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 1: Proposed ML-Based Adaptation Algorithm

Input:

L

is the learning module,

K

is knowledge learned from training,

C

is task
context set such as connection, battery, data, time, associated data, task state, etc.,

A

is attribute set defined for tasks and their results from mobile devices,

P

is processing
module,

T D

is training dataset,

T

is mobile devices computational tasks,

R

is followed
for results obtained from task execution, and

D

is decision set after probability analysis.

T

is declared as tasks,

W

is Wi-Fi.
Output:

A d o p t i v e D e c i s i o n t o o f f l o a d t a s k s

Steps

G e t T a s k \leftarrow T

g e t c o n t e x t f o r t a s k T : x = (t, w)

w h e r e t, w \in T \times W

i f (K \neq \emptyset) t h e n

t r a i n_m o d e l (K, P, T, A, C, L);

w h i l e (d \in D & & r_{i} \in R_{e s}) d o

P r e d i c t_{R e s u l t s ()} \leftarrow r_{i} (d, x)

e n d w h i l e

D e c i s i o n S e l e c t i o n :

d = a r g u m e n t (\min) \sum_{j = 1}^{m} w_{j} * r_{i} (d, x) {r n d}_{n u m b e r} () \leftarrow \{\begin{matrix} n u m \in 0 \\ n u m \in 1 \end{matrix} i f ({r n d}_{n u m b e r} < ϵ) d \leftarrow r a n d o m_d e c i s i o n () e n d i f e n d i f

e l s e

d \leftarrow r a n d o m_d e c i s i o n (p r e d i c t i o n r a t e s) e n d e l s e

D e c i s i o n i f f i n a l i z e d a s d :

e x e c u t e (t a s k) \leftarrow d e c t i o n (d)

T r a i n P r e d i c t i o n M o d e l

S t o r a g e :

x^{D \cup A} = (o_{1} (x), o_{2} (x), o_{3} (x), \dots \dots \dots, o_{n} (x), r_{1} (x, d), r_{2} (x, d), r_{3} (x, d), \dots \dots \dots \dots, r_{m} (x, d), d) \in T D

i f (t i m e \in l e a r n i n g) t h e n K n o w l e d g e l e a r n e d f r o m T r a i n i n g D a t a (T D) K \leftarrow L_{T D} E n d i f

Go to Step 1