A Dynamic Multi–Objective Approach for Dynamic Load Balancing in Heterogeneous Systems

Cabrera, Alberto; Acosta, Alejandro; Almeida, Francisco; Blanco, Vicente

doi:10.1109/tpds.2020.2989869

Cited by 16 publications

(11 citation statements)

References 26 publications

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“…But they do not consider the complexity differences of different task requests. The authors in [12], aims the problem of application performance degradation due to the use of multiple GPUs in heterogeneous environments, proposed a dynamic load balancing algorithm for multi-objective decision making. Constructed two objective decision-making models of performance priority and performance energy balance, and dynamically exchanged the models during the execution of the algorithm, which can make more effective use of system resources.…”

Section: Related Workmentioning

confidence: 99%

“…Algorithm 1 Dynamic load balancing algorithm based on optimal matching of weighted bipartite graph Input: Task request queue set J, server cluster set S, and server initial load indicator set V Output: The optimal matching H (1) while (J is not empty) do; // There are tasks pending assignment in the task pool (2) establish task set J n and server set S n ; // Take out an equal number of n tasks as the server (3) compute R according to Equation ( 5); (4) compute T k according to Equation ( 6); (5) establish G 1 with J n and S n ; // Construct edgeless bipartite graph G 1 (6) for (k, i = 1 : n) do (7) compute t ki according to Equation ( 7); (8) establish E by comparing T k and t ki ; // Construct the edge set E (9) compute W nn according to Equation (8); // Compute the edge weight matrix W nn (10) end for (11) establish G 2 with J n , S n , E and W nn ; // Construct the bipartite graph G 2 (12) establish G 3 by adding edges with zero-weight to G 2 ; // Construct the weighted complete bipartite graph G 3 (13) find equal subgraph K L ; (14) while do (15) find the perfect matching M of K L by using the Hungarian algorithm; (16) if (M is non-existent) then (17) find a new K L ' to replace K L ; (18) continue; // Find the perfect matching M again (19) else (20) end while (21) end if (22) get the optimal matching H by deleting edges with zero-weight from perfect matching M; (23) end while (24) return H;…”

Section: ) Step 4: Find the Optimal Matching H Of The Weighted Bipart...mentioning

confidence: 99%

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Dynamic Load Balancing Algorithm Based on Optimal Matching of Weighted Bipartite Graph

Hou

Meng

et al. 2022

IEEE Access

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When the server cluster is processing concurrent task requests, if the performance difference among servers is not fully considered, task allocation will be unreasonable, which will lead to an increase in task making span and a decrease in cluster resource utilization. As one of the core technologies of server cluster, load balancing is used to balance the load of each server by allocating tasks to each server through an algorithm before the task processing. Therefore, this paper proposes a dynamic load balancing algorithm based on optimal matching of weighted bipartite graph. First, we constructed a bipartite graph with servers and tasks as vertices. The management server collects the load indicators of each server in the cluster in real time, using the real-time processing rate of each server as the load indicator. Each edge of the bipartite graph is determined by comparing the expected completion time of the tasks with the load of each server. The degree of matching between each task amount and each server load capacity is defined as the weight matrix of the edges, and the bipartite graph is weighted to construct a weighted bipartite graph. The Kuhn-Munkres algorithm was used to solve the optimal matching of the weighted bipartite graph, and the optimal assignment of tasks to servers was achieved based on the result of the optimal matching. The proposed algorithm fully considers the differentiation of each task amount and each server load capacity. By building a server cluster example and conducting comparison experiments, it is demonstrated that the algorithm can achieve load balancing of the server cluster and improve the resource utilization efficiency of the cluster, while offsetting the extra time overhead caused by the algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: ) Step 4: Find the Optimal Matching H Of The Weighted Bipart...mentioning

confidence: 99%

Dynamic Load Balancing Algorithm Based on Optimal Matching of Weighted Bipartite Graph

Hou

Meng

et al. 2022

IEEE Access

View full text Add to dashboard Cite

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“…The best way to do it is to distribute load evenly across machines. Equation (2) describes the load of a machine M j and the lower bound of the makespan ω .…”

Section: Scheduling Model and Notationmentioning

confidence: 99%

“…Even though HPC applications may have different characteristics, they all have to be properly scheduled on the available HPC resources to achieve their objectives. As these platforms grow in scale, so does the risk of wasting their costly resources due to load imbalance [1,2].…”

Section: Introductionmentioning

confidence: 99%

PackStealLB: A scalable distributed load balancer based on work stealing and workload discretization

Freitas

Pilla

Santana

et al. 2021

Journal of Parallel and Distributed Computing

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The scalability of high-performance, parallel iterative applications is directly affected by how well they use the available computing resources. These applications are subject to load imbalance due to the nature and dynamics of their computations. It is common that high performance systems employ periodic load balancing to tackle this issue. Dynamic load balancing algorithms redistribute the application's workload using heuristics to circumvent the NP-hard complexity of the problem However, scheduling heuristics must be fast to avoid hindering application performance when distributing the workload on large and distributed environments. In this work, we present a technique for low overhead, high quality scheduling decisions for parallel iterative applications. The technique relies on combined application workload information paired with distributed scheduling algorithms. An initial distributed step among scheduling agents group application tasks in packs of similar load to minimize messages among them. This information is used by our scheduling algorithm, Pack-StealLB, for its distributed-memory work stealing heuristic. Experimental results showed that PackStealLB is able to improve the performance of a molecular dynamics benchmark by up to 41%, outperforming other scheduling algorithms in most scenarios over almost one thousand cores.

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“…The inefficient allocation technology is faced with the challenge of over-utilization and underutilization (unbalanced) of resources, which results in the decline of service performance (in the case of over-utilization) or the waste of resources (in the case of under-utilization) [2,3,12]. The basic idea of resource allocation in avionics systems is to allocate tasks (diversity and complexity) between resources by way of tailored algorithms to avoid load balancing problems [13], so as to achieve the best control effect [14]. Load balancing algorithms should also optimize key performance parameters, such as response time, completion time, reliability, availability, energy consumption, cost, resource utilization, etc [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Load Balancing Method for Avionics Systems via Artificial Bee Colony and Simulated Annealing Algorithms

Chen

et al. 2022

Preprint

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Avionics systems are a crucial part of aircraft, and the heterogeneity of resources also leads to load differentials within these systems. Inefficient load balancing technology faces the dual challenge of over-utilization and under-utilization of resources, which results in the decline of service performance (in the case of over-utilization) or the waste of resources (in the case of under-utilization). However, it is necessary to control avionics systems via an efficient load balancing method under time constraints and resource states. Therefore, this paper proposes a load balancing method for avionics systems by using both artificial bee colony ( ABC ) and simulated annealing (SA) algorithms. First, the load balancing model of avionics systems is established, this model can reflect the demands of the tasks for the resources in detail. Then, the load balancing of avionics systems is realized by artificial bee colony and simulated annealing algorithms, the hybrid algorithm not only retains the advantages of simple and easy implementation of ABC algorithm, but also utilizes the probability jump of SA algorithm to jump out of the local extreme and achieve the effect of global optimization. Finally, compared with the existing algorithms, the experimental results show that the algorithm proposed in this paper produces a good and stable load-balance performance.

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A Dynamic Multi–Objective Approach for Dynamic Load Balancing in Heterogeneous Systems

Cited by 16 publications

References 26 publications

Dynamic Load Balancing Algorithm Based on Optimal Matching of Weighted Bipartite Graph

Dynamic Load Balancing Algorithm Based on Optimal Matching of Weighted Bipartite Graph

PackStealLB: A scalable distributed load balancer based on work stealing and workload discretization

A Load Balancing Method for Avionics Systems via Artificial Bee Colony and Simulated Annealing Algorithms

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