A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.

Cross-Cycled Uplink Resource Allocation over NB-IoT

Before each user equipment (UE) can send data using the narrowband physical uplink shared channel (NPUSCH), each UE should periodically monitor a search space in the narrowband physical downlink control channel (NPDCCH) to decode a downlink control indicator (DCI) over narrowband Internet of Things (NB-IoT). This monitoring period, called the NPDCCH period in NB-IoT, can be flexibly adjusted for UEs with different channel qualities. However, because low-cost NB-IoT UEs operate in the half-duplex mode, they cannot monitor search spaces in NPDCCHs and transmit data in the NPUSCH simultaneously. Thus, as we observed, a percentage of uplink subframes will be wasted when UEs monitor search spaces in NPDCCHs, and the wasted percentage is higher when the monitored period is shorter. In this paper, to address this issue, we formulate the cross-cycled resource allocation problem to reduce the consumed subframes while satisfying the uplink data requirement of each UE. We then propose a cross-cycled uplink resource allocation algorithm to efficiently use the originally unusable NPUSCH subframes to increase resource utilization. Compared with the two resource allocation algorithms, the simulation results verify our motivation of using the cross-cycled radio resources to achieve massive connections over NB-IoT, especially for UEs with high channel qualities. The results also showcase the efficiency of the proposed algorithm, which can be flexibly applied for more different NPDCCH periods.

Gridded Flood Depth Estimates from Satellite Derived Inundations

Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.

A Methodology for Designing Short-Term Stationary Air Quality Campaigns with Mobile Laboratories Using Different Possible Allocation Criteria

Air quality monitoring and control are key issues for environmental assessment and management in order to protect public health and the environment. Local and central authorities have developed strategies and tools to manage environmental protection, which, for air quality, consist of monitoring networks with fixed and portable instrumentation and mathematical models. This study develops a methodology for designing short-term air quality campaigns with mobile laboratories (laboratories fully housed within or transported by a vehicle and maintained in a fixed location for a period of time) as a decision support system for environmental management and protection authorities. In particular, the study provides a methodology to identify: (i) the most representative locations to place mobile laboratories and (ii) the best time period to carry out the measurements in the case of short-term air quality campaigns. The approach integrates atmospheric dispersion models and allocation algorithms specifically developed for optimizing the measuring campaigns. The methodology is organized in two phases, each of them divided into several steps. Fourteen allocation algorithms dedicated to three type of receptors (population, vegetation and physical cultural heritage) have been proposed. The methodology has been applied to four short-term air quality campaigns in the Emilia-Romagna region.

An Intelligent Dynamic Bandwidth Allocation Method to Support Quality of Service in Internet of Things

Worldwide, Internet of Things (IoT) devices will surpass a range of five billion by 2025 and developed countries will extend to advance by supplying almost two-thirds of such connections. With existing infrastructure, allocating bandwidth to billions of IoT devices is going to be cumbersome. This paper addresses the problem of Dynamic bandwidth allocation in IoT devices. We enhanced the dynamic bandwidth allocation algorithms to support QoS in different bandwidth ranges. Our Proposed innovative Machine learning-based Intelligent Dynamic Bandwidth Allocation (IDBA) algorithm allocates the bandwidth effectively between IoT devices based on utilization patterns observed through machine learning methods. Moreover, we showed that an IDBA algorithm results in supporting quality of service in terms of ensuring uninterrupted bandwidth to critical IoT application where bandwidth tolerance is zero percent, along with that IDBA increasing the network throughput correlated to other dynamic bandwidth allocation algorithms. We demonstrate simulations in different applications. The results show that IDBA achieves better throughput even in low bandwidth range.

Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets

Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets

Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets

Graph Coloring and Ant Colony Optimization based Sub-channel Allocation Algorithms for LTE-A HetNets

Admission Control and Bandwidth Allocation for Class A Traffic in RPR Networks

In this thesis, we study the admission control and bandwidth allocation methods for classA traffic in RPR networks. First, we investigate the performance of classA traffic under the current RPR protocol. The simulation results show that RPR networks can support low-delay classA traffic even if the networks are congested with classB and classC traffic. The low-delay performance, however, is subject to the condition that the load of classA traffic must be properly controlled. Consequently, an admission control mechanism must be used for classA traffic. In this thesis, several admission control algorithms are studied. They are the Simple Sum algorithm, the Measured Sum algorithm, and the Equivalent Bandwidth algorithm. The simulation results show that the Equivalent Bandwidth algorithm is the most suitable to use as the admission control mechanism for classA traffic. The admission control mechanism makes admission decision based on the available bandwidth allocated to the classA traffic. The existing RPR standard assumes the bandwidth allocated for classA traffic at each node is fixed. The fixed bandwidth allocation introduces inflexibility and inefficient use of bandwidth for classA traffic. In this thesis, three bandwidth allocation algorithms are proposed to dynamically allocate bandwidth for classA traffic. These algorithms have different levels of complexity and can be applied to different traffic environments. Simulation results show that the proposed algorithms improve the bandwidth efficiency of the RPR networks. The proposed algorithms are also readily integrated with the existing Internet Quality of Services (QoS) paradigms such as Diffserv and RSVP services.