Resource Requests Research Articles

When Cloud Media Meet Network Function Virtualization: Challenges and Applications

Recently, network operators around the world have encountered a paradox. On the one hand, they have made enormous investments to handle the tremendous growth of Internet media services triggered by the ubiquitous penetration of high-speed wireless networks and the increasing popularity of mobile devices. On the other hand, these investments have not delivered the expected revenue, leading to declining profitability. In response to this paradox, network function virtualization (NFV) has been proposed as a revolutionary technology to transform network architecture and operations. This emerging technology opens up significant opportunities to reduce the cost of operating media services. In this article, the authors present a survey on this emerging topic. Specifically, they analyze the forces driving the evolution of the network architecture, then describe a novel NFV-enabled media cloud system from two alternative perspectives: an end-to-end view and a layered view. They then illustrate the architectural changes' technical challenges, which range from optimal resource provision and request routing to automated optimization. Finally, they substantiate the NFV-enabled media cloud platform with proof-of-concept case studies. By demonstrating NFV's flexibility and effectiveness, they show that the cost of providing Internet media services by NFV-enabled systems could be substantially reduced in practice.

Optimal Computing Resource Management Based on Utility Maximization in Mobile Crowdsourcing

Mobile crowdsourcing, as an emerging service paradigm, enables the computing resource requestor (CRR) to outsource computation tasks to each computing resource provider (CRP). Considering the importance of pricing as an essential incentive to coordinate the real-time interaction among the CRR and CRPs, in this paper, we propose an optimal real-time pricing strategy for computing resource management in mobile crowdsourcing. Firstly, we analytically model the CRR and CRPs behaviors in form of carefully selected utility and cost functions, based on concepts from microeconomics. Secondly, we propose a distributed algorithm through the exchange of control messages, which contain the information of computing resource demand/supply and real-time prices. We show that there exist real-time prices that can align individual optimality with systematic optimality. Finally, we also take account of the interaction among CRPs and formulate the computing resource management as a game with Nash equilibrium achievable via best response. Simulation results demonstrate that the proposed distributed algorithm can potentially benefit both the CRR and CRPs. The coordinator in mobile crowdsourcing can thus use the optimal real-time pricing strategy to manage computing resources towards the benefit of the overall system.

5G New Radio: Waveform, Frame Structure, Multiple Access, and Initial Access

Different from conventional mobile networks designed to optimize the transmission efficiency of one particular service (e.g., streaming voice/ video) primarily, the industry and academia are reaching an agreement that 5G mobile networks are projected to sustain manifold wireless requirements, including higher mobility, higher data rates, and lower latency. For this purpose, 3GPP has launched the standardization activity for the first phase 5G system in Release 15 named New Radio (NR). To fully understand this crucial technology, this article offers a comprehensive overview of the state-of-the-art development of NR, including deployment scenarios, numerologies, frame structure, new waveform, multiple access, initial/random access procedure, and enhanced carrier aggregation (CA) for resource requests and data transmissions. The provided insights thus facilitate knowledge of design and practice for further features of NR.

Model of passage of vessels through the waterway section forming the schedule in the course of its implementation

The model of passage of vessels through a section of the waterway is considered, which independently determines the order of passage of vessels with limited capacity of sections of the track. Such a model will consist of a number of standard algorithmic networks. When composing the schedule in the model, the following preference rules were used: first-come-first-served (that is, if the ship occupied the workplace, this decision is not canceled); The rule of the shortest operation; For the swamps the priority of vessels going downstream (the direction of flow from the source to the drain). An algorithmic network that implements the search for an acceptable schedule must implement the following for conflicting operations: the operation that has started is not interrupted; If several operations simultaneously claim for one workplace (port, reach) and their number is greater than its throughput, then the conflict resolution is carried out in accordance with predefined preference rules or based on the user's decision; If the operation is waiting for the release of the workplace, it does not occupy the resource; The resource is returned immediately after the operation is completed. The considered design of algorithmic networks allows to resolve the conflict, with a simultaneous resource request, to take the resource once, remember that it was received and return it after the end of the operation, then the resource receives a contra-controlling operation for execution. However, the use of this design introduces redundancy into the model, even if it is used only for conflicting operations. The model is presented in the language of algorithmic networks and is implemented in the system of modeling automation KOGNITRON.

FJC Grey Literature: Lady Justice Goes Grey

The Center recognizes the significant role of the Internet in getting new and lesser-known resources to the legal community because of the ease of accessibility and locating grey literature to answer legal questions. Therefore, the Center developed its new website to function as an open access repository of its robust grey legal collection. Successes and setbacks are revealed as analytics demonstrate an increase and eventual plateau in electronic resource requests. Center information professionals must overcome access issues through improved architecture and search design to maintain current patrons and attract new users to the website. The Center's contribution to the body of grey literature after more than four decades of existence is astounding, but without effective promotion of our collection on the Internet, the rest of the world will never know about this significant body of work.

Lean Construction Techniques for Transforming Nigeria Project Delivery Process - A Case Study Report

Background/Objective: Practitioners in Nigerian construction industry continues to search for ways of improving productivity in its project delivery process. In this research, we share our experiences on lean construction implementation via case study site, to increase knowledge of construction practitioners on improving productivity using lean construction approach. Thus, the aim of this study is to present a case where lean construction technique was adopted and to highlight on the benefits realised in the implantation process. Methodology: Action research was adopted in a case study of 20 housing units using lean construction principles in Federal University Gashua of Yobe state Nigeria. Empirical qualitative data were collected through participatory observation and document studies in the project delivery. A comparative analysis between lean concept project and traditional concept were made to evaluate the potentials of lean construction concept towards improving productivity. Findings: Lean construction tools are used to optimize value in construction sites by extending planning to downstream to achieve reliable work flow. It makes the project participants to employ their commitment for success of the project in the project planning. Lean construction tools were adopted in design and management of production system of some housing units. The result reveals that, the project realised a more stable work flow and emergency requests of resources drastically reduced. And the project was completed six weeks in advance to its real schedule and recorded 17.24% increase in productivity. This makes the client to use the structure quicker than expected time. Application/Improvements: After assessing the benefits recorded, the client decides to engage in lean practice in its future projects. The result will contribute to literature on lean project management and will be used by construction practitioners.

The efficient framework and algorithm for provisioning evolving VDC in federated data centers

Data center has been working as a cost-efficient infrastructure to store a large amount of data and host service applications. With the virtualization technology, a resource request submitted to data center can be abstracted as a virtual data center (VDC) request which consist of virtual machines (VMs) connected through virtual switches, routers and links with guaranteed bandwidth. As one of the challenges, VDC embedding/provisioning focuses on mapping VDC components onto physical nodes and links in data center. In this paper we study the problem of provisioning/embedding for evolving/dynamic VDC request across federated data centers, such that the total operation cost is minimized. We use the VM migration to reconfigure evolving/dynamic VDC for reducing the total operation cost, as well to consolidate the VDCs on as few servers as possible for reducing the number of active servers and thus lowering energy consumption. We design an efficient framework and algorithm for solving the studied NP-Hard problem. Finally, we evaluate and compare the performance of our proposed approach through extensive simulation experiments. The simulation results show that the proposed approach performs better in terms of lowering total operation cost and energy consumption than existing solution does.

Mixed integer linear programming models for optimal crop selection

In this paper, we propose the modeling of a real-case problem where a farmer has to optimize the use of his/her land by selecting the best mix of crops to cultivate. Complexity of the problem is due to the several factors that have to be considered simultaneously. These include the market prices variability of harvested products, the specific resource requests for each crop, the restrictions caused by limited machines availability, and the timing of operations required to complete each crop cultivation. We provide two different mathematical formulations for the analyzed problem. The first one represents a natural integer programming formulation looking for the crop-mix that maximizes the farmer’s expected profit measured as the difference between revenues obtained by selling the harvested products and the production costs. Since the revenue of each crop depends on the price as quoted at the exchange market and the yield per hectare of harvested product, we define it as a random variable. Then, the second model uses the maximization of the Conditional Value-at-Risk (CVaR) as objective function and looks for the crop-mix that allows to maximize the average expected profit under a predefined quantile of worst realizations. To test and compare the proposed models with the cultivation choice made by the farmer, we use Italian historical data represented by monthly returns of different crops over a time period of 16 years. Computational results emphasize the advantage of using the CVaR model for a risk-averse farmer and provide interesting insights for farmers involved in similar problems.

Optimal distributed task scheduling in volunteer clouds

The ever increasing request of computational resources has shifted the computing paradigm towards solutions where less computation is performed locally. The most widely adopted approach nowadays is represented by cloud computing. With the cloud, users can transparently access to virtually infinite resources with the same aptitude of using any other utility. Next to the cloud, the volunteer computing paradigm has gained attention in the last decade, where the spared resources on each personal machine are shared thanks to the users’ willingness to cooperate. Cloud and volunteer paradigms have been recently seen as companion technologies to better exploit the use of local resources. Conversely, this scenario places complex challenges in managing such a large-scale environment, as the resources available on each node and the presence of the nodes online are not known a-priori. The complexity further increases in presence of tasks that have an associated Service Level Agreement specified, e.g., through a deadline. Distributed management solutions have then be advocated as the only approaches that are realistically applicable.In this paper, we propose a framework to allocate tasks according to different policies, defined by suitable optimization problems. Then, we provide a distributed optimization approach relying on the Alternating Direction Method of Multipliers (ADMM) for one of these policies, and we compare it with a centralized approach. Results show that, when a centralized approach can not be adopted in a real environment, it could be possible to rely on the good suboptimal solutions found by the ADMM.

An integrated method for real time and offline web robot detection

AbstractRecent academic and industry reports confirm that web robots dominate the traffic seen by web servers across the Internet. Because web robots crawl in an unregulated fashion, they may threaten the privacy, function, performance, and security of web servers. There is therefore a growing need to be able to identify robot visitors automatically, in offline and in real time, to assess their impact and to potentially protect web servers from abusive bots. Yet contemporary detection approaches, which rely on syntactic log analysis, finding statistical variations between robot and human traffic, analytical learning techniques, or complex software modifications may not be realistic to implement or remain effective as the behavior of robots evolve over time. Instead, this paper presents a novel detection approach that relies on the differences in the resource request patterns of web robots and humans. It rationalizes why differences in resource request patterns are expected to remain intrinsic to robots and humans despite the continuous evolution of their traffic. The performance of the approach, adoptable for both offline and real time settings with a simple implementation, is demonstrated by playing back streams of actual web traffic with varying session lengths and proportions of robot requests.

Energy cooperation in communication of energy harvesting tags

Energy harvesting wireless devices are recently emerging as a viable infrastructure for internet of things (IoT) applications. In this paper, an energy cooperative transmission strategy is proposed for a network energy harvesting tags, that is adapted to the available energy resource and identification request. We develop an optimal transmission policy to maximize the long-term average throughput performance via a Markov decision process (MDP) formulation. Numerical results are provided to show the performance of the energy cooperative transmission policy under various scenarios.

Mitigating Distributed Denial of Service Attack in Cloud Computing Environment using Threshold based Technique

Objectives: Cloud is becoming a very assertive computing platform now a days due to the availability of resources in a customized manner. But DDoS attack is a very dangerous as it directly affects the availability of resources. So the objective of the paper is to mitigate DDoS attack in cloud network using threshold based technique. Methods/Statistical Analysis: In the proposed solution a list of faulty IP addresses has been prepared based on their performance during the Turing test and named as black list. If the request is from black list than it is directly rejected else forwarded to next step. At the second stage check whether the number of resources available are greater than the request made and also the request for resources is less than the threshold value of resource m, than the resource are allocated to that request else request is rejected. Findings: Cloud resources can be defended from the DDoS attack by any of the three defense mechanisms, i.e. DDoS attack prevention, DDoS attack detection and DDoS attack mitigation and recovery. But it is found that Attack mitigation is the easiest way to defend against the DDos attack because of easily available resources. The paper presented a technique that will easily detect and mitigate the DDos attack and it is very easy to implement with minimum cost and overhead. Application/Improvements: The proposed work can be implemented in any cloud network to save it from wasting the resources for malicious requests. For further improvement client based protection can also be implemented such that the attacker will not be able to form its army for the purpose of DDoS attack.

An Approach to Forecast Queue Time in Adaptive Scheduling: How to Mediate System Efficiency and Users Satisfaction

The minimisation of the total cost of ownership is hard to be faced by the owners of large scale computing systems, without affecting negatively the quality of service for the users. Modern datacenters, often included in distributed environments, appear to be “elastic”, i.e., they are able to shrink or enlarge the number of local physical or virtual resources, also by recruiting them from private/public clouds. This increases the degree of dynamicity, making the infrastructure management more and more complex. Here, we report some advances in the realisation of an adaptive scheduling controller (ASC) which, by interacting with the datacenter resource manager, allows an effective and an efficient usage of resources. In particular, we focus on the mathematical formalisation of the ASC’s kernel that allows to dynamically configure, in a suitable way, the datacenter resources manager. The described formalisation is based on a probabilistic approach that, starting from both a hystorical resources usage and on the actual users request of the datacenter resources, identifies a suitable probability distribution for queue time with the aim to perform a short term forecasting. The case study is the SCoPE datacenter at the University of Naples Federico II.

Qualitative Analysis of Resources and Barriers Related to Treatment of Borderline Personality Disorder in the United States.

Resources and treatment for individuals with borderline personality disorder (BPD) are limited and often difficult to obtain. This article aimed to identify key resources for and barriers to obtaining supportive and treatment services for BPD from the perspective of individuals seeking information or services related to BPD ("BPD care seekers"). Data came from transcripts of resource requests to the Borderline Personality Disorder Resource Center from January 2008 to December 2015 (N=6,253). Basic statistics, including the type of service requested, demographic information for the BPD care seeker, and national distribution of requests, were generated for all eligible transcripts. Qualitative analysis of a random subset of 500 transcripts was used to identify themes, challenges, and common experiences reported by BPD care seekers. The greatest number of requests for primary services or resources among the random subset of transcripts was for outpatient services (51%), informational materials (13%), and day programs (9%). Family services, crisis intervention, and mental health literacy were identified as areas where available resources did not meet current demand and that could be improved or expanded. Factors identified as potential barriers to finding and obtaining appropriate treatment for BPD included stigmatization and marginalization within mental health care systems, financial concerns, and comorbidity with psychiatric or medical disorders. BPD care seekers face numerous barriers to obtaining appropriate care. Expanded services and resources to connect individuals with treatment are needed to meet the current demands and preferences of those seeking care.

Mobility management through access network discovery and selection function for load balancing and power saving in software-defined networking environment

The mobile traffic has grown rapidly with the popularity of smart mobile devices. To accommodate increasing traffic, heterogeneous network integration is considered as a viable solution. By overlapping the coverage of heterogeneous networks (e.g., the long-term evolution (LTE) and Wi-Fi integrated network), the mobile operators can use the offloading service (e.g., Wi-Fi offloading) to reduce network congestion. In this approach, a proper network coordination mechanism is required for load balancing of the LTE and Wi-Fi integrated network. In this paper, we use access network discovery and selection function (ANDSF) to suggest selection of proper base stations (e.g., LTE evolved Node Bs or Wi-Fi access points) to user equipment (UE) for load balancing. We integrate the ANDSF with software-defined networking (SDN) to make the ANDSF-aided network more programmable, flexible, and dynamically manageable. Moreover, we propose a power-saving ANDSF (PSA) algorithm to appropriately assign network resource to UEs and reduce the power consumption of Wi-Fi access points (APs). We have implemented the SDN-based PSA and measured the delay times. We also conduct simulation experiments to show that the successful probability of UEs’ resource requests to PSA is almost the same as the proposed schemes of the previous studies when the network traffic is unbalanced. Our study indicates that for unbalanced traffic, PSA can reduce 15.63 % more power consumption of Wi-Fi AP than the previous approaches.

JINR cloud infrastructure evolution

To fulfil JINR commitments in different national and international projects related to the use of modern information technologies such as cloud and grid computing as well as to provide a modern tool for JINR users for their scientific research a cloud infrastructure was deployed at Laboratory of Information Technologies of Joint Institute for Nuclear Research. OpenNebula software was chosen as a cloud platform. Initially it was set up in simple configuration with single front-end host and a few cloud nodes. Some custom development was done to tune JINR cloud installation to fit local needs: web form in the cloud web-interface for resources request, a menu item with cloud utilization statistics, user authentication via Kerberos, custom driver for OpenVZ containers. Because of high demand in that cloud service and its resources over-utilization it was re-designed to cover increasing users’ needs in capacity, availability and reliability. Recently a new cloud instance has been deployed in high-availability configuration with distributed network file system and additional computing power.

A Mechanism for Configurable Network Service Chaining and Its Implementation

Recently Service Function Chaining (SFC) is promising to innovate the network service mode in modern networks. However, a feasible implementation of SFC is still difficult due to the need to achieve functional equivalence with traditional modes without sacrificing performance or increasing network complexity. In this paper, we present a configurable network service chaining (CNSC) mechanism to provide services for network traffics in a flexible and optimal way. Firstly, we formulate the problem of network service chaining and design an effective service chain construction framework based on integrating software-defined networking (SDN) with network functions virtualization (NFV). Then, we model the service path computation problem as an integer liner optimization problem and propose an algorithm named SPCM to cooperatively combine service function instances with a network utility maximum policy. In the procedure of SPCM, we achieve the service node mapping by defining a service capacity matrix for substrate nodes, and work out the optimal link mapping policies with segment routing. Finally, the simulation results indicate that the average request acceptance ratio and resources utilization ratio can reach above 85% and 75% by our SPCM algorithm, respectively. Upon the prototype system, it is demonstrated that CNSC outperforms other approaches and can provide flexible and scalable network services.

Inclusão de estudante cego em curso de Análise e Desenvolvimento de Sistemas de uma instituição pública de ensino superior: um estudo de caso

A proposta da educação inclusiva tem sido destaque na legislação brasileira nas últimas décadas. Entretanto, a concretização desse modelo é uma das questões que desafiam o sistema educacional de países do mundo todo, uma vez que requer recursos para adaptação dos ambientes físicos, aquisição de software específico, contratação de profissionais especializados, entre outras necessidades. Neste contexto, o objetivo deste artigo é apresentar as ações desenvolvidas para permitir a inclusão de um estudante cego na educação superior, por meio de um estudo de caso que teve como participante o próprio aluno. A apresentação deste estudo aborda as ações iniciais e também as ações específicas para superar as dificuldades encontradas. Os resultados indicam que as ações desenvolvidas alcançaram o efeito desejado de promover a inclusão e permanência do estudante cego, visto que ao término do curso este demonstrou domínio das habilidades e competências previstas no projeto político-pedagógico do curso. Espera-se que as ações apresentadas possam ser repetidas em outras situações nas quais seja necessário realizar práticas inclusivas.

An approximate dynamic programming approach to resource management in multi-cloud scenarios

ABSTRACTThe programmability and the virtualisation of network resources are crucial to deploy scalable Information and Communications Technology (ICT) services. The increasing demand of cloud services, mainly devoted to the storage and computing, requires a new functional element, the Cloud Management Broker (CMB), aimed at managing multiple cloud resources to meet the customers’ requirements and, simultaneously, to optimise their usage. This paper proposes a multi-cloud resource allocation algorithm that manages the resource requests with the aim of maximising the CMB revenue over time. The algorithm is based on Markov decision process modelling and relies on reinforcement learning techniques to find online an approximate solution.

Resource requests prediction in the cloud computing environment with a deep belief network

SummaryAccurate resource requests prediction is essential to achieve optimal job scheduling and load balancing for cloud Computing. Existing prediction approaches fall short in providing satisfactory accuracy because of high variances of cloud metrics. We propose a deep belief network (DBN)‐based approach to predict cloud resource requests. We design a set of experiments to find the most influential factors for prediction accuracy and the best DBN parameter set to achieve optimal performance. The innovative points of the proposed approach is that it introduces analysis of variance and orthogonal experimental design techniques into the parameter learning of DBN. The proposed approach achieves high accuracy with mean square error of [10−6,10−5], approximately 72% reduction compared with the traditional autoregressive integrated moving average predictor, and has better prediction accuracy compared with the state‐of‐art fractal modeling approach. Copyright © 2016 John Wiley & Sons, Ltd.