Spectrally-spatially elastic optical networks (SS-EONs) enable ultra-high data rate transmission, which raises critical concerns about physical-layer security vulnerabilities, particularly against eavesdropping and unauthorized network access. Dynamic resource allocation through lightpath reconfiguration presents an effective approach to improving security by reducing request exposure windows. However, implementing secure reconfiguration in SS-EONs introduces significant complexity due to the complex relationships between spectral allocation and spatial resource management constraints. This paper proposes an analytical model for blocking performance with security-aware reconfiguration (AnalyticalSAR) in SS-EONs based on continuous-time Markov chain analysis to tackle these security challenges. The AnalyticalSAR provides analytical assessment of how spectrum reconfiguration affects both network security and blocking performance while accounting for inter-core and intermode crosstalks. The model generates all viable states accounting for spectrum reconfiguration processes and their corresponding transitions to establish state probabilities. Our analysis incorporates two distinct spectrum allocation policies: core-modespectrum random fit (CMS-RF) and core-mode-spectrum first fit (CMS-FF) policies. Our model supports diverse traffic scenarios, including single-class requests with uniform slot requirements and multi-class requests including heterogeneous bandwidth demands. To overcome computational complexity limitations in single-hop analyses, we develop an heuristic iterative approach and subsequently extend this approach to multi-hop network scenarios. We compare AnalyticalSAR, the heuristic iterative approach, and Monte Carlo simulation studies for a single-hop link. Analytical evaluation reveals that random spectrum reconfiguration substantially improves security metrics while introducing minimal blocking probability increases. These performance trade-offs depend critically on number of spectrum reconfiguration, link and network load conditions, and available link capacity. The results validate that AnalyticalSAR achieves an effective compromise between security enhancement and operational performance, providing a practical framework for secure resource management in SS-EON deployments.

Signal transmission using counter-propagation is increasingly adopted to enhance resource utilization in spectrally-spatially elastic optical networks (SS-EONs). Crosstalk is a well-known drawback of SS-EONs that increases blocking probability. Evaluating blocking probability analytically in counter-propagation is challenging due to additional constraints. Current studies typically employ simulation-based techniques or do not consider dynamic scenarios in their analytical models to estimate blocking probability. This paper proposes AnalyticalCP, an exact analytical continuous-time Markov chain model (CTMC) for SS-EONs considering counter-propagation, which estimates blocking probabilities and enhances resource utilization. AnalyticalCP is constructed in two phases: (i) First, a bipartite graph is formed to generate two separate sets of cores and modes, using an optimization approach focused on minimizing interference among cores and modes. (ii) Second, the resultant sets of cores and modes serve as input parameters for the analytical model, streamlining the generation of all feasible states and transitions to estimate exact blocking probability. AnalyticalCP accommodates both single- and multi-class requests: single-class requests uniformly utilize spectrum slots, while multi-class requests adapt slot usage to match bandwidth requirements. Numerical results indicate that AnalyticalCP and the simulations yield comparable performances in terms of blocking probabilities and resource utilization, with the iterative approximate model also demonstrating acceptable accuracy. AnalyticalCP outperforms a benchmark model that considers vertex elimination during bi-partitioning. We extend our CTMC model with a Markov-modulated Poisson process (MMPP) to evaluate bursty-traffic behavior in SS-EONS.

Spectrally-spatially elastic optical networks (SS-EONs) face challenges such as fragmentation and crosstalk, which lead to a rise in blocking probability. Accurate modeling of fragmentation and blocking probability is complex due to multiple constraints in SS-EONs. We evaluate the average fragmentation without considering spectrum contiguity constraints while simultaneously avoiding inter-core and inter-mode crosstalks in SS-EONs, which has not been studied in previous research. This study employs a continuous-time Markov chain-based analytical model to compute average fragmentation and blocking probability under different spectrum allocation policies—core-mode-spectrum random fit (CMS-RF) and core-mode-spectrum first fit (CMS-FF)—without contiguity constraints while simultaneously avoiding inter-core and inter-mode crosstalks in SS-EONs. The model considers both single-class and multi-class requests, where single-class requests use a uniform number of slots, and multi-class requests have variable slot requirements. Additionally, an iterative approximation model is introduced to improve scalability in single-hop links. Numerical results show that both the analytical model and simulations demonstrate lower blocking probability when contiguity constraints are not enforced, with CMS-FF performing better than CMS-RF in reducing fragmentation.

In elastic optical networks (EONs), spectrum fragmentation occurs when available spectrum resources become split into small, non-contiguous blocks, resulting in inefficient spectrum usage. Traditional methods, which allocate separate, dedicated partitions for each bandwidth demand type, often lead to an underutilized spectrum. Dedicated partitioning limits available slots per demand type, significantly increasing the likelihood of blocking requests, particularly those with higher bandwidth demands—even if sufficient spectrum slots are free in other partitions. To address this challenge, this paper proposes DGSP, a bandwidth demand grouping and spectrum partitioning scheme designed to reduce connection blocking. DGSP utilizes an integer linear programming (ILP) model to optimally group demands based on their slot requirements and determine equitable partition sizes. Within each partition, spectrum allocation is done using the first-last fit (FLF) policy. Simulation studies conducted on two networks confirm that DGSP effectively reduces the blocking probability, achieving improvements of up to 97.51% compared to conventional dedicated partitioning approaches.

The exploration of virtualization in spectrally-spatially elastic optical networks (SS-EONs), particularly focusing on virtual optical network embedding (VONE), is an emerging technology to enhance resource utilization and transport capacity. However, managing both inter-core crosstalk (IC-XT) and inter-mode crosstalk (IM-XT) in virtualized SS-EONs poses significant challenges. To tackle this, the paper proposes an optimization model for VONE in SS-EONs, named VneXT-Aw, which incorporates an XT-aware approach. VneXT-Aw employs node and link mapping techniques to seamlessly integrate VON requests into the substrate SS-EONs, ensuring precise node mapping and capacity adherence. It allocates spectrum slots along routing paths in the substrate network, ensuring spectrum contiguity, continuity, and mode continuity for virtual links, all while considering the XT-aware approach. The optimization problem for VneXT-Aw is formulated as a mixed-integer linear programming (MILP) problem. Recognizing the complexity of MILP for larger instances, the paper introduces two heuristic approaches: MILP-based heuristic (MILP-h) and rank-assisted simulated annealing (Rasa-h). A comparative analysis reveals that MILP-h accommodates more requests than Rasa-h but sacrifices computational efficiency. This trade-off highlights the delicate balance between request accommodation and computational complexity, providing insights into the practical implementation of VON embedding in SS-EONs. Furthermore, VneXT-Aw outperforms the benchmark scheme by utilizing the XT-avoided approach.

Internet-of-Things (IoT) services require high performance regarding low delay and fault tolerance. Distributed server allocation is well-suited for meeting these requirements in IoT monitoring services. Previous work focused on reducing delay but overlooked the need for fault tolerance in distributed server allocation. This paper proposes a distributed server allocation model based on preventive start-time optimization (PSO) for IoT monitoring services against server failure. The proposed model preventively determines the server allocation to minimize the largest maximum delay between IoT devices and application servers and between database and application servers among all failure patterns. We formulate the proposed model as an integer linear programming (ILP) problem. We introduce a server allocation algorithm based on PSO to accelerate the computation to obtain an optimal server allocation, compared to the ILP approach. We prove that the introduced algorithm obtains a PSO-based optimal allocation in polynomial time. Numerical results show that the introduced algorithm outputs an optimal server allocation faster than the ILP approach. We compare the PSO-based server allocation with allocations based on the start-time and run-time optimization. We observe that the PSO-based allocation reduces the largest maximum delay by 5.5% for a network model with eleven servers compared to the start-time optimization and avoids unnecessary network disconnections while increasing the maximum delay by 5.1% compared to the run-time optimization.

Delay-sensitive applications have been provided through a low-delay network utilizing multiple edge clouds. For applications that involve sharing status among multiple users, it is crucial to prevent longer communication delays for users who are farther from the application server compared to those who are closer. To address this issue, this paper proposes a distributed processing network design scheme for virtual processing platforms using low-delay networks and widely distributed servers. The proposed scheme introduces Tapl as a given parameter for correcting events in occurrence order. Events within Tapl delay are sorted in occurrence order. The proposed scheme can change its operation mode from a conservative synchronization to an optimistic synchronization depending on the setting of Tapl. The proposed scheme is formulated as a mixed-integer linear programming problem to determine users’ and servers’ distributed processing network configuration. We evaluate the proposed scheme on two different network topologies. Numerical results indicate that, depending on the setting of Tapl, the proposed scheme can reduce the maximum amount of memory used for rollback processes in optimistic synchronization-based applications or realize a conservative synchronization algorithm. The computation time under the condition of 1000 users is within a maximum of nine sec, an acceptable amount of time for preparation before starting a planned service. These results indicate that the proposed scheme realizes event order correction with excellent delay characteristics and applies to virtual processing platforms.

In recent years, space division multiplexing (SDM) technology, particularly multi-core and multi-mode fibers (MCMMFs), has been investigated to overcome physical limitations and enhance transport capacity. When SDM is integrated with elastic optical networks (EONs), it gives rise to an emerging technology known as spectrally-spatially elastic optical networks (SS-EONs). However, SS-EONs face significant challenges, such as fragmentation and crosstalk (XT), which increase blocking probability. Defragmentation is widely regarded as the most effective technique to mitigate fragmentation and reduce blocking probability. However, analytically assessing blocking probability with defragmentation is challenging due to the added constraints. Current studies on MCMMF-based SS-EONs typically rely on simulations or overlook defragmentation in their analytical models. This paper proposes AnDefrag, the first exact analytical continuous-time Markov chain model designed to calculate blocking probabilities in SS-EONs, taking into account defragmentation and the XT-avoided approach. AnDefrag generates all possible states and transitions, avoiding inter-core and inter-mode XTs for both single-class and multi-class requests. Single-class requests utilize an equal number of slots, while multi-class requests require different numbers of slots to meet clients’ needs. For cases where AnDefrag is not scalable, we introduce an iterative approximate model for single-hop links, which is extended to multi-hop networks. Numerical evaluations indicate that AnDefrag outperforms a non-defragmentation-aware benchmark model, as demonstrated through comparison with Monte Carlo simulations for a single-hop link.

Low-delay networking and edge computing will enable mission-critical applications to be delivered over wide-area networks. We consider this trend to be the realization that all users can share an application space without feeling any distance difference. We propose a distributed processing scheme that keeps the order of event occurrence regardless of the distance between users and an application server. The proposed scheme can be applied to both optimistic synchronization algorithms (OSA) and conservative synchronization algorithms (CSA). In the proposed scheme, arrival events with a delay within a predefined set time (correction time) are sorted in order of occurrence before application processing. We formulate the proposed scheme as an integer linear programming (ILP) problem. The objective function of ILP consists of the number of users excluded from the delay quality, the amount of memory consumed for a rollback in OSA, and the maximum end-to-end delay. The three parts of the objective function are set weight and the sum of parts with weight is minimized. We evaluate the proposed scheme for 1000 users distributed in two types of network models. Numerical results indicate that the proposed scheme reduces memory consumption compared to that of the conventional OSA scheme. The proposed scheme works as CSA in which all events are sorted in the occurrence order if the correction time is set above the delay for the slowest event to arrive at the server.

Recently, multi-core and multi-mode fibers (MCMMFs) have been considered to overcome physical limitations and increase transport capacity. They are combined with elastic optical networks (EONs) to form spectrally-spatially elastic optical networks (SS-EONs), an emerging technology. Fragmentation and crosstalk (XT) are well-known drawbacks of SS-EONs that increase blocking probability; evaluating blocking probability analytically is difficult due to additional constraints. When calculating blocking probabilities in MCMMFs-based SS-EONs, it is observed that all current studies either employ simulation-based techniques or do not consider defragmentation of their analytical models. This paper proposes an exact analytical continuous-time Markov chain model for blocking probabilities, named AnalyticalDF, in SS-EONs, which considers defragmentation and the XT-avoided approach. AnalyticalDF generates all possible states and transitions while avoiding inter-core and inter-mode XTs for single-class and multi-class requests. Single-class requests utilize the same number of slots, whereas multi-class requests adopt varying numbers of slots to accommodate client needs. We introduce an iterative approximation model for a single-hop link when AnalyticalDF is not tractable due to scalability. We evaluate AnalyticalDF, the iterative approximate model, and simulation studies for a single-hop link. The numerical results indicate that AnalyticalDF outperforms a non-defragmentation-aware benchmark model.

This paper proposes a polynomial-time algorithm for a server allocation problem in delay-sensitive Internet-of-Things (IoT) monitoring services. The server allocation problem determines the appropriate servers to which the database and application are allocated to minimize the maximum delay between the latest update of reference data and the start of application processing for monitoring data . The server allocation problem was previously handled by expressing it as an integer linear programming (ILP) problem. Nevertheless, it fails to meet the computational time complexity needed to solve the problem, and it does not offer a more efficient technique than the ILP approach. The proposed algorithm consists of two components. The first step entails choosing utilization servers for both the database and the application. Next, the second phase entails matching each usage server and its corresponding IoT device . We prove that the proposed algorithm obtains an optimal solution in polynomial time . We compare computation times between the ILP approach and the proposed algorithm. Numerical results show that the proposed algorithm obtains the optimal solution faster than the ILP approach.

A well-known drawback of spectrally-spatially elastic optical networks (SS-EONs) is crosstalk, which increases blocking probability; evaluating blocking probability analytically is challenging due to additional constraints. Not surprisingly, all the existing studies at this time mainly use simulation-based techniques to quantify blocking probabilities in multi-core and multi-mode fibers-based SS-EONs. This paper proposes AnalyticalBP, an exact analytical continuous-time Markov chain model for blocking probabilities considering the crosstalk-avoided approach in SS-EONs. AnalyticalBP generates all the feasible states and their transitions while avoiding inter-core and inter-mode crosstalks for both single and multi-class requests. Single-class requests use the same number of slots, whereas multi-class requests adopt different numbers of slots to satisfy clients’ requirements. When AnalyticalBP is not tractable due to scalability, we introduce an iterative approximate model for a single-hop link. We further extend the single-hop model for a multi-hop network. We compare AnalyticalBP, the iterative approximate model, and Monte Carlo simulation studies for a single-hop link. Numerical results indicate that the performances obtained by AnalyticalBP and the simulation studies, in terms of blocking probabilities and resource utilization, are comparable, and the accuracy of the iterative approximate model is also acceptable.

Given the spring-up of diverse new applications with different requirements in metro optical networks, network slicing provides a virtual end-to-end resource connection with customized service provision. To improve the quality-of-service (QoS) of slices with long-term operation in networks, it is beneficial to reconfigure the slice adaptively, referring to the future traffic state. Considering the busy-hour Internet traffic with daily human mobility, the tidal pattern of traffic flow occurs in metro optical networks, expressing both temporal and spatial features. To achieve high QoS of slices, this paper proposes a node-oriented slice reconfiguration (NoSR) scheme to reduce the penalty of slices, where a gradient-based priority strategy is designed to reduce the penalties of slices overall penalties in reconfiguration. Besides, given that a precise traffic prediction model is essential for efficient slice reconfiguration with future traffic state, this paper presents the model combining the graph convolutional network (GCN) and gated recurrent unit (GRU) to extract the traffic features in space and time dimensions. Simulation results show that the presented GCN-GRU traffic prediction model achieves a high forecasting accuracy, and the proposed NoSR scheme efficiently reduces the penalty of slices to guarantee a high QoS in metro optical networks.

Software-defined networks (SDNs) are an emerging technology to improve the scalability and network performance of data-intensive applications with high-volume traffic. Load balancing among controllers with several switch migrations is a major obstacle in a multi-controller SDN, degrading the overall system’s performance. To resolve the above issue, this paper proposes OptiGSM, an optimum greedy-based switch migration approach for load balancing among the controllers. OptiGSM starts the switch migration process from an overloaded controller to an underloaded controller if the mean-square deviation of a load of each controller from the average load of all controllers is greater than a pre-defined threshold. A controller is overloaded if its load is greater than the average load of all controllers and vice-versa. We introduce an algorithm for load balancing and prove that it achieves load balancing using the minimum number of switch migrations. Next, we analyze the time and space complexities of the algorithm. Furthermore, we evaluate OptiGSM using simulation and experimental studies. Numerical results indicate that OptiGSM outperforms conventional approaches in terms of the number of switch migrations, response time, throughput, and delay.

When mission-critical applications are provided over a network, high availability is required in addition to a low delay network. This paper proposes a multi-homing network design model, named MHND, to balance a low delay and high availability when distributed processing applications use multiple processing servers. MHND maintains the event occurrence order with a multi-homing configuration using conservative synchronization. We formulate MHND as an integer linear programming problem to minimize the delay. We prove that the distributed server allocation problem with MHND is NP-complete. Numerical results indicate that, as a multi-homing number, which is the number of servers to which each user belongs, increases, the availability increases while increasing the delay. Two or more multi-homing can achieve approximately an order of magnitude higher availability compared to that of the conventional single-homing at the expense of a delay increase of 1.25 times. By using MHND, flexible network design is achieved based on the acceptable delay in service and the required availability.

This paper proposes a network design approach considering data consistency for a delay-sensitive distributed processing system. The data consistency is determined by collating the own state and the two states of slave servers. If the state is mismatched with other servers, the rollback process is initiated to modify the state to guarantee data consistency. In the proposed approach, the select servers and the master-slave server pairs are determined to minimize the end-to-end delay and the delay for data consistency. We formulate the proposed approach as an integer linear programming problem. We evaluate the delay performance and computation time. The proposed approach reduces the delay for data consistency by 6.8-31.2% compared to that of a typical approach that collates the status of all servers at the master server. The computation time is a few seconds, which is an acceptable time for network design before service launch. These results indicate that the proposed approach is effective for delay-sensitive applications.

Due to cost and service level agreement, upgrading network infrastructure is always challenging for network operators, particularly for backbone optical networks (ONs). Nowadays, to support several bandwidth-hungry applications, network operators adopt the migration process from fixed-grid nodes to flex-grid in backbone ONs. However, without proper planning, it may lead to inefficient resource utilization. This paper proposes optimization models for resource allocation in mixed-grid ONs, which initially identifies the set of eligible nodes for upgradation from fixed-grid to flex-grid capabilities for a given set of offline traffic and then uses them in the network to maximize the resource utilization. We formulate an integer linear programming (ILP) problem, named Opti-node, to determine the set of nodes selected for fixed-grid to flex-grid conversion in mixed-grid ONs. We then introduce an ILP, named Opti-resource, to enhance the resource utilization based on the determined set of nodes converted from fixed-grid to flex-grid using Opti-node. We prove that the decision problem of resource allocation in mixed-grid ONs is NP-Complete. Heuristic approaches based on the ILP-based approach and simulated annealing are introduced for a large problem size when Opti-resource is not tractable. We evaluate the proposed resource allocation model extensively, considering different networks, and observe that the simulated annealing-based approach performs better in terms of blocking ratio and spectrum utilization than the ILP-based heuristic approach, but it requires additional computation time.

This paper proposes an optimistic synchronization algorithm (OSA) based server selection scheme with successive participation scenario. In the scenario, we introduce a participating-domain segmentation and determe recommended servers before user participation. Numerical results indicate that the proposed scheme reduces the latency compared to the non-domain segmentation approach (conventional scheme) and overcomes latency fluctuation.

Optical regenerators are beneficial in resource utilization as they provide additional functionalities, such as modulation format (MF) and spectrum conversion, besides signal regeneration. In spectrally-spatially elastic optical networks (SS-EONs), regenerators can perform core and mode switching, which further improves the spectrum utilization. For the first time, this paper proposes a regenerator-aware routing, spectrum, core, and mode allocation model while avoiding inter-mode and inter-core crosstalks during resource allocation to enhance the spectrum utilization in SS-EONs. The proposed model performs core/mode switching operations at the regeneration sites along with spectrum and MF conversions. Apart from the regeneration sites, the proposed model maintains the spectrum continuity, spectrum contiguity, core continuity, and mode continuity constraints in the remaining intermediate nodes. We model the regenerator-aware resource allocation as an integer linear programming to minimize the highest utilized spectrum slot index under the condition that a limited number of regenerators with their placement are given in the network. We introduce a heuristic when the optimization problem is not tractable. Numerical results indicate that the proposed model improves resource utilization compared to a benchmark model that does not consider core and mode switching.

To accommodate the exponential growth of the inter-network services, infrastructure as a service (IaaS) allows the different parties to share the physical infrastructure of the optical network resources using network virtualization. The virtual optical network embedding (VONE) enables efficient resource virtualization to map several virtual optical network (VON) requests over a substrate optical network. On the other hand, the spectrally-spatially elastic optical network (SS-EONs) is becoming a promising solution for the increasing volume of the demanded traffic due to its higher fiber capacity. For the first time, this paper proposes a routing, spectrum, core, and mode allocation (RSCMA) model for VONE over SS-EONs while avoiding inter-core and inter-mode crosstalks. The proposed model allocates spectrum for VON requests over the substrate SS-EONs while maintaining the spectrum continuity, spectrum contiguity, core continuity, and mode continuity constraints. We model the RSCMA model for the VONE problem over SS-EONs as an integer linear programming (ILP) to maximize the number of VON requests placed in the substrate network. We introduce a heuristic when the optimization problem is not tractable for large networks. Numerical results indicate that the performances of the ILP and heuristic approaches are comparable and the computation time of the heuristic approach is much smaller than that of the ILP approach.

Nowadays, quantum-key-distribution (QKD) gains popularity for providing high-security in high-capacity spectrally-spatially elastic optical network (SS-EONs). Considering QKD in SS-EONs requires addition resources for classical and quantum channels and introduces addition noises. The key challenge during resource allocation in QKD-enabled SS-EONs is to deal with the background noises generated by classical and quantum channels along with inter-core and inter-mode XT while reducing the highest utilized spectrum slot index. For the first time, this paper proposes a routing, spectrum, core, and mode allocation (RSCMA) model in QKD-enabled SS-EONs while avoiding intercore and inter-mode XT and the noises generated by the classical and quantum channels. The proposed model allocates spectrum for the data and classical channels for each request maintaining the spectrum continuity and contiguity constraints. It selects a dedicated mode for the quantum channels along the routing path for each request. We model the QKD-enabled resource allocation as an integer linear programming (ILP) to minimize the highest utilized spectrum slot index. We introduce a heuristic when the optimization problem is not tractable for large networks. Numerical results indicate that the values of the highest utilized spectrum slot index using the ILP and heuristic approaches are comparable and the computation time of the heuristic approach is much smaller than that of the ILP approach.

This article proposes a primary and backup resource allocation model that provides a probabilistic protection guarantee for virtual machines against multiple failures of physical machines in a cloud provider to minimize the required total capacity. A physical machine allocates both primary and backup computing resources for virtual machines. When any failure occurs, the survived physical machines with preplanned backup resources recover the virtual machines on the failed physical machines and take over the workloads. The probability that the protection provided by a physical machine does not succeed is guaranteed within a given number. Providing the probabilistic protection can reduce the required backup capacity by allowing backup resource sharing, but it leads to a nonlinear programing problem in a general-capacity case against multiple failures. We apply robust optimization with extensive mathematical operations to formulate the primary and backup resource allocation problem as a mixed integer linear programming problem, where capacity fragmentation is suppressed. We prove the NP-hardness of considered problem. A heuristic is introduced to solve the optimization problem. The results reveal that the proposed model saves about one-third of the total capacity in our examined cases; it outperforms the conventional models in terms of both blocking probability and resource utilization.

To support the fifth-generation bandwidth-hungry applications, such as the Internet of Things, virtual reality, augmented reality, and cloud computing, elastic optical networks have become the most promising infrastructure that allocates bandwidths for services flexibility. Fragmentation caused by dynamic resource allocation deteriorates the availability of resources in networks, increasing the blocking of requests. The fragmentation occurs not only in the used path but also in the neighboring links that are not included in the used path; they are connected to the used path. This paper proposes a service-driven fragmentation-aware (SDFA) resource allocation scheme to enhance resource utilization by avoiding fragmentation with the joint consideration of the used path and neighboring links. A service-driven fragmentation metric (SDFM) is, for the first time, presented to estimate the fragmentation in the used path and neighboring links. The SDFA scheme prefers to assign services at the spectrum slots, which leads to the minimum value of SDFM. Simulation results indicate that SDFA outperforms four conventional fragmentation-aware resource allocation schemes in terms of blocking probability and resource utilization due to a lower fragmentation in the network.

Signal transmission using counter-propagation nowadays is adopted to enhance resource utilization in spectrally-spatially elastic optical networks (SS-EONs), where inter-mode and inter-core crosstalks always degrade signal quality and become bottlenecks for high transport capacity. In this paper, we propose BPRIA for the first time, a Bi-Partitioning-based crosstalk-avoided Resource Identification and Allocation scheme in SS-EONs to enhance resource utilization while suppressing both inter-mode and inter-core crosstalks. We introduce a bi-partitioning optimization problem with vertex elimination to maximize the number of non-adjacent cores and modes in each partition of the bipartite graph; two distinct sets of cores and modes are used in a counter-propagation manner for lightpath allocation. The optimization problem is formulated as an integer linear programming (ILP) problem, and we prove that the optimization problem is NP-complete. An algorithm for core-mode-spectrum allocation is developed considering two distinct sets of cores and modes obtained by ILP to serve lightpath requests while avoiding inter-mode and inter-core crosstalks. For dynamic scenarios, we present core-mode-spectrum allocation. Numerical results reveal that the blocking probability is reduced by BPRIA in SS-EONs, and it enhances traffic admissibility in the network.

Recently, space-division multiplexing (SDM) has been incorporated with elastic optical networks (EONs) to enhance the fiber capability, which forms space-division multiplexing-based elastic optical networks (SDM-EONs). During transmission of optical signals through multi-core fibers, inter-core crosstalk (XT) and intra-core physical layer impairments (PLIs) arise, which deteriorate the signal quality. Existing models typically handle inter-core XT and intra-core PLIs separately and set a single XT threshold for each modulation format, which results in an unacceptable lightpath due to the degradation of signal quality or leads to inefficient spectrum utilization. This paper proposes a routing, modulation, spectrum, and core allocation (RMSCA) model for SDM-EONs to consider inter-core XT and intra-core PLIs jointly. For each modulation format, it sets different XT thresholds and transmission reaches according to inter-core XT and intra-core PLIs. An optimization problem is formulated as an integer linear programming (ILP) problem. We prove that the RMSCA decision problem is NP-complete. We introduce a heuristic algorithm when the ILP problem is not tractable. Numerical results demonstrate that, by setting several XT limits for each modulation format, the proposed model increases spectrum efficiency compared to a benchmark model based on the literature.

In distributed processing for communication services, a proper server selection scheme is required to suppress delay by ensuring the event occurrence order. Although a conservative synchronization algorithm (CSA) has been used in this issue, an optimistic synchronization algorithm (OSA) can be a potential candidate for synchronizing distributed systems. In comparison with CSA, which reproduces events in occurrence order before processing application, OSA can be feasible to realize low delay communication as the processing events arrive sequentially. This paper proposes an optimal server selection scheme considering OSA for distributed processing systems to minimize end-to-end delay under the condition that the holding time for application status is limited. In other words, the end-to-end delay is minimized based on the allowed rollback time for application design or quality-of-service. Numerical results indicate that the delay of the proposed scheme can be reduced by up to a quarter compared to that of the conventional scheme that is based on CSA.

In the modern era, we need to deploy several functionalities either on central cloud servers or edge cloud servers to provide Internet of Things (IoT)-based services via a wide-area network. Typically a huge database and several non-real-time functions are deployed on the central cloud servers. On the other hand, real-time functions are deployed on edge cloud servers. For delay-sensitive services, this approach has an issue in completing the analysis with the latest information when the delay between the central and edge cloud servers is large. In this paper, we propose an allocation scheme of database and applications, which minimizes the delay from the latest update of the database to analyze the real-time data from IoT devices. In the proposed scheme, the delay is minimized considering two constraints of each server, which are maximum accommodating capacity of the IoT devices and whether the database function can be deployed. Numerical results observe that the proposed scheme reduces the delay of analysis compared to the conventional scheme. These results indicate that the proposed scheme can configure a low-delay network for delay-sensitive IoT services with data analysis.

Space-division multiplexing-based elastic optical networks (SDM-EONs) enhance the fiber capacity. Inter-core crosstalk (XT) and intra-core physical layer impairments (PLIs) occur in the multi-core fiber and degrade the optical signal. An existing model separately considers inter-core XT and intra-core PLIs, and sets a single XT threshold to each modulation format. This can lead to an unacceptable lightpath due to signal degradation or the occurrence of spectrum inefficiency. This paper proposes a routing, modulation, spectrum, and core allocation (RMSCA) model, which jointly considers inter-core XT and intra-core PLIs for SDM-EONs. The proposed model sets multiple XT thresholds for each modulation format based on inter-core XT and intra-core PLIs. We present an optimization problem and formulate it as an integer linear programming (ILP) problem. We introduce a heuristic algorithm for a network where the ILP problem is not tractable. Numerical results observe that the proposed model improves the spectrum efficiency by setting multiple XT thresholds to each modulation format.

Fragmentation with crosstalks is the major obstacle in spectrally-spatially elastic optical networks, which suppresses resource utilization while degrading the quality-of-transmission. To overcome this issue, this article proposes, for the first time, a proactive fragmentation management scheme based on batch processing while satisfying both inter-core and inter-mode crosstalks to enhance resource utilization. The proposed scheme adopts a batch processing method to create batches of lightpath requests received within a time threshold to utilize spectrum resources effectively. In batch processing, lightpath requests are prioritized based on the number of links in their routes and required slots. To maintain fairness in batch processing, when any request is rejected, the proposed scheme triggers a procedure that gives an equal opportunity to all arriving requests within the threshold, irrespective of numbers of hops and requested capacities, for allocation. We formulate the static batch processing of lightpath requests (SBPLR) as an integer linear programming (ILP) problem. We prove that SBPLR is an NP-Complete problem. We introduce a heuristic solution when ILP is intractable. To serve lightpath requests in each batch while avoiding inter-core and inter-mode crosstalks, we develop a core-mode-spectrum allocation algorithm. We present a dynamic batch processing based fragmentation management approach. Numerical results indicate that the proposed scheme outperforms the benchmark schemes.

Spectrally-spatially elastic optical networks (SS-EONs) have been considered nowadays to overcome the physical barrier and enhance the transport capacity, where enhancing spectrum utilization while satisfying inter-core and inter-mode crosstalks is always challenging. This paper proposes a priority-based crosstalk-avoided core, mode and spectrum allocation scheme in SS-EONs, which enhances resource utilization while satisfying both constraints of inter-core crosstalk and inter-mode crosstalk. The proposed scheme creates different groups of cores and modes and assigns a priority to each of them. Core and mode are selected for serving lightpath requests based on their priority order. We define an optimization problem for routing, modulation assignment, spectrum, core, and mode allocation (RA-SCMA) in SS-EONs considering both constraints of inter-core crosstalk and inter-mode crosstalk simultaneously. The optimization problem is formulated as an integer linear programming problem. We prove that the decision version of RA-SCMA is NP-complete. We present crosstalk-avoided core-mode-spectrum allocation considering a dynamic scenario. Numerical results indicate that the proposed scheme reduces the blocking probability in SS-EONs and allows up to 40% increased traffic loads by utilizing the crosstalk-avoided unutilized slots compared to the conventional scheme that adopts a core-mode-spectrum first fit policy.

This paper proposes a server selection scheme in successive participation scenario based on the segmentation of participating-domain to suppress the latency. In the proposed scheme, the users participate for server selection one after another. The proposed scheme determines a recommended server, and a new participating user selects the recommended server first. Recommended servers are determined in advance at the condition that participating users exist in all regions in users' participation domain. A recommended server is determined for each divided region to minimize the latency when there exists one user at each region. The new participating user selects the recommended available server, where the user is located. We formulate an integer linear programming problem to determine the recommended servers for the proposed scheme. We use the outcome of the recommended server finding process as the input for server selection. Numerical results express that smaller latency is obtained using the proposed scheme compared to the conventional greedy based server selection scheme, by employing some additional computations for finding the recommended servers before participating of users.

The lightpath provisioning time or releasing time is adversely affected by the time that optical amplifiers require to adjust to a newly added or terminated signal power. This shortcoming is particularly true with multi-core erbium-doped amplifiers (EDFAs), as multi-core transient-suppressed EDFAs are unavailable at the current time. This paper proposes a fiber span power management scheme based on dummy wavelength signals that are used to shorten the lightpath provisioning and releasing times in multi-core fiber networks. With the shorter time of lightpath provisioning and releasing procedures, the total time that is required to reserve wavelengths in the system is decreased, which means that network resources are used more efficiently. As a result, the blocking performance and average waiting time in the system are improved. To evaluate the performance of the proposed scheme, this paper introduces both analytical model and simulation study. In the introduced model, the ratio of the number of activating and activated dummy wavelengths to the number of dummy wavelengths in each span is considered in the range between 0 and 1. The analysis reveals that the performance of the proposed scheme depends on α, which is the ratio of the number of dummy wavelengths to the number of dummy and lightpath wavelengths in each span, and there exists a point of α where the blocking probability becomes minimum. We further observe that the proposed scheme outperforms the conventional approaches in terms of blocking probability and average waiting time, as traffic loads increase. Finally, we provide the direction on how our introduced model can be considered for a network with multi-span routes.

This work proposes a defragmentation scheme using reroutable backup paths in toggled-based quasi 1+1 path protected elastic optical networks to enhance the efficiency of defragmentation and suppress the fragmentation effect. The proposed scheme allows both reallocation of spectrum slots of backup paths and rerouting of backup paths. By using the path exchanging approach in the proposed scheme, the primary paths become the backup path while the backup path becomes the primary path. This allows to utilize the advantages of defragmentation in both primary and backup paths. Considering rerouting and path exchanging, we present to the key idea to formulate the proposed scheme as an integer linear programming (ILP) problem. A heuristic algorithm is introduced to solve the problem for large networks, when ILP is not tractable. For a dynamic traffic scenario, an approach that suppresses the fragmentation considering rerouting and path exchanging operations is presented. The numerical results indicate that the blocking probability using the proposed scheme is suppressed compared to the conventional scheme.

This paper proposes a backup resource allocation model that provides a probabilistic protection for primary physical machines in a cloud provider to minimize the required total capacity. When any random failure occurs, workloads are transferred to preplanned and dedicated backup physical machines for prompt recovery. In the proposed model, a probabilistic protection guarantee is introduced to prevent the cloud provider from capacity overbooking. We apply robust optimization in our model to formulate the backup resource allocation problem as an integer linear programming problem. A simulated annealing heuristic is adopted to solve the same optimization problem when the cloud provider is large. Finally, the results reveal that the required backup capacity depends on the reliability of primary physical machines. Specifically, the more the resources in primary physical machines share backup capacity when the failure probabilities of primary physical machines are sufficiently small, the less capacity is required for backup resource allocation.

In elastic optical networks (EONs), a major obstacle to using the spectrum resources efficiently is the spectrum fragmentation. In the literature, several defragmentation approaches have been presented. For 1+1 path protection, conventional defragmentation approaches consider designated primary and backup paths. This exposes the spectrum to fragmentations induced by the primary lightpaths, which are not to be disturbed in order to achieve hitless defragmentation. This paper proposes a defragmentation scheme using path exchanging in 1+1 path protected EONs. We exchange the path function of the 1+1 protection with the primary toggling to the backup state, while the backup becomes the primary. This allows both lightpaths to be reallocated during the defragmentation process, while they work as backup, offering hitless defragmentation. Considering path exchanging, we define a static spectrum reallocation optimization problem that minimizes the spectrum fragmentation while limiting the number of path exchanging and reallocation operations. We then formulate the problem as an integer linear programming (ILP) problem. We prove that a decision version of the defined static reallocation problem is NP-complete. We present a spectrum defragmentation process for dynamic traffic, and introduce a heuristic algorithm for the case that the ILP problem is not tractable. The simulation results show that the proposed scheme outperforms the conventional one and improves the total admissible traffic up to 10%.

This paper analyzes the computational time complexity of the allocation problem for data processing functions among multiple users and distributed servers in the distributed processing communication scheme for a real-time network application. In the distributed processing communication scheme, the application is processed on a data processing function in the distributed servers in order to minimize the delay time. We prove that the allocation problem for data processing functions among multiple users and distributed servers is an NP-complete problem.

This paper aims to design a Hadoop system and evaluates the performance of a task allocation scheme. The task allocation scheme splits each job into tasks using an appropriate splitting ratio, and assigns tasks to slave servers based on server processing performance and network resource availability. We experimentally evaluate the performance of the scale out of the task allocation scheme with five machines. We focus on the configuration of jobtracker and tasktracker in Hadoop. In cases with heterogeneous Hadoop clusters, we distribute task blocks to high-capability slaves with proportionally larger-sized tasks than to low-capability slaves. We create an environment in which high-capability slaves perform more work than low-capability slaves. The experimental testbed results indicate that the task allocation scheme is effective.