Showing papers on "Resource management published in 2013"

Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment

Abstract: Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.

Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities

Abstract: This keynote paper: presents a 21 st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on marketbased resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3 rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21 st century vision.

Integrated analysis of climate change, land-use, energy and water strategies

Abstract: Land, energy and water are our most precious resources, but the manner and extent to which they are exploited contributes to climate change. Meanwhile, the systems that provide these resources are themselves highly vulnerable to changes in climate. Efficient resource management is therefore of great importance, both for mitigation and for adaptation purposes. We postulate that the lack of integration in resource assessments and policy-making leads to inconsistent strategies and inefficient use of resources. We present CLEWs (climate, land-use, energy and water strategies), a new paradigm for resource assessments that we believe can help to remedy some of these shortcomings.

Efficiency Resource Allocation for Device-to-Device Underlay Communication Systems: A Reverse Iterative Combinatorial Auction Based Approach

Abstract: Peer-to-peer communication has been recently considered as a popular issue for local area services. An innovative resource allocation scheme is proposed to improve the performance of mobile peer-to-peer, i.e., device-to-device (D2D), communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a reverse iterative combinatorial auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which as bidders compete to obtain business while the packages of the D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit, as a basis of the proposed auction. And then a detailed non-monotonic descending price auction algorithm is explained depending on the utility function that accounts for the channel gain from D2D and the costs for the system. Further, we prove that the proposed auction-based scheme is cheat-proof, and converges in a finite number of iteration rounds. We explain non-monotonicity in the price update process and show lower complexity compared to a traditional combinatorial allocation. The simulation results demonstrate that the algorithm efficiently leads to a good performance on the system sum rate.

Spatial dynamics of ecosystem service flows: A comprehensive approach to quantifying actual services

Abstract: Recent ecosystem services research has highlighted the importance of spatial connectivity between ecosystems and their beneficiaries. Despite this need, a systematic approach to ecosystem service flow quantification has not yet emerged. In this article, we present such an approach, which we formalize as a class of agent-based models termed “Service Path Attribution Networks” (SPANs). These models, developed as part of the Artificial Intelligence for Ecosystem Services (ARIES) project, expand on ecosystem services classification terminology introduced by other authors. Conceptual elements needed to support flow modeling include a service's rivalness, its flow routing type (e.g., through hydrologic or transportation networks, lines of sight, or other approaches), and whether the benefit is supplied by an ecosystem's provision of a beneficial flow to people or by absorption of a detrimental flow before it reaches them. We describe our implementation of the SPAN framework for five ecosystem services and discuss how to generalize the approach to additional services. SPAN model outputs include maps of ecosystem service provision, use, depletion, and flows under theoretical, possible, actual, inaccessible, and blocked conditions. We highlight how these different ecosystem service flow maps could be used to support various types of decision making for conservation and resource management planning.

Achieving Conservation Science that Bridges the Knowledge–Action Boundary

Abstract: There are many barriers to using science to inform conservation policy and practice. Conservation scientists wishing to produce management-relevant science must balance this goal with the imperative of demonstrating novelty and rigor in their science. Decision makers seeking to make evidence-based decisions must balance a desire for knowledge with the need to act despite uncertainty. Generating science that will effectively inform management decisions requires that the production of information (the components of knowledge) be salient (relevant and timely), credible (authoritative, believable, and trusted), and legitimate (developed via a process that considers the values and perspectives of all relevant actors) in the eyes of both researchers and decision makers. We perceive 3 key challenges for those hoping to generate conservation science that achieves all 3 of these information characteristics. First, scientific and management audiences can have contrasting perceptions about the salience of research. Second, the pursuit of scientific credibility can come at the cost of salience and legitimacy in the eyes of decision makers, and, third, different actors can have conflicting views about what constitutes legitimate information. We highlight 4 institutional frameworks that can facilitate science that will inform management: boundary organizations (environmental organizations that span the boundary between science and management), research scientists embedded in resource management agencies, formal links between decision makers and scientists at research-focused institutions, and training programs for conservation professionals. Although these are not the only approaches to generating boundary-spanning science, nor are they mutually exclusive, they provide mechanisms for promoting communication, translation, and mediation across the knowledge-action boundary. We believe that despite the challenges, conservation science should strive to be a boundary science, which both advances scientific understanding and contributes to decision making.

What is conservation physiology? Perspectives on an increasingly integrated and essential science†

Abstract: Globally, ecosystems and their constituent flora and fauna face the localized and broad-scale influence of human activities. Conservation practitioners and environmental managers struggle to identify and mitigate threats, reverse species declines, restore degraded ecosystems, and manage natural resources sustainably. Scientific research and evidence are increasingly regarded as the foundation for new regulations, conservation actions, and management interventions. Conservation biolo- gists and managers have traditionally focused on the characteristics (e.g. abundance, structure, trends) of populations, spe- cies, communities, and ecosystems, and simple indicators of the responses to environmental perturbations and other human activities. However, an understanding of the specific mechanisms underlying conservation problems is becoming increasingly important for decision-making, in part because physiological tools and knowledge are especially useful for developing cause- and-effect relationships, and for identifying the optimal range of habitats and stressor thresholds for different organisms. When physiological knowledge is incorporated into ecological models, it can improve predictions of organism responses to environmental change and provide tools to support management decisions. Without such knowledge, we may be left with simple associations. 'Conservation physiology' has been defined previously with a focus on vertebrates, but here we redefine the concept universally, for application to the diversity of taxa from microbes to plants, to animals, and to natural resources. We also consider 'physiology' in the broadest possible terms; i.e. how an organism functions, and any associated mechanisms, from development to bioenergetics, to environmental interactions, through to fitness. Moreover, we consider conservation physiology to include a wide range of applications beyond assisting imperiled populations, and include, for example, the eradication of invasive species, refinement of resource management strategies to minimize impacts, and evaluation of resto - ration plans. This concept of conservation physiology emphasizes the basis, importance, and ecological relevance of physio- logical diversity at a variety of scales. Real advances in conservation and resource management require integration and inter-disciplinarity. Conservation physiology and its suite of tools and concepts is a key part of the evidence base needed to address pressing environmental challenges.

Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids

Abstract: Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naive auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply-demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.

China's ion-adsorption rare earth resources, mining consequences and preservation

Abstract: The surface mining and heap leaching of China's unique ion-adsorption rare earth resources have caused severe environmental damage, and China needs to develop and implement an integrated rare earth resource management approach for a sustainable rare earth industry.

Cloud Computing: Theory and Practice

Abstract: Cloud Computing: Theory and Practice provides students and IT professionals with an in-depth analysis of the cloud from the ground up. Beginning with a discussion of parallel computing and architectures and distributed systems, the book turns to contemporary cloud infrastructures, how they are being deployed at leading companies such as Amazon, Google and Apple, and how they can be applied in fields such as healthcare, banking and science. The volume also examines how to successfully deploy a cloud application across the enterprise using virtualization, resource management and the right amount of networking support, including content delivery networks and storage area networks. Developers will find a complete introduction to application development provided on a variety of platforms. Learn about recent trends in cloud computing in critical areas such as: resource management, security, energy consumption, ethics, and complex systems Get a detailed hands-on set of practical recipes that help simplify the deployment of a cloud based system for practical use of computing clouds along with an in-depth discussion of several projects Understand the evolution of cloud computing and why the cloud computing paradigm has a better chance to succeed than previous efforts in large-scale distributed computing

On the optimal allocation of virtual resources in cloud computing networks

Abstract: Cloud computing builds upon advances on virtualization and distributed computing to support cost-efficient usage of computing resources, emphasizing on resource scalability and on demand services. Moving away from traditional data-center oriented models, distributed clouds extend over a loosely coupled federated substrate, offering enhanced communication and computational services to target end-users with quality of service (QoS) requirements, as dictated by the future Internet vision. Toward facilitating the efficient realization of such networked computing environments, computing and networking resources need to be jointly treated and optimized. This requires delivery of user-driven sets of virtual resources, dynamically allocated to actual substrate resources within networked clouds, creating the need to revisit resource mapping algorithms and tailor them to a composite virtual resource mapping problem. In this paper, toward providing a unified resource allocation framework for networked clouds, we first formulate the optimal networked cloud mapping problem as a mixed integer programming (MIP) problem, indicating objectives related to cost efficiency of the resource mapping procedure, while abiding by user requests for QoS-aware virtual resources. We subsequently propose a method for the efficient mapping of resource requests onto a shared substrate interconnecting various islands of computing resources, and adopt a heuristic methodology to address the problem. The efficiency of the proposed approach is illustrated in a simulation/emulation environment, that allows for a flexible, structured, and comparative performance evaluation. We conclude by outlining a proof-of-concept realization of our proposed schema, mounted over the European future Internet test-bed FEDERICA, a resource virtualization platform augmented with network and computing facilities.

Small Cell Networks: Deployment, PHY Techniques, and Resource Management

Abstract: This comprehensive resource explores state-of-the-art advances in the successful deployment and operation of small cell networks. A broad range of technical challenges, and possible solutions, are addressed, including practical deployment considerations and interference management techniques, all set within the context of the most recent cutting-edge advances. Key aspects covered include 3GPP standardisation, applications of stochastic geometry, PHY techniques, MIMO techniques, handover, and radio resource management, including techniques designed to make the best possible use of the available spectrum. Detailed technical information is provided throughout, with a consistent emphasis on real-world applications. Bringing together world-renowned experts from industry and academia, this is an indispensable volume for researchers, engineers and systems designers in the wireless communication industry.

A Framework for Cooperative Resource Management in Mobile Cloud Computing

Abstract: Mobile cloud computing is an emerging technology to improve the quality of mobile services. In this paper, we consider the resource (i.e., radio and computing resources) sharing problem to support mobile applications in a mobile cloud computing environment. In such an environment, mobile cloud service providers can cooperate (i.e., form a coalition) to create a resource pool to share their own resources with each other. As a result, the resources can be better utilized and the revenue of the mobile cloud service providers can be increased. To maximize the benefit of the mobile cloud service providers, we propose a framework for resource allocation to the mobile applications, and revenue management and cooperation formation among service providers. For resource allocation to the mobile applications, we formulate and solve optimization models to obtain the optimal number of application instances that can be supported to maximize the revenue of the service providers while meeting the resource requirements of the mobile applications. For sharing the revenue generated from the resource pool (i.e., revenue management) among the cooperative mobile cloud service providers in a coalition, we apply the concepts of core and Shapley value from cooperative game theory as a solution. Based on the revenue shares, the mobile cloud service providers can decide whether to cooperate and share the resources in the resource pool or not. Also, the provider can optimize the decision on the amount of resources to contribute to the resource pool.

‘Green’ Human Resource Benefits: Do they Matter as Determinants of Environmental Management System Implementation?

Abstract: This article analyses whether benefits arising for human resource management from environmental management activities drive environmental management system implementation. Focusing on employee satisfaction and recruitment/retention, it tests this for German manufacturing firms in 2001 and 2006 and incorporates a rare longitudinal element into the analysis. It confirms positive associations of the benefit levels for both variables with environmental management system implementation on a large scale. Also it provides evidence that increasing levels of environmental management system implementation result from higher economic benefits in the human resource domain. In doing so the article supplies needed quantitative evidence on important aspects of how sustainability relates to human resource management.

Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler

Abstract: Research into developing effective computer aided techniques for planning software projects is important and challenging for software engineering. Different from projects in other fields, software projects are people-intensive activities and their related resources are mainly human resources. Thus, an adequate model for software project planning has to deal with not only the problem of project task scheduling but also the problem of human resource allocation. But as both of these two problems are difficult, existing models either suffer from a very large search space or have to restrict the flexibility of human resource allocation to simplify the model. To develop a flexible and effective model for software project planning, this paper develops a novel approach with an event-based scheduler (EBS) and an ant colony optimization (ACO) algorithm. The proposed approach represents a plan by a task list and a planned employee allocation matrix. In this way, both the issues of task scheduling and employee allocation can be taken into account. In the EBS, the beginning time of the project, the time when resources are released from finished tasks, and the time when employees join or leave the project are regarded as events. The basic idea of the EBS is to adjust the allocation of employees at events and keep the allocation unchanged at nonevents. With this strategy, the proposed method enables the modeling of resource conflict and task preemption and preserves the flexibility in human resource allocation. To solve the planning problem, an ACO algorithm is further designed. Experimental results on 83 instances demonstrate that the proposed method is very promising.

A Combinatorial Auction-Based Mechanism for Dynamic VM Provisioning and Allocation in Clouds

Abstract: Cloud computing providers provision their resources into different types of virtual machine (VM) instances that are then allocated to the users for specific periods of time. The allocation of VM instances to users is usually determined through fixed-price allocation mechanisms that cannot guarantee an economically efficient allocation and the maximization of cloud provider's revenue. A better alternative would be to use combinatorial auction-based resource allocation mechanisms. This argument is supported by the economic theory; when the auction costs are low, as is the case in the context of cloud computing, auctions are especially efficient over the fixed-price markets because products are matched to customers having the highest valuation. The existing combinatorial auction-based VM allocation mechanisms do not take into account the user's demand when making provisioning decisions, that is, they assume that the VM instances are statically provisioned. We design an auction-based mechanism for dynamic VM provisioning and allocation that takes into account the user demand, when making provisioning decisions. We prove that our mechanism is truthful (i.e., a user maximizes its utility only by bidding its true valuation for the requested bundle of VMs). We evaluate the proposed mechanism by performing extensive simulation experiments using real workload traces. The experiments show that the proposed mechanism yields higher revenue for the cloud provider and improves the utilization of cloud resources.

Harmony: Dynamic Heterogeneity-Aware Resource Provisioning in the Cloud

Abstract: Data centers today consume tremendous amount of energy in terms of power distribution and cooling. Dynamic capacity provisioning is a promising approach for reducing energy consumption by dynamically adjusting the number of active machines to match resource demands. However, despite extensive studies of the problem, existing solutions for dynamic capacity provisioning have not fully considered the heterogeneity of both workload and machine hardware found in production environments. In particular, production data centers often comprise several generations of machines with different capacities, capabilities and energy consumption characteristics. Meanwhile, the workloads running in these data centers typically consist of a wide variety of applications with different priorities, performance objectives and resource requirements. Failure to consider heterogenous characteristics will lead to both sub-optimal energy-savings and long scheduling delays, due to incompatibility between workload requirements and the resources offered by the provisioned machines. To address this limitation, in this paper we present HARMONY, a Heterogeneity-Aware Resource Management System for dynamic capacity provisioning in cloud computing environments. Specifically, we first use the K-means clustering algorithm to divide the workload into distinct task classes with similar characteristics in terms of resource and performance requirements. Then we present a novel technique for dynamically adjusting the number of machines of each type to minimize total energy consumption and performance penalty in terms of scheduling delay. Through simulations using real traces from Google's compute clusters, we found that our approach can improve data center energy efficiency by up to 28% compared to heterogeneity-oblivious solutions.

A survey on resource allocation in high performance distributed computing systems

Abstract: Classification of high performance computing (HPC) systems is provided.Current HPC paradigms and industrial application suites are discussed.State of the art in HPC resource allocation is reported.Hardware and software solutions are discussed for optimized HPC systems. 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. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

Resource conflict, collective action, and resilience: an analytical framework

Abstract: Where access to renewable natural resources essential to rural livelihoods is highly contested, improving cooperation in resource management is an important element in strategies for peacebuilding and conflict prevention. While researchers have made advances in assessing the role of environmental resources as a causal factor in civil conflict, analysis of the positive potential of collective natural resource management efforts to reduce broader conflict is less developed. Addressing this need, we present a framework on collective action, conflict prevention, and social-ecological resilience, linking local stakeholder dynamics to the broader institutional and governance context. Accounting for both formal and informal relationships of power and influence, as well as values and stakeholder perceptions alongside material interests, the framework aims to provide insight into the problem of (re)building legitimacy of common-pool resource management institutions in conflict-sensitive environments. We outline its application in stakeholder-based problem assessment and planning, participatory monitoring and evaluation, and multi-case comparative analysis.

Energy-saving virtual machine placement in cloud data centers

Abstract: In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.

Wildfire Risk Assessment Framework for Land and Resource Management

Abstract: Emergency management; Emergency management/Emergency response; Emergency management/Emergency preparedness; Environmental issues and disasters; Environmental issues and disasters/Wildfire

Resource Management for Device-to-Device Underlay Communication

Abstract: Device-to-Device (D2D) communication will become a key feature supported by next generation cellular networks, a topic of enormous importance to modern communication. Currently, D2D serves as an underlay to the cellular network as a means to increase spectral efficiency. Although D2D communication brings large benefits in terms of system capacity, it also causes interference as well as increased computation complexity to cellular networks as a result of spectrum sharing. Thus, efficient resource management must be performed to guarantee a target performance level of cellular communication. This brief presents the state-of-the-art research on resource management for D2D communication underlaying cellular networks. Those who work with D2D communication will use this books information to help ensure their work is as efficient as possible. Along with the survey of existing work, this book also includes the fundamental theories, key techniques, and applications.

Resource management in a multicore architecture

Abstract: A resource management and task allocation controller for installation in a multicore processor having a plurality of interconnected processor elements providing resources for processing executable transactions, at least one of said elements being a master processing unit, the controller being adapted to communicate, when installed, with each of the processor elements including the master processing unit, and comprising control logic for allocating executable transactions within the multicore processor to particular processor elements in accordance with pre-defined allocation parameters.

'Glocal' water governance: a multi-level challenge in the

Abstract: The water crisis is a crisis of governance. A literature reviewreveals that this crisis concerns definitional issues, issues ofownership and access, boundary issues, the multiple uses ofwater, and the levels at which water should be managed.Paradigms for managing water have evolved from integratedwater resource management through more experimental andlearning based adaptive governance to understanding thatwater is not a sector but a cross-cutting issue and shouldperhaps be dealt with through the ‘nexus’ approach. Theliterature reveals a toolbox of policy instruments,infrastructures and institutions for managing water butconcludes that solutions need to be crafted in a contextrelevant manner taking the relevant drivers of water use andmisuse into account.

Self-adaptive workload classification and forecasting for proactive resource provisioning

Abstract: As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real-world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.