NACER: a Network-Aware Cost-Efficient Resource allocation method for processing-intensive tasks in distributed clouds

In the distributed cloud paradigm, data centers are geographically dispersed and interconnected over a wide- area network. Due to the geographical distribution of data centers, communication networks play an important role in distributed clouds in terms of communication cost and QoS. Large-scale, processing-intensive tasks require the cooperation of many VMs, which may be distributed in more than one data center and should communicate with each other. In this setting, the number of data centers serving the given task and the network distance among those data centers have critical impact on the communication cost, traffic and even completion time of the task. In this paper, we present the NACER algorithm, a Network-Aware Cost-Efficient Resource allocation method for optimizing the placement of large multi-VM tasks in distributed clouds. NACER builds on ideas of the A * search algorithm from Artificial Intelligence research in order to obtain better results than typical greedy heuristics. We present extensive simulation results to compare the performance of NACER with competing heuristics and show its effectiveness

Sensor Network-based and User-friendly User Location Discovery for Future Smart Homes

User location is crucial context information for future smart homes where a lot of location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently makes conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses to design such a ULD system for context-aware services in future smart homes stressing on the following challenges: (i) users’ privacy, (ii) device/tag-free, and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies such as Internet of Things, embedded systems, intelligent devices and machine-to-machine communication are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors or home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of cheap sensors as well as a context broker with a fuzzy-based decision maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation

Improving learning automata-based routing in Wireless Sensor Networks

©2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Recent research in the field of Wireless Sensor Networks (WSNs) has demonstrated the advantages of using learning automata theory to steer the routing decisions made by the sensors in the network. These advantages include aspects such as energy saving, energy balancing, increased lifetime, the selection of relatively short paths, as well as combinations of these and other goals. In this paper, we propose a very simple yet effective technique, which can be easily combined with a learning automaton to dramatically improve the performance of the routing process obtained with the latter. As a proof-of-concept, we focus on a typical learning automata-based routing process, which aims at finding a good trade off between the energy consumed and the number of hops along the paths chosen. In order to assess the performance of this routing process, we apply it on a WSN scenario where a station S gathers data from the sensors. In this typical WSN setting, we show that our combined technique can significantly improve the decisions made with the automata; and more importantly, even though the proof-of-concept particularizes somehow the automata and their behavior, the technique described in this paper is general in scope, and therefore can be applied under different routing methods and settings using learning automata.This work was supported in part by the Spanish Ministry of Science and Innovation under contract TEC2009-07041, and by the Catalan Government under contract 2009 SGR1508.Peer ReviewedPostprint (author's final draft

EIDA: An Energy-Intrusion aware Data Aggregation Technique for Wireless Sensor Networks

Energy consumption is considered as a critical issue in wireless sensor networks (WSNs). Batteries of sensor nodes have limited power supply which in turn limits services and applications that can be supported by them. An efcient solution to improve energy consumption and even trafc in WSNs is Data Aggregation (DA) that can reduce the number of transmissions. Two main challenges for DA are: (i) most DA techniques need network clustering. Clustering itself is a time and energy consuming procedure. (ii) DA techniques often do not have ability to detect intrusions. Studying to design a new DA technique without using clustering and with ability of nding intrusion is valuable. This paper proposes an energy-intrusion aware DA Technique (named EIDA) that does not need clustering. EIDA is designed to support on demand requests of mobile sinks in WSNs. It uses learning automata for aggregating data and a simple and effective algorithm for intrusion detection. Finally, we simulat

On Analyzing User Location Discovery Methods in Smart Homes: A Taxonomy and Survey

User Location Discovery (ULD) is a key issue in smart home ecosystems, as it plays a critical role in many applications. If a smart home management system cannot detect the actual location of the users, the desired applications may not be able to work successfully. This article proposes a new taxonomy with a broad coverage of ULD methods in terms of user satisfaction and technical features. In addition, we provide a state-of-the-art survey of ULD methods and apply our taxonomy to map these methods. Mapping contributes to gap analysis for existing ULDs and also validates the applicability and accuracy of the taxonomy. Using this systematic approach, the features and characteristics of the current ULD methods are identified (i.e., equipment and algorithms). Next, the weaknesses and advantages of these methods are analyzed utilizing ten important evaluation metrics. Although we mainly focus on smart homes, the results of this article can be generalized to other spaces such as smart offices and eHealth environments

Allocation de ressources pour un cloud green et distribué

L'objectif de cette thèse est de présenter de nouveaux algorithmes de placement de machines virtuelles (VMs) à fin d’optimiser le coût et les émissions de carbone dans les Clouds distribués. La thèse se concentre d’abord sur la rentabilité des Clouds distribués, et développe ensuite les raisons d’optimiser les coûts ainsi que les émissions de carbone. La thèse comprend deux principales parties: la première propose, développe et évalue les algorithmes de placement statiques de VMs (où un premier placement d'une VM détient pendant toute la durée de vie de la VM). La deuxième partie propose des algorithmes de placement dynamiques de VMs où le placement initial de VM peut changer dynamiquement (par exemple, grâce à la migration de VMs et à leur consolidation). Cette thèse comprend cinq contributions. La première contribution est une étude de l'état de l'art sur la répartition des coûts et des émissions de carbone dans les environnements de clouds distribués. La deuxième contribution propose une méthode d'allocation des ressources, appelée NACER, pour les clouds distribués. L'objectif est de minimiser le coût de communication du réseau pour exécuter une tâche dans un cloud distribué. La troisième contribution propose une méthode de placement VM (appelée NACEV) pour les clouds distribués. NACEV est une version étendue de NACER. Tandis que NACER considère seulement le coût de communication parmi les DCs, NACEV optimise en même temps les coûts de communication et de calcul. Il propose également un algorithme de cartographie pour placer des machines virtuelles sur des machines physiques (PM). La quatrième contribution présente une méthode de placement VM efficace en termes de coûts et de carbone (appelée CACEV) pour les clouds distribués verts. CACEV est une version étendue de NACEV. En plus de la rentabilité, CACEV considère l'efficacité des émissions de carbone pour les clouds distribués. Pour obtenir une meilleure performance, la cinquième contribution propose une méthode dynamique de placement VM (D-CACEV) pour les clouds distribués. D-CACEV est une version étendue de notre travail précédent, CACEV, avec des chiffres supplémentaires, une description et également des mécanismes de migration de VM en direct. Nous montrons que notre mécanisme conjoint de réallocation-placement de VM peut constamment optimiser à la fois le coût et l'émission de carbone dans un cloud distribuéVirtual machine (VM) placement (i.e., resource allocation) method has a direct effect on both cost and carbon emission. Considering the geographic distribution of data centers (DCs), there are a variety of resources, energy prices and carbon emission rates to consider in a distributed cloud, which makes the placement of VMs for cost and carbon efficiency even more critical and complex than in centralized clouds. The goal of this thesis is to present new VM placement algorithms to optimize cost and carbon emission in a distributed cloud. It first focuses on cost efficiency in distributed clouds and, then, extends the goal to optimization of both cost and carbon emission at the same time. Thesis includes two main parts. The first part of thesis proposes, develops and evaluates static VM placement algorithms to reach the mentioned goal where an initial placement of a VM holds throughout the lifetime of the VM. The second part proposes dynamic VM placement algorithms where the initial placement of VMs is allowed to change (e.g., through VM migration and consolidation). The first contribution is a survey of the state of the art on cost and carbon emission resource allocation in distributed cloud environments. The second contribution targets the challenge of optimizing inter-DC communication cost for large-scale tasks and proposes a Network-Aware Cost-Efficient Resource allocation method, called NACER, for distributed clouds. The goal is to minimize the network communication cost of running a task in a distributed cloud by selecting the DCs to provision the VMs in such a way that the total network distance (hop count or any reasonable measure) among the selected DCs is minimized. The third contribution proposes a Network-Aware Cost Efficient VM Placement method (called NACEV) for Distributed Clouds. NACEV is an extended version of NACER. While NACER only considers inter-DC communication cost, NACEV optimizes both communication and computing cost at the same time and also proposes a mapping algorithm to place VMs on Physical Machines (PMs) inside of the selected DCs. NACEV also considers some aspects such as heterogeneity of VMs, PMs and switches, variety of energy prices, multiple paths between PMs, effects of workload on cost (energy consumption) of cloud devices (i.e., switches and PMs) and also heterogeneity of energy model of cloud elements. The forth contribution presents a Cost and Carbon Emission-Efficient VM Placement Method (called CACEV) for green distributed clouds. CACEV is an extended version of NACEV. In addition to cost efficiency, CACEV considers carbon emission efficiency and green distributed clouds. It is a VM placement algorithm for joint optimization of computing and network resources, which also considers price, location and carbon emission rate of resources. It also, unlike previous contributions of thesis, considers IaaS Service Level Agreement (SLA) violation in the system model. To get a better performance, the fifth contribution proposes a dynamic Cost and Carbon Emission-Efficient VM Placement method (D-CACEV) for green distributed clouds. D-CACEV is an extended version of our previous work, CACEV, with additional figures, description and also live VM migration mechanisms. We show that our joint VM placement-reallocation mechanism can constantly optimize both cost and carbon emission at the same time in a distributed clou