Resource Reservation Scheme Research Articles

LAMP: A latency-aware MAC protocol for joint scheduling of CAM and DENM traffic over 5G-NR sidelink

Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) are two types of messages used in Intelligent Transport Systems (ITS) for Vehicle-to-Everything (V2X) communication. CAMs are used to periodically exchange the current state of a vehicle with its nearby vehicles and the DENMs are used to aperiodically provide critical and time-sensitive information about current environmental conditions to other vehicles and infrastructure entities. While considerable research has been conducted on the efficient scheduling of CAM and DENM separately or at the network level coexistence, no work has been done to integrate the scheduling of both message types at the granularity of the per-vehicle level. The scheduling of CAM is commonly done through Semi-Persistent Scheduling (SPS). However, no equivalent scheduling algorithm has been developed for the aperiodic and variable-sized DENMs. As a result, there is a need for novel mechanisms that can efficiently prioritize and schedule mixed traffic of CAM and DENM messages. This paper introduces a Quality of Service (QoS) scheduler called LAMP for segregating traffic in the Radio Link Control (RLC) layer for joint sidelink scheduling of CAM and DENM at the vehicular level. The LAMP scheduler is aided with a special resource selection and reservation scheme for mixed traffic scenarios. The groundwork involves an experimental analysis in Network Simulator-3 (NS-3) that uses the New Radio (NR) Vehicle-to-Everything (V2X) module in Mode-2. The simulation findings demonstrate that LAMP could significantly reduce the end-to-end latency of DENM by 89.36% and CAM by 40.2% while also increasing the packet reception rate by 12.1% and 9.74% for CAM and DENM with repetitions, respectively.

Offloading Demand Prediction-Driven Latency-Aware Resource Reservation in Edge Networks

The wide applications of edge computing has brought dawn to terminals with limited computing resources and energy supply. Terminal completes its task through computing offloading to reduce energy consumption and improve performance. In order to improve the efficiency of resource scheduling and utilization, this paper proposes a delay-aware resource reservation strategy based on the prediction of spatialtemporal correlation task offloading demands. Due to the strong time-varying characteristics of terminal task offloading demand, this paper designs a Spatiotemporal Task Offloading Demand prediction model (STOD). It divides the region into multiple subregions and models them as a graph structure so as to predict the task offloading demand of each region separately by considering the complex spatial and temporal dependencies of regional task offloading demands. With this model, we propose a regional Edge Server Resource Reservation (ESRR) algorithm to minimize the terminal task offloading delay. The experimental results based on the real data sets show that ESRR can reduce the average offloading time consumption by 30% under different scenarios and verify its feasibility and effectiveness.

Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework

The Network Slicing (NS) technology, recognized as one of the key enabling features of Fifth Generation (5G) wireless systems, provides very flexible ways to efficiently accommodate common physical infrastructures, e.g., Base Station (BS), multiple logical networks referred to as Network Slice Instances (NSIs). To ensure the required Quality of Service (QoS) levels, the NS-technology relies on classical Resource Reservation (RR) or Service Prioritization schemes. Thus, the current paper aims to propose a Preemption-based Prioritization (PP) scheme “merging” the classical RR and Service Prioritization schemes. The proposed PP-scheme efficiency is evaluated or estimated given a Queueing system (QS) model analyzing the operation of multiple NSIs with various requirements at common 5G BSs. As a key result, the proposed PP-scheme can provide up to 100% gain in terms of blocking probabilities of arriving requests with respect to some baseline.

Outright fit resource allocation approach for advance reservation requests in elastic optical networks

Elastic optical networks (EONs) are a promising technology for the next-generation optical network, and advanced reservation (AR) applications have been growing expeditiously over the past few years. Thus, the routing and spectrum allocation problem for AR requests has become a significant concern. This paper proposes an approach named outright fit advanced resource reservation to improve the spectral resource allocation efficiency for AR connection requests. Our proposed approach works in two stages: The proposed resource reservation strategy first uses an outright fit resource allocation strategy to reserve the spectral resource for a connection request within the absolute best service window and leaves adequate spectral resources for subsequent requests. If a connection request cannot get resources in the first stage, the proposed optimal rescheduling technique then is applied to serve the request. Our proposed optimal rescheduling technique uses spectrum shifting and reallocation without altering the routing paths, and its optimal characteristic decreases the number of rescheduling operations, thereby decreasing the rescheduling computational complexity and cost, which improves the network’s quality of service. Moreover, this rescheduling approach increases the opportunities for successful rescheduling of the requests without inducing many spectrum fragments, significantly reducing the blocking probability. The simulation results illustrate that the proposed approach can considerably enhance the blocking performance and obtains high spectrum utilization.

Energy-Efficient Multivariate Privacy-Aware RF Spectrum Reservation in Wireless Virtualization for Wireless Internet of Things

Next generation communication systems are expected to be heterogeneous and to have varying Quality of Service (QoS) requirements for different Internet of Things (IoT) applications. Wireless virtualization is regarded as an emerging technology capable of handling the ever growing resource demands by those heterogeneous IoT applications and systems by enabling wireless resource sharing through slicing. Scarce wireless resources and expensive physical infrastructure are leased to business units. The business units do not own the infrastructure, but rather gain access through a subleasing process. However, due to the lack of efficient management of wireless resources, energy efficiency of the physical infrastructure cannot be guaranteed. A viable solution to this problem is to anticipate resource needs and proactively implement energy efficient measures to ensure QoS requirements of users are met using a reservation scheme. Previous spectrum reservation approaches do not consider energy efficiency in the reservation process. In this paper, we present an adaptive resource reservation scheme for wireless network virtualization. The proposed scheme relies on an informed estimate of resource needs by a combination of aggregated event data from multiple contributors, and prediction based on previous allocation data. We present a Privacy aware Aggregation Model (PrivAgg) that relies on the truthfulness of contributors by providing secure enrollment and communication. We also present a prediction algorithm, Volume and Bandwidth-conditioned Spectrum Selective Moving Average (VBSSMA), that enforces a multivariate filter for the allocation history in order to increase the accuracy of prediction. We evaluate the performance of the reservation scheme and algorithms using theoretical analysis and numerical results from extensive simulations. Numerical results over multiple configurations for the weight of the aggregator and predictor components shows that VBSSMA results in up to 27% less allocation cost and 4% less error than existing Volume-conditioned Spectrum Selective Moving Average reservation approach.

Dual Connectivity-Aided Proactive Handover and Resource Reservation for Mobile Users

Dual connectivity has been regarded as an effective mechanism for mobility management to reduce the handover latency and improve transmission reliability. In this article, we investigate a dual connectivity-aided proactive mobility management scheme for mobile users requesting realtime service with minimum data rate requirements. The proactive mobility management consists of a predictive resource reservation scheme and a proactive handover scheme. The predictive resource reservation scheme is optimized to improve the network throughput or user fairness with the predicted average channel gains and the predicted set of connected base stations for each user. The proactive handover scheme aims to avoid frequent and delayed handovers, and reduce the computational complexity by partitioning cell-center and cell-edge users and choosing different numbers of connected base stations for the two types of users. Simulation results show that the proposed scheme outperforms reactive counterparts, which can effectively reduce service outages meanwhile support higher network throughput when the traffic load is heavy, or improve user fairness meanwhile cause less service outages when the traffic load is light.

Reinforcement Learning Enabled Dynamic Resource Allocation in the Internet of Vehicles

As an important application scenario of the industrial Internet of things, the Internet of Vehicles can significantly improve road safety, improve traffic management efficiency, and improve people's travel experience. Due to the high dynamics of the Internet of vehicles environment, the traditional resource optimization technologies cannot meet the requirements of the Internet of vehicles for dynamic communication, computing and storage resources optimization management, and artificial intelligence algorithms can adaptively obtain dynamic resource allocation schemes through self-learning. Therefore, adopting artificial intelligence techniques to optimize the dynamic resource of the Internet of Vehicles is the research focus of this article. In this article, we first model the Internet of Vehicles resource allocation problem as a semi-Markov decision process that introduces a resource reservation strategy and a secondary resource allocation mechanism. Then, the reinforcement learning algorithm is used to solve the model. Thereafter, it theoretically analyzes the joint optimization of computing and communication resources, models it as a hierarchical architecture, and uses hierarchical reinforcement learning to obtain the optimal system resource allocation plan. Finally, the results of simulation experiments show that the dynamic resource allocation scheme of the Internet of vehicles based on the reinforcement learning in this article greatly improve resource utilization and user quality of experience with guaranteeing system quality of service compared with the traditional greedy algorithm.

An Experiment Resource Reservation Strategy Based on Rigid Demand and Reputation

With the increasing investment in teaching and scientific research, universities have established high-level experimental teaching centers. In order to make full use of laboratory equipment resources, more and more laboratories have established an open sharing system. However, due to the outstanding problems such as the large number of students, the wide range of majors, the small number of full-time experimental personnel, and the long time opening, there are inevitably some scrambles and sometimes vacancy in the process of opening and sharing, so the experimental resources cannot be used with the maximum efficiency. Based on the traditional time priority reservation strategy, it comprehensively considers the reputation problems caused by breaking the reservation and canceling the reservation, as well as the resource demand weight near the course acceptance, and proposes a multi parameter laboratory resource reservation strategy based on rigid demand and reputation. It uses convenient and fast information technology to balance the density of resource occupation, to achieve a fair and reasonable optimization of reservation strategy, so as to improve the efficiency of laboratory resource use and the quality of practical teaching.

Elastic Timeslot-Based Advance Reservation Algorithm for Enterprise Video Conferencing Systems

In an enterprise network, it is common to have hundreds of video conferences held simultaneously such that a large number of video streams need to be transmitted between participants in different geographical locations. For such a large transmission demand, an advance reservation (AR) system deployed for the video conferencing system can provide quality-of-service (QoS) guarantees to users and improve the resource utilization of the network. However, the effective resource reservation strategies for a heavy traffic case still remains open. In this paper, we propose an algorithm called the elastic timeslot-based advance reservation algorithm (ETARA), which aims at improving the resource utilization and reducing the computational complexity. A new time processing method, elastic timeslot, is proposed for the time domain management. Moreover, ETARA takes not only the processing time but also the resource usage into account. Comparative simulations with the existing two popular approaches, the dynamic timeslot-based approach and timeslot-based flexible approach, have been performed in terms of the acceptance ratio and runtime. The results show that with the same acceptance ratio, the runtime of ETARA can be up to 57 times lower than that of the flexible timeslot-based approach. Though ETARA has a slightly longer runtime than the dynamic approach, the acceptance ratio of ETARA can be twice as high as that of the dynamic timeslot-based approach.

A novel optimized approach for resource reservation in cloud computing using producer–consumer theory of microeconomics

Designing economic pricing mechanisms have recently attracted a great deal of attention in the context of cloud computing. We believe that microeconomics theory is a good candidate to model the resource reservation operations in cloud networks. Producer–consumer theory of microeconomics guarantees the maximization of social welfare of the customers, conditional that the particular consideration concerning customers and producers are met. As is the case in real-world cloud datacenters, the workload associated with each user is fed into the system and then the user is bound to a virtual machine (VM). In this research, we propose a microeconomic-inspired resource reservation scheme for cloud computing. The designed mechanism includes two steps: in the first step, we seek to find a Pareto efficient reservation set concerning bandwidth of VMs, and in the second step, our goal is to place VMs’ reserved bandwidth rates on physical hosts. In our modeling, VMs and the cloud network are considered as consumers and producers of the market, respectively. Also, the bandwidth of requested services is considered as commodity. As is the case in microeconomics, we prove that the aggregation of users’ utilities (users’ social welfare in microeconomics terminology) could reach to global maximum, known as Pareto efficiency. After finding the best set of reserved bandwidth rates in the first step of mechanism, in the second step, the mechanism seeks to find the best placement for VMs on physical hosts. The placement operation is performed in such a way that results in minimization of total consumed power in datacenter. Since the VM placement problem has been proven to be NP-hard, we use a metaheuristic cuckoo search optimization approach to solve the optimization problem. Simulation results, obtained through the CloudSim framework, established the robustness of the proposed method in terms of significant criteria such as users’ welfare, consumed power and Pareto optimality.

Delay-Oriented Cross-Tier Handover Optimization in Ultra-Dense Heterogeneous Networks

To meet the ever-growing demand and service requirement, especially for delay-sensitive services like vehicular communications, small cells consisting of low-power base stations (BSs) are deployed to undertake the traffic pressure of BSs in macro cells. The vehicle may handover from the macro cell tier to the small cell tier when moving toward a small cell. In this paper, we mainly analyze and optimize the cross-tier handover performance in heterogeneous networks from the perspective of delay characteristics. Based on the stochastic geometry theory, we deduce the expression of the effective capacity which reflects the delay to the rate before and after the handover; moreover, a resource reservation scheme is proposed. Based on this scheme, a closed-form expression of the blocking probability for delay-sensitive users is derived. Finally, based on the convex optimization theory, a joint optimization of effective capacity and blocking probability is carried out. The simulations show that the proposed optimization algorithm will greatly reduce the blocking probability while keeping the effective capacity relatively constant, especially beneficial for the delay-sensitive users. The theoretical and simulation results can be applied to the handover strategy and resource reservation strategy selection in heterogeneous networks.

A large-scale nesting ring multi-chip architecture for manycore processor systems

The optical network on chip (ONoC) paradigm has emerged as a promising solution to multi-core/many-core processor systems for offering enormous bandwidth and low power consumption. As chip multiprocessors (CMPs) scale to unprecedented numbers of cores, the performance of next-generation CMPs will be bounded by the process yield and power density of single chip. In earlier work we proposed a multi-chip ONoC architecture that scales to large numbers of CMPs and delivers high performance in terms of delay and throughout. Building on that work, in this paper we propose an optimized architecture for integrating a large number of cores into chips with a novel control strategy, including a contention resolution scheme and a resource reservation scheme. The proposed control strategy is crucial to large scale ONoCs, because the resource reservation scheme ensures efficient wavelength allocation for the traffic while the contention management scheme is effective in reducing the impact of contentions. To sustain good performance and energy efficiency of large-scale ONoC, the topology is optimized to reduce the average transmission distance with minimum increase of power consumption. We evaluate the proposed architecture within a 1000-core processor system and compare it with CMesh and several previously proposed topologies with different control strategies. The simulation results show that, our new large-scale architecture can achieve better performance on throughput and delay.

Towards efficiently supporting database as a service with QoS guarantees

Quality of Service (QoS) is at the core of the vision of Database as a Service (DBaaS), which provides guarantees to database users on the usability of database services, even when the underlying database infrastructure is shared by multiple users. When QoS guarantees are necessitated, traditional approaches in DBaaS often have to reserve computation resources (e.g. CPU and memory) for tenants according to their performance Service Level Objectives (SLOs), so that the database engine always possesses sufficient resources to accomplish the expected workloads under any circumstance. Such resource reservation schemes inevitably result in poor resource utilization, as the actual workloads of tenants are usually below their maximal workload expectation described in their SLOs.In this paper, we propose a novel scheme called FrugalDB to further improve resource utilization and thus reduce operational cost for DBaaS systems with QoS guarantees. FrugalDB accommodates two independent database engines, an in-memory engine for heavy workloads with tight SLOs, and a disk-based engine for light workloads with loose SLOs. The in-memory database is leveraged to migrate temporary heavy workloads from the disk-based database, when the latter itself does not suffice to handle all active tenants’ workloads, and thus it could be relieved of the tremendous data serving pressure yielded by those heavy workloads, and focus on processing massive numbers of light workloads. When the heavy workloads fade out, FrugalDB reversely migrates a tenant’s workload from the in-memory database into the disk-based database, so that the occupied memory resources could be recollected for subsequent workload migrations. By an effective workload estimation method and an efficient migration schedule algorithm, FrugalDB tries to minimize workload migration cost incurred in moving workloads between the two engines. By allocating each tenant’s workload to an appropriate engine via workload migration, this dual-engine scheme can substantially save computation resources, and thus consolidate more tenants on a single database server. We evaluate FrugalDB with extensive experiments, which show that it has a higher tenant consolidation rate with performance SLO guarantees and fewer performance SLO violations than the existing systems, as well as with acceptable response latency.

Kraken: Online and Elastic Resource Reservations for Cloud Datacenters

In cloud environments, the absence of strict network performance guarantees leads to unpredictable job execution times. To address this issue, recently, there have been several proposals on how to provide guaranteed network performance. These proposals, however, rely on computing resource reservation schedules a priori . Unfortunately, this is not practical in today’s cloud environments, where application demands are inherently unpredictable, e.g., due to differences in the input data sets or phenomena, such as failures and stragglers. To overcome these limitations, we designed K raken , a system that allows to dynamically update minimum guarantees for both network bandwidth and compute resources at runtime . Unlike previous work, Kraken does not require prior knowledge about the resource needs of the applications but allows to modify reservations at runtime. Kraken achieves this through an online resource reservation scheme , which comes with provable optimality guarantees. In this paper, we motivate the need for dynamic resource reservation schemes, present how this is provided by Kraken, and evaluate Kraken via extensive simulations and a preliminary Hadoop prototype.

A partial-success-rate-based resource reservation scheme for slotted OBS networks

Optical burst switching (OBS) is a promising technology for next-generation optical networks. Slotted OBS is an improved version of OBS to reduce burst loss rate, in which wavelength channels are divided into time slots. Slotted OBS has an implicit and under-used property that resources for two bursts with the same source and the same destination are interchangeable. The property further means that resource for a long-distance burst can be partially used by a short-distance burst. In this paper, we utilize the property to design a resource reservation scheme for slotted OBS networks. The scheme reserves a batch of slots every time; the specific number of slots is calculated according to number of arrived bursts and partial success rate (a newly introduced conception in this paper) at each node. Simulation results show that the proposed scheme can get lower burst loss rate, comparing with the well-performing two-way signaling scheme.

Power‐efficient real‐time scheduling based on multi‐granularity resource reservation for multimedia services

Recent advances in mobile technologies have led to improved quality of multimedia services (QoMS) in a variety of mobile devices. Because multimedia has become a major form of content consumption for mobile users, satisfying user expectation on QoMS in energy-restricted mobile devices is critical. This need has motivated us to develop an aggressive and conservative low-power work demand analysis with multi-granularity (lpWDA-MG-AGG/CON) algorithm, designed to minimise power consumption in mobile devices by utilising a dynamic voltage scaling technique while simultaneously ensuring QoMS based on a resource reservation scheme. In addition, the authors analytically showed the schedulability of the proposed scheme under the rate monotonic scheduling policy. For performance evaluation, the authors implemented the two lpWDA-MG algorithms and several existing algorithms in a Linux operating system. Specifically, the authors measured power consumption with a power metre and determined that the proposed algorithms consume about 40% less dynamic power than the other existing algorithms. Moreover, the authors found that the proposed algorithms ensure acceptable QoMS.

Resource Reservation Schemes for Optical Burst Switching Networks

Since the current optical communication networks are based on internet protocol and demands for transmission capacity and speed are growing, the optical burst switching presents the solution for future high-speed wavelength division multiplexing optical networks. In order to successfully transmit bursts over optical burst switching network, resource reservation schemes have to be implemented. According to the way of resource reservation, reservation schemes use one-way reservation or two-way reservation. The schemes with one-way reservation are used mainly for optical burst switching networks. The survey of one-way resource reservation schemes is presented. The basic operations and properties of one-way resource reservation schemes are described.

Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds

In computing clouds, burstiness of a virtual machine (VM) workload widely exists in real applications, where spikes usually occur aperiodically with low frequency and short duration. This could be effectively handled through dynamically scaling up/down in a virtualization-based computing cloud; however, to minimize energy consumption, VMs are often highly consolidated with the minimum number of physical machines (PMs) used. In this case, to meet the dynamic runtime resource demands of VMs in a PM, some VMs have to be migrated to some other PMs, which may cause potential performance degradation. In this paper, we investigate the burstiness-aware server consolidation problem from the perspective of resource reservation, i.e., reserving a certain amount of extra resources on each PM to avoid live migrations, and propose a novel server consolidation algorithm, $\sf {QUEUE}$ . We first model the resource requirement pattern of each VM as a two-state Markov chain to capture burstiness, then we design a resource reservation strategy for each PM based on the stationary distribution of a Markov chain. Finally, we present $\sf {QUEUE}$ , a complete server consolidation algorithm with a reasonable time complexity. We also show how to cope with heterogenous spikes and provide remarks on several extensions. Simulation and testbed results show that, $\sf {QUEUE}$ improves the consolidation ratio by up to 45 percent with large spike size and around 30 percent with normal spike size compared with the strategy that provisions for peak workload, and achieves a better balance between performance and energy consumption in comparison with other commonly-used consolidation algorithms.

Dynamic Multi-frame Transmission Technology Using the WiMedia MAC for Multi-hop N-screen Services

N-screen is a promising technology to improve support for multimedia multicasting, content sharing, content mobility, media scalability, and seamless mobility. In this paper, the WiMedia distributed-MAC (D-MAC) protocol is adopted for development of a seamless N-screen wireless service. Furthermore, to provide a multi-hop, one source multi-use N-screen service through point to point streaming in a seamless D-MAC protocol, a dynamic multi-frame transmission technology is proposed. In this technology, a dynamic time slot allocation scheme and a multi-hop resource reservation scheme are combined. In the proposed dynamic time slot allocation scheme, two thresholds, a hard threshold and a soft threshold, are included to satisfy the power consumption and delay requirements. A multi-frame DRP reservation scheme is proposed to minimize end-to-end delay during the multi-hop transmissions between N-screen devices. The proposed dynamic multi-frame transmission scheme enhances Nscreen performance in terms of the multi-hop link establishment success rate and link establishment time compared to the conventional WiMedia D-MAC system.

Location and Direction Estimation-Based Resource Reservation Scheme for the Handoff in Micro Cellular Multimedia Networks

This paper proposes resource reservation scheme for the handoff control to accommodate mobile multimedia traffic using the location estimation and the direction estimation. This proposed scheme uses a novel mobile tracking method based on fuzzy multi-criteria decision making, in which uncertain parameters such as pilot signal strength, distance between the mobile terminal and the base station, the moving direction, and the previous location are used in the decision process using the aggregation function in fuzzy set theory. With the position information, the moving direction is determined. The handoff requests for real time sessions are handled based on the direction prediction and the resource reservation scheme. The radio resources in the estimated adjacent cells should be reserved and allocated to guarantee the continuity of the real-time sessions. In performance analysis, our proposed scheme provides a better performance than the previous schemes.