Optimal Resource Scheduling and Allocation System
Abstract
Most of the existing solutions on task scheduling and resource management in distributed
computing environment are based on the traditional client/ server model, enforcing a
homogeneous policy on making decisions and limiting the flexibility, unpredictable reliability
and scalability of the system.
The present software quality mathematical models and analyzes a three-dimensional
model framework of software project development. Second, the paper constructs optimal
software quality mathematical model, which allocates the limited time and costs to every stage of
software project development to make optimal software qualities, and it suggests a quantifying
basis for project managers to make plans and allocate resources. Finally, we illustrate the
correctness and validity of the mathematical models we have constructed by a specific data and
instance.
Existing System
1) In cloud computing, projects are scheduled on a set of cloud resources that are local active (in
the sense that each resource was determined to be assigned tasks based on its own state and the
state of the environment and its productivity are affected by the amount of tasks that assigned to
it), and corporately structured.
2) Scheduling systems are not immune to Murphy. In cloud project scheduling system, after an
enough power strikes one of the resources, which leads to its productivity reduced or collapsed,
the whole system collapsed, resources CPU overload, resource over- or under-provisioning, or
application misbehaviors. Thus, the system is failed to execute and deliver a project as originally
scheduled.
3) There is a limit on the number of assigned resources beyond which any increase may have the
opposite effect.
4) Allocate resources beyond this limit may lead to disorder/chaotic condition and a
disproportional return on investment in terms of local resource productivity, global system
efficiency and reliability.
Proposed System
1) A project consists of a collection of tasks that have no dependency among each other. Each
task requires amounts of computing demand that are known before the task is submitted for
execution, or at the time it is submitted.
2) Project needs to be completed within deadline and cost budget. A collection of numbers of
cloud resources is rented for running the project.
3) Cloud scheduling system’s behavior as a cellular automaton (CA), specifically as a one
dimension CA network, and then calculate the CA entropy to measure the reliability degree of
such complex system under different scheduling rules and resource allocation strategies.
Modules
Three basic cloud scheduling algorithm



First Come First Served Algorithm (FCFS)
Min-Min Algorithm and
Max-Min Algorithm.
First Come, First Served (FCFS):
Tasks are executed according to the sequence of task submitting. The first come task will be
scheduled on the available resource first as soon as it is submitted and then removed from the
queue.
FCFS provides an efficient, simple and error-free process scheduling algorithm that saves
valuable CPU resources. It uses non preemptive scheduling in which a process is automatically
queued and processing occurs according to an incoming request or process order.
Min-Min:
All the tasks in a project will be ordered by their computing demands first. The task with the
minimum computing demand will be scheduled first on the available resource which the
completion time is minimum and then removed from the queue.
This algorithm is also called Qos guided Min-Min algorithm. At the time of scheduling of tasks
it require high band width than other algorithm.
Max-Min:
All the tasks in a project will be ordered by their computing demands first. The task with the
maximum computing demand will be scheduled first on the available resource which the
completion time is minimum and then removed from the queue.
Optimal Resource Allocation Solution
The optimal resource allocation solution selected by CERRA model meets the following
condition:



Meeting project deadline and within cost budget
Under the reliability threshold that user prefer
With the minimum Cost-Efficiency and Reliability Rate (CERR).
Where n refers to the number of rented resources (number of persons request resources) to
run the project, MS refers to the project’s completed time(here the file uploaded time is taken
as the completion time), cp refers to the cost price of a resource (maximum cost is analyzed
from overall request) and ARE refers to the Average Resource Entropy (speed of file transfer
is calculated from the times (hours,minutes and seconds))
Task Specification
Task
specification
Task ID
1
Computing
Demand
(file
size)(MB)
16265
8
9
14938
17597
Cloud resource type specification
Resource
Type
Task ID
Bandwidth
Scheduling Bandwidth
range (>20)
Resource
Memory
size(>450)
No. of Servers
3
Cost
1MB=Cost
Project Requirements
Task ID
Minutes
Seconds
Get the Minutes Get the Seconds
(MM)
(SS)
File Size
Mb
Speed
Mb+MM*SS
Transfer
Rate
Mb/Speed
Optimal Resource Allocation
Solutions Ranking
Solution
Specification
Scheduling Startegy
Resources
First choice
Second choice
Max-Min
3
Speed(Average
1.4222222
Size+Minutes*Seconds)
Transfer
7. 45999
Rate(CERR=Speed*Filesize)
FCFS
3
Third choice
Min-Min
3
1832.714
1857.7816
8.93622
10299.6826
Conclusion
Resource allocation or resource management is the scheduling of activities and the resources
required by those activities while taking into consideration both the resource availability and the
project time. It is one that is based on Cellular Automaton Entropy, based on minimizing the
CERR of a scheduling system, which indicates both cost-efficiency and higher level of reliability
resource allocation solution thus a more manageable project.