A new task scheduling method for 2 level load balancing in homogeneous distributed system

In this paper, we classify the dynamic, decentralized load balancing algorithms for heterogenous distributed computer systems into three policies: queue adjustment policy (QAP), rate adjustment policy (RAP) and queue and rate adjustment policy (QRAP). We propose two efficient algorithms, referred to as rate based load balancing via virtual routing (RLBVR) and queue based load balancing via virtual routing (QLBVR), which belong to the above RAP and QRAP policies, respectively. We also consider algorithms estimated load information scheduling algorithm (ELISA) that was introduced in the literature, to implement QAP policy. Our focus is to analyze and understand the behaviors of these algorithms in terms of their load balancing ability under varying load conditions (light, moderate, or high) and the minimization of mean response time of jobs. We compare the above classes of algorithms by a number of rigorous simulation experiments to elicit their behavior under some influencing parameters. From these experiments, recommendations are drawn to prescribe the suitability of the algorithms under various situations.

In this paper, we classify the dynamic, decentralized load balancing algorithms for heterogenous distributed computer systems into three policies: queue adjustment policy (QAP), rate adjustment policy (RAP) and queue and rate adjustment policy (QRAP). We propose two efficient algorithms, referred to as rate based load balancing via virtual routing (RLBVR) and queue based load balancing via virtual routing (QLBVR), which belong to the above RAP and QRAP policies, respectively. We also consider algorithms estimated load information scheduling algorithm (ELISA) that was introduced in the literature, to implement QAP policy. Our focus is to analyze and understand the behaviors of these algorithms in terms of their load balancing ability under varying load conditions (light, moderate, or high) and the minimization of mean response time of jobs. We compare the above classes of algorithms by a number of rigorous simulation experiments to elicit their behavior under some influencing parameters. From these experiments, recommendations are drawn to prescribe the suitability of the algorithms under various situations.

The proliferation of multimedia devices and video content availability is producing an increase in video content consumption. Video is now the most significant slice of all Internet traffic produced, and it is still growing. However, Over-The-Top (OTT) video is the one significantly increasing its strength, causing infrastructural and business challenges to Telecommunication Operators (Telcos), which provide traditional video services. Telcos must improve their network infrastructure to sustain future demand for OTT video content while improving user perceived Quality-of-Experience (QoE). A meaningful way to accomplish that is to distribute hardware appliances compatible with the Software-Defined Networking (SDN) paradigm throughout the network, improving flexibility and capacity while introducing new ways of content delivery.Towards this objective, this paper proposes an SDN load balancing approach to distribute OTT video requests through multiple servers. The load balancing decision is performed at the edge of the network influenced by the client's QoE. Comparing to a traditional load balancing scheme, the proposed method outperforms the non-SDN approaches in scenarios in which the server-side infrastructure starts to become stressed.

A multiprocessor system is a computer with two or more central processing units (CPUs) with each one sharing the common main memory as well as the peripherals. Multiprocessor system is either homogeneous or heterogeneous system. A homogeneous system is a cluster of processors joined to a high speed network for accomplishing the required task; also it is defined as parallel computing system. Homogeneous is a technique of parallel computing system. A heterogeneous system can be defined as the interconnection of a number of processors, having dissimilar computational speed. Load balance is a method of distributing work between the processors fairly in order to get optimal response time, resource utilization, and throughput. Load balancing is either static or dynamic. In static load balancing, work is distributed among all processors before the execution of the algorithm. In dynamic load balancing, work is distributed among all processors during execution of the algorithm. So problems arise when it cannot statistically divide the tasks among the processors. To use multiprocessor systems efficiently, several load balancing algorithms have been adopted widely. This paper proposes an efficient load balance algorithm which addresses common overheads that may decrease the efficiency of a multiprocessor system. Such overheads are synchronization, data communication, response time, and throughput.

In this paper we present a hierarchical algo- rithm for performing dynamic load balancing in a distri- buted system The processors in the system are viewed as being in a lightly loaded, heavily loaded, or normally loaded state The goal of the algorithm is to keep all nodes normally loaded by migrating processes from heavily loaded nodes to lightly loaded nodes In addi- tion, the load balancing must involve low communica- tion overhead and respond quickly to load imbalance in the system The system is partitioned into disjoint groups of processors First intra-partition process migra- tion is performed to achieve an acceptable load distribu- tion If this is not sufficient, inter-partition load balanc- ing is carried out

A decentralized algorithm for dynamic load balancing with file transfer

Convergence of the dynamic load balancing problem to Nash equilibrium using distributed local interactions

The client-server model is widely used in many distributed systems in which proper assignment of client is an important factor for the better performance of system. A large number of clients may exist in a distributed system that establishes communication with one another through a variety of intermediary servers. The primary objective of the overall system is to allocate clients to servers to maintain the load balancing as well as communication cost. There are two criteria for server load evaluation. The first one is complete load, followed by task scheduling, with complete load and task scheduling being diametrically opposed measurements. It seems to be NP-hard to figure out the appropriate client-server allotment for a distributed system's total load and load balancing. In this research, we examine distinct server load balancing strategies for the assignment of clients in distributed systems.

In this paper, we classify the dynamic distributed load balancing algorithms for heterogenous distributed computer systems into three policies: queue adjustment policy (QAP), rate adjustment policy (RAP), and queue and rate adjustment policy (QRAP). We propose two efficient algorithms, referred to as rate-based load balancing via virtual routing (RLBVR) and queue-based load balancing via virtual routing (QLBVR), which belong to the above RAP and QRAP policies, respectively. We also consider algorithms estimated load information scheduling algorithm (ELISA) and perfect information algorithm, which were introduced in the literature, to implement QAP policy. Our focus is to analyze and understand the behaviors of these algorithms in terms of their load balancing abilities under varying load conditions (light, moderate, or high) and the minimization of the mean response time of jobs. We compare the above classes of algorithms by a number of rigorous simulation experiments to elicit their behaviors under some influencing parameters, such as load on the system and status exchange intervals. We also extend our experimental verification to large scale cluster systems such as a mesh architecture, which is widely used in real-life situations. From these experiments, recommendations are drawn to prescribe the suitability of the algorithms under various situations

Reduction of the execution time of a job through equitable distribution of work load among the processors in a distributed system is the goal of load balancing. Performance of static and dynamic load balancing algorithms for the extended hypercube, is discussed. Threshold algorithms are very well-known algorithms for dynamic load balancing in distributed systems. An extension of the threshold algorithm, called the multilevel threshold algorithm, has been proposed. The hierarchical interconnection network of the extended hypercube is suitable for implementing the proposed algorithm. The new algorithm has been implemented on a transputer-based system and the performance of the algorithm for an extended hypercube is compared with those for mesh and binary hypercube networks.

Videoconferencing system is widely and successfully used on the Internet and standard/high definition videoconferencing is a new heated topic in research. However, researches in various videoconferencing systems (e.g. Access Grid, DVTS, VLC, and Ultra Grid) are faced with the problem of scalability. Bandwidth is the primary factor that limits the system capacity. In this paper, we propose a scalable and distributed standard definition videoconferencing system and design its architecture as well as forwarding model. We use several data stream transmitters to cooperatively forward video streams and design the system architecture, components' behaviors, and forwarding models on both control and data plane, with reasonable load allocation and balance. We also implement our idea based on DVTS as a multi-point standard-definition distributed DVTS Plus and test it on CERNET. Experiments on CERNET show that distributed DVTS Plus now supports higher system capacity, easier deployment and better scalability. Keywords-videoconferencing; distributed system; load balancing; scalability; large-scale conference management; DVTS

Network reconfiguration for load balancing is the determination of switching-options for a particular set of loads on a distribution system that improves load balancing the most and is performed altering the topological structure of distribution feeders by changing open/closed status of sectionalizing and tie-switches. By transferring loads from the heavily loaded feeders to the lightly loaded ones, network reconfiguration can balance feeder loads and alleviate overload conditions of a network. In this paper a novel method is proposed by formulating an algorithm to reconfigure distribution networks for load balancing. Systematic logical approach is used to determine the feasible switching combinations, select the status of the switches, and find the best combination of switches for maximum improvement in load balancing. The algorithm has been developed in two stages. In the first stage, a limited number of switching combinations is formed by trinary logic principle. A simultaneous switching approach is applied logically to find the best combination by selecting the appropriate switching options, connecting the switches accordingly, and assigning the loads for the newly formed combinations. In the next stage, the search is extended to investigate whether any other switching combination gives rise to maximum improvement in load balancing, compared to the improvement obtained in the first stage. This is carried out by considering one after another, the branches next to the open-branches of the best configuration, and the switching configuration that gives the maximum improvement in load balancing is identified. The proposed method has been tested on a 69-bus test system, and the test results indicate that the algorithm proposed is able to determine the appropriate switching-options of the optimal (or near optimal) configuration with less computation. This paper proposes a scheme to implement the simultaneous switching algorithm for real-time on-line control of three-phase distribution networks, with symmetrical loading per phase.

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Transparently changing the distribution of objects in a distributed system (application, service) at runtime is a way to improve service continuity and performance. For instance, it enables to transparently shutdown execution nodes for necessary maintenance operations. Moreover, optimizing service execution resources consumption, while improving service response time, is a key to both client and service provider satisfaction. However, previous work on load balancing has globally shown practical difficulties in getting effective such an approach. In the context of advanced telecommunication service execution environments, this paper proposes to revisit preemptive dynamic load balancing, by showing an experiment based on the combination between synchronous programming-based reactive objects, a flexible object request broker and a mobile agent framework. We present a full Java-based architecture, including a service example, and we show how peculiarities of synchronous programming give a chance to support low-cost preemptive mobility (also known as strong mobility), and to easily give a relevant computer-independent load indicator.KeywordsLoad BalanceMobile AgentService LogicDynamic DistributionService ExecutionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cloud computing (CC) emerged as one of the important utility such as water and electricity bills, where the user has to pay for whatever quantity they have utilized for their tasks. As cloud users are increasing; so the crucial challenges for the cloud service provider (CSP) is to balance the load among the resources shared by the end-users and curb increased security risks, misuse, or malicious attacks in cloud computing. The functionality of load balancing is divided into two functions, first, there will be allocation of resources and the second is the provisioning of resources along with task scheduling in the distributed system such as CC. Although many state-of-the-art approaches are available in the literature which provides load balancing and better resource utilization. In this paper, we build a dynamic prediction approach for cloud resource usage, discuss a load balancing technique, and identify sudden spikes and failures that are causes of the anomaly using a reactive and proactive approach. The final result demonstrates a comparative analysis of deep learning models for the prediction of cloud resource utilization, insight into dynamic load balancing technique and anomaly detection.KeywordsAnomaly detectionCloud computingCloud service providerDeep learningLoad balancingOutliers