In the era of cloud computing and big data, distributed block storage system has become more and more important with its unique advantages. Load balancing (LB) is an important feature of distributed block storage systems, and it is also one of the hotspots of current distributed block storage research. Based on the existing LB algorithms, researching new and more effective LB techniques is very effective in improving the stability of the system. The main purpose of this paper is to study the LB strategy in the distributed system (DS) of converged workstations. Based on the traditional LB strategy, this paper selects multiple evaluation indicators and establishes a corresponding evaluation function to improve the accuracy of judging the actual load of the node server. According to the situation of different hardware parts, the hierarchical standard of the load state is formulated, and the instantaneous stress test of the improved LB algorithm and the weighted minimum connection algorithm is carried out. The complexity of a LB algorithm. Experiments show that with the increase of the number of request connections, the response time of the system under the two algorithms is longer, but the response time of the improved LB scheduling algorithm has a smaller increase. That is to say, the improved LB algorithm is better than the weighted least connection algorithm in handling the instantaneous pressure of the system.

Rashid Gullun. Distributed System Load Balancing Strategy for Converged Workstations. Distributed Processing System (2022), Vol. 3, Issue 3: 61-71. https://doi.org/10.38007/DPS.2022.030308.

[1] Handur E. Particle Swarm Optimization for LB in Distributed Computing Systems – A Survey. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(1S):257-265.

[2] Augustine J, Cohen A, Peleg D, et al. Distributed Graph Realizations. IEEE Transactions on Parallel and DSs, 2021, PP(99):1-1.

[3] Alam M, Haidri R A, Shahid M. Resource Aware LB Model for Batch of Tasks (BoT) with Best Fit Migration Policy on Heterogeneous Distributed Computing Systems. International Journal of Pervasive Computing and Communications, 2020, 16(2):113-141.

[4] Aktas M F, Behrouzi-Far A, Soljanin E, et al. Evaluating LB Performance in Distributed Storage With Redundancy. IEEE Transactions on Information Theory, 2021, PP(99):1-1.

[5] Rathan K, Roslin S. Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for LB in WMNS. International Journal of Intelligent Engineering and Systems, 2021, 14(1):32-41.

[6] Korndrfer J, Eleliemy A, Mohammed A, et al. LB4OMP: A Dynamic LB Library for Multithreaded Applications. IEEE Transactions on Parallel and DSs, 2021, PP(99):1-1.

[7] Azzouzi S, Hsaini S, Charaf M. A Synchronized Test Control Execution Model of DSs. International Journal of Grid and High Performance Computing, 2020, 12(1):1-17.

[8] Khochare A D, Krishnan A, Simmhan Y. A Scalable Platform for Distributed Object Tracking across a Many-camera Network. IEEE Transactions on Parallel and DSs, 2021, PP(99):1-1.

[9] Giordano A, Rango A D, Rongo R, et al. Dynamic LB in Parallel Execution of Cellular Automata. IEEE Transactions on Parallel and DSs, 2021, 32(2):470-484.

[10] Veen D J V, Kudesia R S, Heinimann H R. An Agent-Based Model of Collective Decision-Making: How Information Sharing Strategies Scale With Information Overload. IEEE Transactions on Computational Social Systems, 2020, PP(99):1-17.

[11] Sim H, Khan A, Vazhkudai S S, et al. An Integrated Indexing and Search Service for Distributed File Systems. IEEE Transactions on Parallel and DSs, 2020, PP(99):1-1.

[12] Cebrian J M, Balem T, Barredo A, et al. Compiler-Assisted Compaction/Restoration of SIMD Instructions. IEEE Transactions on Parallel and DSs, 2021, PP(99):1-1.

[13] Gupta N, Jati A, Chauhan A K, et al. PQC Acceleration Using GPUs: FrodoKEM, NewHope, and Kyber. IEEE Transactions on Parallel and DSs, 2021, 32(3):575-586.

[14] Giordano A, Rango A D, Rongo R, et al. Dynamic LB in Parallel Execution of Cellular Automata. IEEE Transactions on Parallel and DSs, 2021, 32(2):470-484.

[15] Feltus C. Reinforcement Learning's Contribution to the Cyber Security of DSs: Systematization of Knowledge. International Journal of Distributed Artificial Intelligence, 2020, 12(2):35-55.

[16] D'Amico M, Gonzalez J C. Energy hardware and workload aware job scheduling towards interconnected HPC environments. IEEE Transactions on Parallel and DSs, 2021, PP(99):1-1.

[17] Hassanzadeh-Nazarabadi Y, Kupcu A, Ozkasap O. LightChain: Scalable DHT-based Blockchain. IEEE Transactions on Parallel and DSs, 2021, PP(99):1-1.

[18] Schug A K, Werner H. Spatio-Temporal Loop Shaping for Distributed Control of PDE Systems. IFAC-PapersOnLine, 2020, 53(2):4014-4019.