Efficient Resource Management in Cloud Computing

The economic approaches of potential game theory and bargaining theory are applied to the area of power control in CDMA wireless networks. These perspectives help identify suitable equilibrium points, and algorithms that can be shown to converge to them. The bargaining approach also suggests an iterative method for the simultaneous routing and resource allocation problem.

The economic approaches of potential game theory and bargaining theory are applied to the area of power control in CDMA wireless networks. These perspectives help identify suitable equilibrium points, and algorithms that can be shown to converge to them. The bargaining approach also suggests an iterative method for the simultaneous routing and resource allocation problem.

Publication data Barbier, Bruno, 1962-Natural resource management in the hillsides of Honduras : bioeconomic modeling at the microwatershed level / Bruno Barbier, Gilles Bergeron. p. cm. -(Research report ; 123) Includes bibliographical references.

Stephen Wood, David Holman and Christopher Stride are at the ESRC Centre for Organisation and Innovation and the Institute of Work Psychology, University of Sheffield. Using data from a sample of 145 U.K. call centres, the authors test the core propositions of the strategic human resource management (SHRM) approach that: (a) there are coherent links through the SHRM chain from strategy, through operational requirements, to work design and human resource management, and (b) the fit between the human resource practices and market factors determines organizational performance. Little support for these hypotheses is found as only (a) a few direct relationships between the elements of the SHRM chain are found, and (b) direct relationships, rather than ones moderated by market factors, are found between human resource practices and performance. But key operational requirements are linked to work design, which is itself related to a limited number of human resource practices. The direct effects of work design on key performance indicators are more pronounced than those of human resource practices.

The concept of coupling geographically distributed resources for solving large scale problems is becoming increasingly popular forming what is popularly called grid computing. Management of resources in the Grid environment becomes complex as the resources are ...

Available per chapter (IoT in HRM, Cloud/Fog/Edge resource management).

Focus: Evaluates resource management strategies (allocation, scheduling, QoS) in cloud, fog, and edge computing for IoT-driven HRM.

Key takeaway: Fog/edge computing improves time-sensitive HR applications by reducing latency and bandwidth load compared to cloud-only solutions.

Enterprise intelligence systems are finding it more difficult to deal with large-scale, diverse, information with dynamic operation conditions causing delay in insights and optimum decision support. To respond to these problems, the paper will recommend a new adaptive information intelligence framework that will integrate metaheuristic optimization into the information management enterprise lifecycle. This approach is called the Evolutionary Intelligence Regulation Mechanism (EIRM), and it brings in an automated control sphere to self-optimize in the choice of data relevancy, merging of knowledge and propagation of decisions within business settings. In contrast to traditional methods, which are based on a few rules fixed or separate optimization phases, EIRM develops enterprise intelligence management as a highdimensional dynamic search problem, which allows autonomous reconfiguring in reaction to changes in the context and changing the organizations purposes. The structure combines the multi-source data integration and adaptive normalization and semantic relevance analysis to make sure only the information which is strategically questionable gets into the creation of intelligence. The evolutionary strategy is population-based and aims at exploring and refining the configurations of intelligence, tradeoffs between global adaptability and stability on the local scale. The proposed system is a tight relationship between optimization and management paradigms by which one can optimally contribute to the responsiveness of the decision-making process, decrease informational redundancy, and efficiencies of operations, without the additional computational load. The abstracted architecture of EIRM guarantees scalability, resilience to uncertainty, and easy integration with the existing enterprise infrastructures, and thus is viable as a solution to next generation enterprise intelligence infrastructures, which are in complex and data-heavy environments. The proposed solution attains an overall accuracy of 95.8%, signifying exceptionally reliable enterprise intelligence creation across various operational scenarios.

The desire to mobilise effective strategic human resource management in India’s new public management domain has seen the role of organisational trust receive greater scholarly and practical scrutiny. This study explores managers’ perception of HRM’s role in building organisational trust in five public sector organisations in India using exploratory multiple case studies. In implementing HRM, the findings suggest that managers are cognisant of specific human resource practices that can be socially adapted to enhance their effectiveness. Implications from the study’s emergent process model of human resource management and trust highlight the cross-level influences that affect India’s public sector organisations’ outcomes.

Restoration is currently among the most important tools for conserving biodiversity, but participation in restoration by local communities in its planning and design must be improved.We devised a people-centered biocultural approach to restoration that combines Indigenous local knowledge and scientific methods to select species for restoration such that human welfare and biodiversity conservation are considered.We applied the approach to the Caatinga dry forest, for which we simulated agroforestry productive systems based on plant species previously selected by locals for use in restoration, given their importance for water, energy, and food security. We compared functional diversity in the simulated systems with functional diversity in natural systems. Common native and some non-native species with low invasive potential dominated the average productive system. These species had functional diversity similar to that of natural vegetation but had a slightly different functional profile. Simulated systems were dominated by plant species with more acquisitive functional strategies than native flora. The adoption of our biocultural approach to restoration programs in the region illustrates the importance of local participation; local selection of species for restoration had little effect on ecosystem functional diversity. Our framework can be readily adapted to various contexts for evaluating cultural preferences and the ecological efficacy of biocultural restoration initiatives globally.

In this paper we present a welfar e economic (marketbased) resour ce management model that is QoS-based, which models the actual price-formation process of an economy. This approach manages resour ce and QoS allocation optimally so that the total utility of thesystem is maximized through a tatonnement process, in which operating markets for each r esour ce is done sep arately. We use the constructs of application b ene t functions and r esource demand functions to represent the system con guration and to solve the resour ce a l l o cation pr oblems.

A new class of multimedia applications require new mechanisms to consider various Quality of Services with respect to resource constraints so that they could support reliable services and utilize available resources optimally. In this paper we present a new analytical and generic resource management model that is QoS-based. The approach for resource allocation and relevant algorithms is based on a mathematically proved model that manages resource and QoS allocation intelligently so that the total system utility of is maximized. We use the constructs of application benefit functions and resource demand functions to represent the system configuration and to solve the resource allocation problems. Extensive surveys on the related work including systematic and analytical approaches are also presented.

Qualitative and semi-quantitative data from 139 interviews with farmers in Faisalabad, Pakistan, was subjected to cluster analysis to identify homogenous groups of farms regarding production strategies, milk yields and marketing. Four distinct production systems were identified. Semi-commercial smallholder-mixed systems (SSM; 31%) combined crop and livestock production, and fodder was primarily produced for own livestock. Semi-commercial smallholder dairy producers (SSD; 21.6%) had few buffaloes and cattle and low income. Commercial smallholder dairy producers (CSD; 37.4%) were mostly well-off and produced substantial quantities of milk year-round. Commercial large-holder dairy farms (CLD; 10%) showed the highest input and output levels. In all systems, most of the produced milk was sold in the increasing order of percentage (SSM: 69%, SSD: 69%, CSD: 87%; CLD: 94%). Negligence in breeding, wastage of high yielding buffaloes, high costs of feedstuffs, an unfavorable marketing system ...

In this chapter, we review scientific information regarding the conservation and restoration of forest ecosystems on public lands within the Northwest Forest Plan (NWFP, or Plan) area that harbor special value for American Indian tribes and individuals. We highlight advances in understanding how changes in climate, fire, hydrology, vegetation, and resource management regimes have affected tribal ecocultural resources and how land management can promote ecocultural resources in the future. In particular, we examine how distinctive strategies for engaging tribes in restoring ecocultural resources can uphold both tribal rights and federal responsibilities, while supporting other federal land management goals.

This paper elaborates on the design, implementation and performance evaluation of a prototype Radio Resource Management (RRM) framework for TV white spaces (TVWS) exploitation, under an auction-based approach. The proposed RRM framework is applied in a centralised Cognitive Radio (CR) network architecture, where exploitation of the available TVWS by Secondary Systems is orchestrated via a Spectrum Broker. Efficient RRM framework performance, as a matter of maximum-possible resources utilization and benefit of Spectrum Broker, is achieved by proposing and evaluating an auctionbased algorithm. This auction-based algorithm considers both frequency and time domain during TVWS allocation process which was defined as an optimization problem, where maximum payoff of Spectrum Broker is the optimization goal. Experimental tests that were carried-out under controlled conditions environment, verified the validity of the proposed framework, besides identifying fields for further research.

This paper elaborates on the design, implementation and performance evaluation of a prototype Radio Resource Management (RRM) framework for TV white spaces (TVWS) exploitation, under an auction-based approach. The proposed RRM framework is applied in a centralised Cognitive Radio (CR) network architecture, where exploitation of the available TVWS by Secondary Systems is orchestrated via a Spectrum Broker. Efficient RRM framework performance, as a matter of maximum-possible resources utilization and benefit of Spectrum Broker, is achieved by proposing and evaluating an auctionbased algorithm. This auction-based algorithm considers both frequency and time domain during TVWS allocation process which was defined as an optimization problem, where maximum payoff of Spectrum Broker is the optimization goal. Experimental tests that were carried-out under controlled conditions environment, verified the validity of the proposed framework, besides identifying fields for further research.

The provision of very high capacity is one of the big challenges of the 5G cellular technology. This challenge will not be met using traditional approaches like increasing spectral efficiency and bandwidth, as witnessed in previous technology generations. Cell densification will play a major role thanks to its ability to increase the spatial reuse of the available resources. However, this solution is accompanied by some additional management challenges. In this article, we analyze and present the most promising solutions identified in the METIS project for the most relevant network layer challenges of cell densification: resource, interference and mobility management.

The object of research is a special-purpose radio communication system. A special purpose radio communication system is affected by many different destructive influences. The main ones are deliberate interference and cybernetic impact of various purposes. The above causes the search for new scientific approaches to identify and identify the destructive impact on special-purpose radio communications in order to increase the operational efficiency of special-purpose radio communications systems. In this work, the problems of developing a mathematical model for managing the radio resource of special-purpose radio communication systems based on the evolutionary approach are solved. In the course of the research, the authors of the work used the main provisions of the theory of artificial intelligence, the theory of automation, the theory of complex technical systems, as well as general scientific methods of cognition, namely analysis and synthesis. The proposed methodological approach w...

More recently I joined South East Catchments which is an NRM regional body. 3 There are three levels of government in Australia, and we vote to elect representatives to each of these levels: federal, state or territory and local. With the Coalition in power federally and Labor in power at a state level across the country from 2000 -2007. A change from the Coalition to Labor Government at the Federal level occurred on 24 th November 2007. Learning from episodes -More theory for multi-party negotiation processes Chapter 6 (Intermezzo I) draws on learnings from the ASAP NRI and compliance episodes and further delineates the need for investigating and developing a methodology to underpin interventions in the form of facilitated interactive negotiations. Theoretical aspects of organisational change for adaptive environmental management are explored en route to proposing a triple-loop-negotiation model that assists the design, facilitation and conduct of multi-stakeholder negotiation processes. Chapter 7 examines further episodes where triple-loop negotiation is tested as a concept for underpinning planning and implementation of multi-stakeholder interactive decision-making and negotiation processes. The first episode specifically targets the use of negotiation thinking and theory in the development of a collective plan for continuation of the Rural Water Use Efficiency program across Queensland. The second episode connects the New Extension Framework (2000) with negotiations on the role and function of NRM extension in DNR&M. Drawing on learnings from preceding episodes, Chapter 8 (Intermezzo II) explores current research in the organisation and facilitation of multi-stakeholder negotiation processes. Particular attention is paid to the organisational dimensions of such processes with investigations of organisational learning literature. Triple-loop negotiation is further developed as a means of conceptualising and planning these interactive negotiations. A methodology is proposed for enabling and facilitating triple-loop negotiation processes. Chapter 9 examines an in-depth action research process where the triple-loop-negotiation approach is employed aiming to influence extension policy nationally. This involves reflective learnings in the participatory design and implementation of national events particularly focused on extension policy spanning 2003-2004. Included in this is the development of a draft National Framework for Extension in Australia (NEFA). Chapter 10 builds on learnings from these events and reflects on the employment of the triple-loopnegotiation approach in the initiation, plans and progressive outcomes of the State Extension Leaders Network (2005+). This network has a strategic role in influencing coordination and collaboration of key institutional stakeholders in the Australian extension system. Chapter 11 brings together key learnings for organising and facilitating multi-party (and other) negotiations in the NRM system in Queensland and Australia. It also relates these findings to past and contemporary thinking on extension in effort to inform further development of extension (or NRM extension) in Queensland and indeed Australia. The findings from the research journey are drawn into key conclusions and recommendations for the Department of Natural Resource and Mines (and its successors), for NRM extension, and for organising and facilitating multi-stakeholder negotiations in the NRM extension system. Mines Department to form NR&M. A later merger with the Department of Energy to form NRM&E (2003), and then another change back to NR&M (2004) and in 2006 to NRW (Natural Resources and Water). 5 See chapter 2 6 It is important to note that Australia is a federation and constitutional monarchy. Australians elect state and territory legislatures based on the Westminster tradition, as well as a bicameral Parliament of Australia, which is a hybrid of Westminster practices with the uniquely federalist element of the Australian Senate. Under this system, powers are distributed between a national government (the Commonwealth) and the six States (three Territories) (Australian Government 2010) 7 National Action Plan for Salinity and Water Quality 8 Natural Heritage Trust (Round 2) Murray (1999), Marsh and Pannell (2000) and all noted the movement from one-on-one services to group facilitation in public approaches to extension. Emphasis away from individual one-on-one services toward a broader platform reflects increasing environmental and social concerns than the previous 'production/economic' focus 10 . In balance with this shift, Coutts et al. ( ) reports strong one-on-one extension continuing in the private sector. This shift to larger societal concerns and a broader sociological approach sees public extension agencies spending a large amount of time considering the social impacts of collective issues such as land/resource degradation and environmental stewardship. Marsh and Pannell (2000) identified that moves away from top-down approaches, towards groupbased extension activities were advantageous. Emphasis on 'adult learning' and landholder 'ownership' of both problems and solutions were likewise very positive, and facilitated the 9 My italics. It is interesting that Murray saw moves to secure external funding as 'frantic'. 10 Note: Changing priorities see some agencies reversing this trend . 13 entry of rural people other than producers into rural issues (e.g. Landcare). Compared to technical concerns common to other international extension systems, however, it seems that Australian extension systems place far greater importance on group processes (Murray 1999). Marsh and Pannell (2000) caution that the recent emphasis on groups may be excessive, resulting in problems relating to effectiveness of group approaches in all situations, the sustainability of group-based extension and its ability to involve all who need to be involved. State Governments have trended towards a reduction in publicly-supported extension services, with corresponding increases in externally-resourced delivery. A key rationale is that individuals profiting from public advisory services should pay for them and participatory approaches to collectively solving issues and generating funding to support related activities are in the interests of society. Some landholder (stakeholder) groups feel there are advantages associated with not being as dependent on public agencies. Murray (1999) found, however, that some landholders feel that extension (Government) is abdicating its responsibilities. observe the private sector operating in the same sphere as public extension, but continuing to expand, providing individual technical advice as well as undertaking interactive group-based activities. In some cases, however, decreases in State support resulted in State Government extension staff reductions below a 'critical mass' with many extension programs being largely non-functional. While this has led to strategies for seeking funding through cost-recovery, fee-for-service, and external sources, the requirement of extension projects to align with funder priorities may be inconsistent with State objectives (Murray 1999). Key elements in this strategy development process included: 1. Using the framework process (as per Timms and Clark above) 2. Compiling a list of key stakeholders to target 3. Organising regional, metropolitan, and head office workshops 4. Organising voluntary regional extension teams to assist where possible Key Research Question: "What processes and approaches do people in natural resource management use to negotiate outcomes given the different values and paradigms in the Department and the community, and in what ways can these approaches, when reflected against literature, inform, develop and institutionalise Extension in the Department of Natural Resources & Mines?" Key Action 1 -Undertake a comprehensive identification and analysis of current Natural Resource Information practices and initiatives within and across Queensland and Commonwealth Government agencies and other relevant bodies. Key Action 2 -Identify and clarify the authority, role and functions performed by State Government agencies and other stakeholders in Natural Resource Information activities. Key Action 3 -Build on existing initiatives to establish a Queensland Natural Resource Monitoring and Reporting System to inform government and the community on the condition and trend of our natural resource asset and inherent environmental values.

This paper investigates the downlink resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA) Heterogeneous Networks (HetNets) consisting of macrocells and small cells sharing the same frequency band. The focus of this study is to devise an energy efficient scheme that allows shared spectrum access to small cells, while ensuring a certain level of quality of service for the macro cell users. It further enable us to minimize the overall energy consumption by switching the underutilized small cells to sleep mode. To devise such a mechanism, we have used a combination of linear binary integer programming and progressive analysis based heuristic algorithm. We evaluated our proposed solution by comparing the macrocell served users performance against Reuse 1 case. Moreover, we provide an analytical comparison of the network power consumption with and without the sleep mode capabilities. It has been shown that our proposed algorithm not only reduces the overall network energy consumption but also minimizes the interference caused by smalls cells to macrocell served users.

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines. Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Moreover, computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account effect on temperature and it is challenging to simulate the thermal behaviour of CDCs. There is a need for a thermal-aware framework to simulate and model the behaviour of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modelling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyse the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modelling and simulating the thermal behaviour of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy.

We describe a de-centralised approach to resource management and discovery, based on a community of interacting software agents. Each agent either represents a user application, a resource, or a MatchMaking service. The proposed approach can support dynamic registration of resources and user tasks, facilitating the establishment of dynamic clusters. Resource capability and task requirements are described using an object based data model, enabling new types of devices or new features in existing devices to be identified. A comparison with the Discovery and LookUp services in Jini and TSpaces is also provided.

Intensifying global change is propelling many ecosystems toward irreversible transformations. Natural resource managers face the complex task of conserving these important resources under unprecedented conditions and expanding uncertainty. As once familiar ecological conditions disappear, traditional management approaches that assume the future will reflect the past are becoming increasingly untenable. In the present article, we place adaptive management within the resist–accept–direct (RAD) framework to assist informed risk taking for transforming ecosystems. This approach empowers managers to use familiar techniques associated with adaptive management in the unfamiliar territory of ecosystem transformation. By providing a common lexicon, it gives decision makers agency to revisit objectives, consider new system trajectories, and discuss RAD strategies in relation to current system state and direction of change. Operationalizing RAD adaptive management requires periodic review and ...

Owing to the increasing number of IoT gadgets and the growth of big data, we are now facing massive amounts of diverse data that require proper preprocessing before they can be analyzed. Conventional methods involve sending data directly to the cloud, where it is cleaned and sorted, resulting in a more crowded network, increased latency, and a potential threat to users' privacy. This paper presents an enhanced version of the BlueEdge framework-a neural network solution designed for the automated classification of data types on edge devices. We achieve this by utilizing a feed-forward neural network and optimized features to identify the presence of 14 distinct data types. Because of this, input data can be preprocessed near its source, and not in the cloud. We utilized a comprehensive dataset comprising 1400 samples, encompassing various data formats from around the world. Compared with rule-based methods, experimental assessment achieves better performance, and results in reduced data transmission (reduced by 62%) and processing latency (78 times faster than cloud-based systems), with resource efficiency comparable to low-end mobile devices. Additionally, our strategy demonstrates strong performance under various data conditions, achieving accuracy levels of over 85% on datasets that may include variations and a noise level as high as 20%. The approach used here is capable of processing data for IoT devices used in education, which can lead to more efficient connections with the cloud and better privacy preservation.

The Italian National Research Council is a public company with multiple locations and many decentralized human resources departments. With decentralized human resources management, each location controls its own individual personnel issues such as attendances and leaves. One of the goals of decentralized human resources departments is to give autonomy to different locations, allowing them to adapt to the specific scientific climate of the individual site. However, the actual result of this form of human resources management is often disorganization, failure to follow unified standards or an ineffective application of those standards. This article describes a distributed and federated approach to Human Resource Management that is being adopted by the Italian National Research Council (CNR). In particular, this article describes the time and attendance software implemented by the Institute of Informatics and Telematics (IIT). The aim of the article is to describe the architecture of the system, the decision-making and choices regarding implementation adopted as part of the project, the main software components and their ways of interaction and communication.

Marine resource crises have initiated a search for alternative approaches to resource assessment and management that has culminated in a global focus on ecosystem approaches to management (EAM). Here, the ecosystem extends to humans as drivers and recipients of ecosystem change. More specifically, attention is being paid to identifying specific qualities of local resource users' experiences and knowledge that might productively inform resource management, while also providing local users with substantial ''voice'' in shaping new management policies and practices. Here an evaluation is provided of the extent to which local ecological knowledge (LEK) can provide advice for an ecosystem approach to inshore coastal management, specifically, the identification of ecologically and biologically significant areas, based on the results of two comprehensive studies of coastal Nova Scotian commercial harvesters' local ecological knowledge. While spatially explicit, local ecological knowledge displays strengths and limitations that must be explicated for it to prove useful for strengthening ''voice'' and providing EAM inputs.

An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes.

Purpose The purpose of this paper is to identify challenges faced in resource management in the UK construction industry and to propose some solutions to these problems. Design/methodology/approach Based on a qualitative research methodology, 14 experts from the UK construction industry were chosen to be participants in the study. The participants were equally divided into two focus groups to discuss resource management using five projects as case studies. Thematic analysis of the discussion reveals seven key factors that affect resource management. Findings The results show that most of the problems identified are due to poor data management processes and the practice of having data in silos. Overcoming this challenge requires the adoption of big data approaches for resource management to allow the integration of large and different forms of data. Originality/value This study seeks to bring to the fore challenges faced in resource management by the UK construction industry and to o...

In California and other regions vulnerable to water shortages, new technologies are needed to support agricultural producers and water managers in maximizing the benefits of available water supplies. The Satellite Irrigation Management Support (SIMS) project combines NASA's Terrestrial Observation and Prediction System (TOPS) with Landsat and MODIS imagery to map crop evapotranspiration (ET) and develop information products to support irrigation management and other water use decisions. The primary focus of the effort is to develop the computing and data processing systems required to support the use of satellite and weather data for rapid assessment of current crop conditions over large areas, and generate product formats that are convenient for agricultural producers and other end users. Supporting datasets include reference ET grids from the California Irrigation Management Information System (CIMIS), and USDA's Cropland Data Layer. SIMS map products are generated at 30 meter resolution on a daily basis over appx. 60,000 sq. km. of California farmland. Wireless sensor networks and surface renewal instrumentation have been deployed on several commercial farms to monitor ET and soil moisture parameters to facilitate evaluation of SIMS products. To support retrospective analyses, the project is utilizing the NASA Earth Exchange to process the complete Landsat archive for California from 2003 to present.

Fog computing is a promising and challenging paradigm that enhances cloud computing by enabling efficient data processing and storage closer to data sources and users. This paper introduces a game-theoretic approach called GTRADPMFC (Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing) to address resource allocation and dynamic pricing challenges in fog computing environments with limited resources. The proposed model features non-cooperative competition among fog nodes for resources and dynamic pricing mechanisms to encourage efficient resource utilization. Theoretical analysis and simulations demonstrate that GTRADPMFC improves resource efficiency and overall fog computing system performance. Additionally, the paper discusses how to handle situations with insufficient samples and provide flexibility for users unable to meet completion time requirements. GTRADPMFC effectively manages resource allocation by establishing pricing in fog computing, considering potential delays in completion time. This is achieved through research, simulations, convergence analysis, complexity evaluation, and optimization guarantees.

Community based natural resource management programmes had been adopted by many countries as a management approach that can bring better results in the sustainable resource management. This management regime represents a shift from centralised approaches which were previously employed. Zimbabwe is among the pioneers of this programme in the sub-Saharan Africa region. A lot has been done in an attempt to bring resource governance to the people. One of the projects that had registered remarkable success is the Communal Areas Management Programme for Indigenous Resources (CAMPFIRE). Many Rural District Councils are engaged in these projects; and nearly every council is running a CAMPFIRE project. However the same cannot be said of other conservation projects in rural and resettlement areas in the country. There is little success registered especially in the conservation of forest resources. Structures for community based natural resource management (CBNRM) are in place but there are defunct, which explains the uninvited resource degradation in rural areas. This paper seeks to delineate the factors underlying the structures' failure in delivery of effective democratisation of resource governance.

Cloud computing refers to a computer environment in which traditional software systems, installations, and licensing concerns are replaced with comprehensive on demand," pay as you need" internet based services. In this scenario, many cloud customers can request multiple cloud resources at the same time. As a result, there should be a plan in place to ensure that resources must be prepared for the needy customer in proficient way in order complete their needs. In cloud computing systems, resource management is a critical and difficult issue. It must meet numerous service quality requirements and, as a result, reduce SLA violations. This paper survey different resource management technique for cloud infrastructures.

The purpose of this descriptive and correlational study was to examine perceptions of Ohio State University Extension county chairs regarding their human resource management competencies and performance of human resource management activities. The study also sought to describe the relationship between human resource management competencies and performance of human resource management activities of county chairs. County chairs were selected for study because they represent the first line of management most clientele and employees face. They also represent a group of extension managers least likely to have formal education or specialized training in human resource management. The highest human resource management competencies perceived by county chairs were written comprehension, oral comprehension, written expression, information gathering, inductive reasoning, and problem sensitivity. The human resource management activities for which county chairs indicated the highest means were: ...

In this paper, the problem of strategic resource management in fog networks is discussed while considering a payper-use model, similar to that used in cloud. Fog networks are distributed in nature, because of which resource management in these networks is an NP-hard problem. In the existing literature, the researchers focused on resource management in fog networks, while considering the network delay constraint. However, none of these works considered the effect of pricing policy while deciding on resource allocation. Hence, there is a need for pricing-based resource management in fog networks. In this work, we proposed a dynamic pricing-based resource allocation scheme, named FogPrime, for analyzing the trade-off between the service delay and the associated price. In FogPrime, we use dynamic coalition-formation game to decide the resource allocation strategy locally within a cluster. On the other hand, we use utility game to choose the fog nodes, strategically, while considering the aforementioned trade-off. Through simulation, we observed that FogPrime outperforms the existing schemes in terms of satisfaction of the involved entities -the enduser and the fog nodes. Using FogPrime, the satisfaction of the end-users and the fog nodes increases by 24.49-47.82%, respectively. Additionally, we observe that FogPrime ensures an even distribution of profit among the fog nodes and enables the end-users to pay less at most by 15.88-47.27%.

Co-management adalah suatu pengaturan dimana tanggung jawab pengelolaan sumberdaya dibagi antara pemerintah dan pengguna. Peningkatan pemanfatan sumberdaya Taman Nasional Karimunjawa telah menurunkan kualitas sumberdaya dan jika dibiarkan akan mengancam status TNKJ. Tujuan utama studi adalah merumuskan arahan kebijakan pengembangan co-management Taman Nasional Karimunjawa dengan menganalisa kebijakan dan kelembagaan pengelolaan, mengevaluasi kapasitas pengelolaan dan mengidentifikasi faktor kunci pengembangan co-management. Studi dilakukan dari bulan April 2006 sampai Maret 2007 di Karimunjawa, Jepara menggunakan pendekatan deskriptif analitik. Data dianalisa secara kualitatif dengan analisis prospektif. Hasil menunjukkan adanya disharmonisasi peraturan dalam pengelolaan TNKJ dan terbatasnya kapasitas pengelolaan. Untuk itu diperlukan pengelolaan dengan co-management melalui pengaturan koordinasi yaitu pembentukan forum stakeholder dari MOU antara BTNK dan Pemerintah Kabupaten Jepara, pembuatan aturan main dalam pengelolaan dan pemanfaatan sumberdaya TNKJ, dan membuat aksi kegiatan kolaborasi strategis yang komprehensif dengan kerja sinergi stakeholders sesuai kapasitas dan kemampuannya berdasarkan prinsipprinsip saling menghargai, saling mempercayai, saling bertanggung gugat, saling berbagi tanggung jawab dan berbagi keuntungan.

Shared-memory multiprocessors (SMPs) are attractive as general-purpose compute servers. On the software side, they present the same programming paradigm as uniprocessors, and they can run unmodified uniprocessor binaries. On the hardware side, the tight coupling of multiple processors, memory, and I/O provides enormous computing power in a single system, and enables the efficient sharing of these resources. As a compute server, this power can be exploited both by a collection of uniprocessor programs and by explicitly or automatically parallelized applications. This thesis addresses two important performance-related issues encountered in such systems, performance isolation and data locality. The solutions presented in this dissertation address these issues through careful resource management in the operating system. Current shared-memory multiprocessor operating systems provide very few controls for sharing the resources of the system among the active tasks or users. This is a serious limitation for a compute server that is to be used for multiple tasks or by multiple users. The current unconstrained sharing scheme allows the load placed by one user or task to adversely affect the performance seen by another. We show that this lack of isolation is caused by the resource allocation scheme (or lack thereof) carried over from single-user workstations. Multi-user multiprocessor systems require more sophisticated resource management, and we propose "performance isolation", a new resource management scheme for such systems.

In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication networks. Cognitive networks, deep learning, hierarchical generative models, optimization.

In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks. In many scenarios, the learning task is performed in the Cloud, while experience samples are generated directly by edge nodes or users. Therefore, the learning task involves some data exchange which, in turn, subtracts a certain amount of transmission resources from the system. This creates a friction between the need to speed up convergence towards an effective strategy, which requires the allocation of resources to transmit learning samples, and the need to maximize the amount of resources used for data plane communication, maximizing users' Quality of Service (QoS), which requires the learning process to be efficient, i.e., minimize its overhead. In this paper, we investigate this trade-off and propose a dynamic balancing strategy between the learning and data planes, which allows the centralized learning agent to quickly converge to an efficient resource allocation strategy, while minimizing the impact on QoS. Simulation results show that the proposed method outperforms static allocation methods, converging to the optimal policy (i.e., maximum efficacy and minimum overhead of the learning plane) in the long run.

The military resource management problem involves timely distribution and placement of materiel, personnel, and sensor assets to accommodate mission requirements throughout the world. Moreover, the problem is highly dynamic in nature in the sense that requirements are changing almost at every moment due to changing situations in the underlying missions, especially in the current age of asymmetric warfare. and the second is related to logistics.