# Early Performance Prediction in Bioinformatics Systems Using Palladio Component Modeling
## 1 Introduction  
Software performance is an important quality attribute that needs to be considered thoroughly in order to develop effective software systems. Software performance is affected by system responsiveness and system scalability. Software responsiveness necessitates meeting the objectives of a system within the required response time, while system scalability requires continuing to meet the response time requirements and the required throughput of a system while its functions increase. Many software systems are implemented without software performance engineering being taking into consideration in the early phases of the Software Development Lifecycle (SDLC). This leads to such systems usually falling short of their performance goals. Attempting to fix these systems in the late phases of the SDLC is expensive and may cause schedule delays, damaged relations with customers, and loss of profits. In extreme cases, it may cause the system to be redesigned from scratch. Performance is very difficult to adjust in an implemented system; thus, it must be considered an essential requirement from the early stages [1]. Traditional software development approaches prioritize software functionalities, while performance comes later in the process. This approach to development has been called the \"fix-it-later\" approach [2]. Achieving the required level of performance is one of the mostimportant aspects in any software development effort, yet it is not included appropriately in most software development methods. In this paper, we propose to use Palladio Component Modeling (PCM), which is a component-based software engineering tool used to predict the non-functional requirements of a software program--in our case, to predict the performance of a bioinformatics system. PCM aims to meet a software's non-functional requirements, such as performance and reliability. Performance testing is usually carried out after the implementation of the software is finished. In other cases, it is conducted after software deployment because there are factors that affect the performance, such as the network, communications, and processor, that are not determined until the deployment phase [3]. In PCM, these factors are modeled such that their values are included in the model in the early phases of the SDLC. For such systems, it is very important that the application is highly scalable and has the required performance quality from the beginning in order to ensure system success; it is better to evaluate the performance during the early phases of development and to focus on it at all stages rather than only during the implementation and deployment phase. Bioinformatics is defined as \"the emerging field that deals with the application of computers to the collection, organization, analysis, manipulation, presentation, and sharing of biologic data\" [4]. Bioinformatics applications must produce accurate and quick responses as much as possible. Some applications function properly, but their execution is often too slow to provide real-time feedback to users. These applications' poor performance may have a direct effect on the patient's health. One of the popular uses of bioinformatics is sequence alignment, also known as genome alignment or genome comparison. The comparative genomics field allows the comparison of different genomic sequences. Phylogenetic analysis involves studying the evolutionary history of and the relationships between different kinds of organisms [5; 6]. Sequencing technology has been commonly used in clinical studies to clearly identify genes for drug design targets or to create medications for complex diseases [7]. Many approaches seek to satisfy performance goals without taking them into consideration in the early stages of the SDLC. Since sequence alignment is an important and growing field in bioinformatics, there are several tools available for its performance. ACNA [8] is a sequence alignment algorithm that uses a benchmark dataset of simulated non-coding sequences from [9] for performance evaluation.