Energy Aware Resource Allocation for Clouds Using Two Level Ant Colony Optimization

In cloud environment resources are dynamically allocated, adjusted, and deallocated. When to allocate and how many resources to allocate is a challenging task. Resources allocated optimally and at the right time not only improve the utilization of resources but also increase energy efficiency, provider's profit and customers' satisfaction. This paper presents ant colony optimization (ACO) based energy aware solution for resource allocation problem. The proposed energy aware resource allocation (EARA) methodology strives to optimize allocation of resources in order to improve energy efficiency of the cloud infrastructure while satisfying quality of service (QoS) requirements of the end users. Resources are allocated to jobs according to their QoS requirements. For energy efficient and QoS aware allocation of resources, EARA uses ACO at two levels. First level ACO allocates Virtual Machines (VMs) resources to jobs whereas second level ACO allocates Physical Machines (PMs) resources to VMs. Server consolidation and dynamic performance scaling of PMs are employed to conserve energy. The proposed methodology is implemented in CloudSim and the results are compared with existing popular resource allocation methods. Simulation results demonstrate that EARA achieves desired QoS and superior energy gains through better utilization of resources. EARA outperforms major existing resource allocation methods and achieves up to 10.56 % saving in energy consumption

Fuzzy clustering chaotic-based differential evolution for resource leveling in construction projects

 Project scheduling is an important part of project planning in many management companies. Resource lev­eling problem describes the process of reducing the fluctuations in resource usage over the project duration. The goal of resource leveling is to minimize the incremental demands that cause fluctuations of resources, and thus avoid unde­sirable cyclic hiring and firing during project execution. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource leveling (FCDE-RL), is introduced. Fuzzy Cluster­ing Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the optimization algorithm from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-par­ent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental results revealed that the new optimization model is a promising alternative to assist project managers in dealing with construction project resource leveling. First published online: 13 Jul 201

Optimized location-allocation of earthquake relief centers using PSO and ACO, complemented by GIS, clustering, and TOPSIS

© 2018 by the authors. After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO

Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

Optimization of Electric-Vehicle Charging: scheduling and planning problems

The progressive shift from traditional vehicles to Electric Vehicles (EVs ) is considered one of the key measures to achieve the objective of a significant reduction in the emission of pollutants, especially in urban areas. EVs will be widely used in a not-so-futuristic vision, and new technologies will be present for charging stations, batteries, and vehicles. The number of EVs and Charging Stations (CSs) is increased in the last years, but, unfortunately, wide usage of EVs may cause technical problems to the electrical grid (i.e., instability due to intermittent distributed loads), inefficiencies in the charging process (i.e., lower power capacity and longer recharging times), long queues and bad use of CSs. Moreover, it is necessary to plan the CSs installation over the territory, the schedule of vehicles, and the optimal use of CSs. This thesis focuses on applying optimization methods and approaches to energy systems in which EVs are present, with specific reference to planning and scheduling decision problems. In particular, in smart grids, energy production, and storage systems are usually scheduled by an Energy Management System (EMS) to minimize costs, power losses, and CO2 emissions while satisfying energy demands. When CSs are connected to a smart grid, EVs served by CSs represent an additional load to the power system to be satisfied, and an additional storage system in the case of vehicle-to-grid (V2G) technology is enabled. However, the load generated by EVs is deferrable. It can be thought of as a process in which machines (CSs) serve customers/products (EVs) based on release time, due date, deadline, and energy request, as happens in manufacturing systems. In this thesis, first, attention is focused on defining a discrete-time optimization problem in which fossil fuel production plants, storage systems, and renewables are considered to satisfy the grid's electrical load. The discrete-time formalization can use forecasting for renewables and loads without data elaboration. On the other side, many decision variables are present, making the optimization problem hard to solve through commercial optimization tools. For this reason, an alternative method for the optimal schedule of EVs characterized by a discrete event formalization is presented. This new approach can diminish the number of variables by considering the time intervals as variables themselves. Of course, the solution's optimality is not guaranteed since some assumptions are necessary. Moreover, the last chapter proposes a novel approach for the optimal location and line assignment for electric bus charging stations. In particular, the model provides the siting and sizing of some CSs to maintain a minimum service frequency over public transportation lines

Location-allocation models for relief distribution and victim evacuation after a sudden-onset natural disaster

Quick response to natural disasters is vital to reduce loss of and negative impact to human life. The response is more crucial in the presence of sudden-onset, difficult-to-predict natural disasters, especially in the early period of those events. On-site actions are part of such response, some of which are determination of temporary shelters and/ or temporary medical facility locations, the evacuation process of victims and relief distribution to victims. These activities of last-mile disaster logistics are important as they are directly associated with sufferers, the main focus of any alleviation of losses caused by any disaster. This research deals with the last-mile site positioning of relief supplies and medical facilities in response to a sudden-onset, difficult-to-predict disaster event, both dynamically and in a more coordinative way during a particular planning time horizon. Four mathematical models which reflect the situation in Padang Pariaman District after the West Sumatera earthquake were built and tested. The models are all concerned with making decisions in a rolling time horizon manner, but differ in coordinating the operations and in utilization of information about future resource availability. Model I is a basic model representing the current practice with relief distribution and victim evacuation performed separately and decisions made only considering the resources available at the time. Model II considers coordination between the two operations and conducts them with the same means of transport. Model III takes into account future information keeping the two operations separate. Model IV combines the features of Models II and III. The four models are approached both directly and by using various heuristics. The research shows that conducting relief distribution and victim evacuation activities by using shared vehicles and/or by taking into account future information on resource availability improves the current practice . This is clearly demonstrated by the experimental results on small problems. For large problems, experiments show that it is not practical to directly solve the models, especially the last three, and that the solution quality is poor when the solution process is limited to a reasonable time. Experiments also show that the heuristics help improve the solution quality and that the performances of the heuristics are different for different models. When each model is solved using its own best heuristic, the conclusions from results of large problems get very close to those from small problems. Finally, deviation of future information on resource availability is considered in the study, but is shown not to affect the performance of model III and model IV in carrying out relief distribution and victim evacuation. This indicates that it is always worthwhile to take into account the future information, even if the information is not perfect, as long as it is reasonably reliable

Product Configurator Development for Complex Products by ApplyingGrouping Process

У докторској дисертацији је представљен алгоритам за развој конфигуратора сложених производа применом поступка груписања. Детаљно је описано свих осам фаза алгоритма, а потом је извршена емпиријска провера алгоритма на примеру расхладно-термичких уређаја током које је развијен систем за конфигурисање сложених производа. Утврђено је да се развијеним конфигуратором може скратити време потребно да се конфигурише и испоручи производ крајњем кориснику, а самим тим и укупно време потребно да се нови или побољшани производ појави на тржишту. Резултати истраживања су показали оправданост приступа развоју конфигуратора сложених производа применом поступка груписања.U doktorskoj disertaciji je predstavljen algoritam za razvoj konfiguratora složenih proizvoda primenom postupka grupisanja. Detaljno je opisano svih osam faza algoritma, a potom je izvršena empirijska provera algoritma na primeru rashladno-termičkih uređaja tokom koje je razvijen sistem za konfigurisanje složenih proizvoda. Utvrđeno je da se razvijenim konfiguratorom može skratiti vreme potrebno da se konfiguriše i isporuči proizvod krajnjem korisniku, a samim tim i ukupno vreme potrebno da se novi ili poboljšani proizvod pojavi na tržištu. Rezultati istraživanja su pokazali opravdanost pristupa razvoju konfiguratora složenih proizvoda primenom postupka grupisanja

Development of production planning and control models for virtualmanufacturing cells

U doktorskoj disertaciji su predstavljeni modeli za planiranje i upravljanje proizvodnim sistemom organizovanim putem virtuelnih proizvodnih ćelija. U modelima su razvijeni postupci određivanja dužine planskog perioda i broja faza obrade ili montaže. U modelima je razvijen postupak kreiranja virtuelnih proizvodnih ćelija imajući u vidu zahteve planskog i upravljačkog sistema. Modeli omogućuju rekonfiguraciju ćelija usled analize opterećenja tehnoloških sistema i mogućnosti realizacije terminskih planova proizvodnje. Modeli su ispitani na primerima dva proizvodna preduzeća sa teritorije Republike Srbije.Doctoral dissertation presents models for production planning and control of virtual manufacturing cells. Models present developed procedure for the determination of the production planning period and the number of processing or assembly stages. Models also present the process of creating virtual manufacturing cells, bearing the requirements of the production planning and control system.The models enable the reconfiguration of cells due to the analysis of machines workload and due to the analysis of the production schedule. The models were tested on the examples of two production companies from the Republic of Serbia

Ant colony optimization for the nonlinear resource allocation problem

[[abstract]]The nonlinear resource allocation problem addresses the important issue which seeks to find an optimal allocation of a limited amount of resource to a number of tasks for optimizing a nonlinear objective over the given resource constraint. Relevant literature has been focused on the use of mathematical programming approaches, few researches based on meta-heuristic algorithms have been conducted. In this paper we present an ant colony optimization algorithm for conquering the nonlinear resource allocation problem. To ensure the resource constraint is satisfied, we incorporate adaptive resource bounds to guide the search. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. Also, our method converges at a fast rate and a reliable performance guarantee is provided through a worst-case analysis. (c) 2005 Elsevier Inc. All rights reserved.[[note]]SC