Struct smoothed_online_convex_optimization::problem::SmoothedLoadOptimization

pub struct SmoothedLoadOptimization<T> {
    pub d: i32,
    pub t_end: i32,
    pub bounds: Vec<T>,
    pub switching_cost: Vec<f64>,
    pub hitting_cost: Vec<f64>,
    pub load: Vec<T>,
}

Smoothed Load Optimization (SLO).

• SSCO
• hitting costs are time independent and linear in some incoming load

Fields

d: i32

Number of dimensions.

t_end: i32

Finite, positive time horizon.

bounds: Vec<T>

Vector of upper bounds of each dimension.

switching_cost: Vec<f64>

Vector of positive real constants resembling the switching cost of each dimension (strictly ascending). Dimensions must be efficient, i.e. there must not be dimensions with a higher switching and higher hitting cost than onether dimension.

hitting_cost: Vec<f64>

Time-independent cost of each dimension (strictly descending).

load: Vec<T>

Non-negative load at each time step.

Implementations

impl<'a, T> SmoothedLoadOptimization<T> where
    T: Value<'a>, 

pub fn into_sblo(self) -> SmoothedBalancedLoadOptimization<'a, T>

Convert instance to an instance of Smoothed Balanced-Load Optimization.

Trait Implementations

impl<'a, T> BaseProblem for SmoothedLoadOptimization<T> where
    T: Value<'a>, 

fn d(&self) -> i32

Number of dimensions.

fn t_end(&self) -> i32

Finite, positive time horizon.

fn set_t_end(&mut self, t_end: i32)

Updates the time horizon.

fn inc_t_end(&mut self)

Increases the time horizon by one time step.

impl<T: Clone> Clone for SmoothedLoadOptimization<T>

fn clone(&self) -> SmoothedLoadOptimization<T>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug> Debug for SmoothedLoadOptimization<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl DefaultGivenOnlineProblem<i32, SmoothedLoadOptimization<i32>, (), DataCenterModelOutputFailure> for Memory

fn default(o: &Online<IntegralSmoothedLoadOptimization>) -> Self

impl<'a, T> Model<T, SmoothedLoadOptimization<T>, DataCenterOfflineInput, DataCenterOnlineInput, (), DataCenterModelOutputFailure> for DataCenterModel where
    T: Value<'a>, 

fn to(&self, _: DataCenterOfflineInput) -> SmoothedLoadOptimization<T>

Notes:

• Only allows for a single location, source, and job type.
• Assumes full utilization and averages the energy cost over the time horizon.

fn update(
    &self,
    o: &mut Online<SmoothedLoadOptimization<T>>,
    _: DataCenterOnlineInput
)

Performs an online update of the given problem instance $o$ with some $input$ (which may be uncertain).

impl<'a, T> Problem<T, (), DataCenterModelOutputFailure> for SmoothedLoadOptimization<T> where
    T: Value<'a>, 

fn hit_cost(
    &self,
    t: i32,
    x: Config<T>
) -> Cost<(), DataCenterModelOutputFailure>

Hitting cost.

fn movement(&self, prev_x: Config<T>, x: Config<T>, inverted: bool) -> N64

Movement cost.

fn objective_function<'a>(&self, xs: &Schedule<T>) -> Result<Cost<C, D>> where
    T: Value<'a>, 

Objective function. Calculates the cost of a schedule.

fn inverted_objective_function<'a>(
    &self,
    xs: &Schedule<T>
) -> Result<Cost<C, D>> where
    T: Value<'a>, 

Inverted Objective function. Calculates the cost of a schedule. Pays the switching cost for powering down rather than powering up.

fn alpha_unfair_objective_function<'a>(
    &self,
    xs: &Schedule<T>,
    alpha: f64
) -> Result<Cost<C, D>> where
    T: Value<'a>, 

$\alpha$-unfair Objective function. Calculates the cost of a schedule.

fn objective_function_with_default<'a>(
    &self,
    xs: &Schedule<T>,
    default: &Config<T>
) -> Result<Cost<C, D>> where
    T: Value<'a>, 

Objective function starting from an initial configuration other than $\mathbf{0}$.

fn _objective_function_with_default<'a>(
    &self,
    xs: &Schedule<T>,
    default: &Config<T>,
    alpha: f64,
    inverted: bool
) -> Result<Cost<C, D>> where
    T: Value<'a>, 

fn total_movement<'a>(&self, xs: &Schedule<T>, inverted: bool) -> Result<N64> where
    T: Value<'a>, 

Movement in the decision space.

fn _default_config<'a>(&self) -> Config<T> where
    T: Value<'a>, 

impl<'a, T> VerifiableProblem for SmoothedLoadOptimization<T> where
    T: Value<'a>, 

fn verify(&self) -> Result<()>