Self-organized Resource Research Articles

THE CONTENT OF PSYCHOLOGICAL ASSISTANCE FOR FORCED MIGRANTS REGARDING THE RECONSTRUCTION OF THE WORLD IMAGE VALUE-SEMANTIC COMPONENTS

Abstract. The article analyzes the psychological assistance content of migrants in the context of changing the world image value-semantic components. To understand the psychological conflicts that accompany migration processes, it is necessary to use an approach that, in contrast to the traditional understanding of the psyche in its regulatory function (as a reflection that adapts to new conditions) would see it as an important link in the mechanism of human self-organization that provides selective interaction with the outside world. Within the classical paradigm, the migration problem is interpreted as the forced migrants᾿ adaptation problem to the new socio-cultural environment, i.e. the homeostatic variant principle of human functioning in the new conditions is realized. The reflection paradigm realizes the view of the world as a "space for life", which must be mastered through the knowledge acquisition, skills, abilities, thinking ways, and experience gained by mankind. The psychologist acts in this case as a "teacher" and a model of a successful and adaptive lifestyle in the new socio-economic and cultural conditions. The contradiction in the case of forced migration is the contradiction between the established image of the world, the relevant behavioral patterns, and the effective implementation impossibility of these behavioral patterns in the new living conditions, which have not yet become conditions for the individual existence. A migrant needs psychological help in a case, when he lacks the self-organization resources to enter a new life way as a complex system features peculiar to this person of energy and material inherent features exchange with the new environment. The counseling process content is to organize assistance for a person in the transition of a person from homeostatic (adapting for a new environment ‒ here and now) to the future ‒ free, proactive human development in a new environment. To do this, the environment must be transformed into living conditions as soon as possible. As shown in the empirical research, the activation in the psychological period of clients᾿ mental activity counseling, aimed at reflecting on the value-semantic content of a new life way, contributes to the world image development.

Analysis of Dynamic Frequency Reuse Techniques in LTE – A Cellular Network

Inter-cell Interference (ICI) is a significant obstacle which is degrading the efficiency of mobile cellular networks particularly for end users. Frequency reuse is an ICI synchronization technique which helps to mitigate ICI by assigning dissimilar frequency reuse factors on the basis of its position to User equipment (UE). This paper proposes a Self-organized resource allocation (SORA) framework that chooses the reuse factors randomly. The output of this strategy is configured using MATLAB and correlated with numerous reuse factor combinations. Results from simulation show improved efficiency for Cell Edge Users (CEU).

Intelligent-Driven Green Resource Allocation for Industrial Internet of Things in 5G Heterogeneous Networks

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well.

To the theory of collective actions. Part 3. Conditions for achieving an optimum

The subject of this research is opportunities for effective use of human capital members of the collective in the conditions of self-governance and self-organization resources. It is assumed that members of the collective are capable to jointly create an additional cost by making individual efforts. Value of the expected gross income increases with the efforts put by each agent, and subordinated to the law of diminishing returns. The goal of each member of the collective consists in maximization of the own individual profit. Overcoming ineffective equilibrium faced by the collective as a result of independent choice of amount of effort applied by the members, and achievement of Pareto-preferred outcome requires corresponding coordination of collective actions. The key goal of coordination consists in overcoming opportunistic behaviors in form of shirking and such setting of incentive system that would results in application of optimal efforts by all members of the collective. Within the framework of mathematical model structured on the general principles, the author determines the essential conditions for achieving this goal. One of them dictates a corresponding strategy, another one – formulates the ex post rule of distribution of value of the expected gross income, perceived by all members of the collective as ex ante. According to this rule, the portion of each member of the collective in gross income must be equal his portion of efforts. Acceptance of this rule allows implementing a strategy, which motivates each member of the collective to apply efforts that would meet the maximal combined profit.

To the theory of collective actions. Part 2. Mathematical model

The subject of this research is the barriers that must be overcome by a collective of individual for the effective use of their self-governance and self-organization resources. It is assumed that members of the collective are capable to jointly create an additional cost by making individual efforts. Value of the expected gross income increases with the efforts put by each agent, and subordinated to the law of diminishing returns. The goal of each member of the collective consists in maximization of the own individual profit. Within the framework of mathematical modeling, it is demonstrated that in the regime of independent choice of the volume of applied effort, the agents achieve outcome that is the equilibrium by Nash, but inefficient by Pareto. Each member of the collective due to their egoistic intentions is interested in applying the limited amount of efforts that meet the maximum of his individual profit, but all his partners should apply as much efforts as possible. Overcoming of ineffective equilibrium requires coordination of efforts invested by all members of the collective. As follows from the model, the main obstacles on the way to effective usage of human resources by a collective in the regime of self-organization and self-governance is a post-contractual opportunism in form of shirking. The free-rider problem and motivation to opportunistic behavior are generated by the desire of each member of the collective to maximization of their own individual profit in the conditions of force of the law of diminishing returns.

To the theory of collective actions. Part 4. Use of violence potential

The subject of this research is opportunities for effective use of human capital members of the collective in the conditions of self-governance and self-organization resources. It is assumed that members of the collective are capable to jointly create an additional cost by making individual efforts. Value of the expected gross income increases with the efforts put by each agent, and subordinated to the law of diminishing returns. The goal of each member of the collective consists in maximization of the own individual profit. Achievement of the socially optimal level of applied efforts requires coordination of actions based on the high level of trust between all members of the collective. Within the framework of mathematical model, it is demonstrated that the lack of trust to some extent can be compensated by the incentives based on the use of violence potential. Most successful implementation of such type of incentives is possible only in the relatively small collectives in the conditions of inevitability of punishment for violation, and cautions use of force. In large collectives, a low probability of punishment for violence turns out either in low significance of the expected punishment, or extremely high meaning of nominal punishment. In first instance, a threat of punishment can play a role of a constraining factor; while on the other instances – lead to a stiff punishment that may reduce the usefulness of a violator to an extremely low level. Therefore, the achieved by the collective result can be incomparable by Pareto even with Nash equilibrium.

To the theory of collective actions. Part I. Articulation of the problem

The subject of this research is the barriers that must be overcome by a collective of individual for the effective use of their self-governance and self-organization resources. Until the moment when Elinor Ostrom was awarded with the Nobel Prize, the majority of economists and politicians were assured that a collective is incapable of managing the common resource effectively. Most popular opinion was reflected in a metaphor: the tragedy of communities. This opinion was traced by an  entire number of theoretical models and concepts. Due to high incidence of pessimistic view, the corresponding issues were considered resolved, and thus, were displaced to the periphery of economic science. The falseness of such views was established in the course of field and experimental research of E. Ostrom. This article analyzes the factors that served as the foundation for formation of pessimistic views upon ability of the collective to efficient management of common resource. On the reasons consists in inappropriate identification of the regime of open accesses with communal property. Another one – in the axiom on the inability of a collective, which members pursue egoistic intentions, to efficient usage of common resource in the regime of self-governance and self-organization. These prerequisites led to a conclusion on the need for privatization or nationalization of such resource. The goal of this work consists in theoretical analysis of such opportunities that allow a collective to successfully resolve the faced social dilemmas, when each of them tries to achieve a maximal individual profit.  

Self-organised resource assignment for on-demand services in the cloud platform

Self-organised resource assignment for on-demand services in the cloud platform

Self-organised resource assignment for on-demand services in the cloud platform

It is more popular for a multimedia service provider (MCSP) to deploy many data centres (DCs) in different geographic locations over cloud for delivering video-on-demand (VoD) services to a lot of users. One primary task of the MCSP is to maximise its profit while guarantee the user's quality-of-service (QoS) requirements. However, the stochastic arrival of user requests and the capacity restriction of individual DC make resource management in distributed cloud more challenging than in a general cloud. We present a resource assignment strategy that can accommodate heterogeneous network resources and QoS demands by converting the request distribution problem into the constrained function optimisation problem. An online algorithm is developed and certified approximating to the optimum solution. Compared with other alternatives, our algorithm can cut down more than 35% of operational cost without degrading the QoS of end users.

Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

The Internet of Things (IoT) will encompass a massive number of machine type devices that must wirelessly transmit, in near real-time, a diverse set of messages sensed from their environment. Designing resource allocation schemes to support such coexistent, heterogeneous communication is hence a key IoT challenge. In particular, there is a need for self-organizing resource allocation solutions that can account for unique IoT features, such as massive scale and stringent resource constraints. In this paper, a novel finite memory multistate sequential learning framework is proposed to enable diverse IoT devices to share limited communication resources, while transmitting both delay-tolerant, periodic messages and urgent, critical messages. The proposed learning framework enables the IoT devices to learn the number of critical messages and to reallocate the communication resources for the periodic messages to be used for the critical messages. Furthermore, the proposed learning framework explicitly accounts for IoT device limitations in terms of memory and computational capabilities. The convergence of the proposed learning framework is proved, and the lowest expected delay that the IoT devices can achieve using this learning framework is derived. Furthermore, the effectiveness of the proposed learning algorithm in IoT networks with different delay targets, network densities, probabilities of detection, and memory sizes is analyzed in terms of the probability of a successful random access (RA) request and percentage of devices that learned correctly. Simulation results show that, for a delay threshold of 1.25 ms, the average achieved delay is 0.71 ms and the delay threshold is satisfied with probability 0.87. Moreover, for a massive network, a delay threshold of 2.5 ms is satisfied with probability 0.92. The results also show that the proposed learning algorithm is very effective in reducing the delay of urgent, critical messages by intelligently reallocating the communication resources allocated to the delay-tolerant, periodic messages.

Knowledge Management for Self-Organised Resource Allocation

Many instances of socio-technical systems in the digital society and digital economy require some form of self-governance. Examples include community energy systems, peer production systems, participatory sensing applications, and shared management of communal living areas or workspace. Such systems have several features in common, of which three are that they are rule-oriented , self-organising, and value-sensitive , and in operation, this combination of features entails self-modification of the rules in order to satisfice a changeable set of values. This presents a fundamental dilemma for systems design. On the one hand, the system must be sufficiently unrestricted (resilient, flexible) to enable a diverse group but with a shared set of congruent values to achieve their joint purposes in collective action situations. On the other hand, it must be sufficiently restricted (stable, robust) to prevent a subset of the group from exploiting self-determination ‘against itself’ and usurp control of the system for the benefit of its own narrow interests. To address this problem, we consider a study of classical Athenian democracy which investigates how the governance model of the city-state flourished. The work suggests that exceptional knowledge management , i.e., making information available for socially productive purposes, played a crucial role in sustaining its democracy for nearly 200 years, by creating processes for aggregation, alignment, and codification of knowledge. We therefore examine the proposition that some properties can be generalised to resolve the rule-restriction dilemma by establishing a set of design principles intended to make knowledge management processes open, inclusive, transparent, and effective in self-governed social technical systems. We operationalise three of these principles in the context of a collective action situation, namely self-organised common-pool resource allocation, and present the results of a series of experiments showing how knowledge management processes can be used to obtain robust solutions for the perception of fairness, allocation decision, and punishment mechanisms. By applying this operationalisation of the design principles for knowledge management processes as a complement to institutional approaches to governance, we demonstrate empirically how it can satisfice shared values, distribute power fairly, and apply “common sense” in dealing with rule violations. We conclude by arguing that this approach to the design of socio-technical systems can provide a balance between restricted and unrestricted self-modification of conventional rules, and can thus provide the foundations for sustainable and democratic self-governance in socio-technical systems.

Appropriate Resource Allocation for Effective Mobile Communication in 4G LTE- Advanced

Objective: Neighboring cells become progressively worse especially for cell edge users due to Inter Cell Interference (ICI) in Orthogonal Frequency Division Multiple Access (OFDMA). Frequency Reuse Factor is to mitigate ICI by proper allocation of resources to users in network layout. Methods/Statistical Analysis: This study presents a self-organized resource allocation scheme for automatically selecting the reuse factor based on number of cell edge users and cell center user. Findings: This study explains about the new scheduling metric based on transmitted energy per bit and this provides the resources to users which are having low ration of transmitted energy per bit to avoid the wastage of resources. Applications/Improvements: Due to consideration of different reuse factor and also scheduling metric, this algorithm provides better performance for edge users in terms of Energy cell efficiency, Signal to Noise Interference Cell Ratio (SINR) and Fairness Index. Keywords: Center Users, Edge Users, 4G LTE Advanced, Round Robin (RR), Maximum Carrier to Interference Ratio (MCI)

A self-organized resource provisioning for cloud block storage

Modern cloud deployments rely on multiple layers of microservices and a layer of one or more persistent data store systems. The latter has always received a lot of attention as it is the slowest part of the request of an online service. Its unpredictable performance due to workload interference, performance degradation, lack of control, and network partitions has made the cloud less attractive for some services, especially for the applications that require guaranteed throughput, latency, elasticity, and high availability. Motivated by those facts, we propose the Block Software Defined Storage (BSDS), a framework to automate the orchestration, deployment, and management of a storage system by separating the data layer from control. BSDS employs an SLA-aware (Service Level Agreement) and self-learning scheduler that treats the backend media as a black box and abstracts the resource allocation from the state of the storage cluster. Our proposed framework is a step toward the emergence of an intelligent cloud infrastructure that can adapt to unexpected failures, network traffic fluctuations, workload interference perturbations or even changes in the underlying infrastructure. We perform extensive simulations with workloads deduced from an enterprise block storage system and showcase that the proposed scheduler can provide guaranteed Quality of Service (QoS). We finally demonstrate a practical deployment of BSDS on the OpenStack platform.

A lightweight plug-and-play elasticity service for self-organizing resource provisioning on parallel applications

Today cloud elasticity can bring benefits to parallel applications, besides the traditional targets including Web and critical-business demands. This consists in adapting the number of resources and processes at runtime, so users do not need to worry about the best choice for them beforehand. To accomplish this, the most common approaches use threshold-based reactive elasticity or time-consuming proactive elasticity. However, both present at least one problem related to the need of a previous user experience, lack on handling load peaks, completion of parameters or design for a specific infrastructure and workload setting. In this context, we developed a hybrid elasticity service for master–slave parallel applications named Helpar. The proposal presents a closed control loop elasticity architecture that adapts at runtime the values of lower and upper thresholds. The main scientific contribution is the proposition of the Live Thresholding (LT) technique for controlling elasticity. LT is based on the TCP congestion algorithm and automatically manages the value of the elasticity bounds to enhance better reactiveness on resource provisioning. The idea is to provide a lightweight plug-and-play service at the PaaS (Platform-as-a-Service) level of a cloud, in which users are completely unaware of the elasticity feature, only needing to compile their applications with Helpar prototype. For evaluation, we used a numerical integration application and OpenNebula to compare the Helpar execution against two scenarios: a set of static thresholds and a non-elastic application. The results present the lightweight feature of Helpar, besides highlighting its performance competitiveness in terms of application time (performance) and cost (performance × energy) metrics.

Echo State Networks for Self-Organizing Resource Allocation in LTE-U With Uplink–Downlink Decoupling

Uplink-downlink decoupling in which users can be associated to different base stations in the uplink and downlink of heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed band. In contrast, in this paper, the problem of resource allocation with uplink-downlink decoupling is studied for an SCN that incorporates LTE in the unlicensed band. Here, the users can access both licensed and unlicensed bands while being associated to different base stations. This problem is formulated as a noncooperative game that incorporates user association, spectrum allocation, and load balancing. To solve this problem, a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed. This proposed algorithm allows the small base stations to autonomously choose their optimal resource allocation strategies given only limited information on the network's and users' states. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution, which constitutes a mixed strategy Nash equilibrium for their studied game. Simulation results show that the proposed approach yields significant gain, in terms of the sum-rate of the 50th percentile of users, that reaches up to 167% compared with a Q-learning algorithm. The results also show that the ESN significantly provides a considerable reduction of information exchange for the wireless network.

Who is Governing the Commons: Studying Swedish Housing Cooperatives

This study examines current governance structures related to multifamily buildings designed by single actors (developers) and operated in cooperative forms. The study analyses the long-term sustainability of the resource regime of study (multifamily buildings) and inked governance structures by applying Ostrom’s eight design principles for long-term survival of self-organized resource regimes (Common-pool resources or CPR’s). The study also searches for signs of movement towards social innovation and collective action in current governance structures. We argue that the structures governing planning, production and operation of housing cooperatives in Sweden do not fulfil the eight design principles for the long-term survival of the resource regime of study, nor do they encourage movement towards social innovation or collective action. In order to ensure the long-term survival of the resource regime of study and to increase innovation in governance structures, five adjustments are proposed; changes in the structures governing risk/profit distribution, communication, collaboration and information between actors in the Swedish cooperative housing sector.

Self-Organized Resource Allocation for Femtocell Network to Mitigate Downlink Interference

In this paper, we consider the femto users and their mutual interference as graph elements, nodes and weighted edges, respectively. The total bandwidth is divided into a number of resource blocks (RBs) and these are assigned to the femto user equipment (FUEs) using a graph coloring algorithm. In addition, resources blocks are assigned to the femto users to avoid inter-cell interference. The proposed scheme is compared with the traditional scheduling schemes in terms of throughput and fairness and performance improvement is achieved by exploiting the graph coloring scheme.

Simulated annealing variants for self-organized resource allocation in small cell networks

This paper discusses the application of simulated annealing (SA) based meta-heuristics to self-organized orthogonal resource allocation problems in small cell networks (SCN)s, for static and dynamic topologies. We consider the graph coloring formulation of the orthogonal resource allocation problem, where a planar graph is used to model interference relations in a SCN comprising of randomly deployed mutually interfering cells. The aim is to color the underlying conflict graph in a distributed way, for which different variants of SA such as SA with focusing heuristic (i.e., limiting the local moves only to the cells that are in conflict), and fixed temperature, are investigated. For static topologies, distributed algorithms are used, in which no dedicated message-passing is required between the cells, except for the symmetrization of conflict graph. To enable distributed SA in dynamic topologies, a distributed temperature control protocol based on message-passing is considered. Different aspects relevant to self-organizing cellular networks are analyzed using simulations. These include the number of cells with resource conflicts, number of resource reconfigurations required by the cells to resolve the conflicts, requirements on dedicated message-passing between the cells, and sensitivity to the temperature parameter that guides the stochastic search process. Simulation results indicate that the considered algorithms are inherently suitable for SCNs, thereby enabling efficient resource allocation in a self-organized way. Furthermore, the underlying concepts and the key conclusions are general, and relevant to other problems that can be solved by distributed graph coloring.

Self-Organizing Resource Management Framework in OFDMA Femtocells

Next generation wireless networks (i.e., WiMAX, LTE) provide higher bandwidth and spectrum efficiency leveraging smaller (femto) cells with orthogonal frequency division multiple access (OFDMA). The uncoordinated, dense deployments of femtocells however, pose several unique challenges relating to interference and resource management in OFDMA femtocell networks. Towards addressing these challenges, we propose RADION, a distributed resource management framework that effectively manages interference across femtocells. RADION's core building blocks enable femtocells to opportunistically determine the available resources in a completely distributed and efficient manner. Further, RADION's modular nature paves the way for different resource management solutions to be incorporated in the framework. We implement RADION on a real WiMAX femtocell testbed deployed in a typical indoor setting. Two distributed solutions are enabled through RADION and their performance is studied to highlight their quick self-organization into efficient resource allocations.

An Agent-Based Model of Public Participation in Sustainability Management

This article reports on an agent-based simulation of public participation in decision making about sustainability management. Agents were modeled as socially intelligent actors who communicate using a system of symbols. The goal of the simulation was for agents to reach consensus about which situations in their regional environment to change and which ones not to change as part of a geodesign process for improving water quality in the greater Puget Sound region. As opposed to studying self-organizing behavior at the scale of a local 'commons', our interest was in how online technology supports the self-organizing behavior of agents distributed over a wide regional area, like a watershed or river basin. Geographically-distributed agents interacted through an online platform similar to that used in online field experiments with actual human subjects. We used a factorial research design to vary three interdependent factors each with three different levels. The three factors included 1) the social and geographic distribution of agents (local, regional, international levels), 2) abundance of agents (low, medium, high levels), and 3) diversity of preconceptions (blank slate, clone, social actor levels). We expected that increasing the social and geographic distribution of agents and the diversity of their preconceptions would have a significant impact on agent consensus about which situations to change and which ones not to change. However, our expectations were not met by our findings, which we trace all the way back to our conceptual model and a theoretical gap in sustainability science. The theory of self-organizing resource users does not specify how a group of social actors' preconceptions about a situation is interdependent with their social and geographic orientation to that situation. We discuss the results of the experiment and conclude with prospects for research on the social and geographic dimensions of self-organizing behavior in social-ecological systems spanning wide regional areas.