Pedram Saeedi

Bio: Pedram Saeedi is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Simulated annealing & Virtual machine. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters

Abstract: Cloud computing attracted great attention in both industry and research communities for the sake of its ubiquitous, elasticity and economic services. The first class concern of cloud providers is power management for both reducing their total cost of ownership and green computing objectives. To reach the goal, a system framework is presented which has different modules. The main concentration of the paper is on virtual machine (VM) consolidation module which launches users requested VMs on the minimum number of active servers to reduce datacenter total power consumption (TPC). In this paper, the VMs consolidation is abstracted to two-dimensional bin-packing problem and also is formulated to an integer linear programming. Since the papers in the literature scarcely are aware of skewness in resources of requested VMs and for discrete nature of search space, this paper presents the resource skewness-aware VMs consolidation algorithm based on improved thermodynamic simulated annealing approach because resource skewness potentially compels the algorithm to activate additional servers. The proposed SA-based algorithm is validated in extensive scenarios with different resource skewness in comparison with two heuristics and two meta-heuristics. The average results reported from different scenarios proves superiority of proposed algorithm in comparison with other approaches in terms of the number of used servers, TPC, and total resource wastage of datacenter.

Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

Abstract: Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications’ components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application’s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.

A hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environments

Abstract: Cloud providers deliver heterogeneous virtual machines to run complicated jobs submitted by users. The task scheduling issue is formulated to a discrete optimization problem which is well-known NP-Hard. This paper presents a hybrid meta-heuristic algorithm based on genetic and thermodynamic simulated annealing algorithms to solve this problem. In the proposed algorithm, the genetic and simulated annealing algorithms have respective global and local search inclinations covering each other's shortcomings. A novel theorem is presented and applied to produce a semi-conducted initial population. In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space. The thermodynamic simulated annealing algorithm is utilized to improve the efficiency, which considers entropy and energy difference concepts in the cooling schedule process. After obtaining a suitable solution, one of the three novel neighbor operators is randomly called to enhance the given solution potentially. In this way, the efficient balance between exploration and exploitation in the search space is achieved. Simulation results prove that the proposed hybrid algorithm has 10.17%, 9.31%, 7.76%, and 8.21% dominance in terms of makespan, schedule length ratio, speedup, and efficiency against other comparative algorithms.

ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment

Abstract: Cloud computing is a powerful way to provide a suitable platform for data centers and to store data. Along with the so many benefits, there are still some management issues that need to be investigated. Although cloud computing seems to be a very attractive implementation it is facing incredible energy consumption and costs concerns. To avoid energy consumption, a VM consolidation and migration approach is introduced. The main objective of VM consolidation is to perform more jobs while consuming less amount of power. To achieve this, in this paper multi-objective energy-efficient VM consolidation using adaptive beetle swarm optimization (ABSO) algorithm is proposed. The proposed ABSO is a hybridization of particle swarm optimization (PSO) and Beetle swarm optimization (BSO).The proposed method presented with efficient solution representation, derivation of efficient fitness function (or multi-objective function) along with PSO and BSO operator. The effectiveness of the approach is analyzed based on the different evaluation measures and effectiveness is compared with different methods. From the results, our proposed approach consumes only 8.234 J energy for scheduling 100 tasks which are 10.616 J for BSO-based VM consolidation, 11.754 J for PSO-based VM consolidation, and 13.545 J for GA-based VM consolidation.