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

Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

AbstractThe ability to support Quality of Service (QoS) constraints is an important requirement in some scientific applications. With the increasing use of Cloud computing infrastructures, where access to resources is shared, dynamic and provisioned on-demand, identifying how QoS constraints can be supported becomes an important challenge. However, access to dedicated resources is often not possible in existing Cloud deployments and limited QoS guarantees are provided by many commercial providers (often restricted to error rate and availability, rather than particular QoS metrics such as latency or access time). We propose a workflow system architecture which enforces QoS for the simultaneous execution of multiple scientific workflows over a shared infrastructure (such as a Cloud environment). Our approach involves multiple pipeline workflow instances, with each instance having its own QoS requirements. These workflows are composed of a number of stages, with each stage being mapped to one or more physical resources. A stage involves a combination of data access, computation and data transfer capability. A token bucket-based data throttling framework is embedded into the workflow system architecture. Each workflow instance stage regulates the amount of data that is injected into the shared resources, allowing for bursts of data to be injected while at the same time providing isolation of workflow streams. We demonstrate our approach by using the Montage workflow, and develop a Reference net model of the workflow

Contributions to channel modelling and performance estimation of HAPS-based communication systems regarding IEEE Std 802.16TM

New and future telecommunication networks are and will be broadband type. The existing terrestrial and space radio communication infrastructures might be supplemented by new wireless networks that make and will make use of aeronautics-technology. Our study/contribution is referring to radio communications based on radio stations aboard a stratospheric platform named, by ITU-R, HAPS (High Altitude Platform Station). These new networks have been proposed as an alternative technology within the ITU framework to provide various narrow/broadband communication services. With the possibility of having a payload for Telecommunications in an aircraft or a balloon (HAPS), it can be carried out radio communications to provide backbone connections on ground and to access to broadband points for ground terminals. The latest implies a complex radio network planning. Therefore, the radio coverage analysis at outdoors and indoors becomes an important issue on the design of new radio systems. In this doctoral thesis, the contribution is related to the HAPS application for terrestrial fixed broadband communications. HAPS was hypothesised as a quasi-static platform with height above ground at the so-called stratospheric layer. Latter contribution was fulfilled by approaching via simulations the outdoor-indoor coverage with a simple efficient computational model at downlink mode. This work was assessing the ITU-R recommendations at bands recognised for the HAPS-based networks. It was contemplated the possibility of operating around 2 GHz (1820 MHz, specifically) because this band is recognised as an alternative for HAPS networks that can provide IMT-2000 and IMT-Advanced services. The global broadband radio communication model was composed of three parts: transmitter, channel, and receiver. The transmitter and receiver parts were based on the specifications of the IEEE Std 802.16TM-2009 (with its respective digital transmission techniques for a robust-reliable link), and the channel was subjected to the analysis of radio modelling at the level of HAPS and terrestrial (outdoors plus indoors) parts. For the channel modelling was used the two-state characterisation (physical situations associated with the transmitted/received signals), the state-oriented channel modelling. One of the channel-state contemplated the environmental transmission situation defined by a direct path between transmitter and receiver, and the remaining one regarded the conditions of shadowing. These states were dependent on the elevation angle related to the ray-tracing analysis: within the propagation environment, it was considered that a representative portion of the total energy of the signal was received by a direct or diffracted wave, and the remaining power signal was coming by a specular wave, to last-mentioned waves (rays) were added the scattered and random rays that constituted the diffuse wave. At indoors case, the variations of the transmitted signal were also considering the following matters additionally: the building penetration, construction material, angle of incidence, floor height, position of terminal in the room, and indoor fading; also, these indoors radiocommunications presented different type of paths to reach the receiver: obscured LOS, no LOS (NLOS), and hard NLOS. The evaluation of the feasible performance for the HAPS-to-ground terminal was accomplished by means of thorough simulations. The outcomes of the experiment were presented in terms of BER vs. Eb/N0 plotting, getting significant positive conclusions for these kind of system as access network technology based on HAPS

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial