Chapter 1
Introduction
Last updated
Introduction
Last updated
The increasing reliance on cloud-based services has created a demand for efficient and resilient server infrastructures. AWS is a popular cloud services platform providing various computing resources to businesses worldwide. However, bottlenecks can occur within AWS servers, causing performance degradation and poor user experience. This study aims to identify the factors affecting bottleneck process flow under different traffic conditions and proposes methods to mitigate them.
With the rapid expansion of digital services, the efficient management of computer networks has become a crucial aspect of modern life. Bottleneck process flow, which arises when the capacity of a particular network component is exceeded, can significantly reduce network performance and degrade the quality of service. Consequently, understanding the factors that contribute to bottleneck process flow under different traffic conditions is essential for optimizing network performance and meeting user expectations.
This research aims to identify and analyze the factors affecting bottleneck process flow under various traffic conditions in computer networks. By employing a combination of empirical data analysis, simulations, and theoretical frameworks, this study seeks to provide a comprehensive understanding of network bottlenecks, their causes, and potential mitigation strategies. The findings of this research will have significant implications for network administrators, service providers, and software developers, enabling them to enhance network efficiency and user satisfaction.
A bottleneck refers to a situation where the capacity or performance of a model is limited or restricted, often due to the architecture or design of the network. Bottlenecks can occur in very deep networks, which consist of a large number of layers, and can lead to various issues, including poor performance, long training times, or difficulties in optimization.
The Performance Dashboard on AWS is a solution that leverages AWS CloudFormation templates to automatically set up the required cloud resources. It provides flexibility in data uploading, allowing users to either use a standard API for automatic uploads or manually upload data files via the dashboard interface.
AWS CloudWatch monitors your Amazon Web Services (AWS) resources and the applications run on AWS in real time.CloudWatch collect and track metrics, which are variables measure for your resources and applications.
AWS X-Ray provides a real-time view of all requests as they pass through your application, and visualizes them through payloads, functions, traces, services, APIs, and more, all without code and with an intuitive interface. Low-code data movement
VPC Flow Logs The information captured includes information about allowed and denied traffic (based on security group and network ACL rules). It also includes source and destination IP addresses, ports, the IANA protocol number, packet and byte counts, a time interval during which the flow was observed, and an action (ACCEPT or REJECT).
Server Performance Baseline: A server performance baseline refers to a set of performance metrics and data collected from a server during normal operation under typical conditions. It serves as a reference point to establish a standard or baseline for the server's performance. By capturing and analyzing the baseline data, administrators can monitor and compare the server's performance over time, identify deviations or anomalies, and make informed decisions about performance optimizations, capacity planning, and troubleshooting.
Performance Stat Server: A performance stat server, also known as a performance monitoring server, is a dedicated system or software that collects, analyzes, and presents performance statistics and metrics from one or multiple servers or systems. It provides real-time or near-real-time visibility into the performance and health of the monitored servers, allowing administrators to proactively identify and address performance issues.
The details in a performance stat server can vary depending on the specific software or system being used. However, here are some common performance statistics that are typically provided by a performance stat server:
CPU Utilization: This statistic measures the percentage of CPU resources being used by the server. It helps identify the overall workload on the server and can indicate if the CPU is becoming a bottleneck.
Memory Usage: This metric shows the amount of memory (RAM) being utilized by the server. It helps monitor the server's memory capacity and detect potential memory leaks or excessive memory consumption.
Disk I/O: Disk I/O statistics track the read and write operations being performed on the server's storage devices. It includes metrics such as disk throughput (data transfer rate), disk latency (response time), and disk utilization. Monitoring disk I/O helps identify performance bottlenecks related to storage access.
Network Traffic: This statistic measures the amount of incoming and outgoing network traffic on the server. It includes metrics like bandwidth utilization, packet loss, and network latency. Monitoring network traffic helps identify network-related performance issues and bandwidth constraints.
Response Time: Response time measures the time taken by the server to respond to incoming requests. It can be measured at various levels, such as application response time, database query response time, or web server response time. Monitoring response time helps assess the overall performance and user experience of the server.
Application-specific Metrics: Depending on the specific application or software running on the server, there may be additional performance statistics available. For example, a web server might provide metrics such as HTTP request rate, number of active sessions, or page load time. These metrics help assess the performance and responsiveness of the application.
Server Availability and Uptime: This metric tracks the availability and uptime of the server, indicating how often the server is accessible and operational. It helps identify any downtime or availability issues that may impact performance.
Error Rates: Error rates measure the occurrence of errors or failures on the server. It includes metrics such as the number of failed requests, HTTP error codes, or application-specific error messages. Monitoring error rates helps identify potential issues affecting the server's performance and stability.
Resource Usage by Processes: This statistic provides insights into the resource usage (CPU, memory, disk, network) of individual processes running on the server. It helps identify resource-intensive processes that may impact overall server performance.
Trend Analysis and Historical Data: A performance stat server often provides historical data and trend analysis capabilities. This allows you to track performance over time, identify patterns, and make informed decisions based on long-term performance trends.
