Load Balancing Algorithm Research Articles

Path selection for link failure protection in hybrid SDNs

During the transition from traditional networks to pure Software-Defined Networks (SDNs), hybrid SDNs are derived to provide flexible services. Furthermore, developing effective failure protection algorithms is significantly urgent to improve the robustness and resilience of networks. In this paper, we propose several algorithms, including Straightforward Protection Path Selection with the Source and Destination-based Tunneling mechanism (S-PPSSDT), its simplification PPSSDT and Protection Path Selection with Load-Balancing (PPSLB) algorithms. Considering that the Protection Path Length (PPL) will affect the transmission delay of rerouted packets, S-PPSSDT leverages the source and destination-based tunneling mechanism to select optimum protection paths with the shortest length. Based on classification idea and optimization search, PPSSDT prunes unnecessary path searches in S-PPSSDT. Combining the tunneling mechanism with the priority-based Maximum Link Utilization (MLU) minimization approach, PPSLB also focuses on load balancing during failure protection to achieve potential congestion avoidance simultaneously. Besides, these algorithms redirect the traffic through various tunnels for affected paths by the non-shortest path routing capability of SDN switches. Extensive simulation results indicate that our proposed algorithms can effectively reduce the PPL and MLU of hybrid SDNs during failure protection, and have excellent performance under various network parameters compared with the state-of-the-art algorithms.

A New Fault-Tolerant Algorithm Based on Replication and Preemptive Migration in Cloud Computing

Cloud computing is a promising paradigm that provides users higher computation advantages in terms of cost, flexibility, and availability. Nevertheless, with potentially thousands of connected machines, faults become more frequent. Consequently, fault-tolerant load balancing becomes necessary in order to optimize resources utilization while ensuring the reliability of the system. Common fault tolerance techniques in cloud computing have been proposed in the literature. However, they suffer from several shortcomings: some fault tolerance techniques use checkpoint-recovery which increases the average waiting time and thus the mean response time. While other models rely on task replication which reduces the cloud's efficiency in terms of resource utilization under variable loads. To address these deficiencies, an efficient and adaptive fault tolerant algorithm for load balancing is proposed. Based on the CloudSim simulator, some series of test-bed scenarios are considered to assess the behavior of the proposed algorithm.

Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

The path to the efficient exploitation of molecular dynamics simulators is strongly driven by the increasingly intensive use of accelerators. However, they suffer performance portability issues, making it necessary both to achieve technological combinations that allow taking advantage of each programming model and device, and to define more effective load distribution strategies that consider the simulation conditions. In this work, a new load balancing algorithm is presented, together with a set of optimizations to support hybrid co-execution in a runtime system for heterogeneous computing. The new extended design enables the exploitation of custom kernels and acceleration technologies altogether, being encapsulated for the rest of the runtime and its scheduling system. With this support, Mash algorithm allows to simultaneously leverage different workload distribution strategies, benefiting from the most advantageous one per device and technology. Experiments show that these proposals achieve an efficiency close to 0.90 and an energy efficiency improvement around 1.80 over the original optimized version.

An Improved and Efficient and Dynamic Load Balancing Approach in Cloud Computing Environments

Cloud Computing Environment (CCE) is a newly rudimentary knowledge in the IT industry. Central to these problems lies in the establishment of an active LB algorithm. The network load can be CPU load, memory limit, postponement, or system load. In the proposed algorithm, Load Balancing (LB) done by considering priority policy. An Efficient and Dynamic Load Balancing Approach in Cloud Computing (EDBA-CC) proposed, and the related algorithm executed on CloudSim (CS). A resource allocation approach that takes into account Resource Utilization (RU) would lead to better energy efficiency. The outcomes indicate the effectiveness of the proposed algorithm. The outcomes exhibited that the EDBA-CC algorithm reduced Response Time (RT). The decreasing in RT is about 2.23 ms when the instruction length was 275 Byte, and the decreasing became more obviously with 10.12 ms for RT and 5.54 ms for RT when the instruction length was 2000 Byte.

FPSO-GA: A Fuzzy Metaheuristic Load Balancing Algorithm to Reduce Energy Consumption in Cloud Networks

FPSO-GA: A Fuzzy Metaheuristic Load Balancing Algorithm to Reduce Energy Consumption in Cloud Networks

Development of Fog-based Dynamic Load Balancing Framework for Healthcare using Fog Computing

Fog computing has become one of the leading technologies by conquering the many significant challenges in IoT, Big Data, and Cloud. Computing models are inclining toward Fog than Cloud due to faster processing. The numerous idle devices near the users help overcome the issue of latency found in the Cloud. Resource management through load balancing plays an essential role in efficient data processing. Based on the current pandemic situation, Emergency patient’s vital sign monitoring system for COVID and other variants is implemented with support of dynamic resource load balancing environment. Apart from this, previously, we have faced many such diseases such as plague and flu which were pandemic and have become normal diseases now. Apart from them, there are many critical conditions and diseases such as hypertension, kidney failure, heart attack, cancer, lung, and liver disease that need continuous monitoring. It is not feasible to treat all patients at the hospital as the count is increasing very speedily. There is a need for infrastructure to handle resource issues without any delay in the treatment of patients using the fog computing. The proposed approach DynaReLoad would provide prompt health services and prevent early deaths due to critical conditions. An immediate alert to the doctors will be generated when detecting any abnormality. The effectiveness of DynaReLoad has been analyzed with other load balancing algorithms to achieve a low latency with minimum MakeSpan, better scheduling time, and response time, maximizing load balancing level and resource utilization using iFogSim.

A Survey on Generating Load Balancing Algorithm for Cloud Computing

Abstract: The elaboration of IT led Cloud calculating technology crop as a new prototype in furnishing the services to its druggies on rented base at any time or place. Considering the inflexibility of cloud services, innumerous associations switched their businesses to the cloud technology by setting up more data centers. Nonetheless, it has come obligatory to give profitable prosecution of tasks and applicable resource application. A many approaches were outlined in literature to enhance performance, job scheduling, storehouse coffers, QoS and cargo distribution. Cargo balancing conception permits data centers to fore stall over-loading or under- lading in virtual machines that as similar is an issue in cloud computing sphere. Accordingly, it bear the experimenters to layout and apply a proper cargo balancer for cloud terrain. The separate study represents a view of problems and pitfalls faced by the current cargo balancing ways and make the experimenters find more effective algorithms. cargo unbalancing problem is a multi-variant, multi-constraint problem that degrades performance and effectiveness of computing coffers. cargo balancing ways feed the result for cargo unbalancing situation for two undesirable angles- overfilling and under- lading. In disdain of the significance of cargo balancing ways to the stylish of our knowledge, there's no comprehensive, expansive, methodical and hierarchical bracket about the being cargo balancing ways. Further, the factors that beget cargo unbalancing problem are neither studied nor considered in the literature. Keywords: Cloud computing, Taxonomy, Classification, Cloud service consumer, Cloud service provider, Quality of Service, Load unbalancing, Load balancing.

A Novel Load Balancing Technique for Smart Application in a Fog Computing Environment

Internet of things (IoT) induces an immense volume of data every day. Smart IoT-based applications need immediate response and processing of data, for which fog computing was introduced. Fog computing contains many small-scale data centres, which helps to process the incoming data from IoT immediately. More, the data more is the requirement of resources in the fog layer. Hence, there may be overloading of data, which needs to be handled directly. There is a need to provide a framework that can reduce energy consumption and enhance resource utilization during storage, processing, and network functioning. This article proposed smart traffic management architecture, which improves resource utilization and conserves intelligent vehicles' energy. The article also proposes a load balancing algorithm for avoiding the overloading of resources in the proposed architecture while executing a large number of vehicle requests. Further, this paper provides some key challenges and issues of fog computing. The article concludes by providing future directions.

5G environment based CLB-AODV multicast routing for the ZigBee on wireless sensor hetrogenous network

A communication load balanced dynamic topology management algorithm (CLB-AODV) is proposed to extend the wireless sensor network (WSN) lifetime via managing the participation in communication process among all nodes in the network. The idea is that, each time there is a failure in the network topology; the topology is adjusted only on-demand by choosing the best path according to paths weights between source and destination nodes. Simulation results show that CLB-AODV can prolong the lifetime of the network, increase the number of alive nodes and reduce the average routing load when compared with some of the most powerful recent algorithms the integration of Wireless Sensor Networks (WSN), new generation networks or 5G, TCP / IP (IPv6) protocols with the Internet of Things (IoT) that aims to exchange information, applying security, QoS (Quality of Service) and configuration, these three aspects are the problems in the construction of a network in which confidentiality, integrity, availability, authentication, reconfiguration of topology, improvement, high quality of service, addressing, infrastructure, Network and node construction, for M2M (Machine to Machine) communication or end to end. Because 5G cellular networks, in particular, are attractive technologies to provide Internet connectivity to equipment (UE).

Large-System Insensitivity of Zero-Waiting Load Balancing Algorithms

This paper studies the sensitivity (or insensitivity) of a class of load balancing algorithms that achieve asymptotic zero-waiting in the sub-Halfin-Whitt regime, named LB-zero. Most existing results on zero-waiting load balancing algorithms assume the service time distribution is exponential. This paper establishes the large-system insensitivity of LB-zero for jobs whose service time follows a Coxian distribution with a finite number of phases. This result suggests that LB-zero achieves asymptotic zero-waiting for a large class of service time distributions. To prove this result, this paper develops a new technique, called "Iterative State-Space Peeling'' (or ISSP for short). ISSP first identifies an iterative relation between the upper and lower bounds on the queue states and then proves that the system lives near the fixed point of the iterative bounds with a high probability. Based on ISSP, the steady-state distribution of the system is further analyzed by applying Stein's method in the neighborhood of the fixed point. ISSP, like state-space collapse in heavy-traffic analysis, is a general approach that may be used to study other complex stochastic systems.

Load balancing technique based on hybrid resource utilization in cloud computing

Cloud computing uses the internet to supply dynamic services including memory, data, bandwidth and applications. Work schedules have an influence on cloud service reliability and performance. A proper provisioning method is required for a systematic resource allocation, which comprises of large virtual resources. Depending on the present state of the system, load balancing solutions can be distinguished as dynamic or static. Dynamic or static load balancing solutions can be employed to increase server response time or to raise load balancing factors for quicker and more efficient resource utilization. To decrease the load across resources and maximize CPU usage, a hybrid load balancing technique is developed. In the cloud, we have a finite quantity of resources that must be efficiently managed in order to fulfill tasks. Requests are transmitted to a cloud server, which assigns work via quadratic probing. During load balancing, the load is shifted from heavy-weighted servers to lighter-weighted servers, enhancing CPU usage. The suggested methodology's performance was assessed using average mean response time, make-span, average make-span, and average resource utilization. The Load Balancing Algorithm (LBA) is created with the primary purpose of reducing job completion time and increasing the average resource utilization ratio.

A Parallel Fuzzy Load Balancing Algorithm for Distributed Nodes Over a Cloud System

Cloud Computing is an IT organization concept that places the Internet at the heart of business activity, allowing it to use hardware resources. In recent years, Load Balancing has been an active area of research, and has played a very important role in the case of the Cloud environment. There is a wide range of improvements in this arena. In this paper, we propose a new Load Balancing algorithm, based on the weights of the nearest servers in the cloud platform. We use the fuzzy logic to represent the weight of the different nodes. Moreover, we implement separate requests in parallel, and use a token to dispatch tasks efficiently. The several scenarios in this paper are considered for experimentation and compare the result of existing Round Robin, Throttled Load Balancing, Equal Spread Load and proposed algorithm .The experiment results show that this approach improves the Load Balancing process effectively in terms of overall response time, data center processing time, total virtual machine cost, and total data transfer cost.

A Traffic Splitting Algorithm for Load Balancing in Tor.

As the most popular anonymous communication system, Tor provides anonymous protection for users by sending their messages through a series of relays. Due to the use of the bandwidth-weighted path selection algorithm, many more users choose routers with high bandwidth as relays. This will cause the utilization of high bandwidth routers to be much higher than that of low bandwidth routers, which will bring congestion risk. The Quality of Service (QoS) is difficult to guarantee for users who need delay-sensitive services such as web browsing and instant messaging. To reduce the average load of routers and improve the network throughput, we propose a circuit construction method with multiple parallel middle relays and conduct a dynamic load allocation method. The experiment demonstrates that our proposed method can provide better load balancing. Compared with other multipath anonymous communication networks, our proposed method can provide better anonymity.

Dynamic Distributed Multi-Path Aided Load Balancing for Optical Data Center Networks

Benefiting from dense connections in data center networks (DCNs), load balancing algorithms are capable of steering traffic into multiple paths for the sake of preventing traffic congestion. However, given each path’s time-varying and asymmetrical traffic state, this may also lead to worse congestion when some paths are overutilised. Especially in the two-tier hybrid optical/electrical DCNs (Hoe-DCNs), the port contentions and large-grained optical packets of the fast optical switch (FOS) require the top-of-rack (TOR) switch to have microsecond-level load balancing capability for microburst traffic. This paper establishes a leaf-spine Hoe-DCN model to illustrate the principal characteristic of dynamic load balancing in TOR switches for the first time. Moreover, we propose the dynamic distributed multi-path (DDMP) load balancing algorithm that relies on dynamic hashing computing for network flow distribution in DCNs, which dynamically adjusts traffic flow distribution at microsecond level according to the inverse ratio of the buffer occupancy. The simulation results show that our proposed algorithm reduces the TOR-to-TOR latency by 15.88% and decreases the packet loss by 22.06% compared to conventional algorithms under regular load conditions, which effectively improves the overall performance of the Hoe-DCNs. Moreover, our proposed algorithm prevents more than 90% packet loss under low load conditions.

Research on Load Balancing MapReduce Equivalent Join Based on Intelligent Sampling and Multi Knapsack Algorithm

Background: With the rapid development of science, more data is available to human beings. Therefore, the storage and calculation of big data have become the focus of scientific research. MapReduce performs well in the big data processing. However, it is prone to data skew, which affects the overall efficiency of the data processing cluster. Objective: Aiming at the low efficiency of MapReduce data join, this paper proposes an intelligent data join load balancing algorithm based on dynamic programming. The algorithm introduces data sampling and partition algorithms. Due to the high performance of dynamic programming in the data constraint problem, it is used to solve the data skew problem intelligently. Methods: Firstly, the causes of data skew are analyzed and the data partition method is improved. The algorithm introduces a data sampling method. In the task allocation stage, the multidimensional knapsack algorithm is used. Different key values are evenly divided to each computing node through the load cost. Finally, The performance of the improved algorithm is verified by experiments. Results: The experimental results show that compared with the traditional load balancing algorithm and the existing improved algorithm, the new algorithm improves the data processing efficiency, reduces the data skew problem and better solves the problem of data load imbalance. Conclusion: A two-table equivalent join load balancing algorithm based on key cost has been proposed. The algorithm creatively combines dynamic programming with intelligent data sampling, which greatly improves the efficiency and quality of data processing. The algorithm is worthy of popularization and application.

An Ensemble of Bacterial Foraging, Genetic, Ant Colony and Particle Swarm Approach EB-GAP: A Load Balancing Approach in Cloud Computing

Background: In the cloud environment, satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumer and service provider. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objective: The research aim is to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, authors have proposed a new load-balancing algorithm named eB-GAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and Ant-Colony algorithm. Based on the availability of resources and load on a virtual machine, a fitness value is assigned to all virtual machines. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment.

GAJEL-DSDN: an intelligent hybrid genetic-Jaya-based switch migration algorithm for efficient load balancing in distributed SDNs

GAJEL-DSDN: an intelligent hybrid genetic-Jaya-based switch migration algorithm for efficient load balancing in distributed SDNs

Retraction Note to: Performance analysis of nature inspired load balancing algorithm in cloud environment

Retraction Note to: Performance analysis of nature inspired load balancing algorithm in cloud environment

Enhancing Response Time of Cloud Resources Through Energy Efficient Cloud Scheduling Algorithm

Cloud Computing is becoming a dominant trend in providing information technology (IT) services. The cloud comprises many hardware and software resources today, and more people are switching to such services. Users' requests for cloud resources must incur a minimum amount of load on the system while getting a rapid response. In the cloud today, there is too much computational power. Load balancing makes it possible for various components of the cloud computing environment to work efficiently. To balance client requests to available resources so that the system is not overloaded, and the requested resources are delivered as quickly as possible, an effective load balancing strategy is essential. In this research article, we have presented a critical analysis of various existing cloud load balancing and scheduling algorithms. Several task scheduling approaches have been proposed in the literature review, but there appears to be a lack of scheduling algorithms for real-time task works based on historical scheduling records (HSR). The proposed algorithm uses information available in HSR to efficiently distributes incoming user requests to available virtual machines. The proposed scheduling algorithm uses the scaleup and scale down resource algorithm which helps in achieving maximum resource utilization. The algorithm tries to balance the load on VMs by scaling up and down cloud resources. WorkflowSim is used to analyze the performance of the algorithm proposed. The simulation results are compared with the existing scheduling algorithm which shows the proposed algorithm outperforms existing scheduling algorithms in terms of makespan.

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time.