Log10 Loadshare -

(The +1 ensures positivity for servers with capacity=1, as ( \log_10(1) = 0 )). Linear weighting (e.g., sending 10x more traffic to a server with 10x the CPU) seems intuitive, but it leads to diminishing returns. A server with 100 cores is not 10x better than a server with 10 cores—performance gains are sublinear due to lock contention, memory bus limits, and NUMA (Non-Uniform Memory Access) effects.

This article will explore what Log10 Loadshare is, why it matters, how to implement it, and real-world use cases where it outperforms conventional methods. Log10 Loadshare is a load-balancing weight assignment strategy where the share of traffic directed to a given server or resource pool is proportional to the base-10 logarithm of that server’s capacity metric (e.g., CPU cores, memory, network bandwidth, or request-handling throughput). The Formula For a set of servers ( S = s_1, s_2, ..., s_n ), each with a capacity ( c_i ), the load share weight ( w_i ) is calculated as:

[ f_i = \frac\log_10(c_i + 1)\sum_j=1^n \log_10(c_j + 1) ] log10 loadshare

def compute_log10_weights(servers): epsilon = 1e-6 weights = [] for s in servers: w = math.log10(s["cores"] + 1) weights.append(w) total = sum(weights) return [w / total for w in weights]

Log10 sits between linear weight and pure randomization—offering stability without over-committing to large nodes. You can extend Log10 Loadshare to be adaptive by using a moving average of actual request latency or error rate as the capacity metric. (The +1 ensures positivity for servers with capacity=1,

[ w_i = \log_10(c_i + 1) ]

Let effective capacity ( c_i = \textbaseline_i \times (1 - \texterror i) / \textlatency norm ) This article will explore what Log10 Loadshare is,

Whether you are managing a Kubernetes cluster with spot instances, a CDN origin fleet, or a distributed database, adopting Log10 Loadshare can be the difference between a cascade failure and a gracefully degraded service.