Service mesh is a concept that describes the requirements of modern cloud native applications with regards to communication, visibility, and security. It provides features such as resilient connectivity, L7 traffic management, identity-based security, observability, and tracing. However, implementing these features can be challenging and costly, especially when using the traditional sidecar proxy model that involves running a layer 4 proxy in each workload or pod. This model adds overhead, complexity, and security risks to the application.
Fortunately, there is an alternative to the sidecar model that leverages eBPF, a powerful tool that allows user code to run in the kernel with high performance and safety. eBPF can enable a transparent service mesh that does not require any changes to the application code or network configuration. It can also provide L7-aware processing, identity verification, and observability at the kernel level, without relying on proxies or libraries.
In this blog, we will focus on how eBPF can be used to implement traffic management features of service mesh such as load balancing, rate limiting, routing, retrying, etc. We will compare the performance and efficiency of eBPF-based traffic management with the traditional sidecar proxy model. We will also mention some of the challenges or limitations of using eBPF for traffic management.
Load Balancing
Load balancing is a feature that distributes the traffic across multiple instances of a service based on some criteria such as round robin, least connection, weighted random, etc. Load balancing can improve the availability and performance of the service by avoiding overloading or underutilizing any instance.
eBPF can implement load balancing features by intercepting and processing the application layer protocols such as HTTP or gRPC. For example, an eBPF program can be attached to a socket connect() call to redirect the traffic to a local port where another eBPF program is listening. This way, the application thinks that it is connecting to a remote service, but in fact it is connecting to a local eBPF program that handles the load balancing logic.
The eBPF program that listens on the local port can then parse and modify the traffic headers or payloads to achieve load balancing features. For example, it can extract the destination service name from the HTTP Host header or the gRPC Authority header and use it as a key to lookup a hash table that stores the list of available instances for that service. It can then select one of the instances based on some algorithm such as round robin, least connection, weighted random, etc. and rewrite the destination IP address and port in the packet accordingly.
eBPF-based load balancing has some advantages over sidecar proxy-based load balancing such as:
- Performance: Since eBPF runs in the kernel space with direct access to hardware resources such as CPU and memory, it can achieve higher performance than user space proxies. It can also avoid context switches and data copies between user space and kernel space.
- Efficiency: Since eBPF does not require any additional processes or containers to run in each pod or workload, it can reduce resource consumption such as CPU cycles, memory usage, disk space, etc.
- Simplicity: Since eBPF does not require any changes to the application code or network configuration, it can simplify the deployment and management of load balancing. It can also avoid compatibility issues or conflicts between different versions or implementations of proxies.
Rate Limiting
Rate limiting is a feature that limits the amount of traffic that a service can receive or send based on some criteria such as global or local rate limits, token bucket, leaky bucket, etc. Rate limiting can prevent denial-of-service attacks or abuse of resources by malicious or faulty clients or services.
eBPF can implement rate limiting features by intercepting and processing the application layer protocols such as HTTP or gRPC. For example, an eBPF program can be attached to a socket send() or recv() call to monitor and control the traffic between services. This way, the application does not need to be aware of the rate limiting logic or link to any libraries.
The eBPF program that monitors and controls the traffic can then parse and modify the traffic headers or payloads to achieve rate limiting features. For example, it can extract some attributes from the traffic such as source IP address, destination IP address, HTTP method, HTTP path, gRPC method name, etc. and use them as keys to lookup a hash table that stores the current rate limit values for each attribute. It can then compare the current rate limit values with some predefined thresholds and decide whether to allow or drop the traffic accordingly.
Read more about eBPF vs Traditional Packet Filtering Technologies
Routing
Routing is a feature that directs the traffic to different instances of a service based on some criteria such as path-based routing, header-based routing, content-based routing, etc. Routing can enable features such as canary deployments, A/B testing, blue-green deployments, etc.
eBPF can implement routing features by intercepting and processing the application layer protocols such as HTTP or gRPC. For example, an eBPF program can be attached to a socket connect() call to redirect the traffic to a local port where another eBPF program is listening. This way, the application thinks that it is connecting to a remote service, but in fact it is connecting to a local eBPF program that handles the routing logic.
The eBPF program that listens on the local port can then parse and modify the traffic headers or payloads to achieve routing features. For example, it can extract some attributes from the traffic such as HTTP path, HTTP header, HTTP body, gRPC method name, gRPC message, etc. and use them as keys to lookup a hash table that stores the list of available instances for each attribute. It can then select one of the instances based on some algorithm or rule and rewrite the destination IP address and port in the packet accordingly.
Retrying
Retrying is a feature that retries the traffic in case of failures or errors based on some criteria such as exponential backoff, jitter, circuit breaker, etc. Retrying can improve the reliability and resilience of the service by handling transient errors or network failures.
eBPF can implement retrying features by intercepting and processing the application layer protocols such as HTTP or gRPC. For example, an eBPF program can be attached to a socket send() or recv() call to monitor and control the traffic between services. This way, the application does not need to be aware of the retrying logic or link to any libraries.
The eBPF program that monitors and controls the traffic can then parse and modify the traffic headers or payloads to achieve retrying features. For example, it can extract some attributes from the traffic such as HTTP status code, gRPC status code, error message, etc. and use them as keys to lookup a hash table that stores the current retry state for each attribute. It can then decide whether to retry or drop the traffic based on some algorithm or rule such as exponential backoff, jitter, circuit breaker, etc.
Conclusion
In this blog, we have seen how eBPF can be used to implement traffic management features of service mesh such as load balancing, rate limiting, routing, retrying, etc. We have also compared the performance and efficiency of eBPF-based traffic management with the traditional sidecar proxy model. We have also mentioned some of the challenges or limitations of using eBPF for traffic management.
eBPF is a powerful tool that can enable a transparent service mesh with high performance, efficiency, security, and simplicity. It can provide L7-aware processing, identity verification, and observability at the kernel level, without relying on
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