elasticity

The challenge of excessive energy consumption, related environmental issues, and optimal utilization of restructured power systems require proper management on the demand side. In this context, demand response programs, try to encourage, persuade, or force consumers to adjust their consumption patterns within that established by the system operator. In this article, to improve accuracy, nonlinear models for demand response programs (power, exponential, and logarithmic models) for incentive-based and time-based programs, have been developed based on price elasticity and customer profit function. Then, the behavior of the proposed models against changes in elasticity, encouragement/penalty rate, and the effect of consumer participation have been investigated in several scenarios. Since consumer participation in these programs depends on various economic, cultural, and social factors, it cannot be accurately predicted. In addition, consumer participation has a significant effect on the program results. So, the consumer participation percentage has been considered an uncertain parameter based on a normal probability distribution function. The results show that the non-linear models are more accurate and more conservative than the linear model. Moreover, considering consumer participation as an uncertain normal parameter results in more reliable responses with more peak reduction and a greater reduction in total energy consumption.

Today, electricity service providers have to consider economic index as well as social indicators and consumer perspectives. In this paper, by studying the consumer satisfaction function, a new load model based on nonlinear consumer perspective has been developed. The main variable in the proposed model is the coefficient of individual consumer behavior, which requires field and statistical activities to estimate. As an alternative solution in this paper, the relationship between demand elasticity and individual behavior coefficients has been mathematically developed to estimate these coefficients through demand elasticity information. Also, a new demand response program based on individual behavior and satisfaction function has been developed to determine the incentive rate for consumer participation in an optimal way based on their individual perspective. In the numerical results section, the data of Iran's electricity network has been selected for study. Numerical results showed that in order to study the annual consumption change, in addition to increasing prices, inflation rates and increasing consumer incomes also need to be examined. It was also shown that for the Iranian electricity grid, the demand response program implementation for the household consumers has the lowest cost. However, in order to achieve maximum welfare, it is more appropriate these programs have been applied to the public customers.

Elasticity is considered as one of the most important features that distinguishes cloud computing from other distributed system approaches. This feature takes into account the fact that the resource allocation process is considered as a process that can be implemented dynamically. Providing an efficient solution for improving elasticity will be useful for both providers and users of cloud computing services. Using the proposed solution in this paper, providers will be able to evaluate and improve the quality of their services, and increase their qualitative or quantitative advantage in competing with other competitors. In this paper, we present a hybrid solution for improving elasticity through using buffer management and centralized elastic management. Buffer managment controls the input queue of the request and the elastic management by using the reinforcement learning controls the elasticity of the system. Finally, we evaluate the effectiveness of our approach under three real workload traces, namely, Yahoo Cluster, Wikipedia, and Google Cluster workload traces. The experimental results show that the proposed approach reduces the response time by up to 15.2%, and increases the resource utilization by up to 13.2 % and the elasticity by up to 19.8 % compared with the CTMC and ControCity approaches.

Cloud computing is a new technology which its popularity increases every day, a popularity due to its elasticity. On the other words, cloud computing takes into account an unlimited capacity of the resource for the consumer, and the consumer can take resources in demand based on competitive rates and increase or decrease the amount of resources. There have been many improvements to elasticity management by previous researches. However, further reasearches are necessary to manage elasticity more efficiently. In this paper, a model for the elasticity improvement using a colored Petri network is proposed to provide resources in cloud computing. In the proposed model, elasticity management is performed using a colored Petri net in the form of control of the M/M/N queues. In this way, there is a horizontal queue for each request or service in the vertical queue for the need to increase or decrease the virtual machine. The results of the proposed method show an improvment in elasticity, accuracy and speed, compared with the other approaches.

The important defect of the previous  models , is assumed that consumer behavior is based on logic and with a rise in the price their consumption decreased and they accurately have no problems with their control. However, various parameters such as lifestyle, habits and emotions and something like that can be effective. In this paper, we use the new model called the OLG model and the CRRA function, which is one of the economists' favorite functions, to solve these problems with the help of the theory of consumer choice and its two important tools, the utility function and the budget constraint. In order to overcome the problem of existing models, the above three coefficients ρ1 and ρ2  (rate of time preferences) and θ (Constant Relative Risk Aversion) are obtained that show the lifestyle coefficients and the tendency of each household to transfer and reduce load. In this study, it was determined that by setting the Constant Relative Risk Aversion and the consumer rate of time preferences, in addition to reducing the consumer cost, the flat load profile will be created. Also, through this model, the inappropriate performance of the TOU demand response was demonstrated in Tehran.

Over the recent years, one of the important aspects of cloud computing is the dynamic scheduling of a large number of task requests which are submitted with variable rate by users. Task scheduling plays a key role in cloud computing systems, and this type of scheduling can not be done on a single criterion, but many rules and conditions must be considered as an agreement between users and cloud providers. In fact, this agreement is the quality of the services that users expect from providers. Cloud data centers should not only execute these huge tasks, but also should meet the multiple needs of different users. In this paper, a multi-objective task scheduling strategy is proposed using non-dominated sorting, calculate normal and threshold rates. The aim of the proposed approach is considering some of the most important criteria for quality of service at the time of tasks execution, that means deadline and cost. In addition, the cloud elasticity property is considered. The simulation results show improvement in the conditions of makespan, cost, mean utilization of virtual machines and deadlines violation compared to MultiObjective, FCFS, Min-Min, Priority Schedulig and MOF approaches.