Load Balancing Of Applications Research Articles

Research Paper on Analysis Load Balancing Algorithm using Cloud Computing

Cloud load balancing is nothing but distributing the work load among many servers & computing property. It can manage work load demand as well as application demand. This process is done with the help of distributing resources among numerous computers. Cloud load balancing consist of circulation of workload traffic & on time demand of internet. Various techniques were represented in literature to improve performance, quality of services, cargo distribution etc. The issue of cargo balancing is same as cloud computing which is data over loading & under loading. We have to apply proper cargo balancer for cloud terrain. The study represents view of problem faced by current cargo balancing ways. The researchers make more effective algorithm. Cloud computing refers to a services & application that can accessible throughout the world from data center.all services are accessible through online. There are several issues in cloud computing such as scheduling, load balancing, quality of services & storage resources. Here we study load balancing. When many clients send request to server, server has already n number of task. In that case server is over loaded for this condition we have to apply load balancing algorithm. Cloud computing is a internet based network technology that share rapid growth in communication technology, by providing services to customer of various requirement with the help of online computing resources. It has both hardware & software application along with software development platform. Cloud computing becomes a popular word in the area of high performance distributed computing. It provide on demand access to share pool of resources over internet. Cloud computing is still much research is required for broad array of topic. One of the important research issue which need to be focus for effective performance isload balancing. Cloud computing aim is to gives computation & resources over the internet via dynamic provision of services. There are many challenges and issues associated with the implementation of cloud computing. This research paper deliberates one of cloud computing main problem that is load balancing. The main aim of load balancing is equally distribute the load on cloud server such that no host is under load & over load. Several load balancing algorithm have been implemented in literature to satisfied customer request for appropriate cloud node.

NETWORK ANALYSIS AND OPTIMIZATION USING LOAD BALANCING TECHNIQUES

The Internet is currently growing rapidly. The needs and rapid developments in the use of the Internet network require a balance in the provision of Internet facilities. Internet standard service is the continuation of the connectivity of the Internet. Connection from the Internet is required to always be maintained under any circumstances, but connectivity will not always run smoothly, many obstacles or disturbances are encountered so that the connection does not run smoothly. Then backup management is needed from the continuity of the connection from the Internet so that if one connection is interrupted, there will be a backup. One thing that can be done is to use a load-balancing system. The subjects taken in this study focus on implementing backups from Internet network sources or backups from dependency on one Internet service provider. The method used in this study uses literature study and observation, namely direct observation of the network at the Benteng Village Village Office. the design starts with selecting the right ISP source to be used as a backup. The design is continued with the Mikrotik installation and the application of load balancing in Mikrotik. testing of the load balancing system is carried out by technical testing of the implementation that has been carried out. by testing the speed, load balancing function, and testing optimization of the load balancing. Based on the tests carried out in this study, it resulted in the optimal application of load balancing. The application of load balancing can run without changing the existing network and makes the connection run more optimally if there is an increase in network traffic due to the distribution of balanced connection lanes

Progress towards sustainable energy storage: A concise review

AbstractIn this study, the benefits and challenges of existing energy storage systems are presented. The environmental threats and the apparent unreliability of fossil fuel energy sources necessitate the need for alternative sources of electrical power. Energy storage has been sourced from mechanical, electrical, thermal, chemical, and electrochemical systems. Perhaps, an electrochemical energy storage system, is a better option toward achieving a net zero carbon‐dioxide emission by 2050. Energy storage sources, such as: batteries and supercapacitors, can be reliably fabricated from the hybrid of polymers and two‐dimensional materials for electric vehicles, aviation, and grid load balancing applications. The performances of lithium‐ion batteries are yet to meet the requirements for high‐duty machines and devices. Graphite, the anode of lithium‐ion batteries, suffers from low capacity and high volume‐expansion, resulting in cracking and fracture of the electrodes. Herein, based on evidence from literature, this study suggests substituting graphite with polymer nanocomposite or metal‐oxide nanocomposites such as: conducting polymers, copper oxide, and graphene. This approach, giving attention to these materials' excellent electrochemical, thermal, electrical, mechanical, and redox properties, offers possible ways to overcome the limited limitations confronting lithium‐ion battery technology.

Tailoring load balancing of cellular automata parallel execution to the case of a two-dimensional partitioned domain

In this paper, techniques for dynamic load balancing of the cellular automata parallel execution are presented for the case of domain space partitioned along two dimensions. Starting from general closed-form expressions that allow to compute the optimal workload assignment in a dynamic fashion when partitioning takes place along only one dimension, we tailor the procedure to allow partitioning and balancing along both dimensions. Both qualitative and quantitative experiments are carried out that assess performance improvement in applying load balancing for the case of two-dimensional partitioned domain, especially when the load balancing takes place along both dimensions.

Analisis Kinerja Load Balancing Terhadap Jaringan Internet Menggunakan Metode Equal Cost Multi Path (ECMP)

Load balancing on Mikrotik is a technique for distributing loads network traffic on two or more connection lines in a balanced way, so that network traffic can run optimally, maximize transfer speed, reduce response time and avoid excessive capacity usage on one connection line. There are various methods that can be used in load balancing, one of which is the ECMP method. ECMP is a load balancing method that is used to divide the traffic of two or more internet connections with the same load on each line. The application of load balancing on the internet network using the ECMP method has good performance as evidenced by the distribution of a balanced number of connection loads on each gateway, and if one source of the internet connection dies or a fileover occurs, the backup will automatically run itself by taking connections from active internet resources. The QoS measurement results in this study produce good quality because the value meets the THIPON (Telecomunications and Internet Protocol Harmonization Over Network) standard where the average throughput value is more than 2 Mbps, the average delay value is less than 150 ms, the average packet loss value is less than 3% while the mean value of jitter is less than 125 ms. 
 
 Keywords: Load Balancing, ECMP, Internet Network, QoS,

Designing a Component-Based Throttled Load Balancing Algorithm for Cloud Data Centers

Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, “available” and “free.” To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm’s performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm’s performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed “component-based throttled load balancing algorithm” is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization.

LBSMT: Load Balancing Switch Migration Algorithm for Cooperative Communication Intelligent Transportation Systems

We entered an era when the automotive industry is undergoing a digital revolution. Automobiles evolving into automated movable objects are using artificial intelligence capabilities. In contrast, cellular communications networks incorporate emerging technologies, such as SDN (software-defined networking) and NFV (network functions virtualization). Sophisticated software-defined communications networks virtualizes network functions and paving the way for the new design, monitoring, and management strategies. SDN is rising towards the application of load balancing for real time applications due to the heavy load of data on servers. When there is intra-communication between the various switches and domains; migration of switches takes place and the load over servers is imbalanced. An imbalance of the load will increase the response time and decrease the throughput. In intelligent transportation systems (ITS) balance on the servers should be maintained for the network sustainability. To provide a solution for the requirement of ITS, a dynamic QoS-aware load balancing switch migration algorithm (LBSMT) is proposed in this paper. As per the results validated in Python, after the migration LBSWT has improved CPU utilization, memory utilization, throughput and response time over server load, round robin, weighted round robin, LBBSRT and dynamic server algorithms.

Mixed Freight Dynamic Routing Using a Co-Simulation Optimization Approach

The current freight transportation network is highly unbalanced as routing decisions are made by individual users without coordination. Certain routes may become congested when chosen based on current traffic information without any anticipation that if other users do the same, these routes may become congested. In this paper we show how a centrally coordinated load balancing system that considers all vehicles to be diesel can take into account electric trucks as mixed fleets. The electric trucks impose additional constraints due to the limitation of range, charging time of batteries as well as the dependency of the battery charge on traffic conditions. The use of a co-simulation approach as part of the system accounts for these nonlinear dependencies and provides more realistic cost estimates for the optimization part. Traffic simulation results using a realistic road network reveal the benefits of applying load balancing and show that as the number of electric trucks increases, the emissions reduce; however, due to the cost of charging, their operational costs are not necessarily less than those of the corresponding diesel trucks. For the electric trucks to compete with diesel, charging should occur when drivers are off duty since the cost of charging includes the labor cost of the waiting driver. It is also shown that a centrally coordinated truck routing system that considers the characteristics of electric trucks in mixed fleets can reduce the operational cost of trucks and encourage the deployment of electric trucks in order to reduce emissions and improve air quality.

LOAD BALANCING-FAILOVER METHODS USING STATIC ROUTE WITH ADDRESS LIST, ECMP, PCC, AND NTH FOR OPTIMIZING LAN NETWORK : A COMPARISON

Internet network is a means of infrastructure to support the learning and working process. Some internet network problems that often occur are internet connection is lost, the connection is slow, and the output does not match the bandwidth that is owned. This can be accommodated by using 2 (two) internet modem units from different ISP combined with the Load Balancing process. Load Balancing used to combine two internet modems so that one connection can become a backup connection if the main connection is lost. Nowadays Load Balancing has 4 (four) methods, they are Static Router with Address List, ECMP, PCC, and Nth. The problem is choosing the best method for Load Balancing. This research uses the PPDIOO network development method. From the results of tests conducted in this research indicate the balance of internet access and bandwidth performance owned by ISP1 and ISP2 after the Load Balancing process is carried out. From testing 4 (four) Load Balancing methods, the results show that the Nth method produces 79.2% bandwidth by testing for 40 (forty) days. The conclusion is that the Nth method is more effective to be applied on a LAN network compared to other Load Balancing methods because it can divide the internet connection path when browsing, downloading, and can do a failover. Expectations going forward after applying Load Balancing Nth method can improve internet performance that is used every day for learning and working activities in an organization. Keywords: Load Balancing, Failover, Static Route with Address List, ECMP, PCC, Nth.

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.

Energy-Efficient Stable and Balanced Task Scheduling in Data Centers

It is well known that load balancing in data centres can lead to unnecessary energy usage if all servers are kept active. Using dynamic server provisioning, the number of servers that serve requests can be reduced by turning off idle servers and thereby saving energy. However, such a scheme, usually increases the risk of instability of server queues. In this work, we analyze the trade-off between energy usage and stability of servers in a data center when we balance the load by dispatching arriving jobs. We propose algorithms to solve a stability and energy objective stochastic optimization problem with a high degree of flexibility to handle the trade-off between these two objectives. We consider variable size jobs to apply load balancing on selected active servers and find that the optimal solution is an NP-hard problem. We therefore develop two computationally efficient greedy and randomized approximation schemes to achieve the trade-off between these objectives. We investigate the performance of our proposed algorithms in minimizing the risk of queue length growth as well as the number of active servers needed to serve jobs, and compare it with several metrics in heterogeneous load scenarios.

Rancang Bangun Server HAproxy Load Balancing Master to Master MySQL (Replication) Berbasis Cloud Computing

<em>At present, the development of cloud computing technology is experiencing rapid development. Also in line with the increase in the need and use of cloud computing technology itself. This rapid and high increase can cause an increase in load for the existing server so that it can result in overload on the connection path traffic. To avoid and optimize management on the server, it is necessary to share network load using various solutions available in the load balancing method. One solution is to use HAProxy software. HAProxy bridges at least 2 servers to apply load balancing and use the Round Robin scheduling algorithm. In the database management of two servers, replication Master to Master can be used by using MySQL as an existing database management media. From the results of tests conducted in this study, the HAProxy server is considered capable of handling problems compared to a single server. A replication that was tested was also considered to be a solution to bridge changes in existing data on the HAProxy server.</em>

Strengthening Cloud Performance by Applying Load Balancing Methodology and Security Automation

Cloud enrolling gives association of sharing information collecting most extreme and PC authority over the Internet and Intranet. Cloud advancement has another procedure for wide scale dissipated figuring with five star introduction. It is a total of wide get-together of topographically and continuously appropriated heterogeneous assets for illuminating broad scale information and strategy concentrated issues. The heterogeneous technique for mastermind selecting asset makes the advantage association an essentially problematic occupation. Asset association conditions a significant part of the time join asset presentation, asset watching, asset inventories, asset provisioning, charge detainment, plan of autonomic limits and association level association works out. The exibility, security and assurance of cloud organizations can be improved by grasping customer driven access control and character the officials plans. Late advances in cryptography and web progresses empower us to structure security game plans that give the purchasers more noteworthy detectable quality and control over their cloud-based resources and right now some security and insurance concerns and fears related with cloud perspective. By the day's end, these game plans make it feasible for the dares to get cloud perspective and re-proper hardware, establishment and programming while simultaneously keeping up order over their characters and data CloudSim is a .NET-based lattice enlisting structure that gives the Runtime gear and programming condition required to make computational framework. In the wake of setting up system condition, we have concentrated existing change in accordance with inside dissatisfaction in CloudSim in detail, and have chosen the standard clarifications behind disappointments in it. To manage a touch of the apparent needs we have proposed bolster boss idea. In case there should rise an occasion of disappointment of the focal regulator, bolster boss will expel its control and stays from the framework to bite the dust. Assets are quick and heterogeneous in nature. So the pile of each advantage fluctuates with change in course of action of matrix additionally the event of disappointment of focal points is altogether increasingly fundamental. Other than the slip-up of the advantages impacts the work execution mortally. This makes stack changing and acclimation to inside frustration dynamically fundamental if there should arise an occurrence of getting ready framework. This paper second issue presents an estimation which adjusts the heap among the preferences in addition amasses the resolute idea of the grid condition. Undertaking will be moved from vivaciously stacked focus point to deliberately stacked focus point. Weight changing is a structure to update assets, using parallelism, redesign throughput, and to slice reaction time through an authentic dispersal of the applications. The focal objective of weight changing is to give a passed on, immaterial effort, invent that alters the heap over the entirety of the processors. This assessment will help society with lessening the execution time of utilization after disillusionment of structure and move the load beginning with one system then onto the following structure in over-trouble structure.

Implementasi High Availability Cluster Web Server Menggunakan Virtualisasi Container Docker

The increasing demand for information on the internet causes the traffic load on the web server to increase. Therefore it can cause the workload on a web server service to be overloaded (request), so that the server is down (overloaded). Based on previous research the application of load balancing can reduce the burden of traffic on the web server. This research method uses load balancing on servers with round robin algorithm and least connections as well as a single server as a comparison. The parameters measured are throughput, responses time, requests per second, CPU utilization. From the test results obtained Haproxy load balancing system, the least connection algorithm is superior to the round robin algorithm. Generated per-second request value of 2607,141 req / s and throughput of 9.25 MB / s for the least connection, while 2807,171 req / s and 9.30 MB / s for round robin.

MQTT服务器动态负载均衡的研究与应用

MQTT服务器动态负载均衡的研究与应用

LOAD BALANCING-FAILOVER METHODS USING STATIC ROUTE WITH ADDRESS LIST, ECMP, PCC, AND NTH FOR OPTIMIZING LAN NETWORK : A COMPARISON

Internet network is a means of infrastructure to support the learning and working process. Some internet network problems that often occur are internet connection is lost, the connection is slow, and the output does not match the bandwidth that is owned. This can be accommodated by using 2 (two) internet modem units from different ISP combined with the Load Balancing process. Load Balancing used to combine two internet modems so that one connection can become a backup connection if the main connection is lost. Nowadays Load Balancing has 4 (four) methods, they are Static Router with Address List, ECMP, PCC, and Nth. The problem is choosing the best method for Load Balancing. This research uses the PPDIOO network development method. From the results of tests conducted in this research indicate the balance of internet access and bandwidth performance owned by ISP1 and ISP2 after the Load Balancing process is carried out. From testing 4 (four) Load Balancing methods, the results show that the Nth method produces 79.2% bandwidth by testing for 40 (forty) days. The conclusion is that the Nth method is more effective to be applied on a LAN network compared to other Load Balancing methods because it can divide the internet connection path when browsing, downloading, and can do a failover. Expectations going forward after applying Load Balancing Nth method can improve internet performance that is used every day for learning and working activities in an organization. Keywords : Load Balancing, Failover, Static Route with Address List, ECMP, PCC, Nth.

Integrated Load Balancing and Void Healing Routing with Cuckoo Search Optimization Scheme for Underwater Wireless Sensor Networks

In underwater wireless sensor networks, routing play a vital role in selecting an optimal path for packet forwarding. In routing scheme, most of the existing work is suffering from both load balancing and void node issue. This is due to the environmental interference, overloaded data, energy depletion, random deployment and mobility of the nodes. However, it causes loss of packet, high energy depletion and bad network quality. We have resolved this issue by implementing load balancing and void healing routing using cuckoo search optimization (CSO) scheme. In this scheme, first we placed the parent node and identify their child node within the transmission range in each level of the network. Then, we applied load balancing with priority based packet forwarding to maintain the uneven distribution of the load and reduces the end-to-end delay. Next, void healing routing with CSO scheme is addressed to recover the convex and concave void issue in the network. A novel multi-objective fitness function is also formulated for selecting the optimal number of nodes. In packet routing, each child node is responsible for receiving the packets from their neighbor nodes and transferred to the parent node. After receiving the packets at parent node, autonomous underwater vehicle is used for collecting the relevant packets from each parent node through minimum travelling time and send towards the base station. The performance evaluation of proposed scheme shows better network quality, packet delivery ratio, less energy consumption and delay over the existing solutions.

PyWPS: overview, new features in version 4 and existing implementations

PyWPS 4 is a re–make of the Python implementation of the WPS standard. It is the result of the work of over a dozen individual contributors, during a period of almost three years. One of the goals driving this re–implementation was to embrace modern Python technologies and the possibilities they open.This technical note reviews some of the more advanced possibilities this new PyWPS implementation opens. Request activity is now logged into a structured database, relying on a generic Object–Relational Mapping engine. The adoption of WSGI (Web Server Gateway Interface) opens new ways for load balancing request execution and application encapsulation, that are exemplified with modern Python technologies. Furthermore, PyWPS 4 is designed with containerisation in mind, expediting both development and deployment and improving security.

A Robust IOT-Cloud IaaS for Data Availability within Minimum Latency

Sensors in Internet of Things generate a huge amount of data. The massive volume of the captured data is stored on cloud servers. Over time, the unbalanced load servers prevent better resource utilization. It also increases the input and the output response time. Hence, applying load balancing techniques is very important to achieve efficient system performance. Ensuring the critical data availability in such dynamic systems is very essential. In this article, the authors propose ROBUST for ensuring data availability mechanism and a fault-tolerance architecture. ROBUST also realises load balancing among servers within minimum latency, by avoiding the problem of the overloaded sites and unbalanced use of the resources on the servers. Comparing the response time using the ROBUST and Load Balancing in the Cloud Using Specialization (LBCS), ROBUST architecture has given satisfactory results in terms of critical data latency. Compared with LBCS, ROBUST gains 42% critical data recovery from a primary server and 45% when searching for duplicated critical data. The authors implemented the system using the JADE platform.

Troodon: A machine-learning based load-balancing application scheduler for CPU–GPU system

Heterogeneous computing machines consisting of a CPU and one or more GPUs are increasingly being used today because of their higher performance-cost ratio and lower energy consumption. To program such heterogeneous systems, OpenCL has become an industry standard due to the portability across various computing architectures. To exploit the computing capabilities of heterogeneous systems, application developers are porting their cluster and Cloud applications using OpenCL. With the increasing number of such applications, the use of shared accelerating computing devices (such as CPUs and GPUs) should be managed using an efficient load-balancing scheduling heuristic capable of reducing execution time, increasing throughput with high device utilization. Mostly, the OpenCL applications are suited (execute faster) on a specific computing device (CPU or GPU) and with varying data-sizes the speedup obtained by an application on the suitable device varies too. Applications’ mapping to computing devices without considering device suitability and obtainable speedup on a suitable device leads to sub-optimal execution time, lower throughput and load imbalance. Therefore, an application scheduler should consider both the device-suitability and speedup variation for scheduling decisions leading to a reduction in execution time and an increase in throughput. In this paper, we present a novel load-balancing scheduling heuristic named as Troodon that considers machine-learning based device-suitability model that classify OpenCL applications into either CPU suitable or GPU suitable. Moreover, a speedup predictor that predicts the amount of speedup that jobs will obtain when executed on a suitable device is also part of the Troodon. Troodon incorporates the E-OSched scheduling mechanism to map jobs on CPU and GPUs in a load balanced way. This results in reduced applications execution time, increased system throughput, and improved device utilization. We evaluate the proposed scheduler using a large number of data-parallel applications and compared with several other state-of-the-art scheduling heuristics. The experimental evaluation has demonstrated that the proposed scheduler outperformed the existing heuristics and reduced the application execution time up to 38% with increased system throughput and device utilization.