Resilience in microservices relies on caller-side observation and cooperative aggregation. While kernel-layer faults disrupt these channels asymmetrically, this paper provides the first systematic quantification of the resulting informational blind spot in production topologies. Under tc~netem network faults in hub-and-spoke topologies, leaf nodes - receiving only successfully routed packets - self-report zero errors, rendering gossip silent. Across 1,020 trials, we establish a structural utility boundary for cooperative signals and characterise this Gossip Silence as a consequence of Signal-Origin Asymmetry. We propose Inbound-Error Reporting as a formal remedy and demonstrate that the autonomous MAS-ODA agent approaches the performance of a globally informed coordinator while eliminating the single point of failure (SPOF).

Short Message Service (SMS) spam poses significant risks, including financial scams and phishing attempts. Although numerous datasets from online repositories have been utilized to address this issue, little attention has been given to evaluating their effectiveness and impact on SMS spam detection models. This study fills this gap by assessing the performance of ten SMS spam detection datasets using Decision Tree and Multinomial Naïve Bayes models. Datasets were evaluated based on accuracy and qualitative factors such as authenticity, class imbalance, feature diversity, metadata availability, and preprocessing needs. Due to the multilingual nature of the datasets, experiments were conducted with two stopword removal groups: one in English and another in the respective non-English languages. The key findings of this research have led to the recommendation of Dataset 5 for future SMS spam detection research, as evidence from the dataset’s high qualitative assessment score of 3.8 out of 5.0 due to its high feature diversity, real-world complexity, and balanced class distribution, and low detection rate of 86.10% from Multinomial Naïve Bayes. Recommending a dataset that poses challenges for high model performance fosters the development of more robust and adaptable spam detection models capable of handling diverse forms of noise and ambiguity. Furthermore, selecting the dataset with the highest qualitative score enhances research quality, improves model generalizability, and mitigates risks related to bias and inconsistencies.

The rapid increase of internet of things (IoT) devices in our daily lives has highlighted the critical need for strong security measures to protect the integrity and confidentiality of IoT communications. This paper presents a novel solution to this growing problem using a secure and lightweight DNA-based encryption method, elliptic curve encryption (ECC), to secure IoT communications. The research explains how DNA-LWCS (DNA-based lightweight cryptography system) utilizes basic encryption methods to secure data transmission against system complexity while maintaining security effectiveness. The security key ensures enough protection for achieving the necessary level of confidentiality. Three fundamental keys are extracted from publicly accessible DNA sequences to start the procedure during its first phase. When employed together with ECC these keys generate a private key during the second stage of development. During the second stage the keys generate a private key based on ECC (elliptic curve cryptography) protocol. The encryption and decryption of IoT device messages requires this private key during the last operational phase. The combination of intuitive DNA sequences together with ECC generates better security and decreases the strain on systems. Practical evaluations demonstrate that the proposed encryption method offers better security and efficiency compared to existing methods while maintaining weightless operational performance. This makes it an ideal solution to secure IoT data exchange. The encryption method we investigated received detailed study which focused on both security and efficiency criteria during our research timeframe. The research demonstrates our security method outperforms other solutions by maintaining low resource requirements. Our proposed DNA-based encryption system shows potential as a suitable security measure for protecting IoT connections through its lightweight design capabilities.

This paper introduces a novel approach inspired by structural sums, which describes the interactions among inclusions of dispersed composites. These sums serve as the cornerstone for mathematical models of random structures within the framework of the theory of analytical representative volume element (aRVE). We quantified the inter-phase interactions within composites based on the methodological aspects presented.

This paper introduces a novel approach inspired by structural sums, which describes the interactions among inclusions of dispersed composites. These sums serve as the cornerstone for mathematical models of random structures within the framework of the theory of analytical representative volume element (aRVE). We quantified the inter-phase interactions within composites based on the methodological aspects presented.

This research shows unethical user prediction on the peer-to-peer content delivery network against the Over-The-Top media service (OTT). The Web User Behaviour Analysis and unethical user prediction are performed based on the OTT multimedia service platform. The Analysis and unethical user prediction contain the four stages of processing. Firstly "Peer to Peer Content Delivery Network" is used to provide services and ideal resources (User). It uses ResourceCache: an optimized algorithm for giving efficient CDN-based over-the-top video streaming services. Secondly, “Selective destination Bitrate-adaptivity” is employed to provide the OTT Service (Devices). It utilizes bitrate adaptivity to improve the adaptivity of Over-The-Top Television systems. Thirdly, the “Network Functions Virtualization (NFV)” enabled multi-access edge data centers to embed to maintain sufficient data about users and their respective devices. It uses the QoE-based Load Balancing of OTT video content in CDN networks to improve the QoE of OTT multimedia services in wireless scenarios. Finally, the efficient “Review of Recommender System” are taken to collect feedback about services and subscription. It is most useful to check unethical user access. The performance result of the proposed system conceives the analysis based on web users' behavior prediction accuracy of 76%, Average precision of 67.8%, Average recall of 74.2%, and Average f1-measures of 85.9% against the performance of unethical user prediction.

Widespread cloud systems present new challenges time and time again. An essential element of such environments is their management. The Infrastructure as Code model has been gaining popularity for some time. In work presented here, we have proposed an agent-based approach to process execution within the Infrastructure as Code approach and have performed several numerical experiments. The work also includes an original formal agent model of the system. The results obtained allow us to develop trade-offs regarding computational demand and utilization.

AbstractAs cars and other transportation devices become increasingly interconnected, mobility takes on a new meaning, offering new opportunities. The integration of new communications technologies in modern vehicles has generated an enormous variety of data from various communications sources. Hence, there is a demand for intelligent transportation systems that can provide safe and reliable transportation while maintaining environmental conditions such as pollution, CO2 emission, and energy consumption. This chapter provides an overview of the Intelligent Transportation Systems (ITS) models. Briefly, it discusses the most important features of the systems and challenges, mostly related to the security in data and information processing. Fast anomalies detection and prevention of external attacks may help solve the problems of traffic congestion and road safety to prevent accidents. The chapter contains the description of the realistic Smart Transportation System developed by the Wobcom company and implemented in Wolfsburg (Germany). That system is also used for practical validation of the security service components of the platform created in the GUARD project.

Modelling artificial life has been an issue explored for several decades. However, science continues to surprise us with novel approaches to this problem. The aim of this paper is to innovative model a simplified living environment based on the agent paradigm and genetic algorithms. This paper also proposes a novel way of defining agent systems and artificial life embedded in a genetic approach. In the modelled and implemented environment there is one species of fauna and a simple species of flora that serves as food for the fauna. The fauna is implemented using agents inscribed in a genetic representation. The experimental part of the work includes calls to the simulator and the study of the dependencies resulting from the simulation mechanisms.

The intensive development and growth in the popularity of mobile cloud computing services bring a critical need to introduce new solutions that increase the level of cloud and users security. One of the critical issues in highly distributed computational systems is a task scheduling process. This process may be exposed to many external and internal security threats, like task injection, machine failure or generation of incorrect schedule. These problems are especially important in mobile environments. It can be even more complicated if we take into consideration the personalization of the services offered. Recently, blockchain has been gaining rapidly in popularity, combining high efficiency with applications in distributed and highly personalized computational environments. In this paper, we developed and described a novel model for security-aware task scheduling in cloud computing based on blockchain technology. Unlike other blockchain-based solutions, the proposed model uses Proof of Stake, which does not have high requirements for computing power. A series of conducted experiments confirmed the high efficiency of the proposed model.

The monitoring of the computational processes in highly distributed environments remains challenging in today's High Performance Computing. In this paper, we define the agent-based cloud monitoring system for supporting the computational tasks scheduling and resource allocation. The system consists of two types of agents, which may decide about the initialization of the schedule execution and monitor the work of the cloud computational nodes. The decision about running the new scheduling process is based on the expected number of available computational units in the specified time window. The efficiency of the proposed MAS-based model was justified through 40 empirical tests, where clouds without and within the MAS support were compared. The multiagent system (MAS) effectiveness has been expressed in the average number of floating point operations completed at the cloud resources in one second. The obtained results show the importance of setting the optimal initial time for execution of the new schedule. Our experiments show that for running the new schedule, at least 25% of the computing units in the clouds should be in the idle mode. Also the batches of tasks should not be too large, cause the waiting time for new schedule for execution should be short and not greater than 10% of expected batch execution time.

Fifth generation mobile networks (5G) will rather supplement than replace current 4G networks by dramatically improving their bandwidth, capacity and reliability. This way, much more demanding use cases that simply are not achievable with today’s networks will become reality - from home entertainment, to product manufacturing and healthcare. However, many of them rely on Internet of Things (IoT) devices equipped with low-cost transmitters and sensors that generate enormous amount of data about their environment. Therefore, due to large scale of 5G systems, combined with their inherent complexity and heterogeneity, Big Data and analysis techniques are considered as one of the main enablers of future mobile networks. In this work, we recognize 5G use cases from various application domains and list the basic requirements for their development and realization.

Body Area Networks (BANs) connect together nodes attached to a human body and transfer the data to an external infrastructure. The wireless communication channel and a variety of miniature sensor devices have lead to many useful applications of BANs, such as healthcare monitoring, military and emergency coordination, rescue services, sports, and entertainment. The Ultra Wide Band (UWB) communication model is widely used in wireless body area networks. UWB Radio Frequency (RF) technology provides robust and energy efficient transmission of data and signals through wireless networks. This chapter surveys recent models, applications and research challenges for future generation UWB RF technology for BANs. The chapter also discusses the state-of-the art in the cloud-based support for data storage and analysis in mobile health monitoring. Security issues for BANs in general and mobile health monitoring are addressed as a key aspect of the recent developments in the domain.

Cloud Computing is one of the most intensively developed solutions for large-scale distributed processing. Effective use of such environments, management of their high complexity and ensuring appropriate levels of Quality of Service (QoS) require advanced monitoring systems. Such monitoring systems have to support the scalability, adaptability and reliability of Cloud. Most of existing monitoring systems do not incorporate any Artificial Intelligence (Al) algorithms for supporting the change inside the task stream or environment itself. They focus only on monitoring or enabling the control of the system as a part of a separated service. An effective monitoring system for the Cloud environment should gather information about all stages of tasks processing and should actively control the monitored environment. In this paper, we present a novel Multi-Agent System based Cloud Monitoring (MAS-CM) model that supports the performance and security of tasks gathering, scheduling and execution processes in largescale service-oriented environments. Such models are explicitly designed to control the performance and security objectives of the environment. In our work, we focus on prevention of unauthorized task injection and modification, optimization of scheduling process and maximization of resource usage. We evaluate the effectiveness of MAS-CM empirically using an evolutionary driven implementation of Independent Batch Scheduler and FastFlow framework. The obtained results demonstrate the effectiveness of the proposed approach and the performance improvement. (C) 2017 Elsevier B.V. All rights reserved.

Cloud computing (CC) systems are the most popular computational environments for providing elastic and scalable services on a massive scale. The nature of such systems often results in energy-related problems that have to be solved for sustainability, cost reduction, and environment protection. In this paper we defined and developed a set of performance and energy-aware strategies for resource allocation, task scheduling, and for the hibernation of virtual machines. The idea behind this model is to combine energy and performance-aware scheduling policies in order to hibernate those virtual machines that operate in idle state. The efficiency achieved by applying the proposed models has been tested using a realistic large-scale CC system simulator. Obtained results show that a balance between low energy consumption and short makespan can be achieved. Several security constraints may be considered in this model. Each security constraint is characterized by: (a) Security Demands (SD) of tasks; and (b) Trust Levels (TL) provided by virtual machines. SD and TL are computed during the scheduling process in order to provide proper security services. Experimental results show that the proposed solution reduces up to 45% of the energy consumption of the CC system. Such significant improvement was achieved by the combination of an energy-aware scheduler with energy-efficiency policies focused on the hibernation of VMs. (C) 2018 Elsevier Inc. All rights reserved.

This paper presents an overview of techniques developed to improve energy efficiency of grid and cloud computing. Power consumption models and energy usage proles are presented together with energy efficiency measuring methods. Modeling of computing dynamics is discussed from the viewpoint of system identication theory, indicating basic experiment design problems and challenges. Novel approaches to cluster and network-wide energy usage optimization are surveyed, including multi-level power and software control systems, energy-aware task scheduling, resource allocation algorithms and frameworks for backbone networks management. Software-development techniques and tools are also presented as a new promising way to reduce power consumption at the computing node level. Finally, energy-aware control mechanisms are presented. In addition, this paper introduces the example of batch scheduler based on ETC matrix approach.

Task scheduling in large-scale distributed High Performance Computing (HPC) systems environments remains challenging research and engineering problem. There is a need of development of novel advanced scheduling techniques in order to optimise the resource utilisation. In this work, we develop the Agent Supported Non-Deterministic Meta Scheduler for cloud environments. This scheduling model is a simple combination of intelligent agent-based monitoring model for cloud system and security-aware cloud scheduler. In our model, scheduling, monitoring and reporting are provided in nondeterministic time intervals. An empirical case study using a FastFlow task farm was presented. It has demonstrates the effectiveness of the proposed solution.

Supervised classification covers a number of data mining methods based on training data. These methods have been successfully applied to solve multi-criteria complex classification problems in many domains, including economical issues. In this paper we discuss features of some supervised classification methods based on decision trees and apply them to the direct marketing campaigns data of a Portuguese banking institution. We discuss and compare the following classification methods: decision trees, bagging, boosting, and random forests. A classification problem in our approach is defined in a scenario where a bank’s clients make decisions about the activation of their deposits. The obtained results are used for evaluat-ing the effectiveness of the classification rules.

Security is a very complex and challenging problem in Cloud organizations. Ensuring the security of operations within the cloud by also enforcing the users' own security requirements, usually results in a complex tradeoff with the efficiency of the overall system.In this paper, we developed a novel architectural model enforcing cloud security, based on a multi-agent scheme and a security aware non-deterministic Meta Scheduler driven by genetic heuristics. Such model is explicitly designed to prevent Denial of Service and Timing Attacks over the cloud and has been demonstrated to be integrable within the well-known OpenStack platform. Additionally, we proposed two different models for assuring users security demands. The first is a scoring model that allows scheduling tasks only on the Virtual Machines offering proper security level. The second model takes into account the time spent on the necessary cryptographic operations dedicated to particular task.The above scheduling system has been simulated in order to assess the effectiveness of the proposed security architecture, resulting in an increased system safety and resiliency against attacks, without sensibly impacting the performance of the whole cloud environment. (C) 2016 Elsevier B.V. All rights reserved.

Over the decades the rapid development of broadly defined computer technologies, both software and hardware is observed. Unfortunately, software solutions are regularly behind in comparison to the hardware. On the other hand, the modern systems are characterized by a high demand for computing resources and the need for customization for the end users. As a result, the traditional way of system construction is too expensive, inflexible and it doesn’t have high resources utilization. Present article focuses on the problem of effective use of available physical and virtual resources based on the OpenStack cloud computing platform. A number of conducted experiments allowed to evaluate computing resources utility and to analyze performance depending on the allocated resources. Additionally, the paper includes structural and functional analysis of the OpenStack cloud platform.