Efficient load balancing in interconnected LANs using group communication

Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.

Ensuring Quality of Service (QoS) and efficient load balancing in cloud communication networks is critical for optimizing resource allocation, minimizing latency, and enhancing service reliability. Traditional load balancing strategies often fail to scale and adapt to dynamic cloud environments, resulting in network congestion, resource underutilization, and increased operational costs. This study presents a novel Genetic Algorithm (GA)-based hyperheuristic optimization framework integrated with a mathematical model for QoS-aware load balancing, designed to address the challenges of scalability and efficiency. The model is referred to as GAHO\textsubscript{QoS}. We introduce valid inequalities to strengthen the optimization formulation, accelerating convergence and improving solution quality. Our GA-hyperheuristic framework dynamically selects and combines multiple low-level heuristics to optimize task allocation across cloud servers while adhering to QoS constraints such as latency, throughput, and energy efficiency. Experimental evaluations on a range of cloud communication scenarios demonstrate that GAHO\textsubscript{QoS} significantly reduces service latency, balances workload distribution, and optimizes resource utilization. Comparative analysis with existing metaheuristic methods, including $GA-PSO$ and $SA-GA$, confirms that the proposed framework outperforms traditional approaches in terms of computational efficiency, scalability, and QoS satisfaction. GAHO\textsubscript{QoS} provides an adaptable, computationally efficient solution for enhancing cloud network performance, contributing to the development of high-performance, energy-efficient, and robust cloud infrastructures.

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

High-speed routers and high-performance data centers share a common system-level architecture in which multiple processing nodes are connected by an interconnection network for high-speed communications. Load balancing is an important technique for maximizing throughput and minimizing delay of the interconnection network. In this thesis, efficient load balancing schemes are designed and analyzed for next-generation routers and data centers.
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\nIn high-speed router design, two preferred switch architectures are input-queued switch and load-balanced switch. In an input-queued switch, time-domain load balancing can be carried out by an iterative algorithm that schedules packets for sending in different time slots. The complexity of an iterative algorithm increases rapidly with the number of scheduling iterations. To address this problem, a single-iteration scheduling algorithm called D-LQF is designed, in which exhaustive service policy is adopted for reusing the matched input-output pairs in the previous time slots to grow the match size. 
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\nUnlike an input-queued switch, a load-balanced switch consists of two stages of crossbar switch fabrics, where load balancing is carried out in both time and space domains. Among various load-balanced switches, the feedback-based switch gives the best delay-throughput performance. In this thesis, the feedback-based switch is enhanced in three aspects. Firstly, we focus on reducing its switch fabric complexity. Instead of using crossbars, a dual-banyan network is proposed. The complexity of dual-banyan can be further reduced by merging the two banyans to form a Clos network, resulting in a Clos-banyan network. Secondly, we target at improving the delay performance of the feedback-based switch. A Clos-feedback switch architecture is devised where each switch module in the Clos network is a small feedback-based switch. With application-flow based load balancing, packet order is ensured and the average packet delay is reduced from O(N) to O(n), where N and n are the switch and switch module sizes, respectively. Thirdly, we extend the feedback-based switch to support multicast traffic. Based on the notion of pointer-based multicast VOQ, an efficient multicast scheduling algorithm with packet replication at the middle-stage ports only is proposed. In order to provide close-to-100% throughput for any admissible multicast traffic patterns, a three-stage implementation of feedback-based switch is also designed.
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\nIn designing load balancing schemes for data centers, we focus on the most popular fat-tree based data centers. Notably, packet-based load balancing is widely considered infeasible for data centers. This is because the associated packet out-of-order problem will cause unnecessary TCP fast retransmits, and as a result, severely undermine TCP performance. In this thesis, we show that if packet-based load balancing is performed properly, the packet out-of-order problem can be easily addressed by slightly increasing the number of duplicate ACKs required for triggering fast retransmit. Admittedly, in case of a real packet loss, the loss recovery time will be increased. But our simulation results show that such an increase is far less than the reduction in the network queueing delay (due to a better load-balanced network). As compared to a flow-based load balancing scheme, our packet-based scheme consistently provides significantly higher goodput and noticeably smaller delay.

Load balancing (LB) aims at tackling the hot-spot cell problem caused by uneven load distribution and resources shortage. Traditional LB schemes mainly focus on the load reduction of the hot-spot cell, while some partner cells might suffer from aggravating load after receiving inappropriate traffic, which triggers ripple LB and reduces the LB efficiency. To deal with this problem, a self- optimizing LB scheme in fixed relay station (RS) cellular networks is proposed. In the scheme, partner cells share the load jointly to guarantee each partner has similar load after LB. Meanwhile, the partner response mechanism considers the resources usage of partner's RSs to further reduce the ripple LB. Simulation results show that the proposed scheme can effectively reduce the number of ripple LB and reduce the call blocking rate.

The Cloud computing is a rapidly emerging distributed system paradigm that offers a huge amount of IT resources as utility services at a reduced cost and flexible schemes. The key of such flexibility is an efficient load balancer that offers better management and utilization of virtualized underlying cloud infrastructures. However, most of the existing load balancers in cloud computing are based on either centralized or fully distributed architectures while the idea of harnessing multiple load balancers in a hierarchical structure to improve the sever load and job response time is still under studied. Therefore, this paper, aims at bridging this gap by providing a comparative study between the three load balancing architectures in cloud computing: centralized, decentralized and hierarchical load balancers. The experimental results suggest that the hierarchical architecture for load balancers best suits the public cloud environment and call for further research to test whether these results can be generalized for other types of clouds.

ABSTRACTMesh adaption is a powerful tool for efficient unstructured-grid computations but causes load imbalance among proces-sors on a parallel machine. We present a novel method todynamically balance the processor workloads with a globalview. This paper presents, for the first time, the imple-mentation and integration of all major components withinour dynamic load balancing strategy for adaptive grid cal-culations. Mesh adaption, repartitioning, processor assign-ment, and remapping are critical components of the frame-work that must be accomplished rapidly and efficiently soas not to cause a significant overhead to the numerical sim-ulation. Previous results indicated that mesh repartitioningand data remapping are potential bottlenecks for perform-ing large-scale scientific calculations. We resolve these issuesand demonstrate that our framework remains viable on alarge number of processors.1 INTRODUCTIONDynamic mesh adaption on unstructured grids is a power-ful tool for computing unsteady three-dimensional problemsthat require grid modifications to efficiently resolve solu-tion features. By locally refining and coarsening the meshto capture flowfield phenomena of interest, such proceduresmake standard computational methods more cost effective.Highly refined meshes are required to accurately captureshock waves, contact discontinuities, vortices, and shear lay-ers. Local mesh adaption provides the opportunity to obtainsolutions that are comparable to those obtained on globally-refined grids but at a much lower cost.Unfortunately, the adaptive solution of unsteady prob-lems causes load imbalance among processors on a paral-lel machine. This is because the computational intensity isboth space and time dependent. An efficient parallel imple-mentation of such methods is extremely difficult to achieve,primarily because of the dynamically-changing nonuniformgrid. Various methods on dynamic load balancing have beenreported to date [5-9,11-14,16-18,24-26]; however, most ofthem either lack a global view of loads across processors orProceedings of the 9th ACM Symposium on Parallel Al-gorithms and Architectures, Newport, Rhode Island, June22-25, 1997.do not apply their techniques to realistic large-scale appli-cations.Figure 1 depicts our framework for parallel adaptive flowcomputation. It consists of a flow solver and a mesh adap-tor, with a partitioner and a remapper that load balancesand redistributes the computational mesh when necessary.Our goal is to build a portable system for efficiently per-forming adaptive large-scale flow calculations in a parallelmessage-passing environment. The mesh is first partitionedand mapped among the available processors. The flow solverthen runs for several iterations, updating solution variables.Once an acceptable solution is obtained, the mesh adaptionprocedure is invoked. It first targets edges for coarseningand refinement based on an error indicator computed fromthe flow solution. The old mesh is then coarsened, resultingin a smaller grid. Since edges have already been marked forrefinement, it is possible to exactly predict the new meshbefore actually performing the refinement step. Programcontrol is thus passed to the load balancer at this time. Aquick evaluation step determines if the new mesh will be sounbalanced as to warrant a repartitioning. If the currentpartitions will remain adequately load balanced, control mpassed back to the subdivision phase of the mesh adaptor.Otherwise, a repartitioning procedure is used to divide thenew mesh into subgrids. The new partitions are then reas-signed to the processors in a way that minimizes the costof data movement. If the remapping cost is less than thecomputational gain that would be achieved with balancedpartitions, all necessary data is appropriately redistributed.Otherwise, the new partitioning is discarded. The computa-tional mesh is then actually refined and the flow calculationis restarted.Notice from the framework in Fig. 1 that splitting themesh refinement step into two distinct phases of edge mark-ing and mesh subdivision allows the subdivision phase tooperate in a more load balanced fashion. In addition, sincedata remapping is performed before the mesh grows in sizedue to refinement, a smaller volume of data is moved. Thiscan lead to a potentially significant savings in the redistribu-tion cost. The load balancer also balances the computationalload for the flow solver while reducing the runtime commu-nication. This is important because flow solvers are usuallyseveral times more expensive than mesh adaptors. In anycase, it is obvious that mesh adaption, repartitioning, pro-cessor assignment, and remapping are critical componentsof the framework and must be accomplished rapidly and ef-ficiently so as not to cause a significant overhead to the flowcomputation.

Cloud computing is an innovative technology that mainly relies on sharing of computing resources, data as well as information over the internet. Cloud computing has been evolved from technological growth in Grid systems, Virtualization and Web2.0. Efficient Load Balancing (LB) and scheduling techniques are required for optimal utilization of computing resources and to reduce turnaround time. Load Balancing balances the load among nodes by shifting load from over-utilized node to under-utilized node. This paper presents an extensive review of different meta-heuristic techniques that are applicable in grid and cloud systems to generate optimal load balancing solution.

With the augmentation of affordable multimedia wireless gadgets, the ubiquitous availability of the internet access, and the rapid pace of mobile traffic motivate research towards fifth generation (5G) communications to realize energy-efficient cloud radio access networks (C-RAN) with guaranteed quality of experience. Exploiting green energy harvesting for powering the C-RAN substantially alleviates the energy procurement from the utility grid, carbon footprint, and operational expenses. In this paper, we propose a new dynamic point selection coordinated multipoint (DPS CoMP) based load balancing paradigm emphasizing achievable throughput and energy efficiency (EE) by reducing utility grid consumption from a network level perspective. This paper investigates the radio efficiency, EE, and average on-grid energy saving addressing the key challenges of tempo-spatial dynamics of traffic intensity and renewable energy (RE) generation under a wide range of network setup. Endeavoring load balancing technique strives a balance in network utilities such as green energy utilization and user association based on BS coordination technique in a cluster. Provision of cell sleep approach is contemplated for further energy saving by turning off lightly loaded base stations (BSs) during low traffic arrivals. The proposed CoMP based load balancing algorithm proficiently manages resource block allocation to the new users and elevated the energy efficiency over the conventional location and traffic centric mechanisms. Extensive system-level simulations manifest that the suggested framework enables an adjustable trade-off between radio efficiency and EE, and saves 22% on-grid power consumption and increases EE index by 32%. Afterward, an exhaustive comparison of the proposed method with the existing schemes is pledged for further validation highlighting sustainable 5G wireless systems.

ABSTRACTThe performance of 3GPP long term evolution (LTE) network can be significantly influenced by unbalanced user distribution. In heavily loaded cells, users cannot obtain satisfied services because of shortage of resources, whereas in lightly loaded cells, there are plenty of unoccupied resources. Load balancing (LB) is proposed to deal with this problem, through which the load distribution among nearby cells can be greatly balanced. However, because of the usage of full frequency reuse in LTE, the cell‐edge throughput is severely deteriorated by strong intercell interference. Meanwhile, LTE network usually serves users with heterogenous quality‐of‐service (QoS) requirements, which requires LB to be aware of it. Furthermore, call admission control (CAC) is indispensable in the process of LB, and it depends heavily on the long‐term average performance of the scheduling scheme. Thus, design of an efficient LB algorithm with the consideration of intercell interference, QoS, CAC and scheduling together becomes an important issue. To deal with the previous problem, a multi‐objective optimisation problem is formulated in this paper. Its complexity is analysed, and a practical algorithm, which includes hybrid scheduling, QoS‐guaranteed CAC, QoS‐aware intracell and intercell LB, is proposed. Extensive simulations are conducted. Results show that the proposed algorithm can approximate the optimal solution efficiently, thus leading to significantly better performance, that is, a lower new call blocking rate for users with QoS requirement, a larger total utility and a higher cell‐edge throughput for users without QoS requirement, although the cost is a bit degradation of the total throughput. Copyright © 2013 John Wiley & Sons, Ltd.

No abstract available.

P2P Grids could solve large-scale scientific problems by using geographically distributed heterogeneous resources. However, a number of major technical obstacles must be overcome before this potential can be realized. One critical problem to improve the effective utilization of P2P Grids is the efficient load balancing. This chapter addresses the above-mentioned problem by using a distributed load balancing policy. In this chapter, we propose a P2P communication mechanism, which is built to deliver varied information across heterogeneous Grid systems. Basing on this P2P communication mechanism, we develop a load balancing policy for improving the utilization of distributed computing resources. We also develop a P2P resource monitoring system to capture the dynamic resource information for the decision making of load balancing. Moreover, experimental results show that the proposed load balancing policy indeed improves the utilization and achieves effective load balancing.

Any micro service-based programme, including alumni management software, must include load balancing. The effective use of resources is made possible by load balancing, which also ensures that application performance is unaffected by unexpected increases in requests or user usage. In an alumni management application, load balancing can be achieved with a combination of techniques such as round-robin scheduling, weighted load balancing, and dynamic scheduling. These techniques help to ensure that the application remains up and running, even during peak usage times. Additionally, load balancing can be used to ensure users get the best possible experience when using the application. By distributing traffic among multiple back-end services, users can expect faster response times and more reliable performance.Load balancing in a micro services-based alumni management application is a way of distributing incoming web traffic across multiple back end servers in order to efficiently manage traffic, reduce latency, and improve overall performance. Load balancing works by distributing incoming requests across a cluster of servers, and can be done either statically or dynamically. Static load balancing assigns each request to a specific server, while dynamic load balancing uses algorithms to decide which server should handle each request. In the alumni management application, load balancing is important for ensuring that requests are handled efficiently and that the system is not overwhelmed by too many requests. This is especially important when multiple users are accessing the application simultaneously. By distributing requests across multiple servers, the application can remain responsive and reliable even under heavy load.

Nowadays, web servers have to respond to the huge amount of requests; therefore, balancing the load between server replicas becomes vital and critical. Conventional load balancers have been dedicated hardware that is susceptible being a single point of failure, vendor-dependent and not flexible. In this work, we propose a flexible and programmable scheduling algorithm that ponders different type of requests, various server processing capacity based upon their CPU, memory usage, I/O rate, and a number of connections. This algorithm uses optimization algorithm, considering not only load of servers but also network congestion. The proposed algorithm includes four main phases. The first two phases have the responsibility of creating sub-topology that causes to increase performance and scalability of the proposed algorithm by reduction of overall solution space. In the third phase, the joint optimal network and server-side load balancing approach are performed, and in the last phase, the post-processing is performed in order to select the server, creates a path, and injects appropriate flow table entries on the OpenFlow switches. The evaluation results show that the dynamic load balancing algorithm significantly decreases response-time whereas increases throughput, in comparison with the existing load balancing algorithms.

DSMLB: Dynamic switch-migration based load balancing for software-defined IoT network