heterogeneous-resource scheduling

This paper addresses the task scheduling challenge in geo-distributed computing, arising from network heterogeneity, heterogeneous resource pricing, and imbalanced computational capacity. It systematically surveys scheduling techniques across four paradigms: cloud, edge, cloud–edge collaboration, and high-performance computing (HPC). The study introduces the first unified taxonomy covering all four environments, grounded in three core objectives—performance, fairness, and fault tolerance—and identifies cross-cutting challenges including cross-domain latency-sensitive scheduling, multi-regional cost optimization, and elastic fault tolerance. Through bibliometric analysis and qualitative comparative evaluation, it classifies and assesses state-of-the-art approaches—including multi-objective optimization, game-theoretic models, reinforcement learning, and heuristic algorithms. The work traces the technical evolution of scheduling research and proposes six future directions: AI-native schedulers, carbon-aware scheduling, among others—thereby providing theoretical foundations and practical guidance for building adaptive, sustainable distributed scheduling systems.

This paper addresses the QoS-aware scheduling problem for periodic Directed Acyclic Graph (DAG) tasks under strict end-to-end latency constraints in heterogeneous, partially occupied Mobile Edge Computing (MEC) environments. We propose a hyperperiod-feasible scheduling method tailored for pre-emptive MEC systems. Our approach innovatively extends the HEFT algorithm to dynamically identify processor idle intervals, while jointly modeling task periodicity, precedence constraints, and resource limitations. We further introduce VM-level task binding and residual computing capacity awareness to achieve fine-grained task-resource matching without compromising the stability of existing workloads. Experimental results demonstrate that the proposed method significantly improves schedulability, load balancing, and resource utilization. It effectively supports high-density periodic DAG workloads while guaranteeing hard real-time QoS requirements.

This work addresses the scheduling of partially replicable task chains (e.g., SDR communication standards) on heterogeneous multicore platforms, jointly optimizing throughput and power consumption. We formulate the problem—uniquely integrating partial replicability and big-little core co-scheduling—as a dual-resource pipelined workflow scheduling problem. To solve it, we propose: (i) FERTAC/2CATAC, a near-optimal greedy algorithm; and (ii) HeRAD, an optimal dynamic programming algorithm—both unifying pipelined and replication-based parallelism. Experiments show that FERTAC/2CATAC achieves average cycle times within <10% of HeRAD’s, with at most two additional cores overhead. On the StreamPU platform and in real-world DVB-S2 deployments, our approach attains >92% of theoretical peak throughput, significantly improving energy efficiency and scalability.

In multicore real-time systems, joint allocation of memory bandwidth and cache resources remains challenging, leading to unpredictable task execution times. Method: This paper proposes a co-optimization framework for preemptive EDF scheduling, formulating a 0–1 linear programming model and designing a two-level heuristic: an outer-layer Pareto-pruning search for multi-objective trade-offs, and an inner-layer dynamic programming algorithm to efficiently solve the coupled bandwidth-and-cache-partitioning knapsack problem. Contribution/Results: It is the first work to unify memory bandwidth control (MemGuard) and cache set partitioning into a single multi-objective co-allocation scheme, implemented and validated on the Jailhouse virtualization platform. Experimental evaluation on the Xilinx ZCU102 platform demonstrates that our approach significantly improves schedulability and resource utilization over state-of-the-art MIP-based methods, yields a superior Pareto-optimal solution set, and achieves higher computational efficiency—outperforming all existing approaches comprehensively.

To address resource supply-demand imbalances—manifesting as shortages and surpluses—across globally distributed heterogeneous computing clusters, this paper proposes a resource rationing mechanism grounded in real-world market economics. Methodologically, it introduces a periodic simulated-clock auction framework integrating utilization-driven reserve-price setting, long-term resource quota modeling, and supply-demand equilibrium pricing, enabling dynamic price signals to guide users’ autonomous job placement decisions. Its key contribution lies in being the first to systematically embed microeconomic market mechanisms into large-scale distributed resource allocation, replacing static quota or immediate-scheduling paradigms. Evaluated on the Google experimental market, the mechanism significantly incentivizes user migration toward underutilized clusters: resource utilization variance decreases by 32%, and shortage rate drops by 41%. These results empirically validate that price-based incentives can effectively drive system-level behavioral optimization and achieve global resource equilibrium.

The performance characteristics and architectural behaviors of cache-coherent interconnects—particularly Compute Express Link (CXL)—remain poorly understood in multi-vendor heterogeneous systems (e.g., CPU + CXL memory devices). Method: We construct a cross-vendor heterogeneous server cluster and propose Heimdall, the first fine-grained memory performance analysis framework tailored for CXL systems, accompanied by a lightweight microbenchmark suite. Through empirical measurement of CXL 3.0 protocol stack–hardware co-behavior, we systematically characterize memory latency, bandwidth, and coherence semantics across mainstream CXL devices. Contribution/Results: We uncover three previously unknown architectural blind spots and implicit protocol stack constraints. Leveraging these insights, we devise practical, workload-aware memory scheduling strategies for database and AI inference workloads. Our work provides both theoretical foundations and actionable guidelines for designing and optimizing cache-coherent heterogeneous systems.

This work addresses the challenge of real-time resource orchestration in heterogeneous edge computing, where multidimensional resource heterogeneity, diverse task requirements, and dynamic network conditions complicate efficient scheduling. To tackle this, the authors propose HiRL, a hierarchical reinforcement learning framework that decouples the mixed continuous-discrete optimization problem into two coordinated levels: power control via Twin Delayed Deep Deterministic Policy Gradient (TD3) and task assignment using Double Deep Q-Networks (DDQN). Decision coordination is achieved through a five-dimensional queue state representation. The approach innovatively integrates continuous and discrete reinforcement learning with deadline-aware priority scheduling, adaptive sampling based on failure penalties, and a compatibility-aware evaluation mechanism for heterogeneous resources. Experiments demonstrate that HiRL reduces latency by 28% and achieves near-100% task completion rates compared to Single-DDQN, cuts energy consumption by up to 51% under low load, and improves delay by 24% over static methods under high load, significantly enhancing the trade-off between energy efficiency and latency.

This work addresses the challenge of resource allocation in geographically distributed and heterogeneous continuum computing infrastructures, where combinatorial explosion and limited generalization hinder effective deployment. To tackle this, the study introduces, for the first time, the pricing structures commonly found in Software-as-a-Service (SaaS) ecosystems into the resource allocation problem, formulating a unified, price-based representation of the configuration space. The authors propose PRIME, a pricing-aware analysis engine that efficiently searches for cost-optimal deployment configurations satisfying both functional and non-functional constraints. Leveraging synthetic infrastructure topologies and workload generation techniques, the project constructs a comprehensive dataset comprising 9,600 diverse scenarios, demonstrating that the proposed approach achieves both scalability and computational efficiency in complex, heterogeneous environments.

In the post-Dennard era, embedded systems face intricate trade-offs between energy efficiency and latency, rendering traditional heuristic methods ineffective in navigating the high-dimensional, non-smooth scheduling space. This work proposes a Gaussian process-based multi-objective Bayesian optimization framework to automatically discover Pareto-optimal scheduling strategies that balance energy consumption and execution time on heterogeneous multicore architectures. By integrating fANOVA sensitivity analysis and comparing multiple covariance kernels—such as Matérn and RBF—the approach endows the black-box optimizer with physical interpretability, uncovering how key hardware parameters influence system performance. Experimental results demonstrate that the method efficiently approximates the Pareto front, significantly advancing both the automation of scheduling and the understanding of underlying hardware behaviors.

This work addresses the resource scheduling challenges faced by hybrid quantum-classical applications in heterogeneous and dynamic computing environments, where existing high-performance computing (HPC) schedulers lack application semantics awareness and runtime adaptability. To overcome these limitations, the authors propose a four-layer middleware architecture that integrates an abstract execution model for hybrid applications, a Pilot-Quantum dynamic scheduling framework, and the Q-Dreamer performance modeling toolkit. This integrated approach enables application-aware, adaptive resource management and optimized quantum circuit partitioning. The system supports coordinated scheduling across CPU, GPU, and quantum processing unit (QPU) backends and has been validated on the Perlmutter and NVIDIA DGX platforms. Experimental results demonstrate that Q-Dreamer achieves an 82% accuracy rate in predicting optimal circuit-cut configurations.

This study addresses the complex trade-offs among cost, performance, and latency in mobile edge computing scenarios, where existing research lacks a systematic modeling framework for edge–cloud architectures. The authors present the first closed-form queueing network model to quantitatively analyze how workload mobility and system dynamics jointly impact end-to-end latency and operational cost. Through both simulation and real-world validation, the model’s accuracy and practical relevance are demonstrated. The work highlights the critical roles of mobility patterns and system utilization, offering theoretical foundations and actionable insights for resource provisioning and optimization in 5G and future mobile edge computing systems.