# Distributed Load-Balancing Method for CCA Parallel Component Applications
## 1 Introduction  
Parallel computing is essential for tackling numerous complex computational tasks. It finds extensive application in sectors such as aerospace, weather forecasting, molecular dynamics simulations, and other critical areas that impact the national economy and public welfare. Due to its intricate structure and extensive codebase, the development efficiency of parallel computing software has garnered increasing attention. Traditional methods, which rely on hand-crafted code written by domain-specific scientists, present several challenges. These include prolonged development times, inconsistent quality, and difficulties in maintenance and reuse. In response to these issues, in 1998, several US universities and research laboratories collaboratively established the Common Component Architecture (CCA) forum. Since its inception, the forum has been dedicated to researching parallel component technologies to address these challenges [1]. Subsequently, researchers from Britain, France, Italy, China, and other countries have also directed their focus towards parallel component technology research [2; 3; 4; 5]. Component technology, initially developed for serial software, has been integrated into the development process of parallel software. This integration facilitates the efficient development and performance optimization of parallel software by establishing a corresponding component model, architectural specification, and operational framework. In these studies, the CCA specification has emerged as the foundation for many other parallel component technologies due to its standardized and straightforward nature. A parallel component is a software component designed to encapsulate the functionality of parallel computing. Parallel software demands high performance. However, the application of parallel components introduces overhead in component connection and management, making the optimization of application performance crucial. Current techniques for optimizing performance in parallel component applications primarily utilize performance prediction, adaptation, and load-balancing methods [6]. Load-balancing methods aim to distribute the workload evenly across different computing nodes on the execution platform, thereby enhancing resource utilization, throughput, and the overall performance of the system. Figure 1 illustrates an example of load-balancing on a heterogeneous cluster platform. In this example, different task blocks, each represented by a distinct color, are assigned to various nodes for execution. Load-balancing methods for parallel component applications are often adapted from existing strategies used for general parallel applications. These strategies can be broadly categorized into two types: static and dynamic. A static load-balancing strategy utilizes known task information to allocate tasks to different hardware resources before execution, with the load distribution remaining constant during runtime [7]. In contrast, dynamic load-balancing adapts task allocation to meet changing demands based on dynamically generated task information and the current loads on computing nodes. Dynamic load-balancing can be implemented in a centralized or distributed manner. In centralized load-balancing, a designated computing node serves as the management node, responsible for maintaining global load information and making load-balancing decisions [8]. Conversely, in distributed load-balancing, each computing node makes local decisions and shares load information, collectively contributing to global load-balancing [9]. When selecting the target node for load migration, common algorithms include the pol  
Figure 1: Load-balancing on a heterogeneous cluster platform. Squares of varying colors symbolize distinct categories of data. performance-first algorithm [11], and the fastest response time algorithm [12]. In existing methods, the load-balancing of parallel component applications typically employs either a static load-balancing strategy or a centralized dynamic strategy [13].