Request Scheduling Research Articles

Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment

To efficiently realize the parallel task scheduling of SaaS platform in large-scale cloud service environment, this paper studies the parallel task scheduling algorithm of SaaS platform based on dynamic adaptive particle swarm optimization in cloud service environment. Users access the cloud through the user access interface module, and issue task scheduling instructions or send task scheduling requests. After the service management module provides diversified application service support according to the scheduling requirements, the core service module determines the SaaS platform parallel scheduling objective function, and uses dynamic adaptive particle swarm optimization to solve the objective function to obtain the SaaS platform parallel task scheduling results. The test results show that the algorithm has better multi-objective solving ability and can obtain higher quality objective solutions, and the test results of the total execution time of parallel scheduling tasks and the total transmission time of task data on SaaS platform are all within 30 s. The results of virtual machine resource load balancing degree are all below 15%; the utilization rate of virtual machine resources is above 92.2%.

Nonlinear Analysis of EDF Scheduling Framework for Resource Distribution in Cloud Computing

In modern times, the field of information technology is significantly shifting towards cloud computing. This innovative technology involves the permanent storage of resources on servers, which clients access via the internet. Typically, a cloud comprises a collection of resources known as virtual machines, capable of managing both computational tasks and storage needs. As the demand for cloud services continues to rise, the scheduling of these resources presents a considerable challenge. Scheduling refers to the workflow management necessary to complete tasks within the system. An efficient scheduler adapts its scheduling policy dynamically in response to changing conditions and task requirements. Cloud computing represents a recently developed technology where resources, whether physical or virtual machines, are permanently stored on servers and accessed by users through the internet. The cloud offers various service models, including Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS). In this context, IaaS refers to a set of resources, including virtual machines, that provide computational and storage services. Given the increasing adoption of cloud technology, resource scheduling has become a critical task. Hence, optimizing the scheduling of requests to ensure the best utilization of cloud resources is essential. This paper introduces a Nonlinear Analysis of EDF Scheduling Framework for resource allocation that considers deadlines and processing times when assigning resources to specific jobs. The performance metrics, including Average Turnaround Time, Average Waiting Time, and Average Deadline Violation, show a significant improvement when compared to traditional scheduling models such as First-Come, First-Served (FCFS), the Shortest Job First (SJF), and Simple Earliest Deadline First (EDF) models.

Doctor and Nurse Scheduling in Emergency Room Using Firefly Algorithm

The Emergency Room (ER) is a part of the hospital responsible for providing initial treatment to patients with life-threatening conditions. The operational hours of the ER follow the schedule set by the hospital. ER must be ready to serve emergency patients 24 hours a day and 7 days a week. Therefore, the scheduling of doctors and nurses in the ER needs to be well-managed to enhance the efficiency of doctors and nurses in responding emergency patients quickly and effectively. In this study, the problem of doctors and nurses scheduling in the ER is solved using the Firefly Algorithm, in which doctors and nurses represented as fireflies. This algorithm is chosen since its ability to find optimal solutions for complex optimization problems. In this research, doctors and nurses can submit schedule requests to improve job satisfaction. The optimization model is constructed by a number of constraints including the availability of doctors and nurses, schedule requests, and the operational needs of the ER. The Firefly Algorithm is applied to find the optimal solution for the model. Simulation results show that this algorithm can produce an optimal schedule, in which 70.6% of doctors' schedule requests and 98.2% of nurses' schedule requests are being fulfilled.

Agile C-states: A Core C-state Architecture for Latency Critical Applications Optimizing both Transition and Cold-Start Latency

Latency critical applications running in modern datacenters exhibit irregular request arrival patterns and are implemented using multiple services with strict latency requirements (30 μs –250 μs ). These characteristics render existing energy saving idle CPU sleep states ineffective due to the performance overhead caused by the state’s transition latency. Besides the state transition latency, another important contributor to the performance overhead of sleep states is the cold-start latency, or in other words, the time required to warm-up microarchitectural state (e.g., cache contents, branch predictor metadata) that is flushed or discarded when transitioning to a lower-power state. Both the transition latency and cold-start latency can be particularly detrimental to the performance of latency critical applications with short execution times. While prior work focuses on mitigating the effects of transition and cold-start latency by optimizing request scheduling, in this work, we propose a redesign of the Core C-state architecture for latency-critical applications. In particular, we introduce C6Awarm a new Agile Core C-state that drastically reduces the performance overhead caused by idle sleep state transition latency and cold-start latency, while maintaining significant energy savings. C6Awarm achieves its goals by implementing 1) medium-grained power gating, 2) preserving the microarchitectural state of the core and 3) by keeping the clock generator and PLL active and locked. Our analysis for a set of microservices based on an Intel Skylake server, shows that C6Awarm manages to reduce the energy consumption by up to \(70\% \) with limited performance degradation (at-most \(2\% \) ).

Effect of Departmental and Physician-Selected Interventions on Point-of-Care Ultrasound Documentation Completion.

Background Point-of-care ultrasound (POCUS) has been disruptive to many experienced emergency physicians as it requires competence in a new physical skill, real-time image interpretation, and navigation of novel software for submission to the electronic health record (EHR). Incomplete documentation of a performed POCUS study used for clinical decision-making represents a potential medicolegal liability, may expose the patient to repetitive or potentially unnecessary imaging, and is a missed opportunity for reimbursement. Identifying effective facilitators of ED POCUS documentation completion requires additional investigation. Methods In the first part of this mixed-methods study, eligible attending physicians were stratified into levels of use ("high"/"low"/"never") based on recent POCUS documentation performance. Semi-structured interviews were conducted with high and low utilizers to explore their perceptions of the POCUS submission workflow and their receptivity to various proposed interventions. Qualitative data were analyzed using a thematic analysis that explored perceived usefulness and usability. The second part of the study consisted of two intervention phases. First, physicians achieving minimum POCUS documentation numbers were rewarded with additional shift scheduling flexibility. In the second phase, the intervention that garnered the most interview support, daily documentation reminder emails, was implemented. The primary outcome was the individual POCUS documentation rates calculated as all studies submitted divided by all studies performed (submitted plus unsubmitted) per month. Provider-level monthly data was aggregated into a departmental rate. Results Interviews were conducted with 12 physicians, six from the highest and six from the lowest documentation quartiles. Both groups supported the same two proposed interventions: reminder emails ranked first, then monetary rewards ranked second. High utilizers emphasized the clinical utility of POCUS, whereas low utilizers expressed concerns over "double billing" and exposure to medicolegal liability with uncertain scan interpretations. For low utilizers, a documentation decision could be dependent on the performing resident physician's displayed confidence. Both groups voiced frustration with the need to use a separate program, Qpath (Telexy Healthcare, Inc, Maple Ridge, British Columbia, Canada), for POCUS documentation. During intervention phase one, the aggregate departmental documentation rate increased from 44.6% to 60.1% with the introduction of the schedule request incentive. This improvement was seen across all documentation quartiles. The departmental rate remained stable and did not improve further following the addition of the daily documentation reminder emails in intervention phase two. When reminder emails ceased yet the day-off request incentive continued, the departmental rate did not drop. Conclusions The implementation of a non-financial shift scheduling incentivecorrelated with the largest increase in departmental POCUS documentation rate. Interviewees incorrectly predicted that email reminders would be the most influential intervention highlighting a mismatch between physician perception and effective drivers of behavior change.Further investigation may focus on determining the size and longevity of the isolated impact of a schedule request incentive, as one might expect diminishing marginal utility.

Enhanced Call Admission Control based on History in BWA Networks

WiMAX is a wireless technology that supports many blooming applications. To efficiently assign resources to traffic flows, bandwidth allocation and Connection Admission Control (CAC) modules are included in MAC layer and Burst Allocation (BA) module is added to PHY layer. It is essential to lessen delay and effectively schedule requests for BA. In this paper, CAC-based on History (CACH) and scheduler based on Delay Tolerance (SDT) for BA are proposed for WiMAX networks. CACH accepts/ rejects connections based on values in Contention Window (CW) in former Time Frame (TF). SDT and a Bucket-based BA (BBA) assign resources to flows with least amount of DT. Performance is analyzed based on throughput, Packet Loss Ratio (PLR) and delay. Index Terms: WiMAX, Contention Window, Contention Delay, Delay Tolerance, Traffic Priority, Connection Admission Control, Burst Allocation

Migration-aware slot-based memory request scheduler to guarantee QoS in DRAM-PCM hybrid memories

Emerging hybrid memory technologies composed of non-volatile memories (NVM) and DRAMs exhibit significant access speeds and capacity improvement. High application performance is feasible by dynamic migration (or relocation) of pages (data) between the memory types. While NVM is used for its density during memory allocation, moving write-intensive pages from NVM to DRAM helps to improve the execution and response time of applications. Existing techniques propose solutions to dynamically identify the pages that need to be moved immediately or in regular intervals. Such an immediate or interval-based rigid migration regime may hamper the service of the regular memory requests, which in turn affects the memory service rate.To alleviate the impact on service rate and improve the quality-of-service (QoS) of the device, this paper proposes a scheduling method to identify the instant at which to migrate the eligible page. Along with regular memory requests, these eligible migration candidate pages are given reserved time slots by taking into account the current memory request rate. Our proposed methods aim to optimize the migration overheads by avoiding unnecessary migrations and at the same time guaranteeing future accesses to the migrated pages. This results in improved execution time and memory response time for the applications.

Predictive Service Provisioning With Online Learning in Wireless Edge Networks

Mobile Edge Computing (MEC) technology can be implemented at cellular base stations, enabling flexible and configurable provisions of services for mobile users to access. Nevertheless, the conventional solutions mainly focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">statical</i> service provisioning, which ignores the dynamic nature of the arriving service requests. In this work, we first conduct comprehensive data-driven observations on over 4 million service requests throughout 9,800 base stations. Our key findings suggest that users' demands intrinsically exhibit spatial and temporal patterns, which inevitably lead to performance degradation in statical service provisioning. Motivated by that, we design and implement MobiEdge, a predictive service provisioning system with online learning in wireless edge networks. We propose a graph embedding learning-based model for representation learning, thus to achieve accurate request prediction at different base stations. Then, based on the prediction of incoming service requests, we study the service provisioning reconfiguration problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , how to jointly optimize service placement and corresponding request scheduling across dual timescales, under constraints of network resources and the total budget. By leveraging the submodular technique, we transform the research issue into a submodular function maximization problem under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> -independence system constraint, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> is a positive constant related to the ratio of coefficients in constraint conditions. On this basis, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/(1+q)$</tex-math></inline-formula> approximation algorithm with rigorous theoretical analysis on the bounded maximum utility. Extensive trace-driven evaluations are conducted over networks of different scales, and MobiEdge shows remarkable performance enhancements by achieving the accuracy of up to 98% in service prediction and an average utility of 92.9% to the optimal solution in service provisioning.

Power-Efficient Dispatching of Time-Sensitive Requests in the Fog-Enabled Industrial Internet of Things

Recent advancements in service offerings and the emergence of new application areas have reveal the limitations of cloud computing as a solely solution. One of these new areas is the industrial internet of things, where industrial processes are controlled in a network-based and automated way which brings many new challenges. To solve the challenges, fog computing has been introduced as a complementary paradigm to the cloud. Resource management is one of those challenges need further investigation. The industrial internet of things by its own characteristics has led traditional methods to be inefficient within this evolving ecosystem. Therefore, fully utilizing potential of the new system necessitates effective resource management. This manuscript aims to investigate the problem of resource management in the emerging cloud-fog ecosystem of the industrial internet of things. Considering the dynamic nature of users, this study proposes the algorithms for dynamic resource allocation and provisioning based on deadlines. We formulate the problem as a multiple criteria stochastic problem and introduced an online solution. Our proposed solution takes into account the unique characteristics of Fog computing and the dynamic behavior of users. To effectively handle the scheduling of requests, the research suggests utilizing the Lyapunov optimization method and Lyapunov drift theory. The Lyapunov method helps to merge and measure all the related objectives in terms drift. The objective function is optimized, in each time slot, using a drift-plus-penalty weight parameter. By adopting this approach, the system can operate efficiently while meeting the constraints and performance requirements of IoT applications.

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI-based services be placed across Function-as-a-Service (FaaS) based edge computing systems in order to maximize total Quality-of-Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time-scales (one for placement and one for scheduling). We first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP-hard. We then propose a novel placement algorithm that places services while considering device-to-device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI-based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning-based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

EdgeOptimizer: A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters

Edge computing has garnered significant attention in recent years, leading to the evolution of more delay-sensitive applications towards a three-tier architecture with edge-cloud collaboration. Concurrently, technologies associated with containerization have been maturing. Notably, K8s(Kubernetes) emerges as a prominent solution for the management of extensive, dynamically evolving, and intricate container clusters. However, optimizing performance in a K8s-based architecture requires careful consideration, as intelligent algorithms cannot be easily rehearsed or retracted, and a poorly functioning algorithm can result in significant damage. To enhance the success rate of time-critical task execution in real production environments, it is crucial to provide an algorithm optimizer for service orchestration and request dispatching, especially when dealing with different services. Traditionally, building a K8s-based experimental system during the early stages of experiments has been time-consuming and involved significant programming efforts. In this paper, we introduce EdgeOptimizer, a decoupled and modularized optimizer for scheduling in multiple clusters. EdgeOptimizer also serves as an online testbed for algorithm verification in K8s-based systems, offering detailed configuration options to facilitate cluster management. By collecting system information and employing an interface-oriented system architecture, EdgeOptimizer enables users to quickly develop, deploy, and switch between various algorithms, significantly reducing the upfront costs associated with setting up an experimental environment. We evaluated the performance of EdgeOptimizer based on overall overload and demonstrated its scalability and effectiveness in verifying the efficacy of service orchestration and request dispatching algorithms for time-critical tasks. Our findings illustrate the value of EdgeOptimizer in improving the overall success rate of executing time-critical tasks, thus highlighting its potential in real-world scenarios.

Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem

The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully utilized. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests.

Reduce the delivery time and relevant costs in a chaotic requests system via lean-Heijunka model to enhance the logistic Hamiltonian route

Online supply chain management (OSCM) is the smart way to deal with the vast amounts of data that come in from customers in a disorganized system to meet the quantities, volumes, and types of customer packages during both delivery and pick-up phases using a new design of vehicle boxes managed by IoT and to track their requests based on scheduling requests and sorting them to make a Hamiltonian route that guarantees the shortest travel distance. The OSCM framework consists of two sequential phases. 1st phase has four recruitment stages. The 1st stage discusses exploration resources (the relationship between the client and the vehicle) using IoT to receive customers' requests (Heijunka growth radius), then moves to exploration maturity to build a one-way Hamiltonian growth route direction. The 1st stage is based on tackling a Heijunka matrix fed through deep learning to classify the matrix into many conditional clusters according to customers' request forecasting and make the prediction value, which is the stop condition of cluster radius through next three stages. This study finds that XGboost outperforms Ada-boost by 14.352 % in the prediction stage. A heuristic rule based on NWBS enhances the FP-Growth algorithm over ECLAT by 7.648 % in the classification stage. Phase II is interested in reducing load and unloading activity time. This problem describes needing more than a different service at the same point (i.e., chaotic and unstable interaction leads to unstable delivery). Therefore, the online scheduling and tracking of the logistic routing using the IoT that Smart Lean Heijunka supports will enhance the SCM, increasing the visited points by 31.2 % and improving the profit by 41 %.

Blockage‐free storage assignment and storage/retrieval scheduling in autonomous vehicle storage and retrieval systems

AbstractAutonomous vehicle storage and retrieval systems have greatly increased in popularity in the last decade. In such a system, at each tier multiple roaming vehicles transport totes between the storage locations and the lifts. However, this may lead to vehicle interference. We study in which order and by which vehicle the storage and retrieval requests should be executed to minimize the makespan, without vehicle interference. The optimal storage locations for incoming totes are also determined. A blocking mitigation protocol is proposed to address vehicle interference. We propose a two‐phase matheuristic, where in the first phase, the tier is divided into zones, with each zone assigned its own vehicle. The second phase focuses on reassigning requests between adjacent vehicles to obtain improved solutions. The models proposed in both phases are solved to optimality in polynomial time and pseudo‐polynomial time, respectively. Computational experiments show that the matheuristic produces high‐quality solutions within a few seconds, even for large‐sized instances, making it suitable for real‐time decisions. Compared to methods commonly used in practice, our matheuristic can reduce the makespan by up to 15%. Our results show that making integrated decisions that combine storage assignment and request scheduling, is more beneficial than sequential optimization in terms of throughput performance, space utilization and overall system cost. We also find that increasing the number of vehicles has a diminishing return effect on the makespan. Another finding is that the system with a large number of short storage aisles leads to higher throughput capacity than that with a small number of long storage aisles.

Improving Patient Access and Provider Availability in Classical Hematology: The Development of an Advanced Practice Provider Led Virtual Anemia Consult Clinic

Background: Classical hematology entails the diagnosis and treatment of many complex disease processes. The growing volume of referrals to classical hematology is challenged by staff hematology provider shortages. Our institutional goal for new patient access is to be seen within 7 days of the scheduling request. In Q2 of 2022, the leadership team identified the average wait time for a hematology consult was greater than 3 weeks and anemia consults comprised over 2/3 of these consultation requests. To address this need, a taskforce was developed to utilize trained classical hematology advanced practice providers (APPs) to see anemia consults virtually for initial work up and management. To optimize APP consult utilization, a centralized virtual anemia consult clinic (VACC) was developed to address patient access issues and improve staff physician consult availability for more complex hematologic diseases. VACC strived to provide greater access to local metropolitan areas as well as reaching those in-state several hours away, breaking down barriers in transportation and access. Methods: In September 2022, a centralized VACC was launched and staffed by hematology APPs to provide availability for patients with an anemia diagnosis. APPs performed triage, assessment, diagnosis, and provided treatment for anemia diagnoses. VACC is staffed Monday-Friday with 5 new consults per day (40 minutes for each consult) for a total of 25 anemia consults per week. Common diagnoses such as iron deficiency and nutritional anemias were seen in VACC (Figure 1A). A staff hematologist and APPs huddled once weekly to discuss complex patients to triage, diagnose, and treat appropriately. Results: APPs in VACC saw a total of 610 in-state virtual consults between September 2022 and May 2023. The median wait for a virtual anemia consult was 4 days. APPs were able to see virtual anemia consults in 31 Ohio counties, reaching counties at the northern most part of the state, to the southern most tip (Figure 1B). This provided patients with flexibility to be evaluated at home without driving hours for a consult. Of patients seen, 82% were female, 17.9% male and 0.1% non-binary. Racial demographics included 29.8% black, 1.7% Asian, 0.7% American Indian, 4.4% multiracial, and 60.7% white patients. Classical hematology APP revenue increased 276%. VACC slot utilization during this time averaged 91%. Overall classical hematology access for physicians and APPs improved from its peak of 20 days just before VACC started, to 8 days in May 2023. The majority of patients (73%) seen for initial consultation were incorporated into the APP practice for follow up visits and further treatment, increasing slot utilization and productivity for hematology APP practice. Conclusions: Collaboration between hematology physicians and hematology APPs within our institute led to an initiative increasing patient access for hematology diagnoses through the development of an APP staffed VACC. VACC led to a decrease in days wait for patients to be seen with an anemia diagnosis and provided increased consult slots to our staff hematologists to see more complex hematologic cases. Barriers such as transportation and limited scheduling options were removed through the option of scheduling anemia consults into VACC centralized clinic. Weekly huddles between staff hematologist and APPs led to collaborative relationships, mentoring, education, and increased expertise of colleagues to better serve patients and provide quality care in assessment, diagnosis, and treatment of patients with anemia diagnoses. Recommendations: APP led virtual consult clinics in classical hematology clinics are valuable tools to increase patient access and APP productivity, improve scheduling workflows, and decrease barriers related to patient inconveniences and social barriers. Hematology physician shortages call for an increase in education and collaboration with hematology APPs to execute comprehensive care to hematology patients. Future efforts for this clinic will focus on patient outcomes, addressing further social barriers identified to hematology patients, and consideration of expanding diagnoses seen in virtual consult clinic, such as thrombocytopenia, to expand timely access to care.

Approach to Evaluate Scheduling Strategies in Container Orchestration Systems

This article offers an in-depth examination of scheduling strategies in container orchestration systems, emphasizing the importance of selecting the most suitable strategy for a system's specific needs. The article explores the evolution of container systems and challenges around cloud reliability and fault tolerance. A structured experimental methodology was introduced to compare different strategies systematically, utilizing various testing methods like packing until first rejection, packing until the cluster is full, and introducing different malfunctions such as liveness and network partitioning. Comparative results between the “binpack” and “spread” strategies highlighted that while the former excels in higher scheduling requests acceptance rate, the latter is superior in offering higher availability. The findings show the need for aligning the scheduling strategy with system objectives, be it resource optimization, fault tolerance or other desired characteristics

Energy-delay aware request scheduling in hybrid Cloud and Fog computing using improved multi-objective CS algorithm

Energy-delay aware request scheduling in hybrid Cloud and Fog computing using improved multi-objective CS algorithm

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.

Novel Access Methods in Cellular Networks Based on the Analog Bloom Filter

In cellular networks, the User Equipment (UE) often needs to send control information to the base station. For example, to initiate a connection, a UE transmits a Zadoff-Chu (ZC) sequence on a channel called PRACH, where the sequence is randomly selected from up to 64 sequences. Also, a connected UE may send the Scheduling Request (SR) bit on a channel called PUCCH to start an uplink data transmission. In this paper, novel access methods, namely, ABF PRACH and ABF SR, are proposed based on the Analog Bloom Filter (ABF). With ABF PRACH, a UE transmits a pattern, which consists of multiple ZC sequences. The performance is improved, because the number of patterns, currently 2304, is much larger than the number of sequences, resulting in a much lower collision probability. Similarly, with ABF SR, each connected UE is assigned a unique pattern. The system capacity is improved, because the resource is shared by many UEs, instead of dedicated to individual UEs. Both ABF PRACH and ABF SR have been demonstrated in real-world experiments. Simulations show that ABF PRACH significantly outperforms the existing PRACH. Also, ABR SR achieves a 2.52-fold increase of capacity, while maintaining very low error ratios.

Reveal: Robustness-aware VNF placement and request scheduling in edge clouds

In the edge cloud network, service providers place virtual network functions (VNFs) in edge clouds to serve users’ requests. Thus, it is essential to consider VNF placement and request scheduling in edge clouds. Existing works often focus on minimizing request completion time or maximizing network throughput to utilize network resources and ensure users’ QoS efficiently. However, they ignore two practical factors: malicious users and failed VNFs, leading to poor network robustness. To this end, this paper studies robustness-aware VNF placement and request scheduling, named Reveal. Specifically, we limit the number of VNFs each user can access and the number of users each VNF can serve to control the influence scope of malicious users and VNF failures. Since placing VNFs is time-consuming and requests arrive dynamically, we solve this problem through two phases: robust VNF placement and assignment, and online request scheduling. For the first phase, we design an efficient knapsack-based rounding algorithm with bounded approximation factors. For online request scheduling, we propose a primal–dual based algorithm with a competitive ratio of 1−ϵ,O(log1/ϵ) where ϵ∈(0,1). Experiment and simulation results show that Reveal can achieve better performance and robustness than other alternatives.