Scheduling optimization and load balancing in scalable distributed systems
SPECIAL ISSUE ON:
Scheduling optimization and load balancing in scalable distributed systems
SCOPE and DETAILS
Modern computing infrastructure is highly dependent on the ability to efficiently distribute tasks and data among multiple resources. Scalable distributed systems must be able to handle different workloads, optimize resource utilization, and ensure scalability to adapt to the growing complexity and scale of current computing tasks. Scheduling optimization and load balancing are the basis of scalable distributed system performance because they directly affect the system's ability to handle dynamic workloads, heterogeneous environments, and complex modern applications.
Although significant progress has been made in the technical research and application exploration of scalable distributed systems, there are still many complex challenges in scheduling optimization and load balancing. First, the system is usually composed of multiple types of computing resources, such as CPU, GPU and FPGA, which have significant differences in performance, energy consumption and applicable tasks. How to use real-time monitoring data to dynamically adjust task allocation to achieve the optimal utilization of each node's computing power has become the primary problem; second, scheduling optimization needs to take into account multiple goals such as response time, system throughput, energy efficiency and task priority at the same time. Traditional balanced allocation strategies are often difficult to cope with such multi-dimensional performance requirements; in addition, the workload of distributed systems is highly dynamic and uncertain, which requires scheduling algorithms to make real-time decisions in milliseconds or even shorter time, and have adaptive adjustment capabilities to cope with sudden tasks or node failures; finally, as the scale of the system continues to expand, the computational overhead and communication delay of the scheduling algorithm itself may also affect the overall performance, so it is particularly important to design a low-complexity and well-scalable optimization scheme.
This special issue aims to bring together the latest research results on scheduling optimization and load balancing to improve the overall performance of distributed systems and solve practical problems such as resource heterogeneity, dynamic scheduling and non-balanced computing power allocation. This special issue is aimed at researchers and practitioners in the fields of distributed computing, cloud computing,
parallel computing and scalable computing. We encourage the proposal of innovative algorithmic theories, model frameworks, and their performance evaluation in practical applications.
TOPICS
- Load balancing and real-time scheduling in scalable distributed systems
- Fault detection and fault-tolerant scheduling in scalable distributed systems
- Safety-aware scheduling in scalable distributed systems
- Task scheduling in scalable distributed databases
- Load balancing in heterogeneous distributed environments
- QoS-aware load balancing optimization
- Hybrid scheduling strategy design based on cloud computing and edge computing
- Self-adjusting and self-optimizing scheduling based on machine learning and big data
- Implementation and effect evaluation of multi-objective optimization strategies in computing power allocation
- Unbalanced computing power allocation and task priority scheduling
- Energy-saving scheduling and low-power algorithms in green computing
- Advanced case analysis and application of scheduling optimization and load balancing
IMPORTANT DATES
- Manuscript submission deadline: October 16, 2025
- Notification of acceptance: January 15, 2026
- Submission of final revised paper: March 12, 2026
- Publication of special issue (tentative): May 13, 2026
Main Guest Editor
Jingsha He, Beijing University of Technology, China, jsh.bjut@gmail.com
Guest Editors
Janusz Kacprzyk, Polish Academy of Sciences, Poland, kacprzyk@ibspan.waw.pl
Dimitros A. Karras, National and Kapodistrian University of Athens (NKUA), Greece, dakarras@uoa.gr
Jasmine Kah Phooi Seng, University of Sunshine Coast, Australia, jseng@usc.edu.au