Dynamic load balancing strategies in heterogeneous distributed system

Distributed heterogeneous computing is being widely applied to a variety of large size computational problems. This computational environments are consists of multiple het- erogeneous computing modules, these modules interact with each other to solve the prob-lem. Dynamic load balancing in distributed computing system is desirable because it is an important key to establish dependability in a Heterogeneous Distributed Computing Systems (HDCS). Load balancing problem is an optimization problem with exponential solution space. The complexity of dynamic load balancing increases with the size of a HDCS and becomes difficult to solve effectively. The solution to this intractable problem is discussed under different algorithm paradigm.The load submitted to the a HDCS is assumed to be in the form of tasks. Dynamic allocation of n independent tasks to m computing nodes in heterogeneous distributed computing system can be possible through centralized or decentralized control. In central-ized approach,we have formulated load balancing problem considering task and machine heterogeneity as a linear programming problem to minimize the time by which all task completes the execution in makespan.The load balancing problem in HDCS aims to maintain a balanced allocation of tasks while using the computational resources. The system state changes with time on arrival of tasks from the users. Therefore,heterogeneous distributed system is modeled as an M/M/m queue. The task model is represented either as a consistent or an inconsistent expected time to compute (ETC) matrix. A batch mode heuristic has been used to de-sign dynamic load balancing algorithms for heterogeneous distributed computing systems with four different type of machine heterogeneity. A number of experiments have been conducted to study the performance of load balancing algorithms with three different ar-rival rate for the task. A better performance of the algorithms is observed with increasing of heterogeneity in the HDCS.A new codification scheme suitable to simulated annealing and genetic algorithm has been introduced to design dynamic load balancing algorithms for HDCS. These stochastic iterative load balancing algorithms uses sliding window techniques to select a batch of tasks, and allocate them to the computing nodes in the HDCS. The proposed dynamic genetic algorithm based load balancer has been found to be effective, especially in the case of a large number of tasks

The relative performance of scalable load balancing algorithms in loosely-coupled distributed systems. Available in 2 volumes.

The computer systems of today are characterised by data and program control that are distributed functionally and geographically across a network. A major issue of concern in this environment is the operating system activity of resource management for different processors in the network. To ensure equity in load distribution and improved system performance, load balancing is often undertaken. The research conducted in this field so far, has been primarily concerned with a small set of algorithms operating on tightly-coupled distributed systems. More recent studies have investigated the performance of such algorithms in loosely-coupled architectures but using a small set of processors. This thesis describes a simulation model developed to study the behaviour and general performance characteristics of a range of dynamic load balancing algorithms. Further, the scalability of these algorithms are discussed and a range of regionalised load balancing algorithms developed. In particular, we examine the impact of network diameter and delay on the performance of such algorithms across a range of system workloads. The results produced seem to suggest that the performance of simple dynamic policies are scalable but lack the load stability of more complex global average algorithms

Flexible Filters: Load Balancing through Backpressure for Stream Programs

Stream processing is a promising paradigm for programming multi-core systems for high-performance embedded applications. We propose flexible filters as a technique that combines static mapping of the stream program tasks with dynamic load balancing of their execution. The goal is to improve the system-level processing throughput of the program when it is executed on a distributed-memory multi-core system as well as the local (core-level) memory utilization. Our technique is distributed and scalable because it is based on point-to-point handshake signals exchanged between neighboring cores. Load balancing with flexible filters can be applied to stream applications that present large dynamic variations in the computational load of their tasks and the dimension of the stream data tokens. In order to demonstrate the practicality of our technique, we present the performance improvements for the case study of a JPEG encoder running on the IBM Cell multi-core processor

Node Availability for Distributed Systems considering processor and RAM utilization

Abstract Node-Availability is a new metric based on processor utilization and free RAM at a node. It compares different workload levels at two or more nodes participating in a distributed system, providing a decision criterion to be implemented in conjunction with a common load-balancing algorithm. Dynamic scheduling and Load-Balancing in distributed systems can achieved through the Node-Availability metric, even without previously knowing the execution time of the processes, nor other information about them such as process communication requirements. This paper also presents a case study which shows that the metric is feasible to implement in conjunction with a dynamic Load-Balancing algorithm, obtaining an acceptable performance

Decentralized load balancing in heterogeneous computational grids

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research

A study of adaptive load balancing algorithms for distributed systems

With the advent of distributed computer systems with a largely transparent user interface, new questions have arisen regarding the management of such an environment by an operating system. One fertile area of research is that of load balancing, which attempts to improve system performance by redistributing the workload submitted to the system by the users. Early work in this field concentrated on static placement of computational objects to improve performance, given prior knowledge of process behaviour. More recently this has evolved into studying dynamic load balancing with process migration, thus allowing the system to adapt to varying loads. In this thesis, we describe a simulated system which facilitates experimentation with various load balancing algorithms. The system runs under UNIX and provides functions for user processes to communicate through software ports; processes reside on simulated homogeneous processors, connected by a user-specified topology, and a mechanism is included to allow migration of a process from one processor to another. We present the results of a study of adaptive load balancing algorithms, conducted using the aforementioned simulated system, under varying conditions; these results show the relative merits of different approaches to the load balancing problem, and we analyse the trade-offs between them. Following from this study, we present further novel modifications to suggested algorithms, and show their effects on system performance

Node Availability for Distributed Systems considering processor and RAM utilization for Load Balancing

Node-Availability is a new metric that based on processor utilization, free RAM and number of processes queued at a node, compares different workload levels of the nodes participating in a distributed system. Dynamic scheduling and Load-Balancing in distributed systems can be achieved through the Node-Availability metric as decision criterion, even without previously knowing the execution time of the processes, nor other information about them such as process communication requirements. This paper also presents a case study which shows that the metric is feasible to implement in conjunction with a dynamic Load-Balancing algorithm, obtaining an acceptable performance

J2EE application for clustered servers : focus on balancing workloads among clustered servers : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Science in Computer Science at Massey University, Albany, New Zealand

J2EE has become a de facto platform for developing enterprise applications not only by its standard based methodology but also by reducing the cost and complexity of developing multi-tier enterprise applications. J2EE based application servers keep business logic separate from the front-end applications (client-side) and back-end database servers. The standardized components and containers simplify J2EE application design. The containers automatically manage the fundamental system level services for its components, which enable the components design to focus on the business requirement and business logic. This study applies the latest J2EE technologies to configure an online benchmark enterprise application - MG Project. The application focuses on three types of components design including Servlet, entity bean and session bean. Servlets run on the web server Tomcat, EJB components, session beans and entity beans run on the application server JBoss and the database runs on the database server Postgre SQL. This benchmark application is used for testing the performance of clustered JBoss due to various load-balancing policies applied at the EJB level. This research also focuses on studying the various load-balancing policies effect on the performance of clustered JBoss. As well as the four built-in load-balancing policies i.e. First Available, First Available Identical All Proxies, Random Robin and Round Robin, the study also extend the JBoss Load balance Policy interface to design two dynamic load-balancing policies. They are dynamic and dynamic weight-based load-balancing policies. The purpose of dynamic load-balancing policies design is to ensure minimal response time and obtain better performance by dispatching incoming requests to the appropriate server. However, a more accurate policy usually means more communications and calculations, which give an extra burden to a heavily loaded application server that can lead to drops in the performance

Dynamic load balancing algorithm complexity

This paper presents a theoretical analysis of the asymptotic complexity inherent in a load balancing algorithm in a loosely-coupled network, where processor communication is achieved by message passing. The load balancing complexity depends on the network topology and the overhead of processor communication for each polling strategy. The best, worst, and average case analysis of the load balancing algorithms for the various polling topologies are presented. The polling strategies considered are local, global, and random polling. The complexity is presented as a function of the number of processors in the network