Youness Teimouri

Bio: Youness Teimouri is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Economic efficiency & Double auction. The author has an hindex of 1, co-authored 1 publications receiving 214 citations.

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

Abstract: There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

FESTAL: Fault-Tolerant Elastic Scheduling Algorithm for Real-Time Tasks in Virtualized Clouds

Abstract: As clouds have been deployed widely in various fields, the reliability and availability of clouds become the major concern of cloud service providers and users Thereby, fault tolerance in clouds receives a great deal of attention in both industry and academia, especially for real-time applications due to their safety critical nature Large amounts of researches have been conducted to realize fault tolerance in distributed systems, among which fault-tolerant scheduling plays a significant role However, few researches on the fault-tolerant scheduling study the virtualization and the elasticity, two key features of clouds, sufficiently To address this issue, this paper presents a fault-tolerant mechanism which extends the primary-backup model to incorporate the features of clouds Meanwhile, for the first time, we propose an elastic resource provisioning mechanism in the fault-tolerant context to improve the resource utilization On the basis of the fault-tolerant mechanism and the elastic resource provisioning mechanism, we design novel f ault-tolerant e lastic s cheduling algorithms for real-time ta sks in c l ouds named FESTAL, aiming at achieving both fault tolerance and high resource utilization in clouds Extensive experiments injecting with random synthetic workloads as well as the workload from the latest version of the Google cloud tracelogs are conducted by CloudSim to compare FESTAL with three baseline algorithms, ie, N on- M igration-FESTAL (NMFESTAL), N on- O verlapping-FESTAL (NOFESTAL), and E lastic F irst F it (EFF) The experimental results demonstrate that FESTAL is able to effectively enhance the performance of virtualized clouds

Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance

Abstract: Cloud computing is an emerging Internet-based computing paradigm, with its built-in elasticity and scalability. In cloud computing field, a service provider offers a large number of resources like computing units, storage space, and software for customers with a relatively low cost. As the number of customer increases, fulfilling their requirements may become an important yet intractable matter. Therefore, service allocation is one of the most challenging issues in the cloud environments. The problem of service allocation in the cloud computing is thought to be a combinatorial optimization problem to a large company for numbers of their customers and owned resources could be huge enough. This paper considers three conflicting objectives, namely maximizing revenue for users and providers as well as finding the optimal solution at desired time. We use a Multi-Objective Particle Swarm Optimization based on Crowding Distance (MOPSO-CD) to solve the problem because MOPSO-CD is highly competitive in converging towards the Pareto front and generates a well-distributed set of non-dominated solutions. In addition, fuzzy set theory is employed to specify the best compromise solution. We simulate the proposed method using Matlab and compare the performance of the method against the performance of two other multi-objective algorithms, in order to prove that the proposed method is highly competitive with respect to them. Finally, the experiments results show that the method improves the speed of the execution of the resources allocation algorithm while generating high revenue for both the users and the providers and increasing the resource utilization.