Showing papers on "Resource management published in 2012"

Design aspects of network assisted device-to-device communications

Abstract: Device-to-device (D2D) communications underlaying a cellular infrastructure has been proposed as a means of taking advantage of the physical proximity of communicating devices, increasing resource utilization, and improving cellular coverage. Relative to the traditional cellular methods, there is a need to design new peer discovery methods, physical layer procedures, and radio resource management algorithms that help realize the potential advantages of D2D communications. In this article we use the 3GPP Long Term Evolution system as a baseline for D2D design, review some of the key design challenges, and propose solution approaches that allow cellular devices and D2D pairs to share spectrum resources and thereby increase the spectrum and energy efficiency of traditional cellular networks. Simulation results illustrate the viability of the proposed design.

Short Introduction to Strategic Human Resource Management: What is strategy?

Abstract: This book is about human resource (HR) strategy – the decisions, processes, and choices that organizations make about managing people . It is designed as a primer for students in master of business administration (MBA) or HR programs, as well as for HR and organization leaders and general managers. It aims to provide an overview of the elements of human resource plans at the strategic, operational, unit, and functional levels. It is more than that, however. A unique aspect of this book is that we have incorporated a consistent perspective that human resource or human capital strategy is also about risk optimization and management. It is difficult to consider any arena of management without attention to risk, and this is especially true in the arena of human capital. Integrating risk into human resource strategy is a less traditional way to approach the topic, but an increasingly uncertain world demands such a perspective. Not only is it important to incorporate risk more explicitly into the framework of human capital strategy, but also, we believe, doing so will enhance and extend the paradigms of human capital planning in new and useful directions, producing a unique perspective for leaders inside and outside the HR function. We will have much more to say about risk optimization and management in later chapters. The purpose of this opening chapter is to explore some of the fundamental ideas that underpin organizational strategy in general, because organizational strategy is the foundation of human resource strategy.

Sustainable supply management: an empirical study

Abstract: Sustainable business development has received much attention over the past decade owing to the significant attention given by governments and both profit and not-for-profit organizations to environmental, social and corporate responsibility. The emergence of a changing economic order has also made companies around the world seriously think about manufacturing and service sustainability. Global markets and operations have prompted companies to revisit their corporate, business and functional strategies in addition to focusing on outsourcing, virtual enterprise and supply chain management. Sustainability research on supply management has received limited attention. Nevertheless, considering the physically disbursed enterprise environment, supply management is critical for organizational competitiveness. Realizing the importance of sustainability in supply management, an attempt has been made to develop a theoretical framework and then to study the framework by means of an empirical study using perceptions and practices of selected French companies. Finally, a summary of findings and conclusions are reported.

Human Resource Management and Industrial Relations

Abstract: you have excellent communication skills, the ability to manage relationships with diverse stakeholders, a commitment to helping people and organisations improve performance? Do you also have the ability to handle details and see the big picture? If so a career in human resource management and industrial relations could be for you. The first port of call for many job applications is the human resources department of an organisation or a recruitment consultancy. Industrial relations experts are called in when employers and unions are in dispute about wages and conditions. But recruitment and bargaining are just two of many roles that human resource management and industrial relations (HRM & IR) professionals are involved in. Other roles include those of change management, strategic planning , training and development, and remuneration. Human resource management is about managing people so that businesses are competitive and successful. To do this in a fast-changing global economy, HRM & IR professionals keep up with issues and trends that affect employment relationships the labour market and economics, the product or service market, the political environment, environmental concerns, technological change, employment regulations, organisational psychology and social trends. The human resource management (HRM) function of an organisation manages the individual aspects of the employment relationship-from employee recruitment and selection to international employment relations, salaries and wages. HRM is a complex blend of science and art, creativity and common sense. At one level, HR practice draws on economics, psychology, sociology, anthropology, political studies, and strategic and systems thinking. At an operational level, success depends on interpersonal relationships. HR professionals are often the \" go to \" people in an organisation for advice and information. When things go wrong employees rely on the integrity and ability of HRM staff to manage and advise on issues without taking sides. They may also train and develop staff to ensure the business performs well, that it meets its goals and continually improves within legislative frameworks. HRM practitioners also keep up-to-date with legislation and analyse contemporary employment issues. Industrial relations is also a multidisciplinary field that studies the collective aspects of the employment relationship. It is increasingly being called employment relations (ER) because of the importance of non-industrial employment relationships. IR has a core concern with social justice through fair employment practices and decent work. People often think industrial relations is about labour relations and unionised employment situations, but it is more than that. Industrial relations …

Development Through Bricolage: Rethinking Institutions for Natural Resource Management

Abstract: Why, despite an emphasis on 'getting institutions right', do development initiatives so infrequently deliver as planned? Why do many institutions designed for natural resource management (e.g. Water User Associations, Irrigation Committees, Forest Management Councils) not work as planners intended? This book disputes the model of development by design and argues that institutions are formed through the uneven patching together of old practices and accepted norms with new arrangements. The managing of natural resources and delivery of development through such processes of 'bricolage' is likened to 'institutional 'DIY' rather than engineering or design. The author explores the processes involved in institutional bricolage; the constant renegotiation of norms, the reinvention of tradition, the importance of legitimate authority and the role of people themselves in shaping such arrangements. Bricolage is seen as an inevitable, but not always benign process; the extent to which it reproduces social inequalities or creates space for challenging them is also considered. The book draws on a number of contemporary strands of development thinking about collective action, participation, governance, natural resource management, political ecology and wellbeing. It synthesises these to develop new understandings of why and how people act to manage resources and how access is secured or denied. A variety of case studies ranging from the management of water (Zimbabwe, India, Pakistan), conflict and cooperation over land, grazing and water (Tanzania), and the emergence of community management of forests (Sweden, Nepal), illustrate the context specific and generalised nature of bricolage and the resultant challenges for development policy and practice.

Value of long-term ecological studies

Abstract: Long-term ecological studies are critical for providing key insights in ecology, environmental change, natural resource management and biodiversity conservation. In this paper, we briefly discuss five key values of such studies. These are: (1) quantifying ecological responses to drivers of ecosystem change; (2) understanding complex ecosystem processes that occur over prolonged periods; (3) providing core ecological data that may be used to develop theoretical ecological models and to parameterize and validate simulation models; (4) acting as platforms for collaborative studies, thus promoting multidisciplinary research; and (5) providing data and understanding at scales relevant to management, and hence critically supporting evidence-based policy, decision making and the management of ecosystems. We suggest that the ecological research community needs to put higher priority on communicating the benefits of long-term ecological studies to resource managers, policy makers and the general public. Long-term research will be especially important for tackling large-scale emerging problems confronting humanity such as resource management for a rapidly increasing human population, mass species extinction, and climate change detection, mitigation and adaptation. While some ecologically relevant, long-term data sets are now becoming more generally available, these are exceptions. This deficiency occurs because ecological studies can be difficult to maintain for long periods as they exceed the length of government administrations and funding cycles. We argue that the ecological research community will need to coordinate ongoing efforts in an open and collaborative way, to ensure that discoverable long-term ecological studies do not become a long-term deficiency. It is important to maintain publishing outlets for empirical field-based ecology, while simultaneously developing new systems of recognition that reward ecologists for the use and collaborative sharing of their long-term data sets. Funding schemes must be re-crafted to emphasize collaborative partnerships between field-based ecologists, theoreticians and modellers, and to provide financial support that is committed over commensurate time frames.

Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells

Abstract: Both cognitive radio and femtocell have been considered as promising techniques in wireless networks. However, most of previous works are focused on spectrum sharing and interference avoidance, and the energy efficiency aspect is largely ignored. In this paper, we study the energy efficiency aspect of spectrum sharing and power allocation in heterogeneous cognitive radio networks with femtocells. To fully exploit the cognitive capability, we consider a wireless network architecture in which both the macrocell and the femtocell have the cognitive capability. We formulate the energy-efficient resource allocation problem in heterogeneous cognitive radio networks with femtocells as a Stackelberg game. A gradient based iteration algorithm is proposed to obtain the Stackelberg equilibrium solution to the energy-efficient resource allocation problem. Simulation results are presented to demonstrate the Stackelberg equilibrium is obtained by the proposed iteration algorithm and energy efficiency can be improved significantly in the proposed scheme.

New horizons for managing the environment: A review of coupled social-ecological systems modeling

Abstract: Conventional approaches to natural resource management are increasingly challenged by environmental problems that are embedded in highly complex systems with profound uncertainties. These so-called social-ecological systems (SESs) are characterized by strong links between the social and the ecological system and multiple interactions across spatial and temporal scales. New approaches are needed to manage those tightly coupled systems; however, basic understanding of their nonlinear behavior is still missing. Modeling is a traditional tool in natural resource management to study complex, dynamic systems. There is a long tradition of SES modeling, but the approach is now being more widely recognized in other fields, such as ecological and economic modeling, where issues such as nonlinear ecological dynamics and complex human decision making are receiving more attention. SES modeling is maturing as a discipline in its own right, incorporating ideas from other interdisciplinary fields such as resilience or complex systems research. In this paper, we provide an overview of the emergence and state of the art of this cross-cutting field. Our analysis reveals the substantial potential of SES models to address issues that are of utmost importance for managing complex human-environment relationships, such as: (i) the implications of ecological and social structure for resource management, (ii) uncertainty in natural and social systems and ways to address it, (iii) the role of coevolutionary processes in the dynamics of SESs, and (iv) the implications of microscale human decision making for sustainable resource management and conservation. The complexity of SESs and the lack of a common analytical framework, however, also pose significant challenges for this emerging field. There are clear research needs with respect to: (i) approaches that go beyond rather simple specifications of human decision making, (ii) development of coping strategies to deal with (irreducible) uncertainties, (iii) more explicit modeling of feedbacks between the social and ecological systems, and (iv) a conceptual and methodological framework for analyzing and modeling SESs. We provide ideas for tackling some of these challenges and indicate potential key focal areas for SES modeling in the future.

Energy-Efficient Resource Allocation for Secure OFDMA Systems

Abstract: In this paper, resource allocation for energy-efficient secure communication in an orthogonal frequency-division multiple-access (OFDMA) downlink network is studied The considered problem is modeled as a nonconvex optimization problem that takes into account the sum-rate-dependent circuit power consumption, multiple-antenna eavesdropper, artificial noise generation, and different quality-of-service (QoS) requirements, including a minimum required secrecy sum rate and a maximum tolerable secrecy outage probability The power, secrecy data rate, and subcarrier allocation policies are optimized for maximization of the energy efficiency of secure data transmission (bit/joule securely delivered to the users) The considered nonconvex optimization problem is transformed into a convex optimization problem by exploiting the properties of fractional programming, which results in an efficient iterative resource allocation algorithm In each iteration, the transformed problem is solved by using dual decomposition Simulation results illustrate that the proposed iterative resource allocation algorithm not only converges in a small number of iterations but maximizes the system energy efficiency and guarantees a nonzero secrecy data rate for the desired users as well In addition, the obtained results unveil a tradeoff between energy efficiency and secure communication

Dynamic resource management using virtual machine migrations

Abstract: Virtualization is a key concept in enabling the "computing-as-a-service" vision of cloud-based solutions. Virtual machine related features such as flexible resource provisioning, and isolation and migration of machine state have improved efficiency of resource usage and dynamic resource provisioning capabilities. Live virtual machine migration transfers "state" of a virtual machine from one physical machine to another, and can mitigate overload conditions and enables uninterrupted maintenance activities. The focus of this article is to present the details of virtual machine migration techniques and their usage toward dynamic resource management in virtualized environments. We outline the components required to use virtual machine migration for dynamic resource management in the virtualized cloud environment. We present categorization and details of migration heuristics aimed at reducing server sprawl, minimizing power consumption, balancing load across physical machines, and so on. We conclude with a discussion of open research problems in the area.

Collaborative Engagement of Local and Traditional Knowledge and Science in Marine Environments: A Review

Abstract: Local and traditional ecological knowledge (LTK) is increasingly recognized as an important component of scientific research, conservation, and resource management. Especially where there are gaps in the scientific literature, LTK can be a critical source of basic environmental data; this situation is particularly apparent in the case of marine ecosystems, about which comparatively less is known than terrestrial ones. We surveyed the global literature relating to the LTK of marine environments and analyzed what knowledge has been collected and with what aims and results. A large proportion of LTK which has been documented by researchers consists of species-specific information that is important for traditional resource use. However, knowledge relating to marine ecology, environmental change, and contemporary resource management practices is increasingly emphasized in the literature. Today, marine LTK is being used to provide historical and contemporary baseline information, suggest stewardship techniques, improve conservation planning and practice, and to resolve management disputes. Still, comparatively few studies are geared toward the practicalities of developing a truly collaborative, adaptive, and resilient management infrastructure that is embracive of modern science and LTK and practices in marine environments. Based on the literature, we thus suggest how such an infrastructure might be advanced through collaborative projects and "bridging" institutions that highlight the importance of trust-building and the involvement of communities in all stages of research, and the importance of shared interest in project objectives, settings (seascapes), and outcomes.

Radio Resource Allocation for OFDMA Systems in High Speed Environments

Abstract: In high speed train (HST) system, real-time multimedia entertainments are very important applications in which a data stream often contains packets with different quality of service requirements For example, video stream encoded with scalability contains the base layer packets with high quality (HQ) bit error rate (BER) requirement and enhancement layers' packets with low quality (LQ) BER requirement When a conventional allocation approach, which only considers one BER constraint for one data stream, is applied to orthogonal frequency division multiple access (OFDMA) systems, the BER constraint will be the strictest one among multiple requirements from different types of packets, which leads to inefficient allocation when each data stream has multiple BER requirements This paper aims to develop novel resource allocation approach by considering multiple BER requirements for different types of packets in one data stream In order to not only simplify the resource allocation, but also to compensate for the channel estimation error caused by Doppler shift in the HST environment, a proper number of contiguous subcarriers are grouped into chunks and spectrum is allocated chunk by chunk Simulation results show that the developed resource allocation scheme outperforms the conventional scheme, particularly when the BER ratio of HQ packets to LQ packets is larger than one Furthermore, in order to reduce the complexity of resource allocation further, an empirical allocation scheme is proposed to allocate better chunks to HQ packets It is shown that the performance of the empirical allocation scheme is quite close to that of the optimal scheme

A Survey on Delay-Aware Resource Control for Wireless Systems—Large Deviation Theory, Stochastic Lyapunov Drift, and Distributed Stochastic Learning

Abstract: In this paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalentrate constraint approach, the Lyapunov stability drift approach, and the approximate Markov decision process approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity, and implementation issues. For each of the approaches, the problem setup, the general solution, and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multihop routing designs in general multihop networks are elaborated. Finally, the delay performances of various approaches are compared through simulations using an example of the uplink OFDMA systems.

Intelligent Energy Resource Management Considering Vehicle-to-Grid: A Simulated Annealing Approach

Abstract: This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G users' profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

DejaVu: accelerating resource allocation in virtualized environments

Abstract: Effective resource management of virtualized environments is a challenging task. State-of-the-art management systems either rely on analytical models or evaluate resource allocations by running actual experiments. However, both approaches incur a significant overhead once the workload changes. The former needs to re-calibrate and re-validate models, whereas the latter has to run a new set of experiments to select a new resource allocation. During the adaptation period, the system may run with an inefficient configuration. In this paper, we propose DejaVu - a framework that (1) minimizes the resource management overhead by identifying a small set of workload classes for which it needs to evaluate resource allocation decisions, (2) quickly adapts to workload changes by classifying workloads using signatures and caching their preferred resource allocations at runtime, and (3) deals with interference by estimating an "interference index". We evaluate DejaVu by running representative network services on Amazon EC2. DejaVu achieves more than 10x speedup in adaptation time for each workload change relative to the state-of-the-art. By enabling quick adaptation, DejaVu saves up to 60% of the service provisioning cost. Finally, DejaVu is easily deployable as it does not require any extensive instrumentation or human intervention.

Resource Allocation with Interference Avoidance in OFDMA Femtocell Networks

Abstract: Interference control and quality-of-service (QoS) awareness are the major challenges for resource management in orthogonal frequency-division multiple access femtocell networks This paper investigates a self-organization strategy for physical resource block (PRB) allocation with QoS constraints to avoid the co-channel and co-tiered interference Femtocell self-organization including self-configuration and self-optimization is proposed to manage the large femtocell networks We formulate the optimization problem for PRB assignments where multiple QoS classes for different services can be supported, and interference between femtocells can be completely avoided The proposed formulation pursues the maximization of PRB efficiency A greedy algorithm is developed to solve the resource allocation formulation In the simulations, the proposed approach is observed to increase the system throughput by over 13% without femtocell interference Simulations also demonstrate that the rejection ratios of all QoS classes are low and mostly below 10% Moreover, the proposed approach improves the PRB efficiency by over 82% in low-loading scenario and 13% in high-loading scenario

Resource Discovery and Allocation in Network Virtualization

Abstract: Network virtualization is considered an important potential solution to the gradual ossification of the Internet. In a network virtualization environment, a set of virtual networks share the resources of a common physical network although each virtual network is isolated from others. Benefits include increased flexibility, diversity, security and manageability. Resource discovery and allocation are fundamental steps in the process of creating new virtual networks. This paper surveys previous work on, and the present status of, resource discovery and allocation in network virtualization. We also describe challenges and suggest future directions for this area of research.

Metabolism of the Anthroposphere : Analysis, Evaluation, Design

Abstract: Over the last several thousand years of human life on Earth, agricultural settlements became urban cores, and these regional settlements became tightly connected through infrastructures transporting people, materials, and information. This global network of urban systems, including ecosystems, is the anthroposphere; the physical flows and stocks of matter and energy within it form its metabolism. This book offers an overview of the metabolism of the anthroposphere, with an emphasis on the design of metabolic systems. It takes a cultural historical perspective, supported with methodology from the natural sciences and engineering. The book will be of interest to scholars and practitioners in the fields of regional development, environmental protection, and material management. It will also be a resource for undergraduate and graduate students in industrial ecology, environmental engineering, and resource management. The authors describe the characteristics of material stocks and flows of human settlements in space and time; introduce the method of material flow analysis (MFA) for metabolic studies; analyze regional metabolism and the material systems generated by basic activities; and offer four case studies of optimal metabolic system design: phosphorus management, urban mining, waste management, and mobility. This second edition of an extremely influential book has been substantially revised and greatly expanded. Its new emphasis on design and resource utilization reflects recent debates and scholarship on sustainable development and climate change.

Spectrum-Aware Mobility Management in Cognitive Radio Cellular Networks

Abstract: Cognitive radio (CR) networks have been proposed as a solution to both spectrum inefficiency and spectrum scarcity problems. However, they face several challenges based on the fluctuating nature of the available spectrum, making it more difficult to support seamless communications, especially in CR cellular networks. In this paper, a spectrum-aware mobility management scheme is proposed for CR cellular networks. First, a novel network architecture is introduced to mitigate heterogeneous spectrum availability. Based on this architecture, a unified mobility management framework is developed to support diverse mobility events in CR networks, which consists of spectrum mobility management, user mobility management, and intercell resource allocation. The spectrum mobility management scheme determines a target cell and spectrum band for CR users adaptively dependent on time-varying spectrum opportunities, leading to increase in cell capacity. In the user mobility management scheme, a mobile user selects a proper handoff mechanism so as to minimize a switching latency at the cell boundary by considering spatially heterogeneous spectrum availability. Intercell resource allocation helps to improve the performance of both mobility management schemes by efficiently sharing spectrum resources with multiple cells. Simulation results show that the proposed method can achieve better performance than conventional handoff schemes in terms of both cell capacity as well as mobility support in communications.

Characterization and Comparison of Cloud versus Grid Workloads

Abstract: A new era of Cloud Computing has emerged, but the characteristics of Cloud load in data centers is not perfectly clear. Yet this characterization is critical for the design of novel Cloud job and resource management systems. In this paper, we comprehensively characterize the job/task load and host load in a real-world production data center at Google Inc. We use a detailed trace of over 25 million tasks across over 12,500 hosts. We study the differences between a Google data center and other Grid/HPC systems, from the perspective of both work load (w.r.t. jobs and tasks) and host load (w.r.t. machines). In particular, we study the job length, job submission frequency, and the resource utilization of jobs in the different systems, and also investigate valuable statistics of machine's maximum load, queue state and relative usage levels, with different job priorities and resource attributes. We find that the Google data center exhibits finer resource allocation with respect to CPU and memory than that of Grid/HPC systems. Google jobs are always submitted with much higher frequency and they are much shorter than Grid jobs. As such, Google host load exhibits higher variance and noise.

What Makes a Resource Valuable? Identifying the Drivers of Firm-Idiosyncratic Resource Value

Abstract: We fill a gap in the resource-based literature by identifying conditions and mechanisms that make a resource valuable to a firm ex ante-that is, before a decision on acquiring or building it is made. These conditions are (1) the firm's ex ante market position; (2) its ex ante resource base, which allows for complementarities; (3) its position in interorganizational networks, which gives it access to privileged information; and (4) the prior knowledge and experience of its managers, which allow superior judgment concerning the value-creating potential of the resource. These factors help explain why firms initially differ in how much value they attribute to a resource and, subsequently, why firms differ in their resource endowments. Our results also contribute to resource management theories by highlighting the role of managerial judgment in acquiring and accumulating resources and, thus, shaping firms' paths toward superior competitive positions. Furthermore, identifying firms' market positions and managerial judgment about demand-side value creation opportunities as resource value drivers highlights the importance of demand-side factors to strategic outcomes. We also discuss how our findings may open avenues for further studies and provide a basis for empirical tests of the resource-based view of strategic management.

When cloud meets eBay: Towards effective pricing for cloud computing

Abstract: The rapid deployment of cloud computing promises network users with elastic, abundant, and on-demand cloud services. The pay-as-you-go model allows users to be charged only for services they use. Current purchasing designs, however, are still primitive with significant constraints. Spot Instance, the first deployed auction-style pricing model of Amazon EC2, fails to enforce fair competition among users in facing of resource scarcity and may thus lead to untruthful bidding and unfair resource allocation. Dishonest users are able to abuse the system and obtain (at least) short-term advantages by deliberately setting large maximum price bids while being charged only at lower Spot Prices. Meanwhile, this may also prevent the demands of honest users from being satisfied due to resource scarcity. Furthermore, Spot Instance is inefficient and may not adequately meet users' overall demands because it limits users to bid for each computing instance individually instead of multiple different instances at a time. In this paper, we formulate and investigate the problem of cloud resource pricing. We propose a suite of computationally efficient and truthful auction-style pricing mechanisms, which enable users to fairly compete for resources and cloud providers to increase their overall revenue. We analytically show that the proposed algorithms can achieve truthfulness without collusion or (t; p)-truthfulness tolerating a collusion group of size t with probability at least p. We also show that the two proposed algorithms have polynomial complexities O(nm + n2) and O(nm), respectively, when n users compete for m different computing instances with multiple units. Extensive simulations show that, in a competitive cloud resource market, the proposed mechanisms can increase the revenue of cloud providers, especially when allocating relatively limited computing resources to a potentially large number of cloud users.

Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments

Abstract: The recent emergence of clouds is making the vision of utility computing realizable, i.e., computing resources and services can be delivered, utilized, and paid for as utilities such as water or electricity. This, however, creates new resource provisioning problems. Because of the pay-as-you-go model, resource provisioning should be performed in a way to keep resource costs to a minimum, while meeting an application's needs. In this work, we focus on the use of cloud resources for a class of adaptive applications, where there could be application-specific flexibility in the computation that may be desired. Furthermore, there may be a fixed time-limit as well as a resource budget. Within these constraints, such adaptive applications need to maximize their Quality of Service (QoS), more precisely, the value of an application-specific benefit function, by dynamically changing adaptive parameters. We present the design, implementation, and evaluation of a framework that can support such dynamic adaptation for applications in a cloud computing environment. The key component of our framework is a multi-input-multi-output feedback control model-based dynamic resource provisioning algorithm which adopts reinforcement learning to adjust adaptive parameters to guarantee the optimal application benefit within the time constraint. Then a trained resource model changes resource allocation accordingly to satisfy the budget. We have evaluated our framework with two real-world adaptive applications, and have demonstrated that our approach is effective and causes a very low overhead.

Evaluating participation in water resource management: A review

Abstract: [1] Key documents such as the European Water Framework Directive and the U.S. Clean Water Act state that public and stakeholder participation in water resource management is required. Participation aims to enhance resource management and involve individuals and groups in a democratic way. Evaluation of participatory programs and projects is necessary to assess whether these objectives are being achieved and to identify how participatory programs and projects can be improved. The different methods of evaluation can be classified into three groups: (i) process evaluation assesses the quality of participation process, for example, whether it is legitimate and promotes equal power between participants, (ii) intermediary outcome evaluation assesses the achievement of mainly nontangible outcomes, such as trust and communication, as well as short- to medium-term tangible outcomes, such as agreements and institutional change, and (iii) resource management outcome evaluation assesses the achievement of changes in resource management, such as water quality improvements. Process evaluation forms a major component of the literature but can rarely indicate whether a participation program improves water resource management. Resource management outcome evaluation is challenging because resource changes often emerge beyond the typical period covered by the evaluation and because changes cannot always be clearly related to participation activities. Intermediary outcome evaluation has been given less attention than process evaluation but can identify some real achievements and side benefits that emerge through participation. This review suggests that intermediary outcome evaluation should play a more important role in evaluating participation in water resource management.

Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity

Abstract: In this paper, resource allocation for energy efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs) is studied. The considered problem is formulated as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate for joint BS zero-forcing beamforming (ZFBF) transmission. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation solution is derived with a low complexity suboptimal subcarrier allocation policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency and network capacity.