Adaptive Resource Management for Deployable HPC Systems

In wide-area networks (WANs), efficiently allocating distributed resources presents a significant challenge due to increasing node numbers and complex network conditions. Traditional algorithms, which heavily depend on global information, encounter scalability limitations. This study proposes an adaptive distributed optimal resource allocation consistency algorithm designed for WANs. The approach dynamically adjusts resource allocation based on real-time network conditions and user requirements, reducing dependence on global data. A time window distribution model and an information coding model are employed to optimize the resource allocation process. By integrating a Q-learning algorithm and extending the Paxos algorithm to WANs, global consistency among network nodes is ensured. The experimental results demonstrate the algorithm’s superior performance, particularly in low-bandwidth WAN scenarios (below 2 Mb/s), where it significantly enhances throughput. An average resource utilization rate exceeding 97% is achieved, along with rapid convergence and high parallelism. This research underscores the algorithm’s effectiveness and scalability, providing a robust solution to distributed resource allocation challenges in complex WAN environments.

Mobile cloud computing (MCC) enables mobile devices to outsource their computing, storage and other tasks onto the cloud to achieve more capacities and higher performance. One of the most critical research issues is how the cloud can efficiently handle the possible overwhelming requests from mobile users when the cloud resource is limited. In this paper, a novel MCC adaptive resource allocation model is proposed to achieve the optimal resource allocation in terms of the maximal overall system reward by considering both cloud and mobile devices. To achieve this goal, we model the adaptive resource allocation as a semi-Markov decision process (SMDP) to capture the dynamic arrivals and departures of resource requests. Extensive simulations are conducted to demonstrate that our proposed model can achieve higher system reward and lower service blocking probability compared to traditional approaches based on greedy resource allocation algorithm. Performance comparisons with various MCC resource allocation schemes are also provided.

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive deep neural network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this article, we propose an edge-enabled multivideo analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov decision process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion-deep-reinforcement-learning-based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes.

This paper presents a number of key radio resource management strategies across physical (PHY) and medium access control (MAC) layers for reducing base station energy consumption including power efficient link adaptation (LA), exploitation of multi-user diversity and trading bandwidth for energy efficiency. By collectively taking advantage of these radio resource management strategies, a multi-user adaptive power and resource allocation algorithm is proposed to ease the power requirements of a base station, whilst maintaining the same levels of service. The scheduling algorithm is evaluated within a LTE downlink simulator for various traffic load conditions. The results show that the proposed algorithm achieves a significant energy saving over a conventional non-energy aware resource allocation scheme.

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

Faced with a backlog of nuclear waste and weapons plutonium, as well as an ever-increasing public concern about safety and environmental issues associated with conventional nuclear reactors, many countries are studying new, accelerator-driven technologies that hold the promise of providing safe and effective solutions to these problems. Proposed projects include accelerator transmutation of waste (ATW), accelerator-based conversion of plutonium (ABC), accelerator-driven energy production (ADEP), and accelerator production of tritium (APT). Also, next-generation spallation neutron sources based on similar technology will play a major role in materials science and biological science research. The design of accelerators for these projects will require a major advance in numerical modeling capability. For example, beam dynamics simulations with approximately 100 million particles will be needed to ensure that extremely stringent beam loss requirements (less than a nanoampere per meter) can be met. Compared with typical present-day modeling using 10,000–100,000 particles, this represents an increase of 3–4 orders of magnitude. High performance computing (HPC) platforms make it possible to perform such large scale simulations, which require 10’s of GBytes of memory. They also make it possible to perform smaller simulations in a matter of hours that would require months to run on a single processor workstation. This paper will describe how HPC platforms can be used to perform the numerically intensive beam dynamics simulations required for development of these new accelerator-driven technologies.

Network virtualization technology is generally envisaged as a promising technology to consequently satisfy various types of service requirements. On the other hand, non-orthogonal multiple access (NOMA) technology has the potential to significantly increase the spectral efficiency of the system. However, previous works that jointly address these two issues have not considered the dynamic resource allocation issue in this context. In this paper, we propose a slice-based virtual resources scheduling scheme with NOMA technology to enhance the quality-of-service (QoS) of the system. We formulate the power granularity allocation and subcarrier allocation strategies into a constrained Markov decision process problem, aiming at the maximization of the total user rate. In order to further avoid the curse of dimensionality and the expectation calculation in the optimal value function, we develop an adaptive resource allocation algorithm based on approximate dynamic programming to solve the problem. Extensive simulation works have been conducted under various system settings, and the results demonstrate that the proposed algorithm can significantly reduce the outage probability and increase the user data rate.

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Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computation- ally expensive, the procedure is limited in practical applications. It is hence important to seek ways to reduce the execution time. A suitable solution is the use of high performance computing. This study focuses on the fully Bayesian algorithm for a conventional IRT model so that it can be implemented on a high performance parallel machine. Empirical results suggest that this parallel version of the algorithm achieves a considerable speedup and thus reduces the execution time considerably.

Purpose of the article: the design of a multi-level monitoring and response system for impacts, ensuring the resilience of complex ergatic systems through the use of a backup control loop and adaptive resource allocation. Research methods: system analysis, modeling, architecture synthesis, resource allocation. Research results: a multi-level architecture of a monitoring and response system for impacts has been developed, aimed at improving the resilience of complex ergatic systems. A structural solution is proposed, including primary and backup control loops, which ensures continuous monitoring and coordinated response even in cases of partial degradation or failure of the communication infrastructure. A mechanism for adaptive resource reallocation between architecture components has been designed, ensuring efficient system operation under variable loads and limited computing and network capabilities. The theoretical significance of the work lies in advancing scientific knowledge on the design of multi-level architectures for the protection of ergatic systems, ensuring their functionality under complex impacts. The practical significance is determined by the possibility of applying the designed architectural solutions in the creation and modernization of distributed automated complexes of various purposes to increase their resilience and the efficiency of monitoring processes. Scientific novelty: for the first time, a multi-level architecture of a monitoring and response system for impacts is proposed, implementing a separation into primary and backup control loops to ensure operational resilience under communication degradation and failures. For the first time, a mechanism for adaptive resource reallocation between system levels has been developed, allowing the maintenance of monitoring and response efficiency under variable loads and limited computing and network capabilities.

The coronavirus 2019 disease (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pre-existing cardiovascular disease (CVD) increases the morbidity and mortality of COVID-19, and COVID-19 itself causes serious cardiac sequelae. Strategies to minimise the risk of viral transmission to health care workers and uninfected cardiac patients while prioritising high quality cardiac care are urgently needed. We conducted a rapid literature appraisal and review of key documents identified by the Cardiac Society of Australia and New Zealand Board and Council members, the Australian and New Zealand Society of Cardiac and Thoracic Surgeons, and key cardiology, surgical and public health opinion leaders. Common acute cardiac manifestations of COVID-19 include left ventricular dysfunction, heart failure, arrhythmias and acute coronary syndromes. The presence of underlying CVD confers a five- to tenfold higher case fatality rate with COVID-19 disease. Special precautions are needed to avoid viral transmission to this population at risk. Adaptive health care delivery models and resource allocation are required throughout the health care system to address this need. Cardiovascular health services and cardiovascular health care providers need to recognise the increased risk of COVID-19 among CVD patients, upskill in the management of COVID-19 cardiac manifestations, and reorganise and innovate in service delivery models to meet demands. This consensus statement, endorsed by the Cardiac Society of Australia and New Zealand, the Australian and New Zealand Society of Cardiac and Thoracic Surgeons, the National Heart Foundation of Australia and the High Blood Pressure Research Council of Australia summarises important issues and proposes practical approaches to cardiovascular health care delivery to patients with and without SARS-CoV-2 infection.

In this paper, a new adaptive resource allocation and grouping scheme is proposed to enhance the capacity of device-to-device (D2D) systems underlaying cellular networks. In order to increase resource utilization, we propose to divide the outer region of the cell into multiple parts and the corresponding D2D pairs located in the symmetrical parts are designed to reuse the same cellular user equipment's (CUE's) resources. To further improve spectrum efficiency, three grouping modes for D2D pairs reusing the same resources are devised and these three modes are proposed to be employed adaptively according to the values of the channel gains between CUEs and base station (BS). Furthermore, by considering interference powers from CUEs to D2D UEs (DUEs) and channel gains of D2D pairs together, an equivalent channel gain is proposed to be exploited to select D2D pairs in symmetrical parts to reuse the same resources of CUEs. Simulation results show that the proposed scheme can increase the capacity of D2D systems up to 3.5 times compared with random resource allocation one.

The system capacity for future mobile communication needs to be increased to fulfill the emerging requirements of mobile services and innumerable applications. The cellular topology has for long been regarded as the most promising way to provide the required increase in capacity. However with the emerging densification of cell deployments, the traditional cellular structure limits the efficiency of the resource, and the coordination between different types of base stations is more complicated and entails heavy cost. Consequently, this study proposes frameless network architecture (FNA) to release the cell boundaries, enabling the topology needed to implement the FNA resource allocation strategy. This strategy is based on resource pooling incorporating a new resource dimension-antenna/antenna array. Within this architecture, an adaptive resource allocation method based on genetic algorithm is proposed to find the optimal solution for the multi-dimensional resource allocation problem. Maximum throughput and proportional fair resource allocation criteria are considered. The simulation results show that the proposed architecture and resource allocation method can achieve performance gains for both criteria with a relatively low complexity compared to existing schemes.

In real-time embedded systems, there is requirement for adapting both energy consumption and Quality of Services (QoS) of tasks according to their importance. This paper proposes an adaptive power-aware resource allocation method to resolve a trade-off between the energy consumption and QoS levels according to their importance with guaranteeing fairness. The proposed resource allocator consists of two components: the total resource optimizer to search for the optimal total resource and QoS-fairness-based allocator to allocate resource to tasks guaranteeing the fairness. These components adaptively achieve the optimal resource allocation formulated by a nonlinear optimization problem with the time complexity O(n) for the number of tasks n even if tasks' characteristics cannot be identified precisely. The simulation result shows that the rapidness of the convergence of the resource allocation to the optimal one is suitable for real-time systems with large number of tasks.

When signal coordination techniques can not eliminate all crosstalk in a digital subscriber line (DSL) system, competition for data rate among different users is strong. In such scenarios, employing a static resource allocation fails to capitalize on the time dependent nature of the traffic carried by the DSL network. An alternative approach is adaptive resource allocation, consisting of dividing time into slots of short duration and using a different resource allocation in each slot. A cross-layer scheduler then decides on the resource allocation for each time slot by solving a network utility maximization (NUM) problem. For many DSL systems however, this NUM problem is non-convex and solving it is NP-hard. This paper presents a fast algorithm for finding a local solution to the NUM problem, which is referred to as NUM-DSB. The algorithm is able to handle many DSL deployment scenarios, and is applicable regardless of the utility function's properties.