Hossein Morshedlou

Bio: Hossein Morshedlou is an academic researcher from University of Shahrood. The author has contributed to research in topics: Service provider & Learning automata. The author has an hindex of 5, co-authored 15 publications receiving 125 citations. Previous affiliations of Hossein Morshedlou include Amirkabir University of Technology.

Decreasing Impact of SLA Violations:A Proactive Resource Allocation Approachfor Cloud Computing Environments

Abstract: User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures SLA violation as an important factor can decrease users’ satisfaction level The amount of this decrease depends on user's characteristics Some of these characteristics are related to QoS requirements and announced to service provider through SLAs But some of them are unknown for service provider and selfish users are not interested to reveal them truly Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters So, two users with different characteristics but similar SLAs have equal importance for the service provider In this paper, we use two user's hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations New methods based on learning automaton for estimation of these characteristics are provided as well To validate our approach we conducted some numerical simulations in critical situations The results confirm that our approach has ability to improve users’ satisfaction level that cause to gain in profitability

Virtual machine placement in cloud systems using Learning Automata

Abstract: In recent years, the IT infrastructure due to the demand for computing power which used by applications are rapidly growing and modern data centers in cloud computing are hosting a variety of advanced applications. The high energy cost and green-house gas emissions are significant problems that have emerged as results of using large data centers. Thus providing an efficient method to reduce energy consumption by data centers is highly regarded by researchers. In this paper we present a new approach based on Learning Automata for dynamic replacement of virtual machines over data centers to reduce power consumption. Live migration and forcing idle nodes to sleep constitute main policies of this approach. To evaluate the proposed method, the workload is used in the real world. Simulation results show that the performance of the proposed method significantly reduces the energy consumption However, the efficiency of the system is preserved to a considerable extent.

A New Local Rule for Convergence of ICLA to a Compatible Point

Abstract: Many problems in the modern world have a decentralized and distributed nature. Irregular cellular learning automata (ICLA) is a powerful mathematical model for decentralized problems and applications. Convergence of ICLA to a compatible point is very important because this convergence can provide efficient solutions for the problems. The local rule of ICLA can play a key role in this convergence. A local rule that simply rewards or punishes learning automata just based on the response of environment and actions of neighbors does not guarantee convergence of ICLA to a compatible point. In this paper, we present a new local rule that guarantees convergence to a compatible point. Formal proofs for the convergence are provided and results of the conducted experiments support our theoretical findings.

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Abstract: A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.

Optimal Multiserver Configuration for Profit Maximization in Cloud Computing

Abstract: As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes critically important. To maximize the profit, a service provider should understand both service charges and business costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an application environment, the configuration of a multiserver system, the service-level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low-quality service, the cost of renting, the cost of energy consumption, and a service provider's margin and profit. Our approach is to treat a multiserver system as an M/M/m queuing model, such that our optimization problem can be formulated and solved analytically. Two server speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. The probability density function of the waiting time of a newly arrived service request is derived. The expected service charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server size and the optimal server speed are demonstrated.

Customer-Satisfaction-Aware Optimal Multiserver Configuration for Profit Maximization in Cloud Computing

Abstract: Along with the development of cloud computing, an increasing number of enterprises start to adopt cloud service, which promotes the emergence of many cloud service providers. For cloud service providers, how to configure their cloud service platforms to obtain the maximum profit becomes increasingly the focus that they pay attention to. In this paper, we take customer satisfaction into consideration to address this problem. Customer satisfaction affects the profit of cloud service providers in two ways. On one hand, the cloud configuration affects the quality of service which is an important factor affecting customer satisfaction. On the other hand, the customer satisfaction affects the request arrival rate of a cloud service provider. However, few existing works take customer satisfaction into consideration in solving profit maximization problem, or the existing works considering customer satisfaction do not give a proper formalized definition for it. Hence, we first refer to the definition of customer satisfaction in economics and develop a formula for measuring customer satisfaction in cloud computing. And then, an analysis is given in detail on how the customer satisfaction affects the profit. Lastly, taking into consideration customer satisfaction, service-level agreement, renting price, energy consumption, and so forth, a profit maximization problem is formulated and solved to get the optimal configuration such that the profit is maximized.

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Abstract: Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this article, for the first time, we apply the latest metaheuristics whale optimization algorithm (WOA) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called I mproved W OA for C loud task scheduling (IWC) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks.