Articles

Request-Aware Fuzzy Load Balancing for Heterogeneous Computing Systems

/0 Comments/in /by

In modern heterogeneous computing systems, efficient load balancing remains one of the key challenges affecting system performance, response time, and resource utilization. Traditional load balancing algorithms such as Round Robin and Least Connection generally distribute requests without considering the computational complexity and priority of incoming tasks, which may lead to resource imbalance and performance degradation under dynamic workloads. To address this limitation, this study proposes a Request-Aware Fuzzy Load Balancing (RA-FLB) model based on a Mamdani-type Fuzzy Inference System (FIS). The proposed approach evaluates both request characteristics, including URL structure, payload size, header information, and computational weight, together with the real-time state of virtual machines such as CPU utilization and workload level. Based on fuzzy inference rules, the system dynamically selects the most appropriate server for each incoming request. In addition, a dynamic feedback mechanism continuously updates server states after task execution, enabling adaptive and real-time decision-making. The proposed model was implemented and evaluated in the CloudSim Plus simulation environment. Experimental results demonstrate that the RA-FLB approach improves response time, throughput, and load distribution efficiency compared with conventional algorithms. The proposed method provides a scalable and adaptive solution for intelligent resource allocation in cloud and distributed computing environments.

Request-Aware Fuzzy Load Balancing for Human Action Recognition and Monitoring in Video Streams

/0 Comments/in /by

real-time human action recognition and behavior monitoring within video streams impose significant computational strains on backend server infrastructures. Traditional distributed system load balancers assign dynamic incoming media tasks based exclusively on infrastructure-side metrics like CPU utilization or memory bandwidth, completely omitting request-specific computational requirements. This mismatch results in suboptimal task allocation, frame drops, and execution latencies when multi-scale convolutional operations or dense optical flow models are triggered unpredictably. To resolve this bottleneck, this paper introduces a novel Request-Aware Fuzzy Load Balancing (RAFLB) framework. The proposed paradigm establishes an adaptive, two-phase scheduling ecosystem. First, high-throughput video streams are frame-decomposed and pre-processed using spatial-temporal filtering kernels and Lucas-Kanade optical flow equations to extract intrinsic stream metadata (resolution, frame rate, structural intensity). Second, a multi-input Mamdani Fuzzy Inference Engine computes real-time routing priorities by simultaneously processing the localized Request Weight (RQ) alongside Server Busy (SB) telemetry. Experimental simulations show that RAFLB drastically reduces structural frames latency by up to 34% and prevents cluster choke points compared to conventional round-robin and resource-only load balancers.