# Indetermsoft-Set-Based D* Extra Lie Framework for Resource Provisioning in Cloud Computing
## 4 Proposed Work
### Indetermsoft Set Task Manager (ISSTM)
```
1: Start
2: Input user task set \(\textit{RCI}=\{\textit{RCI}_{1},\textit{RCI}_{2},\textit{RCI}_{3},\ldots, \textit{RCI}_{m}\}\)
3: Output ISF of user task set \(\textit{ISF}(\textit{RCI})=\{\textit{ISF}(\textit{RCI}_{1}),\textit{ISF}( \textit{RCI}_{2}),\textit{ISF}(\textit{RCI}_{3}),\ldots,\textit{ISF}(\textit{ RCI}_{n})\}\)
4: Training phase of ISSRM
5: For each training resource center instance \(\textit{RCI}_{i}\in\textit{RCI}\) do
6: For each training resource center instance attributes do \(\textit{RCI}_{i}^{alt}=\{H_{i}(\textit{vm}_{1}^{alt}),\ldots,H_{i}(\textit{vm }_{i}^{alt})\}\) in \(\textit{RCI}\)
7: Train \(\forall\textit{is}f_{i}(H_{i}(\textit{vm}_{1}^{alt})\ \in H_{i}(\textit{vm})^{ alt})\) initalize \(\sigma(\textit{is}f_{i}(H_{i}(\textit{vm}_{k}^{alt})))=\text{NULL}\)
8: Calculate training ISF of resource center instances
9:\(\textit{ISF}(\textit{RCI}_{1}):H_{i}(\textit{vm}_{k}^{alt})\to P \big{(}H(H_{i}(\textit{vm}_{k}^{alt}))\big{)},H\subseteq\textit{RCI}\)
10: End For
11: End For
12: Testing phase of ISSRM
13: For each testing user task set \(\textit{RCI}_{i}\in\textit{RCI}\) do
14: For each testing user task set attributes do \(\textit{RCI}_{i}^{alt}=\{H_{i}(\textit{vm}_{1}^{alt}),H_{i}(\textit{vm}_{i}^ {alt})\}\) in \(\textit{RCI}\)
15: Test \(\forall\textit{is}f_{i}\big{[}H_{i}(\textit{vm}_{1}^{alt})\in H_{i}(\textit{vm })^{alt})\) initalize \(\sigma\big{(}\textit{is}f_{i}\big{(}H_{i}(\textit{vm}_{k}^{alt})\big{)}\big{)} =\text{NULL}\)
16: Compute aggregation of Indetermsoft set function \(\textit{ISF}(\textit{RCI}):=\textit{ISF}(\textit{RCI}_{i})\cup(\textit{RCI}_{i} ^{alt},\forall\textit{is}f_{i}\big{(}H_{i}(\textit{vm}_{k}^{alt})))\)
17: End For
18: End For
19: Output \(\textit{ISF}(\textit{RCI})=\{\textit{ISF}(\textit{RCI}_{1}),\textit{ISF}( \textit{RCI}_{2}),\textit{ISF}(\textit{RCI}_{3}),\ldots,\textit{ISF}(\textit{ RCI}_{n})\}\)
20: Stop
```
**Algorithm 2**Working of ISSRM

### D* Extra Lite (D*EL)

The D*EL component of the D* extra lite framework accepts the Indetermsoft set function of the user task set \(\textit{ISF}(\textit{UIT})=\{\textit{ISF}(\textit{UIT}_{1}),\textit{ISF}( \textit{UIT}_{2}),\ldots,\textit{ISF}(\textit{UIT}_{m})\}\) and Indetermsoft set function of resource center instances to generate D* extra lite resource-provisioning policies \(D^{*}(\textit{RPP})=\{\ \ D^{*}(\textit{RPP}_{1}),\ldots,D^{*}(\textit{RPP}_{n})\ \}\). The working of the D*EL component is shown in Algorithm 3, which is composed of training and testing phases. The user tasks and resource center instances are represented in terms of an acyclic tree composed of nodes and edges. During training, D*EL uses an incremental heuristic search technique for the robust navigation of user tasks to ideal resource center instances. During testing, for the incoming user tasks, an ideal path is determined by using a priority queue data structure. The time incurred in training and testing is less as the user tasks are aligned in a priority queue which prevents frequent reordering and fast re-planning during user task navigation. The efficiency lies in re-expanding the parts of the search space that are registered for changes. The CALCULATE KEY function uses the key value for assigningpriority to the list elements, and this value is determined through the sum of heuristic values. The REINITIALIZE function performs fast re-initialization of the affected search space using a search tree. Quick re-computation of the optimal path is performed by keeping track of visited and unvisited states of the resource center instances using the CUT BRANCHES function. ```
1:Start
2:Input
3:\(ISF(UT)=\{ISF(UT_{1}),ISF(UT_{2}),ISF(UT_{1}),\ldots,ISF(UT_{m})\}\)
4:\(ISF(RCI)=\{ISF(RCI_{1}),ISF(RCI_{2}),ISF(RCI_{3}),\ldots,ISF(RCI_{n})\}\)
5:Output \(D^{\prime}(RP)=\{I(RP),D^{\prime}(RP)_{2},\ldots,D^{\prime}(RPp_{n})\}\)
6:Function CALCURET KER (StateS)
7:Return \([g(s)+H(S_{start},S)+K_{n}g(s)]\), where h is the heuristic value, \(g(s)\) is the cost from goal state, and \(K_{m}\) is the bias value.