Advance Reservation Requests Research Articles

Link Resource-Aware RMSA Algorithm for Advance Reservation Requests in Semi-Filterless Optical Networks

This paper investigates the routing, modulation, and spectrum assignment (RMSA) problem for advance reservation (AR) requests in semi-filterless optical networks (semi-FONs). A link resource-aware (LRA) algorithm consisting of three parts is proposed. First, in the request scheduling phase, a request sorting strategy based on the flexible window size of the starting time is proposed. This strategy schedules AR requests based on the size of a flexible window with respect to the starting time. Next, in the routing phase, a link resource-aware strategy is proposed. This strategy evenly distributes the requests across the network resources, thereby avoiding request blocking due to excessive resource occupation on a single or few links. In the spectrum assignment phase, a frequency–time block-based metric is employed to measure the availability of each frequency–time block for AR requests. This metric is a sum of two factors: the distance to the block and spectrum boundaries, and the time interval between the request start time and the earliest start time. Our simulation results demonstrate that the proposed LRA algorithm has a lower blocking probability compared to the benchmark algorithms.

2-D fragmentation-aware RMSA based on the relative flexibility of advance reservation requests in elastic optical networks

With the advantages of fine granularity spectrum allocation (SA) and large capacity, elastic optical networks (EONs) are considered as the most promising development direction of optical network in the future. The emergence of two-dimensional (2-D) fragmentation during dynamic SA of EONs not only reduces the availability of resources but also increases the blocking probability of requests. In addition, the advance reservation (AR) request has the characteristics of resource reservation and time flexibility, which further complicates the fragmentation issue. To solve the above issues, in this paper, a fragmentation-aware routing, modulation and spectrum assignment (RMSA) algorithm for AR requests is proposed. First, delayed resource allocation is developed for AR requests. Second, a resource allocation sorting strategy based on the relative flexibility of requests is proposed. The strategy makes full use of the time characteristics of AR request to determine the allocation order of requests. In addition, from the perspective of the impact on adjacent resources, a 2-D path resource model is introduced to evaluate the impact of 2-D fragmentation from the perspective of resource matching degree and resource connectivity. Moreover, simulation results show that the proposed algorithm is better than the benchmark algorithm in terms of blocking probability, resource utilization and fragmentation rate.

DRL-Based Deadline-Driven Advance Reservation Allocation in EONs for Cloud–Edge Computing

The ongoing roll-out of cloud–edge computing and Internet of Things (IoT) has been simulating the boom of new advance reservation (AR) services, such as bulk-data migration and virtual machine backup, driving the development of substrate elastic optical networks (EONs). These AR requests are initial-delay-insensitive if they are guaranteed to be completed before a predefined deadline. Therefore, the routing, modulation, and spectrum assignment (RMSA) problem is extended to the time-spectrum domain rather than the single spectrum domain. Traditional heuristic RMSA algorithms follow static procedures under handcrafted rules and assumptions, and thus cannot be optimized automatically. To solve this problem, we propose a deep reinforced deadline-driven allocation (DRDA) algorithm. To the best of our knowledge, this work is the first to leverage deep reinforcement learning (DRL) methods to solve the AR resource allocation problem. Moreover, compared with the single experiment scenario of many existing works, the DRDA algorithm is evaluated in both static and dynamic scenarios. Simulation results show that our DRDA algorithm outperforms the other leading algorithms in both static scenario and dynamic scenario.

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.

Temporal and Spectral 2D Fragmentation-Aware RMSA Algorithm for Advance Reservation Requests in EONs

In this paper, we explore the problem of routing, modulation and spectrum assignment (RMSA) for advance reservation (AR) requests in elastic optical networks (EONs), with the objective to further reduce the blocking probability and improve the spectrum utilization. On the one hand, we propose a minimum resource consumption routing policy (MRC) for leaving more free resources for future requests. On the other hand, we introduce a two-dimensional (2D) path resource model, in which each AR request can be transformed into a rectangle block whose length and width are related to request bandwidth and request duration respectively. Therefore, the spectrum assignment problem of AR requests is simplified to the two-dimensional rectangle packing problem. Furthermore, we design several factors to evaluate the two-dimensional fragmentation caused by a rectangle packing operation, where we consider the fragmentation situation in the rectangle block neighborhood for the first time. Based on these factors, we propose a two-dimensional fragmentation-aware spectrum assignment strategy (2D-FA). Through the combination of MRC and 2D-FA, we propose a novel RMSA algorithm named MRC-2D-FA for dynamically provisioning AR requests in EONs. Simulation results show that MRC-2D-FA can achieve lower bandwidth blocking probability and higher spectrum utilization compared with several well-performed benchmark algorithms.

A Survey of Minimum Wavelength Conversion Routing and Wavelength Assignment in WDM Networks

Generally, research on routing and wavelength task over wavelength directed WDM networks is worried about immediate reservation (IR) requests. An IR demand typically does not specify a holding time for data transmission and the start time of the data transmission are assumed to be immediate (i.e. when the connection request arrives). The idea of advance reservation (AR) has as of recently been picking up consideration for optical networks. An AR request commonly determines data about the beginning of the information transmission or a cutoff time, just as the holding time of the transmission. AR has a few significant applications for both wide-region networks and Grid networks. For instance, AR can be utilized for modifying virtual geographies to adjust to predictable peak hour traffic use. It very well may be utilized to give high- bandwidth transmission administrations, for example, video conferencing and in Grid applications requiring the booked circulation of enormous records and for co-allotment of organization and grid assets. AR can likewise be advantageous to the organization by permitting the organization administrator to more readily design asset use and accordingly increment use. Information on the holding time can prompt more ideal choices for asset distribution. This translates to better quality of service for users. In this paper we give a complete study of the past and current work on booking ahead of time for optical organizations. There have been numerous varieties of the advance reservation of time idea proposed, so we will likewise give an expansive grouping. Notwithstanding the review, we will examine what we accept are significant regions of future work and open difficulties for early booking on optical organizations. Keywords: Advance reservation, scheduled demands, WDM, survey, wavelength-routed, and RWA

Allocating resources via price management systems: a dynamic programming-based approach

In this paper, a novel model for price management systems in resource allocation problems is proposed. Stochastic customer requests for resource allocations and releases are modelled as constrained parallel Birth–Death Processes (BDP). We address both instant (i.e. the customer requires a resource to be allocated immediately) and advance (i.e. the customer books a resource for future use) reservation requests, the latter with both bounded and unbounded time interval options. Algorithms based on Dynamic Programming (DP) principles are proposed for the calculation of suitable price profiles. At the core of such algorithms, there is the resolution of stochastic optimisation problems. In particular, the maximisation of the expected total revenue is formulated via a constrained Stochastic Dynamic Programming (SDP) approach, which becomes time-variant in case of advance reservation requests. Approximate Dynamic Programming (ADP) techniques are adopted in case of large state spaces. Simulations are performed to show the effectiveness of the proposed models and the related algorithms.

Crosstalk-aware spectrum defragmentation by re-provisioning advance reservation requests in space division multiplexing enabled elastic optical networks with multi-core fiber.

Space division multiplexing enabled elastic optical networks (SDM-EONs) with multi-core fiber (MCF) have become a promising candidate for future optical transport networks, due to their high capacity and flexibility. Meanwhile, driven by the development of cloud computing and data centers, more types of requests are allowed in the networks, i.e., the usual immediate reservation (IR) requests, which need to be served immediately, and advance reservation (AR) requests, which support initial-delay tolerance services. However, with the introduction of AR requests, spectrum fragments occur frequently in both spatial and time dimension as lightpaths are set up and torn down, and the issue of spectrum fragmentation could be much more serious in SDM-EONs than in simple EONs. To measure fragments status in both spatial and time dimension in SDM-EONs, we first design a metric, i.e., time-dimensional spectrum compactness (TSC). Then, based on TSC, we propose a crosstalk-aware AR requests re-provisioning algorithm with two strategies to optimize the fragments in SDM-EONs. The performance of the proposed algorithm is evaluated via software simulation in terms of spectrum compactness, blocking probability, spectrum utilization, average moving times, average re-provisioning latency and average start time delay. The results show that the proposed re-provisioning algorithm can effectively improve spectrum compactness and spectrum efficiency of the networks. We also evaluate the proposed re-provisioning algorithm in different TSC thresholds, and it turns out that the proposed re-provisioning algorithm in higher threshold performs better in terms of spectrum compactness and spectrum utilization.

Farsighted Spectrum Resource Assignment Method for Advance Reservation Requests in Elastic Optical Networks

Farsighted Spectrum Resource Assignment Method for Advance Reservation Requests in Elastic Optical Networks

Re-Provisioning of Advance Reservation Applications in Elastic Optical Networks

Driven by the development of cloud computing and mobile Internet, network-based applications have become diverse. Besides the usual immediate reservation (IR) applications, which need to be served immediately, advance reservation (AR) applications have been introduced to support initial-delay-tolerance services, such as grid computing, virtual machine backup, and so on. The provisioning mechanism of AR applications is different from that of IR applications, because AR requests don’t require to be served immediately, and the network operator must decide the exact serving time in the AR service provisioning process. Based on the scheduling features of AR requests, this paper formulates a resource model and sets up a control framework for elastic optical networks to support AR services. We propose an optimization algorithm with three reprovisioning policies to dynamically reprovision the AR applications, which have already been scheduled. Both the proposed control framework and algorithm are developed on the basis of an open network operating system, which is an open-source software-defined networking controller. Also, the performance of the proposed algorithm is evaluated via software simulation. Demonstration results show the benefits of reprovisioning, and simulation results show that the reprovisioning optimization algorithm can improve the efficiency of network spectrum resource.

Dynamic time and spectrum fragmentation-aware service provisioning in elastic optical networks with multi-path routing

Abstract In this paper, we propose a multi-path fragmentation-aware routing, modulation and spectrum assignment algorithm (RMSA) for advance reservation (AR) and immediate reservation (IR) requests in elastic optical networks. Immediate reservation requests should be provided with service immediately, while advance reservation requests have specific starting times and holding times. As lightpaths are set up and torn down, fragmentation may occur in both spectrum and time domains. To decrease the two-dimensional fragmentation and to solve the problem of resource scarcity, we propose splitting requests into different parts and transferring these parts along one or more paths utilizing sliceable bandwidth variable transponders. We first introduce a model to solve the problem and propose a two-dimensional fragmentation occurrence measurement in spectrum and time domains. Then we propose a multi-path fragmentation-aware RMSA algorithm (MPFA). Simulation results show that MPFA can achieve better performance than existing algorithms in terms of blocking probability and spectrum utilization.

Service provisioning of flexible advance reservation leases in IaaS clouds

Resource provisioning is a very important and challenging task in cloud computing. The techniques employed for resource provisioning needs to avoid under-provisioning and over-provisioning of resources. It also needs to ensure the cloud providers that the required SLAs are met. In this paper, an efficient resource provisioning and scheduling algorithm is proposed to schedule service requests in IaaS clouds. Service requests in the cloud are of four types: immediate, best-effort, advance reservation and deadline sensitive. In the proposed work, advance reservation requests with flexible start times (flexible advance reservation requests) are considered. The experiments are conducted in a cloud environment developed using OpenNebula. The proposed algorithm is compared with the existing algorithms of Haizea and SAB. The experimental results indicate that the proposed algorithm gives better request acceptance rate and reduced resource fragmentation than the existing ones.

Deadline-aware advance reservation scheduling algorithms for media production networks

In the media production process a substrate network can be shared by many users simultaneously when different media actors are geographically distributed. This allows sophisticated media productions involving numerous producers to be concurrently created and transferred. Due to the predictable nature of media transfers, the collaboration among different actors could be significantly improved by deploying an efficient advance reservation system. In this paper, we propose a model for the advance bandwidth reservation problem, which takes the specific characteristics of media production networks into account. Flexible and time variable bandwidth reservations, meeting delivery deadlines, supporting splittable flows and interdependent transfers and all types of advance reservation requests imposed by the media production transfers are incorporated into this model. In addition to the optimal scheduling algorithms, which are presented based on this model, near optimal alternatives are also proposed. The experimental results show that the proposed algorithms are scalable in terms of physical topology and granularity of time intervals and obtain a satisfactory performance, executing significantly faster than an optimal algorithm and within 8.78% of the optimal results.

Cross-stratum resource reservation (CSRR) algorithm for deadline-driven applications in datacenter networks

Nowadays, datacenters interconnected with optical networks have become the fundamental infrastructure to accommodate high-performance datacenter applications. Lacking of layer interaction between datacenters and networks during service provisioning, many end user applications cannot efficiently utilize the network capabilities, nor can they achieve the desired quality of service objectives. In response to these challenges, cross-stratum optimization has been studied to optimize the computing and network resources utilization from a united view. Meanwhile, the type of network applications becomes diverse. Compared to traditional immediate reservation (IR) request, a new type of request called advance reservation (AR) has recently been gaining attention for optical networks. IR's start time of data transmission is assumed to be immediate, while AR request typically specifies the earliest time or the deadline of data transmission. According to the time features of AR request, AR applications should be scheduled in time dimension. Thus, it is both important and challenging to reserve computing and network resources in efficient manners. In this study, a cross-stratum resource model considering time dimension is set up, and a cross-stratum resource reservation (CSRR) algorithm is proposed to schedule AR applications and to reserve cross-stratum resource. Simulation results show that CSRR can reduce the failure rates of AR applications and improve the resource consumption ratio significantly.

On Hybrid IR and AR Service Provisioning in Elastic Optical Networks

This paper investigates hybrid immediate reservation (IR) and advance reservation (AR) service provisioning in elastic optical networks, with the objective to minimize IR/AR service conflicts. We design algorithms to coordinate service provisioning of IR and AR requests. Specifically, both proactive and reactive IR provisioning schemes are considered to minimize IR service failures. Our AR scheduling algorithm can coordinate AR service provisioning with various IR traffic patterns, balance spectrum utilization in both time and spectral domains, and reduce IR/AR service conflicts. Simulation results verify that our proposed IR + AR schemes can significantly reduce IR service failures as well as routing and spectrum allocation reconfigurations in IR service provisioning. Moreover, the results indicate that the proposed IR + AR schemes can achieve more performance gain from AR flexibility when compared with two existing benchmarks.

Implementation and Demonstration of Revenue-Driven Provisioning for Advance Reservation Requests in OpenFlow-Controlled SD-EONs

This paper aims to realize OpenFlow-controlled revenue-driven advance reservation (AR) provisioning in software-defined elastic optical networks (SD-EONs). We design control plane framework, propose two algorithms for the OpenFlow controller, implement the whole system in a semi-practical SD-EON testbed, and conduct experiments with two payment models. Experimental results indicate the proposed system performs well and can increase total revenue-gain effectively.

Resource availability-aware advance reservation for parallel jobs with deadlines

Advance reservation is important to guarantee the quality of services of jobs by allowing exclusive access to resources over a defined time interval on resources. It is a challenge for the scheduler to organize available resources efficiently and to allocate them for parallel advance reservation jobs with deadline constraint appropriately. This paper provides a slot-based data structure to organize available resources of multiprocessor systems in a way that enables efficient search and update operations and formulates a suite of scheduling policies to allocate resources for dynamically arriving advance reservation requests. The performance of the scheduling algorithms were investigated by simulations with different job sizes and durations, system loads, and scheduling flexibilities. Simulation results show that job sizes and durations, system load and the flexibility of scheduling will impact the performance metrics of all the scheduling algorithms, and the \(\textit{PE}\; \textit{Worst Fit}\) algorithm becomes the best algorithm for the scheduler with the highest acceptance rate of advance reservation requests, and the jobs with the \(\textit{First Fit}\) algorithm experience the lowest average slowdown. The data structure and scheduling policies can be used to organize and allocate resources for parallel advance reservation jobs with deadline constraint in large-scale computing systems.

Dynamic Service Provisioning of Advance Reservation Requests in Elastic Optical Networks

The elastic optical network (EON) based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology is a promising candidate for accommodating the uncertainty and heterogeneity of the traffic across future Internet. Advance reservation (AR) is essential to support initial-delay-tolerant services, such as e-science and grid computing, which are making significant contributions to the Internet traffic. Therefore, we expect that it is necessary for future EONs to support AR requests. In this paper, we study dynamic service provisioning of AR requests in EONs. These AR requests permit certain initial-delay during setting-up, as long as the resources are allocated before a preset deadline. We propose several algorithms that combine request scheduling in the time domain with routing, modulation and spectrum assignment (RMSA) in the spectrum domain. Specifically, with the combination of a routing path selection policy and a request scheduling strategy, the algorithm constructs a weight matrix for each pending AR request and provisions the request with it. We design numerical simulations to investigate the algorithms' performance in terms of three metrics, i.e., blocking probability, average spectrum efficiency, and average initial-delay. Based on the simulation results and computational complexity analysis, we provide suggestions on how to choose routing path selection policy and request scheduling strategy for provisioning AR requests dynamically in EONs. To the best of our knowledge, this is the first attempt to address dynamic service provisioning of AR requests in EONs.

Static Routing and Wavelength Assignment for Multicast Advance Reservation in All-Optical Wavelength-Routed WDM Networks

In this paper, we investigate the static multicast advance reservation (MCAR) problem for all-optical wavelength-routed WDM networks. Under the advanced reservation traffic model, connection requests specify their start time to be some time in the future and also specify their holding times. We investigate the static MCAR problem where the set of advance reservation requests is known ahead of time. We prove the MCAR problem is NP-complete, formulate the problem mathematically as an integer linear program (ILP), and develop three efficient heuristics, seqRWA, ISH, and SA, to solve the problem for practical size networks. We also introduce a theoretical lower bound on the number of wavelengths required. To evaluate our heuristics, we first compare their performances to the ILP for small networks, and then simulate them over real-world, large-scale networks. We find the SA heuristic provides close to optimal results compared to the ILP for our smaller networks, and up to a 33% improvement over seqRWA and up to a 22% improvement over ISH on realistic networks. SA provides, on average, solutions 1.5-1.8 times the cost given by our conservative lower bound on large networks.

Hierarchical scheduling strategies for parallel tasks and advance reservations in grids

Recently, the advance reservation functionality gained high importance in grids due to increasing popularity of modern applications that require interactive tasks, co-allocation of multiple resources, and performance guarantees. However, simultaneous scheduling, both advance reservations and batch tasks affects the performance. Advance reservations significantly deteriorate flow time of batch tasks and the overall resource utilization, especially in hierarchical scheduling structures. This is a consequence of unknown batch task processing times and the lack of possibility of altering allocations of advance reservations. To address these issues we present a common model for scheduling both computational batch tasks and tasks with advance reservation requests. We propose simple on-line scheduling policies and generic advices that reduce negative impact of advance reservations on a schedule quality. We also propose novel data structures and algorithms for efficient scheduling of advance reservations. A comprehensive experimental analysis is presented to show the influence of advance reservations on resource utilization, mean flow time, and mean tardiness—the criteria significant for administrators, users submitting batch tasks, and users requesting advance reservations, respectively. All experiments were performed with a well-known real workload using the GSSIM simulator.