Flexible Resource Allocation Strategies Research Articles

NOMA for Integrating Sensing and Communications Toward 6G: A Multiple Access Perspective

This article focuses on the development of integrated sensing and communications (ISAC) from a multiple access (MA) perspective, where the idea of non-orthogonal multiple access (NOMA) is exploited for harmoniously accommodating the sensing and communication functionalities. We first reveal that the developing trend of ISAC is from orthogonality to non-orthogonality, and introduce the fundamental models of the downlink and uplink ISAC while identifying the design challenges from the MA perspective. (1) For the downlink ISAC, we propose two novel designs, namely NOMA-empowered downlink ISAC and NOMA-inspired downlink ISAC to effectively coordinate the inter-user interference and the sensingto- communication interference, respectively. (2) For the uplink ISAC, we first propose a pure-NOMA-based uplink ISAC design, where a fixed communication-to-sensing successive interference cancellation order is employed for distinguishing the mixed sensing-communication signal received over the fully shared radio resources. Then, we propose a general semi-NOMA-based uplink ISAC design, which includes the conventional orthogonal multiple access-based and pure-NOMA-based uplink ISAC as special cases, thus being capable of providing flexible resource allocation strategies between sensing and communication. Along each proposed NOMA-ISAC design, numerical results are provided for showing the superiority over conventional ISAC designs.

Optimizing Task Scheduling in Cloud Data Centres with Dynamic Resource Allocation Using Genetic Algorithm (TSOGA)

Nowadays, Massive business applications are increasingly giving attention to cloud computing data centres because of its high potential, adaptability, and efficiency in supplying several sources of both software and hardware to support networked consumers. The criteria for autonomy of virtual machines necessitate a flexible resource allocation strategy for Virtual Machines (VMs) .The majority of resource utilization models were inaccurate, making it impossible to determine the virtual machine's energy usage directly from the hardware. Due to the size of modern data centres and the constantly changing character of their resource supply, efficient scheduling solutions must be developed to oversee these resources and meet the objectives of both cloud service providers and cloud customers. Hence an algorithm called Task Scheduling Optimization based Genetic Algorithm (TSOGA) has been proposed to dynamically allocate the resources in pursuit of scheduling the tasks in cloud data centers. The proposed module initially focuses on task scheduling process, followed by optimized running time of task execution. For data centres with dynamic resource allocation, the goal of TSOGA is to efficiently assign jobs to resources while minimizing execution time and optimizing resource utilization. Thus, to manage the data centres while achieving high levels of efficiency in resource allocation, we constructed a virtual node for our research. Incorporation of Genetic Algorithm is to determine an ideal or nearly ideal schedule for carrying out tasks using the available resources while taking into account a variety of restrictions and goals, such as minimizing execution and waiting time of task during dynamic scheduling process and efficient resource utilization.

Lessons Learned from Mobilising Research for Impact During the Covid-19 Pandemic

During the Covid-19 pandemic, research organisations have strived to be resilient. This means navigating through the technical, operational, and political challenges to achieving successful research implementation. Particularly for local policy research thinktanks, the pandemic has made these challenges even more difficult to address. From the experience of the Cambodia Development Resource Institute (CDRI) in implementing large-sample research in the formal and informal sectors during the pandemic, these challenges are countered through: (1) the incorporation of a technical advisory team; (2) the adoption of a flexible resource allocation strategy; and (3) the implementation of a quality assurance system. Policy research is only impactful when the knowledge produced serves its purpose as evidence to inform policymaking and guide programme intervention. To realise this objective, CDRI implements three types of engagement activities (consultation, coordination, and validation) that provide opportunities for interaction between researchers and relevant stakeholders.

Flexible Resource Allocation Strategies: Resource Redeployment and Resource Idling

Flexible Resource Allocation Strategies: Resource Redeployment and Resource Idling

A network slicing algorithm for cloud-edge collaboration hybrid computing in 5G and beyond networks

Edge computing can effectively provide a promising solution with ultra-low latency for a variety of user-oriented services in 5G and beyond networks. The existing studies, however, are unable to provide a flexible resource allocation strategy for various network slices with different user demands in edge computing. This paper proposes a new network slicing algorithm for cloud–edge collaboration hybrid computing (CECHC) in 5G and beyond networks. This algorithm designs a flexible and effective resource allocation strategy primarily for three network slices based on diversified requirements. Moreover, the CECHC model is designed with optimally distributed units (DUs) and centralized units (CUs) deployments to improve storage capability and computing power. This allows for more convenient function partitioning for different network slices. To validate the performance of the proposed algorithm, a series of agent-based simulations are conducted in various network models. The experimental results demonstrate that the proposed algorithm deployed in CECHC outperforms other network models, including fog computing (FC), multi-access edge computing (MEC), and the original CECHC models. It provides the lowest latency for various network slices and achieves all successful runs with different storage capabilities and computing power.

Reinforcement learning based framework for COVID-19 resource allocation

In this paper, a reinforcement learning based framework is developed for COVID-19 resource allocation. We first construct an agent-based epidemic environment to model the transmission dynamics in multiple states. Then, a multi-agent reinforcement-learning algorithm is proposed based on the time-varying properties of the environment, and the performance of the algorithm is compared with other algorithms. According to the age distribution of populations and their economic conditions, the optimal lockdown resource allocation strategies of Arizona, California, Nevada, and Utah in the United States are determined using the proposed reinforcement-learning algorithm. Experimental results show that the framework can adopt more flexible resource allocation strategies and help decision makers to determine the optimal deployment of limited resources in infection prevention.

Coordinated process scheduling algorithms for coupled earth system models

AbstractIt is becoming increasingly significant for humans to predict and understand future climate changes using coupled climate system models. Although the performance and scalability of individual physical components have improved over the past few years, coupled climate systems still suffer from low efficiency. This paper focuses on the process scheduling problem for the widely applied coupled earth system model (CESM). The proposed resource allocation strategies allow components to execute on a compromised suboptimal setup and still maintain approximately the best parallel speedup. With this flexible resource allocation strategy, we further propose a coordinated process scheduling algorithm (CPSA). More notably, we propose an upgraded version called CPSA‐B, which makes efficient resource sharing configurations, including resource allocation and process layout of components. We integrate CPSA and CPSA‐B as pre‐arrangement tools into the CESM program and deploy them on the Huawei Kunpeng platform. The speedup curves of the CESM components are prepared in advance, based on sampling tests. Experimental data show that CPSA‐B reduces up to 58% of the execution time compared with the CESM default strategy. The algorithm has low complexity and can efficiently find solutions for large input sizes.

Pharaoh ant colonies dynamically regulate reproductive allocation based on colony demography

The success of social insect colonies is dependent upon efficient and dynamic allocation of resources to alternate queen and worker castes. The developmental and molecular mechanisms regulating the caste fate of individual larvae in response to environmental cues have been the focus of intense study. However, the mechanisms regulating colony-level resource allocation into alternate castes (i.e., caste allocation ratios) are less well studied. Here, we systematically manipulate colony demography to elucidate the social regulatory mechanisms of caste allocation in the ant Monomorium pharaonis. By measuring the effects of demographic manipulation on colony productivity, we infer that caste allocation results from differences in timing and efficiency of culling of very young reproductive-destined larvae, which are always present in colonies. Based on our results, we develop a conceptual model depicting how colonies integrate numerous individual-level caste determination decisions to regulate colony-level caste allocation. We propose that adult workers make decisions about culling larvae based on the ratio of the number of workers to the number of eggs contained in colonies, likely signaled by pheromone present on eggs. This strategy enables the dynamic alteration of colony demography in response to internal and external conditions, which is likely key to the ability of M. pharaonis and similar ants to thrive in disturbed habitats and to become widespread invasive species. The defining feature of social insect societies is the presence of alternate queen (reproductive) and worker (non-reproductive) castes of individuals. The fitness of social insect colonies is dependent upon efficient allocation of resources to alternate castes, particularly in the case of highly polygynous (multiqueen) societies. However, the mechanisms by which such societies regulate caste allocation are largely unknown. In this study, we perform a range of manipulative studies to disentangle social mechanisms of caste allocation in polygynous ant societies. Based on our results, we develop a model in which colonies manipulate their production of queens (and also males) versus workers according to the present density of eggs in the colony, a reliable indicator of queens’ fertility. Provided egg density is high, colonies kill queen- and male-destined larvae; when egg density falls, colonies begin to rear queens and males. This flexible resource allocation strategy is key to the ability of highly polygynous species to thrive in marginal (often human-associated) habitats.

How to get the most bang for your buck: the evolution and physiology of nutrition-dependent resource allocation strategies.

All organisms use resources to grow, survive and reproduce. The supply of these resources varies widely across landscapes and time, imposing ultimate constraints on the maximal trait values for allocation-related traits. In this review, we address three key questions fundamental to our understanding of the evolution of allocation strategies and their underlying mechanisms. First, we ask: how diverse are flexible resource allocation strategies among different organisms? We find there are many, varied, examples of flexible strategies that depend on nutrition. However, this diversity is often ignored in some of the best-known cases of resource allocation shifts, such as the commonly observed pattern of lifespan extension under nutrient limitation. A greater appreciation of the wide variety of flexible allocation strategies leads directly to our second major question: what conditions select for different plastic allocation strategies? Here, we highlight the need for additional models that explicitly consider the evolution of phenotypically plastic allocation strategies and empirical tests of the predictions of those models in natural populations. Finally, we consider the question: what are the underlying mechanisms determining resource allocation strategies? Although evolutionary biologists assume differential allocation of resources is a major factor limiting trait evolution, few proximate mechanisms are known that specifically support the model. We argue that an integrated framework can reconcile evolutionary models with proximate mechanisms that appear at first glance to be in conflict with these models. Overall, we encourage future studies to: (i) mimic ecological conditions in which those patterns evolve, and (ii) take advantage of the 'omic' opportunities to produce multi-level data and analytical models that effectively integrate across physiological and evolutionary theory.

Call Level Performance Analysis for Multiservices Wireless Cellular Networks With Adaptive Resource Allocation Strategies

In this paper, a novel mathematical approach to evaluate the performance of adaptive or flexible resource allocation (FRA) strategies with uniform quality of service (QoS) provisioning in multiservices wireless cellular networks, in terms of the call blocking probabilities and transmission delay, is proposed. FRA strategies improve channel utilization by dynamically adjusting user transmission rates. Based on the available cell capacity, the bandwidth offered to users can be adjusted in accordance with the elasticities of their service types. When an FRA strategy provides similar QoS to all the calls of the same service type, it is known as an FRA with uniform QoS provisioning. In multiservices wireless cellular networks, calls can be identified as belonging to one of four service classes (i.e., conversational, streaming, interactive, and background). Transmission delay is one of the most important performance measurements of interactive and background service classes. Transmission delay, however, has not been addressed in previous studies on FRA with uniform QoS provisioning either in conjunction with interactive or background service classes. This is because such studies have been based on the Markov property of the negative exponential distribution of the service time which lacks time delay information. The analytical approach in this paper is based on the fact that in FRA strategies with uniform QoS provisioning, calls of all service types tend to use an average number of resources (i.e., the number of resources available for a service type divided by the number of active users of that service type). This feature facilitates the assessment of the mean service time and the number of resources allocated to a call during its lifetime and, consequently, a call's transmission delay. The study also considers the fact that the probability density function (pdf) of the normalized transmission delay is almost a symmetrical function and has low variance. The accuracy of the proposed mathematical analysis is then corroborated by means of semianalytical methods and by discrete event computer simulation.

Flexible Resource Allocation Strategies for Class-Based QoS Provisioning in Mobile Networks

Flexible resource-allocation (FRA) strategies have been proposed in the literature to mitigate the high blocking rate caused in high-speed mobile communication networks when resource aggregation is used to increase the data rate. In this paper, new FRA strategies that cope with scenarios with multiple service types and multiple priorities are proposed. These are called the FRA strategy with differentiated priorities and quality of service (FRAQoS) and the FRA strategy with prioritized levels (FRASPL). The main distinguishing feature of these strategies is their capacity to prioritize some service types over others. FRAQoS prioritizes the quality of service (QoS) of particular service types over others by introducing the concepts of prioritized call degradation and compensation. However, FRAQoS provides a limited ability to prioritize particular service types over the rest because any incoming call, irrespective of its service type and priority, may trigger resource reallocations to service it. The FRASPL overcomes this limitation by introducing a call-admission mechanism that, when necessary, rejects calls originated by low-priority service types. This enhances the protection to high-priority service types. By prioritizing some services over the others, FRASPL can trade off capacity against QoS. FRAQoS represents particular limiting cases of FRASPL. A mathematical model is developed to investigate the performance of FRAQoS and FRASPL. Then, they are compared with other FRA strategies in a scenario with multiple differentiated service types. Results show that the proposed strategies effectively prioritize service types, providing them with better QoS. This makes FRAQoS and FRASPL specially suitable for class-based QoS provisioning in mobile networks.

Equal resource sharing allocation with QoS differentiation for conversational services in wireless communication networks

Flexible resource allocation (FRA) strategies are used in cellular systems to dynamically adjust the quality of service (QoS) of active calls according to the prevailing traffic conditions. This is done by assigning a variable number of resources to active calls. When the offered load is high, a small number of resources is assigned to offer high capacity while providing a QoS lower than the maximum requested. When the offered load is low, the opposite occurs. A new flexible resource allocation strategy called equal resource sharing allocation with differentiated QoS (ERSAQoS) is presented. This strategy is suitable for operation in environments with integrated conversational services (i.e. voice, video, videophone) each with different QoS and priorities. ERSAQoS consists of a two-phase allocation process. Resources are first distributed among different active calls with the same service type, respecting priorities, and then equally distributed among active calls with the same service type. The proposed strategy allows fine or soft control of differentiated QoS provisioning. Results show that ERSAQoS meets the upper capacity bound for FRA strategies while improving the QoS of the prioritised types of service.

Equal resource sharing scheduling for PCS data services

For high speed mobile communication applications, the data rate can be increased by using multiple channels (or time slots) instead of one channel. To reduce the high blocking rate of multiple channels assignment, flexible resource allocation strategies have been proposed. This paper proposes the Equal Resource Sharing Allocation scheme (ERSA scheme) for flexible resource allocation. The ERSA scheme dynamically averages the allocated resource to the call requests based on the number of calls in a base station. The scheme accommodates the maximum number of requests while providing acceptable quality to the admitted requests. We developed an analytic model to investigate the performance of ERSA, and conducted simulation experiments to validate the analytic model. We define satisfaction indication SI as the performance measurement of the resource allocation algorithm. The experiment results indicate that the ERSA scheme outperforms other resource allocation algorithms proposed in our previous study.

Dynamic resource assignment strategies for PCS data services

For high speed mobile communication applications, the data rate can be increased by using multiple channels (or time slots) instead of a single channel (or time slot). To reduce the high blocking rate of multiple channels assignment, flexible resource allocation strategies are exercised. This paper proposes a generic Mobile Switching Center (MSC) resource allocation algorithm called MRA algorithm for Personal Communications Services (PCS) data services. The algorithm may combine with any base station resource allocation algorithms proposed in our previous study. A simulation model is proposed to investigate the performance of these algorithms. We define satisfaction indication SI to measure the performance of the resource allocation. SI indicates the customer's satisfaction about the call connection, it can also be used by the service provider as a charge indication.