Problem Of Dynamic Load Balancing Research Articles

Hybrid Nearest-Neighbor Ant Colony Optimization Algorithm for Enhancing Load Balancing Task Management

Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex problems, adding advantages to many areas of research. Dynamic load balancing is feasible in distributed computing systems since it is a significant key to maintaining stability of heterogeneous distributed computing systems (HDCS). The challenge of load balancing is an objective function of optimization with exponential complexity of solutions. The problem of dynamic load balancing raises with the scale of the HDCS and it is hard to tackle effectively. The solution to this unsolvable issue is being explored under a particular algorithm paradigm. A new codification strategy, namely hybrid nearest-neighbor ant colony optimization (ACO-NN), which, based on the metaheuristic ant colony optimization (ACO) and an approximate nearest-neighbor (NN) approaches, has been developed to establish a dynamic load balancing algorithm for distributed systems. Several experiments have been conducted to explore the efficiency of this stochastic iterative load balancing algorithm; it is tested with task and nodes accessibility and proved to be effective with diverse performance metrics.

Dynamic Load Balancing Based on Genetic Algorithm

Load balancing has been the focus of research over the current days in many domains but more importantly they are crucial for distributed computing. The research mainly focuses towards distributing load based on the current usage of nodes to facilitate effective resource utilization and obtain better performance from the system. Balancing load is to distribute the tasks on to the available or idle nodes so that resources are utilized fairly in a distributed environment. By developing strategies to assign the processes on a heavily loaded processor to an idle/under loaded processor in a way that balances out the load, the total processing time can be reduced hence achieving improved processor utilization. Genetic Algorithm(GA) is a search based approach that is robust and that can adapt to the search space for optimizing the solution are gaining immense popularity. GA in the proposed work considers the load as a parameter to evaluate fitness of the strings. The strings are also generated based on the load information of the nodes. The fitness evaluates the strings to identify only the underutilized or idle nodes which can take the transmitted load. Hence the work proposed explores and illustrates how GA could be employed to solve the problem of dynamic load-balancing.

Modeling and Deploying IoT-Aware Business Process Applications in Sensor Networks.

The concept of the Internet of Things (IoT) is an important part of the next generation of information. Wireless sensor networks are composed of independent distributed smart sensor nodes and gateways. These discrete sensors constantly gather external physical information, such as temperature, sound, and vibration. Owing to the diversity of sensor devices and the complexity of the sensor sensing environment, the direct modeling of an IoT-aware business process application is particularly difficult. In addition, how to effectively deploy those designed applications to discrete servers in the heterogeneous sensor networks is also a pressing problem. In this paper, we propose a resource-oriented modeling approach and a dynamic consistent hashing (DCH)-based deploying algorithm to solve the above problems. Initially, we extended the graphic and machine-readable model of Business Process Model Notation (BPMN) 2.0 specification, making it able to support the direct modeling of an IoT-aware business process application. Furthermore, we proposed the DCH-based deploying algorithm to solve the problem of dynamic load balancing and access efficiency in the distributed execution environment. Finally, we designed an actual extended BPMN plugin in Eclipse. The approach presented in this paper has been validated to be effective.

Unevenness measurement using the support vector machine and dynamic multiservice load balancing with modified genetic algorithm in cloud-based multimedia system

Cloud computing is the most concerned and growing field in the real world, which is used by various fields to handle and manage the multimedia application due to availability of more amount of resources. At the time of multiple multimedia requests entering into the cloud server, fining and provisioning the required resources for the incoming multimedia requests of different king would be a more difficult task. In the existing system centralised hierarchical cloud-based multimedia system (CMS) is introduced which consists of elements such as resource manager, cluster head and server clusters. This word can assign the user required resources effectively with reduced cost. Also, existing research overcomes the load balancing problem which might occur at the time of multiple multimedia services with different characteristics entering into the system and allocating them in the server without considering their load capacity level using genetic algorithm. However, genetic algorithm would fail to find the optimal resource with optimal load level due to local search optimisation problem. A, load balancing cannot be done effectively in case of arrival of multimedia tasks with varying characteristics. This problem is resolved in the proposed research methodology by introducing the novel load balancing system in which both task unevenness problems and the genetic algorithms local search optimisation problems are resolved. This paper proposed an effective multimedia load balancing (MLB) scheme for CMS using support vector machine (SVM) and dynamic multiservice load balancing with adaptive genetic algorithm (AGA) (MLB-SVM-AGA). In this work, SVM is introduced for the purpose of quantifying the unevenness in the utilisation of multiple resources on a resource manager on the client side and confirmed at the server side in the each cluster. Unevenness scenario can be modelled as a mathematical hyperplane problem, but in most cases it is computationally intractable. Here, AGA is used for the purpose of solving the problem of dynamic load balancing. The experimental evaluation is conducted in the CloudSim toolkit for both proposed and existing research methodologies. The performance evaluation is done and the results demonstrate that AGA approach is able to dynamically spread the multimedia task load equally.

Multiservice Load Balancing with Hybrid Particle Swarm Optimization in Cloud-Based Multimedia Storage System with QoS Provision

Load balancing is a method of workload distribution across various computers or instruction data centres for maximizing throughput and minimizing work load on resources. To perform load balancing techniques in cloud computing environments, various challenges such as data security, and proper distribution exist which requires serious attention. The most important challenge posed by cloud applicationsis the provision of Quality of Service (QoS) provision as it develops the problem of resource allocation to the application so as to guarantee a service level along dimensions such as performance, availability and reliability. A centralized hierarchical Cloud-based Multimedia System (CMS) consisting of a resource manager, cluster heads, and server clusters is being considered by which the resource manager assigns clients' requests to server clusters for performing multimedia service tasks based on the job features after which each the job is assigned to the servers within its server cluster by the cluster head. Designing an effective load balancing algorithm for CMS however being a complicated and challenging task, enables spreading of multimedia service job load on servers at the minimal cost for transmitting multimedia data between server clusters and clients without exceeding the maximal load limit of each server cluster. In the present work, the Multiple Kernel Learning with Support Vector Machine (MKL-SVM) approach is proposed to quantify the disturbance in the utilization of multiple resources on a resource manager at client side and then verifying at the server side in the each cluster. Also, Fuzzy Simple Additive Weighting (FSAW) method is introduced for QoS provision for improving the system performance. The proposed model CMSdynMLB serves as the multiservice load balancing while considering the integer linear programming problem having unevenness measurement. In order to solve the problem of dynamic load balancing, Hybrid Particle Swarm Optimization (HSPO) is proposed as it holds well for dynamic problems. From the simulation results, it is determined that proposed MKL-SVM algorithm can efficiently manage the dynamic multiservice load balancing.

Service Oriented Load Balancing Framework in Computational Grid Environment

Grid computing or computational grid has become a vast research field in academics. It is a promising platform that provides resource sharing through multi-institutional virtual organizations for dynamic problem solving. Such platforms are much more cost-effective than traditional high performance computing systems. Due to the provision of scalability of resources, these days grid computing has become popular in industry as well. However, computational grid has different constraints and requirements to those of traditional high performance computing systems. In order to fully exploit such grid systems, resource management and scheduling are key challenges, where issues of task allocation and load balancing represent a common problem for most grid systems as because the load scenarios of individual grid resources are dynamic in nature. The objective of this paper is to review different existing load balancing algorithms or techniques applicable in grid computing and propose a layered service oriented framework for computational grid to solve the prevailing problem of dynamic load balancing.

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

Load balancers distribute workload across multiple nodes based on a variation of the round robin algorithm, or a more complex algorithm that optimizes a specified objective or allows for horizontal scalability and higher availability. In this paper, we investigate whether robust load balancing can be achieved using a local co-operative mechanism between the resources (nodes). The local aspect of the mechanism implies that each node interacts with a small subset of the nodes that define its neighborhood. The co-operative aspect of the mechanism implies that a node may offload some of load to its neighbor nodes that have lesser load or accept jobs from neighbor nodes that have higher load. Each node is thus only aware of the state of its neighboring nodes and there is no central entity that has the knowledge of the state of all the nodes. We model the overall mechanism of load balancing based on local interactions as a congestion game and show that convergence to the Nash equilibrium is possible using only local interactions. We derive worst case bounds on the number of transfers (time) required to achieve global load balancing under this setup. We also include simulation results to demonstrate emergent global load balancing based only on local interactions and local information.

On the Stability of Dynamic Diffusion Load Balancing

We consider the problem of dynamic load balancing in arbitrary (connected) networks on n nodes. Our load generation model is such that during each round, n tasks are generated on arbitrary nodes, and then (possibly after some balancing) one task is deleted from every non-empty node. Notice that this model fully saturates the resources of the network in the sense that we generate just as many new tasks per round as the network is able to delete. We show that even in this situation the system is stable, in that the total load remains bounded (as a function of n alone) over time. Our proof only requires that the underlying “communication” graph be connected. (It of course also works if we generate less than n new tasks per round, but the major contribution of this paper is the fully saturated case.) We further show that the upper bound we obtain is asymptotically tight (up to a moderate multiplicative constant) by demonstrating a corresponding lower bound on the system load for the particular example of a linear array (or path). We also show some simple negative results (i.e., instability) for work-stealing based diffusion-type algorithms in this setting.

A study of the contribution made by evolutionary learning on dynamic load-balancing problems in distributed computing systems

A computer simulation model that investigated the contribution made by evolutionary learning techniques on load-balancing problems was proposed. Three parameters for controlling the load-balancing activity of a node were used. The system was tested in different distributed systems, including different processing and communication speeds as well as network structures. Our experimental results showed that the system demonstrated an effective learning capability in balancing load among different processing nodes. It also showed that each of these three parameters played an important role in contributing to load-balancing, and that the system performance increased upon increasing the number of parameter changes simultaneously. The contribution made by evolutionary learning was significant as the variety of node processing speeds increased.

Dynamic load balancing in IP-over-WDM optical burst switching networks

In this paper, we consider the problem of dynamic load balancing in wavelength division multiplexing (WDM)-based optical burst switching (OBS) networks. We propose a load balancing scheme based on adaptive alternate routing aimed at reducing burst loss. The key idea of adaptive alternate routing is to reduce network congestion by adaptively distributing the load between two pre-determined link-disjoint alternative paths based on the measurement of the impact of traffic load on each of them. We develop two alternative-path selection schemes to select link-disjoint alternative paths to be used by adaptive alternate routing. The path selection schemes differ in the way the cost of a path is defined and in the assumption made about the knowledge of the traffic demands. Through extensive simulation experiments for different traffic scenarios, we show that the proposed dynamic load balancing algorithm outperforms the shortest path routing and static alternate routing algorithms.

Observations on using genetic algorithms for dynamic load-balancing

Load-balancing problems arise in many applications, but, most importantly, they play a special role in the operation of parallel and distributed computing systems. Load-balancing deals with partitioning a program into smaller tasks that can be executed concurrently and mapping each of these tasks to a computational resource such as a processor (e.g., in a multiprocessor system) or a computer (e.g., in a computer network). By developing strategies that can map these tasks to processors in a way that balances out the load, the total processing time will be reduced with improved processor utilization. Most of the research on load-balancing focused on static scenarios that, in most of the cases, employ heuristic methods. However, genetic algorithms have gained immense popularity over the last few years as a robust and easily adaptable search technique. The work proposed here investigates how a genetic algorithm can be employed to solve the dynamic load-balancing problem. A dynamic load-balancing algorithm is developed whereby optimal or near-optimal task allocations can "evolve" during the operation of the parallel computing system. The algorithm considers other load-balancing issues such as threshold policies, information exchange criteria, and interprocessor communication. The effects of these and other issues on the success of the genetic-based load-balancing algorithm as compared with the first-fit heuristic are outlined.

A comparison of some dynamic load-balancing algorithms for a parallel adaptive flow solver

In this paper we contrast the performance of a number of different parallel dynamic load-balancing algorithms when used in conjunction with a particular parallel, adaptive, time-dependent, 3D flow solver. An overview of this solver is given along with a description of the dynamic load-balancing problem that results from its use. Two recently published parallel dynamic load-balancing software tools are then briefly described and a number of recursive parallel dynamic load-balancing techniques are also outlined. The effectiveness of each of these algorithms is then assessed when they are coupled with the parallel adaptive solver and used to tackle a model 3D flow problem.

Reconfiguration and Dynamic Load Balancing in Broadcast WDM Networks

In optical WDM networks, an assignment of transceivers to channels implies an allocation of the bandwidth to the various network nodes. Intuition suggests, and our recent study has confirmed, that if the traffic load is not well balanced across the available channels, the result is poor network performance. Hence, the time-varying conditions expected in this type of environment call for mechanisms that periodically adjust the bandwidth allocation to ensure that each channel carries an almost equal share of the corresponding offered load. In this paper we study the problem of dynamic load balancing in broadcast WDM networks by retuning a subset of transceivers in response to changes in the overall traffic pattern. Assuming an existing wavelength assignment and some information regarding the new traffic demands, we present two approaches to obtaining a new wavelength assignment such that (a) the new traffic load is balanced across the channels, and (b) the number of transceivers that need to be retuned is minimized. The latter objective is motivated by the fact that tunable transceivers take a non-negligible amount of time to switch between wavelengths during which parts of the network are unavailable for normal operation. Furthermore, this variation in traffic is expected to take place over larger time scales (i.e., retuning will be a relatively infrequent event), making slowly tunable devices a cost effective solution. Our main contribution is a new approximation algorithm for the load balancing problem that provides for tradeoff selection, using a single parameter, between two conflicting goals, namely, the degree of load balancing and the number of transceivers that need to be retuned. This algorithm leads to a scalable approach to reconfiguring the network since, in addition to providing guarantees in terms of load balancing, the expected number of retunings scales with the number of channels, not the number of nodes in the network.

Theoretical Analysis of the Heterogeneous Dynamic Load-Balancing Problem Using a Hydrodynamic Approach

This paper presents a hydrodynamic framework for solving the dynamic load-balancing problem on a network of heterogeneous computers. In this approach, each processor is viewed as a liquid cylinder where the cross-sectional area corresponds to the capacity of the processor, the communication links are modeled as liquid channels between the cylinders, the workload is represented as liquid, and the load-balancing algorithm describes the flow of the liquid. It is proven that all algorithms under this framework converge geometrically to the state of equilibrium, in which the heights of the liquid columns are the same in all the cylinders. In this way, each processor obtains an amount of workload proportional to its capacity. The parameters that affect the convergence rates of the algorithms are also identified and discussed.

Dynamic Load Balancing by Random Matchings

The fundamental problems in dynamic load balancing and job scheduling in parallel and distributed networks involve moving load between processors. In this paper we consider a new model for load movement in synchronous machines. In each step of our model, load can be moved across only a matching set of communication links but across each link any amount of load can be moved. We present an efficient local algorithm for the dynamic load balancing problem under our model of load movement. Our algorithm works on networks of arbitrary topology under possible failure of links. The running time of our algorithm is related to the eigenstructure of the underlying graph. We also present experimental results analyzing issues in load balancing related to our algorithms.

LOAD BALANCING: A PROGRAMMER’S APPROACH OR THE IMPACT OF TASK-LENGTH PARAMETERS ON THE LOAD BALANCING PERFORMANCE OF PARALLEL PROGRAMS

We consider the problem of dynamic load balancing in an n processor parallel system. The scheduling process of a parallel program is modeled by randomly throwing weighted balls into n holes. For a given program A, the ball weights (task lengths) are chosen according to a probability distribution , for which we know only some of the following parameters: the expectation μ, variance σ2, maximum M and minimum m. From these parameters, we derive an upper bound for the number of tasks to be generated by A in order to achieve a load balancing ratio for which the run-time is optimal up to a factor (1+ε)2 for any 0<ε≤0.5, with very high probability. Using the derived relations, the programmer may control the load-balancing of his program by tuning the global parameters of the generated tasks. This can be done regardless of the underlying scheduler used by the parallel machine. We also give experimental results of marine-life simulation in support of our claims.

A multi-level diffusion method for dynamic load balancing

We consider the problem of dynamic load balancing for multiprocessors, for which a typical application is a parallel finite element solution method using non-structured grids and adaptive grid refinement. This type of application requires communication between the subproblems which arises from the interdependencies in the data. A load balancing algorithm should ideally not make any assumptions about the physical topology of the parallel machine. Further requirements are that the procedure should be fast and accurate. An new multi-level algorithm is presented for solving the dynamic load balancing problem which has these properties and whose parallel complexity is logarithmic in the number of processors used in the computation.

Dynamic and static load balancing for solving block bordered circuit equations on multiprocessors

A special class of large-scale nonlinear systems of equations of block bordered structure is generated in VLSI circuit simulation from the sparse and irregular nature of the circuit matrices. Several Newton-like parallel methods for solving the block bordered equations are presented. Static and dynamic load balancing problems that arise in solving the circuit equations on multiprocessors are discussed. Computational results obtained using the BBN GP1000, a shared memory multiprocessor through a distributed architecture, are presented to show the effectiveness of dynamic and static load balancing. These experiments include a simulation of the op-amp 741 amplifier and a large analog filter simulation that leads to a multilevel block bordered system of nonlinear equations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A distributed dynamic load balancer and its implementation on multi-transputer systems for molecular dynamics simulation

A new and efficient approach is described to the dynamic load-balancing problem which is central in concurrent computing. A transputer-based implementation is tested on a molecular dynamics simulation of spinodal phase separation.