The paper introduces a family of caching protocols designed to reduce or eliminate hot spots in distributed networks, particularly the Internet. Hot spots occur when a large number of clients simultaneously access data from a single server, leading to service degradation or loss. The protocols are based on consistent hashing, a hashing technique that minimizes changes in the function as the range changes. This allows the protocols to function effectively even when clients have inconsistent views of the network. The authors propose two main tools: random cache trees and consistent hashing. Random cache trees combine aspects of previous approaches to distribute requests across multiple caches, ensuring no single cache becomes overwhelmed. Consistent hashing ensures that changes in the network, such as the addition or removal of caches, do not cause significant disruptions in data distribution. The paper also includes a detailed analysis of the protocols, proving that they can prevent swamping, minimize cache memory requirements, and minimize browser delay. The analysis considers both static and temporal models of request patterns and provides high-probability bounds on the number of requests a cache receives. The authors also discuss the implementation details and theoretical properties of consistent hashing, including its monotonicity, universality, and smoothness. Overall, the paper presents a robust and efficient solution for managing hot spots in distributed networks, making it suitable for large-scale applications like the Internet.The paper introduces a family of caching protocols designed to reduce or eliminate hot spots in distributed networks, particularly the Internet. Hot spots occur when a large number of clients simultaneously access data from a single server, leading to service degradation or loss. The protocols are based on consistent hashing, a hashing technique that minimizes changes in the function as the range changes. This allows the protocols to function effectively even when clients have inconsistent views of the network. The authors propose two main tools: random cache trees and consistent hashing. Random cache trees combine aspects of previous approaches to distribute requests across multiple caches, ensuring no single cache becomes overwhelmed. Consistent hashing ensures that changes in the network, such as the addition or removal of caches, do not cause significant disruptions in data distribution. The paper also includes a detailed analysis of the protocols, proving that they can prevent swamping, minimize cache memory requirements, and minimize browser delay. The analysis considers both static and temporal models of request patterns and provides high-probability bounds on the number of requests a cache receives. The authors also discuss the implementation details and theoretical properties of consistent hashing, including its monotonicity, universality, and smoothness. Overall, the paper presents a robust and efficient solution for managing hot spots in distributed networks, making it suitable for large-scale applications like the Internet.