Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

. 2024 Jan 29;24(3):865. doi: 10.3390/s24030865

Algorithm 2: MCVS Scheduling Algorithm

Input: Input data from Table 1 and Algorithm 1.
Output:

Task Scheduling

Steps

T_{i \dots . . n} \leftarrow Data (decision);

: Algorithm 1 Decision function

Init \leftarrow V_{l \dots n};

ρ_{k} \leftarrow Nil;

: MCVS server with zero attributes.

{Data attribute \to D}_{A_{n}};

ϑ_{T} = \sum_{l = 1}^{n} L_{j} * R_{j}

: Cost function of the system

{M C V S}_{k} = {dataset};

: MCVS server set with data.
Start:

while (H) do

While (R) do

While (L_{j}) d o

if ((L_{j} \leftarrow R) \neq \emptyset)

then

Attribute Match \to A_{i . . j}

: Match all the attributes with resources.

T_{j} \leftarrow R_{k}

d_{r}^{k} \equiv T_{j}

T_{j} (threshold) \to M_{k} W_{j} \leftarrow W_{j} |\{T_{j}\}|

Decision \{H_{kl}, τ_{k}\} = 1

K_{j} \leftarrow K_{j} \cup \{R_{k}\}

V_{l \dots n} \leftarrow U_{jk}

: Checking the context of tasks.

K_{i} = K_{i} \cup \{K_{i}\}

\{K_{gi}, K_{g 2}\} \leftarrow W_{i, j, k};

if ({(K}_{g 1} > K_{g 2}) \leftarrow A) then

switch (T_{i} (K_{g 1}) to T_{i} {(K}_{g 2}))

a l l o c a t e

(D_{i (g 1)} = O n e (1))

{(V}_{l \dots n}) * \leftarrow E_{ij}

: Optimal Assignment

e l s e i f (T_{i} \leftarrow K_{g 2} = N u l l) t h e n M \leftarrow M |K_{g 2}| K_{g 2} (L i m i t e d c o s t E) w h i l e E n d W h i l e E n d L o o p s - E n d