TCP FOP outperforms TLS over TFO when websites are served from multiple IP addresses.

Minimizing Deadline Misses and Total Run-time with Load Balancing for a Connected Car Systems in Fog Computing

Cloud computing helps in providing the applications with a few number of resources that are used to unload the tasks. But there are certain applications like coordinated lane change assistance which are helpful in cars that connects to internet has strict time constraints, and it may not be possible to get the job done just by unloading the tasks to the cloud. Fog computing helps in reducing the latency i.e the computation is now done in local fog servers instead of remote datacentres and these fog servers are connected to the nearby distance to clients. To achieve better timing performance in fog computing load balancing in these fog servers is to be performed in an efficient manner.The challenges in the proposed application includes the number of tasks are high, client mobility and heterogeneous nature of fog servers. We use mobility patterns of connected cars and load balancing is done periodically among fog servers. The task model presented here in this paper solves scheduling problem and this is done at the server level and not on the device level. And at last, we present an optimization problem formulation for balancing the load and for reducing the misses in deadline, also the time required for running the task in these cars will be minimized with the help of fog computing. It also performs better than somecommon algorithms such as active monitoring, weighted round robin and throttled load balancer.

Semi-Online Computational Offloading by Dueling Deep-Q Network for User Behavior Prediction

Task offloading could optimize computational resource utilization in edge computing environments. However, how to assign and offload tasks for different behavior users is an essential problem since the systems dynamic, intelligent application diversity, and user personality. With user behavior prediction, this paper proposes soCoM, a semi-online Computational Offloading Model. We explore the user behaviors in sophisticated action space by reinforcement learning for catching unknown environment information. With Dueling Deep-Q Network, both the prediction accuracy of users' behaviors and the server load balance are well-considered, while increasing the computational efficiency and decreasing the resource costing. We propose a dynamic simulation environment of edge computing to demonstrate that user behavior is the critical factor for impacting system utilization. As the action space increasing, Dueling DQN performs better than state-of-art DQN and other improved strategies, and also load balance in multiple different server scenario.