Function plot_function_scheduling_latency

artifact_evaluation/plot_azure_500.py:118–170

()

def plot_function_scheduling_latency():
    def get_per_invocation(path):
        df = pd.read_csv(path)

        df['responseTime'] -= df['actualDuration']
        df['responseTime'] /= 1000  # to ms

        return cdf(df, 'responseTime')

    def get_per_function(path):
        df = pd.read_csv(path)

        df['responseTime'] -= df['actualDuration']
        df['responseTime'] /= 1000  # to ms

        df = df.apply(lambda x: x.replace({'(\-[0-9]+(\-[0-9]+\-deployment\-[0-9A-Za-z\-]+){0,1})$': ""}, regex=True))

        test = df.groupby('instance')['responseTime'].mean().to_frame()

        return cdf(test, 'responseTime')

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))

    if os.path.exists(knative_path):
        ax1.plot(get_per_invocation(knative_path), label='Knative', color='tab:blue', linestyle='dotted')
        ax2.plot(get_per_function(knative_path), label='Knative', color='tab:blue', linestyle='dotted')

    if os.path.exists(dirigent_containerd):
        ax1.plot(get_per_invocation(dirigent_containerd), label='Dirigent', color='tab:green')
        ax2.plot(get_per_function(dirigent_containerd), label='Dirigent', color='tab:green')

    ax1.set_xlabel('Per-invocation scheduling\nlatency [ms]')
    ax1.set_ylabel('CDF')
    ax1.set_xscale('log')
    ax1.set_xticks([1, 10, 100, 1_000, 10_000, 100_000, 1_000_000])
    ax1.set_xlim([1, 10 ** 6])
    ax1.grid()
    ax1.legend()

    ax2.set_xlabel('Average per-function\nscheduling latency [ms]')
    ax2.set_ylabel('CDF')
    ax2.set_xscale('log')
    ax2.set_xticks([1, 10, 100, 1_000, 10_000, 100_000, 1_000_000])
    ax2.set_xlim([1, 10 ** 6])
    ax2.grid()
    ax2.legend()

    fig.set_figheight(4)
    fig.set_figwidth(8)

    plt.tight_layout()

    plt.savefig("azure_500_scheduling_latency.png")


plot_per_function_slowdown()