Request Queue Research Articles

Genetic Cache: A Machine Learning Approach to Designing DRAM Cache Controllers in HBM Systems

DRAM memory controller plays a critical role in maximizing the performance of High Bandwidth Memory by efficiently managing data transfers between the CPU and the memory modules. Thus, they are suitable for low-power data-intensive applications. However, the complexity of DRAM command scheduling combined with tag management overheads in the cache makes the design of in-package cache controllers significantly challenging. Traditional memory controllers often face challenges with static, inflexible access scheduling designed for general applications, leading to suboptimal performance in dynamic environments. On the other hand, while some advanced controllers employing reinforcement learning offer adaptability to workload fluctuations, they tend to introduce hardware complexity and incur longer training latencies. Our approach aims to design low-power and efficient DRAM cache controllers using a machine learning model that dynamically adjusts to workload changes with optimized hardware efficiency and reduced training time. Therefore, we propose a machine learning approach to design low-power and efficient DRAM cache controllers that will leverage the trained model to produce optimal cache command schedules at runtime. The model considers several conditions for each request queue, and chooses the best response among the options. The simulation results show the superiority of our proposed design over the previous algorithms in a set of twelve data-intensive applications; our model is able to improve the performance up to 40% in some cases, and an average of 15% in performance and 10% in power consumption.

Evaluating the Performance of SPDK-based io uring and AIO Block Device

Abstract: In the context of contemporary data processing and storage, this article examines the performance of asynchronous I/O interfaces that are provided by the Linux Kernel. Specifically, this study utilizes an SPDK-based block device module of io uring and AIO which offers features like request queueing and lockless queues. Our benchmarks comparing io uring and aio performance revealed an interesting dichotomy in their handling of random I/O patterns. Both approaches exhibited similar performance trends for random-read operations, showcasing a clear benefit from increasing the IO queue depth. This suggests both methods efficiently leverage parallel processing to boost read throughput. However, the story flips for random-write operations. Unlike read workloads, increasing queue depth for writes yielded no performance improvement. This increase significantly inflated latency, introducing undesired delays.

Satellite Return Link Resource Allocation Strategy Based on DVB-RCS2

This article improves the priority of the request queue packets for the Return Channel Satellite Terminal (RCST) of the Geosynchronous Earth Orbit (GEO) satellite system. It designs an inverted numbered slot tree resource allocation algorithm based on the Request Queue Maximum Weighted Delay Priority Algorithm (RQM-LWDF) in conjunction with existing slot allocation algorithms. It builds a simulation model of the DVB-RCS2 return link using the satellite communication module (SNS3) on the NS3 (Network Simulator 3) software simulation platform. It studies the throughput, packet loss rate, and delay performance of the algorithm under specific conditions and evaluates the algorithm.

Large-Scale Service Function Chaining Management and Orchestration in Smart City

In the core networking of smart cities, mobile network operators need solutions to reflect service function chaining (SFC) orchestration policies while ensuring efficient resource utilization and preserving quality of service (QoS) in large-scale networking congestion states. To offer this solution, we observe the standardized QoS class identifiers of smart city scenarios. Then, we reflect the service criticalities via cloning virtual network function (VNF) with reserved resources for ensuring effective scheduling of request queue management. We employ graph neural networks (GNN) with a message-passing mechanism to iteratively update hidden states of VNF nodes with the objectives of enhancing allocation of resource blocks, accurate detection of availability statuses, and duplication of heavily congested instances. The deployment properties of smart city use cases are presented along with their intelligent service functions, and we aim to activate a modular architecture with multi-purpose VNFs and chaining isolation for generalizing global instances. Experimental simulation is conducted to illustrate how the proposed scheme performs under different congestion levels of SFC request rates, while capturing the key performance metrics of average delay, acceptance ratios, and completion ratios.

Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station

The primary purpose of green communication is to reduce energy use. The base station (BS) is a radio receiver/transmitter that acts as the wireless network’s hub. It serves as a link between a wired and wireless network. To receive and transmit messages, BS uses a lot of energy. The use of effective sleep and wake-up/setup activities with an acceptable delay helps reduce base station power consumption. In this paper, the BS’s service process is modelled as a finite buffer queue with close down, sleep, and setup. After a certain number of user requests (URs) have accumulated in the system, to awaken the BS from multiple sleeps (MS) the -Policy is implemented. To produce probability generating functions, the supplementary variable approach is applied. The UR’s mean delay and the BS’s mean power consumption are calculated using simulation. According to computational studies, multiple sleeps with -policy consume less power than multiple sleeps without -policy.

Technique and Instrument for Effective CPU and GPU Access Request Arbitration Using On-Chip Cache

This research paper presents a novel on-chip cache procedure and device that effectively handles access requests from both CPUs and GPUs. The proposed procedure involves classification caching based on the access request type, arbitrating different types of access requests for caching, and optimizing access time for CPU requests through cache while bypassing cache for GPU requests. The device includes CPU and GPU request queues, a moderator, and cache performance elements. By considering the distinct access characteristics of CPUs and GPUs simultaneously, this approach offers high performance, simple hardware implementation, and minimal cost.

Community Request Queue Management System for Local Government Unit of Cabanatuan City

The Community Request Queue Management System for Local Government Unit of Cabanatuan City aimed to give solutions to the following struggles of Barangay officials: pen and paper storage management, complicated retrieval of records, and compromised set up due to the COVID-19 pandemic. The system went through the stages of the Software Development Life Cycle, namely: Stage 1 – Requirement Analysis; Stage 2 – System Design; Stage 3 – Development; Stage 4 – Testing; Stage 5 – Deployment; and Stage 6 - Maintenance. The system took ten months to design and develop. Different softwares were used for the development of the user and admin interface using programming languages, which are HTML, Java, and PHP. For the development of the database, MySQL was utilized. It has been strongly agreed by Barangay Officials and users based on the following criteria: functionality, usability, efficiency, portability, reliability, and security. The system gave the barangay officials a modern, convenient, and efficient way of performing their services. It lessened the people coming in and out in the barangay hall, thus resulting in the adherence to the COVID-19 protocols. The Community Request Queue Management System for Local Government Unit of Cabanatuan City provided the Sumacab Sur Citizens an alternative way of submitting inquiries, filing complaints and requesting documents. The Barangay Personnel were able to produce printed requested documents and give their response via e-mail.

High-performance adaptive texture streaming for planetary-scale high-mobility information visualization

Planetary-scale terrain requires streaming of world-scale high-resolution data sets in real time. High-performance streaming of world-scale data sets to achieve high mobility in terms of speed and view such as look-around, side-ways, or backward for high fidelity display systems is challenging. Moreover, streaming data sets without any preprocessing overhead require high-performance decompression and re-projection in real time. This work proposes a high-performance adaptive texture streaming system to capture high-speed scenes with view switching capability over a real-world terrain for high-fidelity display system. The system proposes multi-stage pipeline to stream multi-resolution data sets with a stable high-performance rendering rate. The proposed pipeline stages are asynchronously connected using request queues with a cache hierarchy. Efficient algorithms for resource management, prefetching and caching, prioritization of load requests, and hardware-accelerated processing are proposed for texture streaming without frame-rate stuttering. The proposed algorithms adapt to viewer navigation, display resolution, and dynamic execution environment. The proposed method is evaluated for complex scenes, mobility in terms of speed and view, and high-fidelity display. Our method achieved a ground-speed of 2.4x105,1.2x105,and9.0x104 km/h for HD (1024x720), FHD (1920x1080), and UHD (3840x2160) display resolutions respectively with a stable frame rate of 850, 590, and 420 frames per second.

Pull request latency explained: an empirical overview

Pull request latency evaluation is an essential application of effort evaluation in the pull-based development scenario. It can help the reviewers sort the pull request queue, remind developers about the review processing time, speed up the review process and accelerate software development. There is a lack of work that systematically organizes the factors that affect pull request latency. Also, there is no related work discussing the differences and variations in characteristics in different scenarios and contexts. In this paper, we collected relevant factors through a literature review approach. Then we assessed their relative importance in five scenarios and six different contexts using the mixed-effects linear regression model. We find that the relative importance of factors differs in different scenarios, e.g., the first response time of the reviewer is most important when there exist comments. Meanwhile, the number of commits in a pull request has a more significant impact on pull request latency when closing than submitting due to changes in contributions brought about by the review process.

A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network

Nowadays, the Long-Term Evolution-Advanced system is widely used to provide 5G communication due to its improved network capacity and less delay during communication. The main issues in the 5G network are insufficient user resources and burst errors, because it creates losses in data transmission. In order to overcome this, an effective Radio Resource Management (RRM) is required to be developed in the 5G network. In this paper, the Long Short-Term Memory (LSTM) network is proposed to develop the radio resource management in the 5G network. The proposed LSTM-RRM is used for assigning an adequate power and bandwidth to the desired user equipment of the network. Moreover, the Grid Search Optimization (GSO) is used for identifying the optimal hyperparameter values for LSTM. In radio resource management, a request queue is used to avoid the unwanted resource allocation in the network. Moreover, the losses during transmission are minimized by using frequency interleaving and guard level insertion. The performance of the LSTM-RRM method has been analyzed in terms of throughput, outage percentage, dual connectivity, User Sum Rate (USR), Threshold Sum Rate (TSR), Outdoor Sum Rate (OSR), threshold guaranteed rate, indoor guaranteed rate, and outdoor guaranteed rate. The indoor guaranteed rate of LSTM-RRM for 1400 m of building distance improved up to 75.38% compared to the existing QOC-RRM.

Internet Entrepreneurship Information Resource Sharing System Based on Improved Deep Learning Algorithm

In order to promote the circulation of Internet entrepreneurial information, based on information theory and general security theory, this paper explains how to construct the attack channel and defense channel of the k-anonymity model, so as to provide a reliable network foundation for the transmission of related information. In order to fully solve the problems existing in multiuser requests, this paper proposes two modules, single-request sharing mode and multirequest sharing mode, to process query requests in the request queue to maximize the reuse of request results. Moreover, the model constructed in this paper needs to be processed through deep learning first and then shared through the intelligent model. Therefore, the data learning and data sharing of the model are mainly analyzed during model validation. The experimental simulation results show that the intelligent sharing model of Internet entrepreneurial information resources based on deep learning proposed in this paper has good data learning and data transmission effects.

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.

Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging

Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like average queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging.

Flow control oriented forwarding and caching in cache-enabled networks

The cache-enabled network architecture is very promising to improve the efficiency of content distribution and reduce the network congestion. In this paper, we propose a maximizing weighted throughput (MWT) algorithm for joint request forwarding, cache placement and flow control to dynamically optimize network performance. Specifically, a dual queue system that includes requests and data is established to retrieve the global content demands and traffic congestion information. In order to improve throughput performance as well as stabilize the queue, we formulate the flow-level throughput and design the request-level control policy by optimizing the throughput function and Lyapunov drift. The request forwarding policy adaptively allocates request forwarding rates for every link according to the differences among adjacent request queue backlogs. A novel cache priority function and a threshold-based request-dropping policy are used in the caching policy and flow control respectively to alleviate network overload. In addition, we prove the MWT algorithm achieves throughput near-optimal performance, and testify the dual queue system is stable by deducing the upper bound of the all queue backlogs. The experimental results verify the MWT algorithm stability and demonstrate the superiority of the MWT algorithm, compared to the state-of-the-art caching algorithms which are combined with back-pressure algorithm.

Multi-UAV Trajectory and Power Optimization for Cached UAV Wireless Networks With Energy and Content Recharging-Demand Driven Deep Learning Approach

In this paper, we propose a novel joint trajectory and communication scheduling scheme for multiple unmanned aerial vehicles (UAVs) enabled wireless caching networks. To exploit the favorable propagation of air-to-ground channels, we consider an ultra dense UAVs enabled content-centric wireless transmission network, where massive UAVs are deployed to transmit cached contents to a group of random distributed ground users. We formulate the problem as an infinite horizon ergodic stochastic differential game (SDG) for optimizing the users' quality-of-experience (QoE). In particular, stochastic dynamics of channel states, UAVs' mobility, energy queues and content request queues are modeled in this game. To deal with the state coupling between the UAVs, we consider a limiting problem for large number of UAV based on mean field analysis. A reduced-complexity decentralized solution can be obtained through mean-field equilibrium analysis. To further reduce the solution complexity on each UAV, we propose a model-specific deep neural network (DNN) to learn the optimal control solution in an online manner. The DNN is not arbitrarily generated but tailored to the structural properties of the value function and stationary distribution based on the homotopy perturbation method analysis. Finally, simulation results are provided to show that the proposed solution can achieve significant gain over the existing baselines.

An algorithm determining the optimal length of the queue of requests for data transmission over the channels of a wireless ad-hoc network

An algorithm determining the optimal length of the queue of requests for data transmission over the channels of a wireless ad-hoc network

Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing

Virtualization technology provides a new way to improve resource utilization and cloud service throughput. However, the randomness of task arrival, tight coupling between resource load imbalance and node heterogeneity, high computing power, and other factors have hindered the energy consumption optimization and cost reduction objectives of the existing technology. Consequently, task scheduling failure cannot be easily eliminated, and cloud computing performance is decreased dramatically. In this study, a strong agile response task scheduling optimization algorithm is proposed on the basis of the peak energy consumption of data centers and the time span of task scheduling. Agile response optimization techniques are also adopted. From the perspective of task failure rate, the proposed algorithm can be used to investigate the strong agile response optimization model, explore the probability density function of the task request queue overflow, and request a timeout to avoid network congestion. Experimental results indicate that the proposed algorithm can achieve the stability and efficiency of task scheduling and effectively improve the throughput of the cloud computing system.

Towards Deadline Guaranteed Cloud Storage Services

More and more organizations move their data and workload to commercial cloud storage systems. However, the multiplexing and sharing of the resources in a cloud storage system present unpredictable data access latency to tenants, which may make online data-intensive applications unable to satisfy their deadline requirements. Thus, it is important for cloud storage systems to provide deadline guaranteed services. In this paper, to meet a current form of service level objective (SLO) that constrains the percentage of each tenant’s data access requests failing to meet its required deadline below a given threshold, we build a mathematical model to derive the upper bound of acceptable request arrival rate on each server. We then propose a Deadline Guaranteed storage service (called DGCloud ) that incorporates three basic algorithms. Its deadline-aware load balancing scheme redirects requests and creates replicas to release the excess load of each server beyond the derived upper bound. Its workload consolidation algorithm tries to maximally reduce servers while still satisfying the SLO to maximize the resource utilization. Its data placement optimization algorithm re-schedules the data placement to minimize the transmission cost of data replication. We further propose three enhancement methods to further improve the performance of DGCloud . A dynamic load balancing method allows an overloaded server to quickly offload its excess workload. A data request queue improvement method sets different priorities to the data responses in a server’s queue so that more requests can satisfy the SLO requirement. A wakeup server selection method selects a sleeping server that stores more popular data to wake up, which allows it to handle more data requests. Our trace-driven experiments in simulation and Amazon EC2 show the superior performance of DGCloud compared with previous methods in terms of deadline guarantees and system resource utilization, and the effectiveness of its individual algorithms.

Research of Queuing Model Based on Request Queue in P2P Network System

Research of Queuing Model Based on Request Queue in P2P Network System

QoS Promotion in Energy-Efficient Datacenters Through Peak Load Scheduling

To build energy-efficient datacenters, one widely used way is to dynamically manipulate the quantity of available hardware resources on demand. However, when bursty workloads appear, additional time overhead is required for resource gearing, thus incurring a performance degradation. To explore this problem, we present an intelligible analysis on the request handing in a VM. From the analysis, we find that, when workloads are overloaded (i.e., peak loads appear), the number of QoS guaranteed requests can be greatly increased by deferring the scheduling of a few requests. Inspired by this finding, we propose a Peak Load Scheduling Control (PLSC) method to promote the Quality of Service (QoS) of peak loads for modern energy-efficient datacenters. However, peak loads are usually difficult to identify. To overcome this difficulty, PLSC tracks the number of requests residing in a VM by leveraging a two-tier request queue maintained by it. When the number exceeds the capability of the VM, it means that peak loads appear. In this case, PLSC adds some delay-tolerant requests to the secondary queue. The scheduling of requests in the secondary queue is controlled with a lower priority than that of requests in the primary queue. Sequentially, with critical requests maintained in the primary queue, PLSC shortens the response time of critical requests. In addition, PLSC expands the number of QoS-guaranteed requests. Comprehensive experiments are conducted to attest the effectiveness of PLSC, by simulating a typical energy-efficient datacenter. The experimental results show that PLSC significantly promotes the QoS of workloads with a negligible impact on energy saving.