This paper presents a detailed analysis of network traffic patterns within Facebook's datacenters, highlighting significant differences from previously reported datacenter traffic characteristics. The study focuses on the unique behaviors of Facebook's core Web service and cache infrastructure, which contrast with traditional datacenter workloads. Key findings include the stable and predictable nature of Facebook's network traffic, which is neither strictly rack-local nor all-to-all, but exhibits varying degrees of locality that remain consistent over time. This stability has implications for network architecture, traffic engineering, and switch design, suggesting that traditional assumptions about datacenter traffic may not hold in Facebook's environment. The research also reveals that many flows are long-lived but not heavy, with traffic distributed evenly across hosts. This leads to stable traffic demands over sub-second intervals, making it difficult to identify consistent heavy hitters. Additionally, the paper notes that packets are small and do not exhibit on/off arrival behavior, with traffic often directed to a limited number of racks. The study compares Facebook's traffic patterns to those reported in the literature, showing that many previous characterizations do not fully represent Facebook's demands. This discrepancy calls into question the applicability of existing proposals for network design and traffic management. The paper emphasizes the importance of understanding the specific characteristics of different datacenter workloads, as these can significantly impact the effectiveness of network solutions. The research also discusses the implications of these findings for network fabric design, suggesting that non-uniform fabrics may be more suitable for Facebook's traffic patterns. The study highlights the need for traffic engineering techniques that can adapt to the stable and predictable nature of Facebook's traffic, rather than relying on assumptions about bursty or unpredictable behavior. Overall, the paper provides valuable insights into the unique characteristics of Facebook's datacenter traffic, offering a foundation for future research and development in network design and traffic management.This paper presents a detailed analysis of network traffic patterns within Facebook's datacenters, highlighting significant differences from previously reported datacenter traffic characteristics. The study focuses on the unique behaviors of Facebook's core Web service and cache infrastructure, which contrast with traditional datacenter workloads. Key findings include the stable and predictable nature of Facebook's network traffic, which is neither strictly rack-local nor all-to-all, but exhibits varying degrees of locality that remain consistent over time. This stability has implications for network architecture, traffic engineering, and switch design, suggesting that traditional assumptions about datacenter traffic may not hold in Facebook's environment. The research also reveals that many flows are long-lived but not heavy, with traffic distributed evenly across hosts. This leads to stable traffic demands over sub-second intervals, making it difficult to identify consistent heavy hitters. Additionally, the paper notes that packets are small and do not exhibit on/off arrival behavior, with traffic often directed to a limited number of racks. The study compares Facebook's traffic patterns to those reported in the literature, showing that many previous characterizations do not fully represent Facebook's demands. This discrepancy calls into question the applicability of existing proposals for network design and traffic management. The paper emphasizes the importance of understanding the specific characteristics of different datacenter workloads, as these can significantly impact the effectiveness of network solutions. The research also discusses the implications of these findings for network fabric design, suggesting that non-uniform fabrics may be more suitable for Facebook's traffic patterns. The study highlights the need for traffic engineering techniques that can adapt to the stable and predictable nature of Facebook's traffic, rather than relying on assumptions about bursty or unpredictable behavior. Overall, the paper provides valuable insights into the unique characteristics of Facebook's datacenter traffic, offering a foundation for future research and development in network design and traffic management.