An optimal migration algorithm for dynamic load balancing

Hu, Yue; Blake, Richard; Emerson, David R.

doi:10.1002/(sici)1096-9128(199805)10:6<467::aid-cpe325>3.0.co;2-a

Cited by 88 publications

(48 citation statements)

References 19 publications

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“…Of course, one needs to apply the permutation obtained from the partitioning before performing this assignment. Note that the loop invariant mentioned for Algorithm 1 also shows that a process always has sufficient data to send to those that it needs to; it need not wait to receive data from any other process in order to have sufficient data to send, unlike some other dynamic load balancing algorithms [11].…”

Section: The Alias Methods Based Algorithm For Dynamic Load Balancingmentioning

confidence: 99%

“…We wish to minimize the time taken in the actual migration step, which is performed in a decentralized manner by each process. In some load balancing algorithms [11], a process may not have all the data that it needs to send, and so the migration step has to take place iteratively, with a process sending only data that it has in each iteration. In contrast, the t ij s generated by our algorithm never require sending more data than a process initially has, and so the migration step can be completed in one iteration.…”

Section: Load Balancing Model Definitionsmentioning

confidence: 99%

“…Another scheme, proposed in [11], is also based on sending tasks to neighbors. It is based on solving a linear system involving the Laplacian of the communication graph.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic load balancing for petascale quantum Monte Carlo applications: The Alias method

Sudheer

Krishnan

Srinivasan

et al. 2013

Computer Physics Communications

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Diffusion Monte Carlo is a highly accurate Quantum Monte Carlo method for the electronic structure of materials, but it requires frequent load balancing or population redistribution steps to maintain efficiency on parallel machines. This step can be a significant factor affecting performance, and will become more important as the number of processing elements increases. We propose a new dynamic load balancing algorithm, the Alias Method, and evaluate it theoretically and empirically. An important feature of the new algorithm is that the load can be perfectly balanced with each process receiving at most one message. It is also optimal in the maximum size of messages received by any process. We also optimize its implementation to reduce network contention, a process facilitated by the low messaging requirement of the algorithm: a simple renumbering of the MPI ranks based on proximity and a space filling curve significantly improves the MPI Allgather performance. Empirical results on the petaflop Cray XT Jaguar supercomputer at ORNL showing up to 30% improvement in performance on 120,000 cores. The load balancing algorithm may be straightforwardly implemented in existing codes. The algorithm may also be employed by any method with many near identical computational tasks that requires load balancing.

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Section: The Alias Methods Based Algorithm For Dynamic Load Balancingmentioning

confidence: 99%

Section: Load Balancing Model Definitionsmentioning

confidence: 99%

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Dynamic load balancing for petascale quantum Monte Carlo applications: The Alias method

Sudheer

Krishnan

Srinivasan

et al. 2013

Computer Physics Communications

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show abstract

“…The authors in [7] designed algorithms for statically balancing the load on trees, considering that the total load is fixed. The paper [8] proposes an efficient algorithm for data migration in dynamic load balancing by calculating the Lagrange multiplier associated with the Euclidean form of transferred weight. This work can minimize the data movement in homogenous environments in an efficient manner, but it does not support the distributed heterogeneous environments.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Load Balancing Algorithms in Cloud Computing

Kumar¹,

Prashar²

2015

IJCA

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Cloud computing is a business oriented IT-technology, which is composed of multiple computing technologies accessed via internet. With the rapid increase in cloud usage, it becomes a challenge to deliver the cloud services effectively and efficiently to the cloud consumers on the pay-per usage basis. In this concern Balancing of load has become one of the essential components for the cloud computing environment to perform the effective operations. Scheduling of virtual machines or data centers has to be done properly by using an appropriate load balancing technique. Hence, several algorithms have been developed to process the client's request towards the cloud nodes.. In this present work, a hybridized swarm intelligence technique is proposed to evenly distribute the incoming task requests among the virtual machines or server. Additionally, the performance analysis has been performed using the CloudAnalyst simulator. This paper gives a comprehensive performance analysis of the proposed approach and compares its results with existing Round Robin (RR), Equally Spread Current Execution (ESCE) and ant colony optimization (ACO) techniques. Simulation results have demonstrated that the proposed technique shows a significant outcome in terms of response time, data center processing time and total cost in cloud computing.

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“…The load can be balanced dynamically or statically. In some situations, the load on a particular node cannot be foreseen [1]. This remains a key issue specifically in distributed networks.…”

Section: Introductionmentioning

confidence: 99%

A Prolific Scheme for Load Balancing Relying on Task Completion Time

Anand

Anuradha

2018

IJECE

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In networks with lot of computation, load balancing gains increasing significance. To offer various resources, services and applications, the ultimate aim is to facilitate the sharing of services and resources on the network over the Internet. A key issue to be focused and addressed in networks with large amount of computation is load balancing. Load is the number of tasks"t" performed by a computation system. The load can be categorized as network load and CPU load. For an efficient load balancing strategy, the process of assigning the load between the nodes should enhance the resource utilization and minimize the computation time. This can be accomplished by a uniform distribution of load of to all the nodes. A Load balancing method should guarantee that, each node in a network performs almost equal amount of work pertinent to their capacity and availability of resources. Relying on task subtraction, this work has presented a pioneering algorithm termed as E-TS (Efficient-Task Subtraction). This algorithm has selected appropriate nodes for each task. The proposed algorithm has improved the utilization of computing resources and has preserved the neutrality in assigning the load to the nodes in the network.

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An optimal migration algorithm for dynamic load balancing

Cited by 88 publications

References 19 publications

Dynamic load balancing for petascale quantum Monte Carlo applications: The Alias method

Dynamic load balancing for petascale quantum Monte Carlo applications: The Alias method

Performance Analysis of Load Balancing Algorithms in Cloud Computing

A Prolific Scheme for Load Balancing Relying on Task Completion Time

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