Evaluation of Program Code Caching for Mobile Agent Migrations

Abstract: Mobile agents are able to migrate among machines to achieve their tasks. This feature is attractive to design, implement, and maintain distributed systems because we can implement both client-side and server-side programming in one mobile agent. However, it involves the increase of data traffic for mobile agent migrations. In this paper, we propose program code caching to reduce the data traffic caused by mobile agent migrations. A mobile agent consists of many program codes that define a task executed in each machine they migrate; thus, the mobile agent migration involves the transfer of their program codes. Therefore, our method reduces the number of the transfer of program codes by using program code cache. We have implemented our method on a mobile agent framework called Maglog and conducted experiments on a meeting scheduling system

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

An enhanced ant colony system algorithm for dynamic fault tolerance in grid computing

Fault tolerance in grid computing allows the system to continue operate despite occurrence of failure. Most fault tolerance algorithms focus on fault handling techniques such as task reprocessing, checkpointing, task replication, penalty, and task migration. Ant colony system (ACS), a variant of ant colony optimization (ACO), is one of the promising algorithms for fault tolerance due to its ability to adapt to both static and dynamic combinatorial optimization problems. However, ACS algorithm does not consider the resource fitness during task scheduling which leads to poor load balancing and lower execution success rate. This research proposes dynamic ACS fault tolerance with suspension (DAFTS) in grid computing that focuses on providing effective fault tolerance techniques to improve the execution success rate and load balancing. The proposed algorithm consists of dynamic evaporation rate, resource fitness-based scheduling process, enhanced pheromone update with trust factor and suspension, and checkpoint-based task reprocessing. The research framework consists of four phases which are identifying fault tolerance techniques, enhancing resource assignment and job scheduling, improving fault tolerance algorithm and, evaluating the performance of the proposed algorithm. The proposed algorithm was developed in a simulated grid environment called GridSim and evaluated against other fault tolerance algorithms such as trust-based ACO, fault tolerance ACO, ACO without fault tolerance and ACO with fault tolerance in terms of total execution time, average latency, average makespan, throughput, execution success rate and load balancing. Experimental results showed that the proposed algorithm achieved the best performance in most aspects, and second best in terms of load balancing. The DAFTS achieved the smallest increase on execution time, average makespan and average latency by 7%, 11% and 5% respectively, and smallest decrease on throughput and execution success rate by 6.49% and 9% respectively as the failure rate increases. The DAFTS also achieved the smallest increment on execution time, average makespan and average latency by 5.8, 8.5 and 8.7 times respectively, and highest increase on throughput and highest execution success rate by 72.9% and 93.7% respectively as the number of jobs increases. The proposed algorithm can effectively overcome load balancing problems and increase execution success rates in distributed systems that are prone to faults

Evolutionary Approach to Efficient Provisioning and Self-organization in Wireless Sensor Networks (WSN)

Advances in low-power digital integration and microelectro-mechanical systems (MEMS) have paved the way for micro-sensors. These sensors are equipped with data processing capabilities along with sensory circuits. Sensor data are processed on these individual sensors and transmitted to the target (sink). Lowcost integration and small sizes of these sensors have generated special interest in the area of disposable-sensors and large scale platform management. Queries to these sensors are addressed to nodes which have data satisfying the same condition. However, these sensors may be constrained in energy, bandwidth, storage, and processing capabilities. Large number of such sensors along with these constraints creates a sensor-management problem. At the network layer it amounts to setting up the efficient route that transmits the non-redundant data from source to the sink in order to maximize one or more sensor objectives (e.g. battery (and sensor's) life, Sensor-Data yield). This is done while adapting to changing connectivity due to failure of some nodes and new nodes powering up. First part of the thesis propose a reduced-complexity genetic algorithm (GA) for optimization of multi-hop battery-constrained sensor networks. The goal of the system is to generate optimal number of sensor-clusters with cluster-heads. It results in minimization of the power consumption of the sensor system while maximizing the sensor objectives (coverage and exposure). The genetic algorithm is used to adaptively create various components such as cluster-members, cluster-heads, and next-cluster. These components are then used to evaluate the average fitness of the system based on the sequence of communication links towards the sink. We then enhance the genetic algorithm (GA) approach for secure deployment of resource constrained multi-hop sensor networks. The goal in this case is to achieve secure coverage and improve battery life by dynamically optimizing security attributes (Like authentication and encryption). Further, we augment the GA approach for intrusion detection of resource constrained multi-hop sensor networks. Traditional intrusion detection mechanisms have limited applicability to the sensor networks due to scarce battery and processing resources. Therefore, we propose an effective scheme that would offer a power efficient and lightweight approach to identify malicious attacks. We evaluate sensor node attributes by measuring the perceived threat and its suitability to host local monitoring node (LMN) that acts as trusted proxy agent for the sink and capable of securely monitoring its neighbors. Security attributes in conjunction with genetic algorithm jointly optimizes the selection of monitoring nodes (i.e., LMN) by dynamically evaluating node fitness by profiling workloads patterns, packet statistics, utilization data, battery status, and quality-of-service compliance. Second part of the thesis delves into application of Information Technology (and Industrial) Systems and devices where the use of sensor networks can deliver non-intrusive and effective telemetry for group-based server management. These systems (Like Data Centers or Shipment tracking) face major challenges in seamless integration of telemetry and control data that is essential to various autonomic management functions related to power, thermal, reliability, predictability, survivability, locality and adaptability. Such systems that are supported by a dense network of sense-points operating in noisy environment (Metals, Cables) are required to deliver reliable trends, measurements and analysis in a timely fashion. The traditional approaches to provide distributed observability and control using wired solutions are static, expensive, and nonscalable. We apply the proposed GA approach for this unique environment that replaces static wired sensors with dynamically reconfigurable battery-powered wireless sensors. The proposed technique employs machine learning approach to optimize sensor node function assignment, clustering decisions, route establishment and data collection trees for improved throughput that results in effective controls

Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers

CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers. A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Recreation, tourism and nature in a changing world : proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 2010

Proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 201

End to end architecture and mechanisms for mobile and wireless communications in the Internet

Architecture et mécanismes de bout en bout pour les communications mobiles et sans fil dans l'Internet. La gestion performante de la mobilité et l'amélioration des performances des couches basses sont deux enjeux fondamentaux dans le contexte des réseaux sans fil. Cette thèse apporte des solutions originales et innovantes qui visent à répondre à ces deux problématiques empêchant à ce jour d'offrir des possibilités de communication performantes et sans couture aux usagers mobiles accédant à l'Internet via des réseaux d'accès locaux sans fil (WLAN). Ces solutions se distinguent en particulier par l'impact minimum qu'elles ont sur les protocoles standards de l'Internet (niveaux transport et réseau) ou de l'IEEE (niveaux physique et liaison de données). S'inscrivant dans les paradigmes de "bout en bout" et "cross-layer", notre architecture permet d'offrir des solutions efficaces pour la gestion de la mobilité : gestion de la localisation et des handover en particulier. En outre, nous montrons que notre approche permet également d'améliorer l'efficacité des transmissions ainsi que de résoudre efficacement plusieurs syndromes identifiés au sein de 802.11 tels que les anomalies de performance, l'iniquité entre les flux et l'absence de contrôle de débit entre la couche MAC et les couches supérieures. Cette thèse résout ces problèmes en combinant des modèles analytiques, des simulations et de réelles expérimentations. Ces mécanismes adaptatifs ont été développés et intégrés dans une architecture de communication qui fournit des services de communication à haute performance pour réseaux sans fils tels que WIFI et WIMAX. ABSTRACT : Wireless networks, because of the potential pervasive and mobile communication services they offer, are becoming the dominant Internet access networks. However, the legacy Internet protocols, still dominant at that time, have not been designed with mobility and wireless in mind. Therefore, numerous maladjustments and “defaults of impedance” can be observed when combining wireless physical and MAC layers with the traditional upper layers. This thesis proposes several solutions for a pacific coexistence between these communication layers that have been defined and designed independently. Reliable mobility management and Low layer performance enhancements are two main challenging issues in the context of wireless networks. Mobility management (which is mostly based on mobile IP architecture nowadays) aims to continuously assign and control the wireless connections of mobile nodes amongst a space of wireless access networks. Low layer performance enhancements mainly focus on the transmission efficiency such as higher rate, lower loss, interference avoidance. This thesis addresses these two important issues from an original and innovative approach that, conversely to the traditional contributions, entails a minimum impact on the legacy protocols and internet infrastructure. Following the “end to end” and “cross layer” paradigms, we address and offer efficient and light solutions to fast handover, location management and continuous connection support through a space of wireless networks. Moreover, we show that such an approach makes it possible to enhance transmission efficiency and solve efficiently several syndromes that plague the performances of current wireless networks such as performance anomaly, unfairness issues and maladjustment between MAC layer and upper layers. This thesis tackles these issues by combining analytical models, simulations and real experiments. The resulting mechanisms have been developed and integrated into adaptive mobility management communication architecture that delivers high performing communication services to mobile wireless systems, with a focus on WIFI and WIMAX access networks

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment

Resource Management in Mobile Edge Computing for Compute-intensive Application

With current and future mobile applications (e.g., healthcare, connected vehicles, and smart grids) becoming increasingly compute-intensive for many mission-critical use cases, the energy and computing capacities of embedded mobile devices are proving to be insufficient to handle all in-device computation. To address the energy and computing shortages of mobile devices, mobile edge computing (MEC) has emerged as a major distributed computing paradigm. Compared to traditional cloud-based computing, MEC integrates network control, distributed computing, and storage to customizable, fast, reliable, and secure edge services that are closer to the user and data sites. However, the diversity of applications and a variety of user specified requirements (viz., latency, scalability, availability, and reliability) add additional complications to the system and application optimization problems in terms of resource management. In this thesis dissertation, we aim to develop customized and intelligent placement and provisioning strategies that are needed to handle edge resource management problems for different challenging use cases: i) Firstly, we propose an energy-efficient framework to address the resource allocation problem of generic compute-intensive applications, such as Directed Acyclic Graph (DAG) based applications. We design partial task offloading and server selection strategies with the purpose of minimizing the transmission cost. Our experiment and simulation results indicate that partial task offloading provides considerable energy savings, especially for resource-constrained edge systems. ii) Secondly, to address the dynamism edge environments, we propose solutions that integrate Dynamic Spectrum Access (DSA) and Cooperative Spectrum Sensing (CSS) with fine-grained task offloading schemes. Similarly, we show the high efficiency of the proposed strategy in capturing dynamic channel states and enforcing intelligent channel sensing and task offloading decisions. iii) Finally, application-specific long-term optimization frameworks are proposed for two representative applications: a) multi-view 3D reconstruction and b) Deep Neural Network (DNN) inference. Here, in order to eliminate redundant and unnecessary reconstruction processing, we introduce key-frame and resolution selection incorporated with task assignment, quality prediction, and pipeline parallelization. The proposed framework is able to provide a flexible balance between reconstruction time and quality satisfaction. As for DNN inference, a joint resource allocation and DNN partitioning framework is proposed. The outcomes of this research seek to benefit the future distributed computing, smart applications, and data-intensive science communities to build effective, efficient, and robust MEC environments