Creative Thinking

1.Creative thinking as defined by Okpara is the act of generating solution to problems by the force of
imagination and reasoning. It is an ability of the mind seeking to find answers to some life’s questions. In
a dynamic and changing world, the challenges of man are not static. They take on new forms and require
a deep understanding of the creative approach. Celebrating achievements of great investors were not
necessarily by accident but by product of deep creative thinking that has delivered the visible products we
see and enjoy today. Bill gates, Steve Job, the Wright brothers, Ford, Rock feller etc have all at one time
followed the part of creative thinking.

2.It is necessary to know that we live in a thinker’s world. It is therefore, not surprising to see that the
men/women who are ahead are those who see ahead with the eyes of their mind. Men and women who
have engaged their minds in resourceful thinking to generate idea and products, which stand the test of
time.

3.Every idea is a product of thinking and every product is the manifestation of idea naked in a thinker’s
mind. These are people who see problems as opportunities to improve and do something new or
something better, people who keep these two vital questions on their mind.

 Demand Forecasting:

 On the basis of analysis and interpretation of information gathered
about various aspects of market and demand from primary and secondary sources, an attempt
is made to forecast the future demand of the proposed product or service. There are various
methods of demand forecasting available to the market analyst.

Methods of Demand Analysis

The various methods of forecasting demand may be grouped under the following categories:

1) Opinion Polling Method:

In this method, the opinion of the buyers, sales force and experts
could be gathered to determine the emerging trend in the market. The opinion polling
methods of demand forecasting are of three kinds:

i) Consumers Survey Methods: The most direct method of forecasting demand in the short-
run is survey method. Surveys are conducted to collect information about future purchase
plans of the probable buyers of the product. Survey methods include:

a) Complete Enumeration Survey:

Under the Complete Enumeration Survey, the firm has to
go for a door to door survey for the forecast period by contacting all the households in the
area.
b) Sample Survey and Test Marketing: Under this method some representative households
are selected on random basis as samples and their opinion is taken as the generalized opinion.
This method on random basis as samples and their opinion is taken as the generalized
opinion. This method is based on the basic assumption that the sample truly represents the
population. A variant of sample survey technique is test marketing. Product testing essentially
involves placing the product with a number of users for a set period. Their reactions to the
product are noted after a period of time and an estimate of likely demand is mad from the
result.
c) End–use Method: In this method, the sale of the product under consideration is projecting
on the basis of demand survey of the industries using this product and intermediate product.
In other words, demand for the final product is the end use demand of the intermediate
product used in the production of this final product.

ii) Sales Force Opinion Method:

This is also known as Collective Opinion Method. In this
method, instead of consumers, the opinion of the salesman is sought. It is sometimes referred
as the “grass roots approach” as it is a bottom-up method that requires each sales person in
the company to make an individual forecast for his or her particular sales territory. These
individual forecasts are discussed and agreed with the sales manager. The composite of all
forecasts then constitutes the sales forecast for the organization.

iii) Delphi Method:

This method is also known as Expert opinion method of investigation. In
this method instead of depending upon the opinions of buyers and salesmen, firms can obtain
views of the specialists or experts in their respective fields. Opinions of different experts are
sought and their identity is kept secret. These opinions are than exchanged among the various
experts and their reactions are sought and analysed. The process goes on until some sort of

unanimity is arrived at among all the experts. This method is best suited in circumstances
where intractable changes are occurring.

2) Statistical or Analytical Methods: Statistical methods are considered to be superior

techniques of demand estimation because:
i) The element of subjectivity in this method is minimum,
ii) Method of estimation is scientific,
iii) Estimation is based on the theoretical relationship between the dependents and
independents variables,
iv) Estimates are relatively more reliable and
v) Estimation involves smaller cost.
The statistical methods, which are frequently used, for making demand projections are:

i) Thread Projection Method:

An old firm can use its data of past years regarding its sales
in past years. These data are known as time series of sales. A trend line can be fitted by
graphic method or by algebraic equations. Equations method is more appropriate. The trend
can be estimated by using any one of the following methods.
a) Graphical Method: A trend line can be fitted through a series graphically. Old values of
sales for different areas are plotted on a graph and a free hand curve is drawn passing through
as many points as possible. The direction of this free hand curve shows the trend. The main
drawback of this method is that it may show the trend but not measure it.
b) Least Square Method: The least square method is based on the assumption that the past
rate of change of the variable under study will continue in the future. It is a mathematical
procedure for fitting a line to a set of observed data points in such a manner that the sum of
the squared difference between the calculated and observed value is minimized. This
technique is used to find a trend line which best fit the available data. The trend is then used
to project department variable in the future. This method is very popular because it is simple
and in expensive.
c) Time Series Methods: Time series forecasting methods are based on analysis of historical
data (time series; a set of observations measured at successive times or over successive
periods). They make the assumption that past patterns in data can be used to forecast future
data points. Moving averages (simple moving average, weighed moving average); forecast is
based on arithmetic average of a given number of past data points.

Components of Time series Demand
• Average: The mean of the observations over time.

• Trend: A gradual increase or decrease in the average over time.
• Seasonal Influence: Predictable short-term cycling behavior due to time of day, week,
month, season, year, etc.
• Cyclical Movement: Unpredictable long-term cycling behavior due to business cycle or
product/service life cycle.
• Random Error: Remaining variation that cannot be explained by the other four components.
Exponential Smoothing: It is one of the methods of trend projection methods.
Exponential smoothing is distinguishable by the special way it weights ach past demand. The
pattern of weights is exponential in form. Demand for the most recent period is weighted
most heavily; the weights placed on successively older periods decrease exponentially. In
other words, the weights decrease in magnitude the future back in time the data are weighted;
the decrease is non-linear (exponential).

ii). Regression method:

This is a very common method of forecasting demand. Under this
method a relationship is established between quantities demanded (dependent variable) and
independent variables such as income, price of the good, prices of the related goods etc. Once
the relationship is established, we drive regression equation assuming relationship between
dependent and independent variables. Once the regression equation is derived the value of Y
i.e. quantity demanded can be estimated for any given value of X.

iii). Simultaneous equations Methods of Forecasting:

 The econometric model forecasting involves estimating several simultaneous equations,
which are, generally, behavioural equations, mathematical identities and market-clearing
equations. The econometric model technique is also known as simultaneous equations
method and complete system approach to forecasting. This technique uses sophisticated
mathematical and statistical tools.

iv). Barometric Method:

 It is also known as ‘leading indicators forecasting’. National bureau of Economic Research
of U.S.A. has identified three types of indicators, coincidental indicators and Lagging
indicators. The analyst should establish relationship between the sales of the product and the
economic indicators to project the correct sales and to measure to what extent these indicators

affect the sales. To establish relationship is not easy task especially in case of new product
where there is no past record.

operator overloading

Function To Add Complex number using operator overloading

Program:




// Online C++ compiler to run C++ program online

#include <bits/stdc++.h>

using namespace std;

class Complex {

  private:

    int a, b;

  public:

    void setcomplex(int x, int y)

  {

    a = x;

    b = y;

  }

  Complex operator + (Complex p1)

  {

    Complex tmp;

    tmp.a = p1.a + a;

    tmp.b = p1.b + b;

    return tmp;

  }

  void showValue() {

    cout << a << " " << b;

  }

};

int main() {

  Complex el;

  el.setcomplex(2, 4);

  Complex pq;

  pq.setcomplex(2, 5);

  Complex ans = el + pq;

  ans.showValue();

  return 0;

}

Designing new methods for DAG scheduling problem for cloud computing

Objective:- Designing new methods for DAG scheduling problem for cloud computing.

Abstract:-

It is a scheduling layer in a spark which implements stage-oriented scheduling. It converts logical execution plan to a physical execution plan. When an action is called, spark directly strikes to DAG scheduler. It executes the tasks those are submitted to the scheduler.

The objective of DAG scheduling is to minimize the overall program finish-time by proper allocation of the tasks to the processors and arrangement of execution sequencing of the tasks. Scheduling is done in such a manner that the precedence constraints among the program tasks are preserved. The overall finish-time of a parallel program is commonly called the schedule length or make span. Some variations to this goal have been suggested. For example, some researchers proposed algorithms to minimize the mean flow-time or mean finish-time, which is the average of the finish-times of all the program tasks [25], [110]. The significance of the mean finish-time criterion is that minimizing it in the final schedule leads to the reduction of the mean number of unfinished tasks at each point in the schedule. Some other algorithms try to reduce the setup costs of the parallel processors [159]. We focus on algorithms that minimize the schedule length.

INTRODUCTION:-

The Cloud is a huge, interconnected system of Powerful servers that provides businesses and individuals with services [1] The concept (Cloud Computing) refers to the ability for online users to share resources offered by the service provider. Without needing to buy expensive hardware, to leverage the high-service provider's capabilities[2]. The main goal of the cloud computing model is to allow users to share resources and data, Software as a service (SaaS), application as a service (PaaS), and infrastructure as a service (IaaS). As the number of cloud users has grown in recent years, the number of tasks that must be managed propositionally has increased, necessitating task scheduling[3]. methodology is based on Reinforcement learning

RELATED WORK:-

The task scheduling algorithm's main goal is to ensure that tasks are completed as efficiently as possible. List scheduling algorithms are used in the task scheduling process. In list scheduling algorithms, there are two distinct phases. The first phase entails determining the tasks' priority, and the second phase entails assigning tasks to the processor in the order determined[3], They will be discussed as follow. In 2017 (Wei et al.)[4] t has been proposed a task scheduling algorithm based on Q-learning and the mutual value function (QS).

Workflow model:-

A directed acyclic graph, G=(V,E), represents an application, with V representing the set of v tasks and E representing the set of e edges between the tasks. Each edge (imp) E represents a precedence constraint, requiring task to finish before task can begin.

Data is a v×v matrix of communication data, with indicating the amount of data to be transmitted from task to task . DAG scheduling object: node tasks are assigned object resources that must satisfy a chronological order constraint in order to reduce the total time to completion.

Components of proposed algorithm:-

RL, MDP, and the Q-learning algorithm

Proposed Scheduling Algorithm:- Input: DAG all Tasks.

Output: The make span.

Procedure:

1: Create DAG for all tasks.

2: Set gamma parameter, environment rewards in matrix R. 3: Initialize matrix Q to zero.

4: Repeat for each episode. 5: Select an initial state.

6: While the goal state not reached Do.

7: Select possible actions for the current state. 8: Go to the next state.

9: Get maximum Q value with E.g. (6). 10: Set next state as a current state. 11:Update Q(state, action) with E.g. (6).

12: Obtain tasks order according to updated Q-table.

13: Map task to the processor which have the minimum execution time. 14: Calculate the make span.

15: Until no longer changes in make span

Conclusion:-

Existing scheduling algorithms focused on the time. The main goal of these schedulers is to reduce the overall Make span of the workflow. Gaps in current workflow scheduling strategies in the cloud environments were studied in this thesis, and an effective scheduling method for workflow management in the cloud setting was proposed based on the gap analysis. It has b even determined that the current scheme is effective enough to make the best use of the available resources. There are two stages to the algorithm design theor