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

EFFICIENT LOAD BALANCING IN PEER-TO-PEER SYSTEMS USING VIRTUAL SERVERS

Load balancing is a critical issue for the efficient operation of peer-to- peer networks. With the notion of virtual servers, peers participating in a heterogeneous, structured peer-to-peer (P2P) network may host different numbers of virtual servers, and by migrating virtual servers, peers can balance their loads proportional to their capacities. Peers participating in a Distributed Hash Table (DHT) are often heterogeneous. The existing and decentralized load balance algorithms designed for the heterogeneous, structured P2P networks either explicitly construct auxiliary networks to manipulate global information or implicitly demand the P2P substrates organized in a hierarchical fashion. Without relying on any auxiliary networks and independent of the geometry of the P2P substrates, this paper present ,a novel efficient, proximity-aware load balancing algorithm by using the concept of common virtual servers, that is unique in that each participating peer is based on the partial knowledge of the system to estimate the probability distributions of the capacities of peers and the loads of virtual servers. The movement cost can be reduced by using common virtual serve

Distributed and Grid Computing: An Analytical Comparison

Abstract-CPU utilization is an important aspect of distributed and grid computing environment. The computing nodes can be overloaded, i.e., they can have more jobs than their capacity such that no more jobs can be associated to them and in that case, the load from the overloaded node can be shifted to other nodes those are under loaded(i.e. doing little work or sitting idle). For this, load balancing is required. In load balancing the workload is redistributed among the computing nodes of the system. This improves the job response time and CPU utilization. Dynamic load balancing schemes operate on the decisions that based on the current state of the system. They do not require the previous state of the system for making the load balancing decisions. In this paper, we present an analytical comparison of the various dynamic load balancing schemes in distributed and grid computing environment. This comparison depicts which scheme is better in distributed environment and which is better in grid environment on a particular quality metrics

The PARSE Programming Paradigm. Part I: Software Development Methodology. Part II: Software Development Support Tools

The programming methodology of PARSE (parallel software environment), a software environment being developed for reconfigurable non-shared memory parallel computers, is described. This environment will consist of an integrated collection of language interfaces, automatic and semi-automatic debugging and analysis tools, and operating system —all of which are made more flexible by the use of a knowledge-based implementation for the tools that make up PARSE. The programming paradigm supports the user freely choosing among three basic approaches /abstractions for programming a parallel machine: logic-based descriptive, sequential-control procedural, and parallel-control procedural programming. All of these result in efficient parallel execution. The current work discusses the methodology underlying PARSE, whereas the companion paper, “The PARSE Programming Paradigm — II: Software Development Support Tools,” details each of the component tools

A mapping strategy for MIMD computers

In this paper, a heuristic mapping approach which maps parallel programs, described by precedence graphs, to MIMD architectures, described by system graphs, is presented. The complete execution time of a parallel program is used as a measure, and the concept of critical edges is utilized as the heuristic to guide the search for a better initial assignment and subsequent refinement. An important feature is the use of a termination condition of the refinement process. This is based on deriving a lower bound on the total execution time of the mapped program. When this has been reached, no further refinement steps are necessary. The algorithms have been implemented and applied to the mapping of random problem graphs to various system topologies, including hypercubes, meshes, and random graphs. The results show reductions in execution times of the mapped programs of up to 77 percent over random mapping

A Probabilistic Analysis of a Locality Maintaining Load Balancing Algorithm

This paper presents a simple load balancing algorithm and its probabilistic analysis. Unlike most of the previous load balancing algorithms, this algorithm maintains locality. We show that the cost of this load balancing algorithm is small for practical situations and discuss some interesting applications for data remapping

On the Design of Mutually Aware Optimal Pricing and Load Balancing Strategies for Grid Computing Systems

Abstract-Managing resources and cleverly pricing them on computing systems is a challenging task. Resource sharing demands careful load balancing and often strives to achieve a win-win situation between resource providers and users. Toward this goal, we consider a joint treatment of load balancing and pricing. We do not assume static pricing to determine load balancing, or vice versa. Instead, we study the relationship between the price that a computing node is charged and the load and revenue that it receives. We find that there exists an optimal price which maximizes the revenue. We then consider a multiuser environment and explore how the load from a user can be balanced on processors with existing loads. Finally, we derive an optimal price that maximizes the revenue in the multi-user environment. We evaluate the performance of the proposed algorithms through simulations

Semi-Distributed Load Balancing for Massively Parallel Multicomputer Systems

This paper presents a semi-distributed approach, for load balancing in large parallel and distributed systems, which is different from the conventional centralized and fully distributed approaches. The proposed strategy uses a two-level hierarchical control by partitioning the interconnection structure of a distributed or multiprocessor system into independent symmetric regions (spheres) centered at some control points. The central points, called schedulers, optimally schedule tasks within their spheres and maintain state information with low overhead. We consider interconnection structures belonging to a number of families of distance transitive graphs for evaluation, and using their algebraic characteristics, show that identification of spheres and their scheduling points is, in general, an NP-complete problem. An efficient solution for this problem is presented by making an exclusive use of a combinatorial structure known as the Hadamard Matrix. Performance of the proposed strategy has been evaluated and compared with an efficient fully distributed strategy, through an extensive simulation study. In addition to yielding high performance in terms of response time and better resource utilization, the proposed strategy incurs less overhead in terms of control messages. It is also shown to be less sensitive to the communication delay of the underlying network