Certain performance aspects of optimal load balancing in distributed computer systems

A distributed computer system is considered to be a collection of autonomous computers (nodes) located at possibly different sites and connected by a communication network. Through the communication network, resources of the system can be shared by users at different locations. Performance enhancement is one of the most important issues in distributed systems. The performance of a distributed computer system can often be improved to an acceptable level by redistributing the workload among nodes. The problem of load redistribution in distributed computer systems is called load balancing. Load balancing policies may be either static or dynamic. Static load balancing policies use only the statistical information on the system (e.g., the average behavior of the system) in making load balancing decisions. On the other hand, dynamic load balancing policies attempt to dynamically balance the workload reflecting the current system state and are therefore thought to be able to further improve the system performance. Generally, the purpose of load balancing policies either static or dynamic is to improve the performance of the system by redistributing the workload among nodes. We can choose between several distinct objectives for performance optimization in many systems including communication networks, distributed computer systems, transportation flow networks, etc. Among them, we have the following three typical objectives or optima: ...Thesis (Ph. D. in Engineering)--University of Tsukuba, (A), no. 3425, 2004.3.25Includes bibliographical reference

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

An Algorithm for Dynamic Load Balancing of Synchronous Monte Carlo Simulations on Multiprocessor Systems

We describe an algorithm for dynamic load balancing of geometrically parallelized synchronous Monte Carlo simulations of physical models. This algorithm is designed for a (heterogeneous) multiprocessor system of the MIMD type with distributed memory. The algorithm is based on a dynamic partitioning of the domain of the algorithm, taking into account the actual processor resources of the various processors of the multiprocessor system.Comment: 12 pages, uuencoded figures included, 75.93.0

The effect of time delays on the stability of load balancing algorithms for parallel computations

A deterministic dynamic nonlinear time-delay system is developed to model load balancing in a cluster of computer nodes used for parallel computations. The model is shown to be self consistent in that the queue lengths cannot go negative and the total number of tasks in all the queues and the network are conserved (i.e., load balancing can neither create nor lose tasks). Further, it is shown that using the proposed load balancing algorithms, the system is stable in the sense of Lyapunov. Experimental results are presented and compared with the predicted results from the analytical model. In particular, simulations of the models are compared with an experimental implementation of the load balancing algorithm on a distributed computing network

Joint Dynamic Radio Resource Allocation and Mobility Load Balancing in 3GPP LTE Multi-Cell Network

Load imbalance, together with inefficient utilization of system resource, constitute major factors responsible for poor overall performance in Long Term Evolution (LTE) network. In this paper, a novel scheme of joint dynamic resource allocation and load balancing is proposed to achieve a balanced performance improvement in 3rd Generation Partnership Project (3GPP) LTE Self-Organizing Networks (SON). The new method which aims at maximizing network resource efficiency subject to inter-cell interference and intra-cell resource constraints is implemented in two steps. In the first step, an efficient resource allocation, including user scheduling and power assignment, is conducted in a distributed manner to serve as many users in the whole network as possible. In the second step, based on the resource allocation scheme, the optimization objective namely network resource efficiency can be calculated and load balancing is implemented by switching the user that can maximize the objective function. Lagrange Multipliers method and heuristic algorithm are used to resolve the formulated optimization problem. Simulation results show that our algorithm achieves better performance in terms of user throughput, fairness, load balancing index and unsatisfied user number compared with the traditional approach which takes resource allocation and load balancing into account, respectively

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances

Dynamic load balancing via thread migration

Light-weight threads are becoming increasingly useful for parallel processing. This is particularly true for threads running in a distributed memory environment. Light-weight threads can be used to support latency hiding techniques, communication and computation overlap, and functional parallelism. Additionally, dynamic migration of light-weight threads supports both data locality and load balancing. Designing a thread migration mechanism presents some very unique and interesting challenges. One such challenge is maintaining communication between mobile threads. A potentially more difficult challenge involves maintaining the correctness of pointers within mobile threads. Since traditional pointers have no concept of address space, moving threads from processor to processor has a strong impact on the use of pointers. Options for dealing with pointers include restricting their use, adding a layer of software to support pointers referencing non-local data, and binding data to threads such that referenced data is always local to the thread. This dissertation presents the design and implementation of Chant, an efficient light-weight threads package which runs in a distributed memory environment. Chant was designed and implemented as a runtime system using MPI like and Pthreads like calls. Chant supports point-to-point message passing between threads executing in distributed address spaces. We focus on the use of Chant as a framework to support dynamic load balancing based on thread migration. We explore many of the issues which arise when designing and implementing a thread migration mechanism, as well as the issues which arise when considering the use of thread migration as a means for performing dynamic load balancing. This load balancing framework uses both system state information, including communication history, and user input. One of the basic functionalities of this load balancing framework is the ability of the user to customize the load balancing to fit particular classes of problems. This dissertation provides implementation details as well as discussion and justification of design choices. We go on to show that the overhead associated with our approach is within an acceptable range, and that significant performance gains can be achieved through the use of thread migration as a means of performing dynamic load balancing

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

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