Resource management with adaptive capacity in C-RAN

This work was supported in part by the Spanish ministry of science through the projectRTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by theUPC. It has been done under COST CA15104 IRACON EU project.Efficient computational resource management in 5G Cloud Radio Access Network (CRAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with Quality of Service (QoS) constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modeling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. However, the assumption of a fixed computational capacity at the BBU pools may result in underutilized or oversubscribed resources, thus affecting the overall QoS. As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). For this reason, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). For this reason, two new strategies are proposed and tested: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 99.9 % and 98 % compared to the DRM-AC, respectively

Energy Efficiency and Quality of Services in Virtualized Cloud Radio Access Network

Cloud Radio Access Network (C-RAN) is being widely studied for soft and green fifth generation of Long Term Evolution - Advanced (LTE-A). The recent technology advancement in network virtualization function (NFV) and software defined radio (SDR) has enabled virtualization of Baseband Units (BBU) and sharing of underlying general purpose processing (GPP) infrastructure. Also, new innovations in optical transport network (OTN) such as Dark Fiber provides low latency and high bandwidth channels that can support C-RAN for more than forty-kilometer radius. All these advancements make C-RAN feasible and practical. Several virtualization strategies and architectures are proposed for C-RAN and it has been established that C-RAN offers higher energy efficiency and better resource utilization than the current decentralized radio access network (D-RAN). This project studies proposed resource utilization strategy and device a method to calculate power utilization. Then proposes and analyzes a new resource management and virtual BBU placement strategy for C-RAN based on demand prediction and inter-BBU communication load. The new approach is compared with existing state of art strategies with same input scenarios and load. The trade-offs between energy efficiency and quality of services is discussed. The project concludes with comparison between different strategies based on complexity of the system, performance in terms of service availability and optimization efficiency in different scenarios

Identifying a guiding list of high involvement practices in human resource management

[EN] In today global competitiveness, it is becoming increasingly frequent, the introduction of new management practices to organizations, seeking to enhance performance as a form of Management Innovation (MI). This is so because such practices are usually difficult to replicate exactly from one company to another, provided they are well rooted in the daily work in the organization. The main purpose of this paper is, by reviewing the previous work done in this area, to present a general list of Human Resource Practices (HRM practices) geared towards improving organizational effectiveness and hence better performance outcomes. Many work has been done to date within this topic, but it is difficult to find a consensus about the best way to address to this practices, and this fact makes difficult to compare different studies and their results. We present a detailed but synthesized list of those HR practices to be used as a starting point in any sector whether industrial or servicesPerelló Marín, MR.; Ribes Giner, G. (2014). Identifying a guiding list of high involvement practices in human resource management. Working Papers on Operations Management. 5(1):31-47. doi:10.4995/wpom.v5i1.1495SWORD314751Adamides, E. D., & Pomonis, N. (2009). The co-evolution of product, production and supply chain decisions, and the emergence of manufacturing strategy. International Journal of Production Economics, 121(2), 301-312. doi:10.1016/j.ijpe.2006.11.025Ahmad, S., & Schroeder, R. G. (2002). The impact of human resource management practices on operational performance: recognizing country and industry differences. Journal of Operations Management, 21(1), 19-43. doi:10.1016/s0272-6963(02)00056-6Alfes, K., Shantz, A. D., Truss, C., & Soane, E. C. (2013). 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Human Resource Bundles and Manufacturing Performance: Organizational Logic and Flexible Production Systems in the World Auto Industry. ILR Review, 48(2), 197-221. doi:10.1177/001979399504800201Marchington, M., & Grugulis, I. (2000). «Best practice» human resource management: perfect opportunity or dangerous illusion? The International Journal of Human Resource Management, 11(6), 1104-1124. doi:10.1080/09585190050177184Marin-Garcia, J. A., Miralles, C., Garcia-Sabater, J. J., & Perello-Marin, M. R. (2011). Alternative tools to mass production and human performance indicators in sheltered work centers of Valencian community (Spain). Journal of Industrial Engineering and Management, 4(3). doi:10.3926/jiem.2011.v4n3.p467-480McClean, E., & Collins, C. J. (2011). High-commitment HR practices, employee effort, and firm performance: Investigating the effects of HR practices across employee groups within professional services firms. 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(2010). Human resource management and firm performance in Europe through the lens of business systems: best fit, best practice or both? The International Journal of Human Resource Management, 21(7), 933-962. doi:10.1080/09585191003783371Tang, T.-W., & Tang, Y.-Y. (2012). Promoting service-oriented organizational citizenship behaviors in hotels: The role of high-performance human resource practices and organizational social climates. International Journal of Hospitality Management, 31(3), 885-895. doi:10.1016/j.ijhm.2011.10.007Tarí, J. J., Molina, J. F., & Castejón, J. L. (2007). The relationship between quality management practices and their effects on quality outcomes. European Journal of Operational Research, 183(2), 483-501. doi:10.1016/j.ejor.2006.10.016Thang, L. C., Rowley, C., Quang, T., & Warner, M. (2007). To what extent can management practices be transferred between countries? Journal of World Business, 42(1), 113-127. doi:10.1016/j.jwb.2006.11.005De Waal, A. A. (2007). 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South and South-East Asian coastal fisheries: their status and directions for improved management: conference synopsis and recommendations

As a step to address the problems of coastal fisheries in Asia, the WorldFish Center joined forces with fisheries agencies from eight developing Asian countries (Bangladesh, India, Indonesia, Malaysia, The Philippines, Sri Lanka, Thailand and Vietnam) and the Asian Development Bank, to implement a project entitled “Sustainable Management of Coastal Fish Stocks in Asia” (also known as the “TrawlBase” project). The project was implemented between 1998 and 2001. The main achievements of this partnership were: (a) Development of a database called “Fisheries Resource Information System and Tools” (FiRST), which contains trawl research survey data and socioeconomic information for selected fisheries, and facilitates its analysis; (b) Evaluation of the extent of resource decline and over-fishing, both biological and economic, in the region; (c) Identification of the measures needed to manage coastal fisheries in the participating countries, resulting in draft strategies and action plans; and (d) Strengthening of national capacity in coastal fisheries assessment, planning and management

Proactive or Reactive? Optimal Management of an Invasive Forest Pest in a Spatial Framework

This paper offers a preliminary investigation into the conditions under which it might be optimal to engage in proactive management of a non-timber forest resource in the presence of an invasive species whose spread is unaffected by management action. Proactive management is defined as treating an uninfected area in order to encourage healthy ecosystem function, given that the arrival of the invasive is inevitable. Inspired by the problem of white pine blister rust in the Rocky Mountain west, the model was solved under varying assumptions concerning the scale of management action, benefit and costs, the discount rate, and uncertainty of spread. Results showed that proactive strategies tended to be optimal when, ceteris paribus, a) more resources are available for treatment; b) the costs of treatment are rapidly increasing in forest health, or conversely, the benefits of healthy and unhealthy stands are relatively similar; and c) the discount rate is low. The introduction of uncertainty did not significantly affect the likelihood of a proactive management strategy being optimal, but did show that the conditional probabilities of infection play important role in the decision of which uninfected stand should be treated if a choice is available to the manager.Crop Production/Industries, Resource /Energy Economics and Policy,