Future Requests Research Articles

Routing Cost Minimization and Throughput Maximization of NFV-Enabled Unicasting in Software-Defined Networks

Data transfer in contemporary networks usually is associated with strict policy enforcement for data transfer security and system performance purposes. Such a policy is represented by a service chain consisting of a sequence of network functions such as firewalls, intrusion detection systems, transcoders, etc. Due to the high cost and inflexibility of managing hardware-based network functions, network function virtualization (NFV) has emerged as a promising technology to meet the stringent requirement imposed on the service chain of each data transfer request in a low-cost and flexible way. In this paper, we study policy-aware unicast request admissions with and without end-to-end delay constraints in a software defined network. We aim to minimize the operational cost of admitting a single request in terms of both computing resource consumption for implementing the NFVs in the service chain and bandwidth resource consumption for routing its data traffic, or maximize the network throughput for a sequence of requests without the knowledge of future request arrivals. We first formulate four novel optimization problems and provide a generic optimization framework for the problems. We then develop efficient algorithms for the admission of a single NFV-enabled request with and without the end-to-end delay constraint, where NFV-enabled requests are defined as the requests with policy enforcement requirements. We also devise online algorithms with a guaranteed performance for dynamic admissions of requests without the knowledge of future arrivals. In particular, we provide the very first online algorithm with a provable competitive ratio for the problem without the end-to-end delay requirement. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising and outperform existing heuristics.

Deep-Learning-Assisted Network Orchestration for On-Demand and Cost-Effective vNF Service Chaining in Inter-DC Elastic Optical Networks

This work addresses the relatively long setup latency and complicated network control and management caused by on-demand virtual network function service chain (vNF-SC) provisioning in inter-datacenter elastic optical networks. We first design a provisioning framework with resource pre-deployment to resolve the aforementioned challenge. Specifically, the framework is designed as a discrete-time system, in which the operations are performed periodically in fixed time slots (TS). Each TS includes a pre-deployment phase followed by a provisioning phase. In the pre-deployment phase, a deep-learning (DL) model is designed to predict future vNF-SC requests, then lightpath establishment and vNF deployment are performed accordingly to pre-deploy resources for the predicted requests. Then, the system proceeds to the provisioning phase, which collects dynamic vNF-SC requests from clients and serves them in real-time by steering their traffic through the required vNFs in sequence. In order to forecast the high-dimensional data of future vNF-SC requests accurately, we design our DL model based on the long/short-term memory-based neural network and develop an effective training scheme for it. Then, the provisioning framework and DL model are optimized from several perspectives. We evaluate our proposed framework with simulations that leverage real traffic traces. The results indicate that our DL model achieves higher request prediction accuracy and lower blocking probability than two benchmarks that also predict vNF-SC requests and follow the principle of the proposed provisioning framework.

Prediction of User Future Request Utilizing the Combination of Both ANN and FCM in Web Page Recommendation

Abstract This paper explains about the web page recommendation system. This procedure encompasses consumers’ upcoming demand and web page recommendations. In the proposed web page recommendation system, potential and non-potential data can be categorized by use of the Levenberg–Marquardt firefly neural network algorithm, and forecast can be made by using the K-means clustering algorithm. Consequently, the projected representation demonstrates the infrequent contact format with the help of the representation that integrates the comparable consumer access model data that belong to the further consumer. Thereafter, the impending user data are specified to the clustering progression. The third phase of the projected process is collecting potential data with the aid of the improved fuzzy C-means clustering algorithm. The last step of our projected process is envisaging the upcoming demand for the subsequent consumer. The presentation of the projected procedure will be compared to the obtainable procedure.

Approximate dynamic programming approaches for appointment scheduling with patient preferences.

During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. In contrast to existing models, the evaluation of a booking decision in this model focuses on the extent to which preferences are satisfied. Characteristics of the model are analysed to develop a system for formulating booking policies. Based on these characteristics, two types of approximate dynamic programming algorithms are developed to avoid the curse of dimensionality. Experimental results suggest directions for further fine-tuning of the model, as well as improving the efficiency of the two proposed algorithms.

Value function approximation for dynamic multi-period vehicle routing

In practical applications like parcel or technician services, customers request service during the day. Service providers decide whether to accept a customer for same-day service or to defer a customer due to resource limitations. Some requests are therefore postponed to the following day. To satisfy customer expectations, service providers aim on a high number of same-day services. Still, acceptance decisions not only affect the performance on the current, but also on the following day. Suitable acceptance, postponement, and routing decisions therefore should anticipate future routing and requests in both the current and the next day(s). The resulting decision problem is a dynamic multi-period vehicle routing problem with stochastic service requests. To approximately solve the Markov decision process of the presented problem, we present an anticipatory dynamic policy based on approximate dynamic programming. This policy estimates the potential of problem states with respect to future same-period services within and over the periods. Our policy draws on value function approximation, state space aggregation, and on a classification of the periods. We compare our policy to several policies from the literature. We analyze how and when multi-period anticipation improves the solution quality significantly and how the newly developed state space classification is essential to achieve anticipation. We finally show how multi-period anticipation changes the acceptance behavior to less discrimination of rural customers and to a fairer geographical distribution of same-day services in comparison to single-period anticipation.

Look-Ahead Insertion Policy for a Shared-Taxi System Based on Reinforcement Learning

This paper proposed a reinforcement learning method to improve the level-of-service (LOS) for a shared-taxi system. In practice, shared-taxi operators usually insert a new arrival request into a vehicle routing system that can minimize current total waiting time and detour distance. However, the LOS of a shared-taxi system does not involve only the total waiting time and detour distance but also the quantity of serviced trip volume. If operators emphasize only on the reduction of the total waiting time and detour distance for current requests, the transport capacity of a shared-taxi system can be excessively expended and cannot reflect future requests effectively. This could lead to a high rejection rate for future requests and damage the global LOS. The proposed reinforcement learning method takes into account the uncertainty of future requests and can make a look-ahead decision to help the operator improve the global LOS of a shared-taxi system. We also tested the proposed method on large-scale networks to verify the performance of the method.

Mining Historical Changes to Predict Software Evolution

Software evolution reflects the progress volume of the development process. This increasing volume is based on a sequence of incremental changes and maintenance activities. As volume increases, more resources are needed to control and handle future change requests. Developers and designers need to be able to analyse and predict how the software evolves in advance, in order to better allocate needed resources. Better and correct predictions help in estimating the required resources as cost and maintainers. Prediction can also help in setting the strategies and assumptions to solve the problems that can be encountered when evolving a project from version to the next. This paper presents a prediction model to predict the evolution of software projects. A set of hypotheses are examined to predict the evolution of specific parameters based on machine learning techniques. Data mining approaches are used to test the hypothesis on different versions of two software projects. Experimental results showed that future changes in software systems can be predicted using changed parameters of the previous version.

HBS-CRA: scaling impact of change request towards fault proneness: defining a heuristic and biases scale (HBS) of change request artifacts (CRA)

Accurately calculating the impact of existing change requests is vital for estimating the probability of fault occurrence in future change requests. For a new request like bug fixing, effectiveness in current change requests is required. In a real-time scenario, bug trackers are deployed to save change requests and their associated information. The trackers and associated information are saved in CVS version control systems and these systems assist programmers to carry out multiple analytical functions and generating descriptions. In our earlier works, we devised the set of change request artifacts and also proposed novel statistical bipartite weighted graphical models to evaluate DFP degree of future change requests. With the motivation gained from this model, here we propose a novel strategy that estimates the DFP of the request by assessing the impact of a change request artifact towards fault-proneness that considers the correlation between code blocks as another factor, which is in addition to our earlier strategy. A novel heuristic and biases scale to evaluate the effectiveness of change request for DFP is devised here in this paper that titled as “Defining a Heuristic and Biases Scale (HBS) of Change Request Artifacts (CRA)”, in short HBS-CRA. The devised model makes use of information retrieval methods to identify the change request artifacts of the request. In addition, it also checks for DFP scope through HBS-CRA. The HBS-CRA is empirically assessed by applying on concurrent versioning and Change request logs of the production level maintenance project.

A model-based approximation of opportunity cost for dynamic pricing in attended home delivery

For online retailers with attended home delivery business models, the decisive factor for promising dynamic time slot pricing decisions is the quality of the opportunity cost approximation concerning incoming customer requests. For this purpose, we present a novel approximation approach based on mixed-integer linear programming that we integrate into the de facto standard dynamic pricing framework prevalent in the academic literature. Our approximation combines the most current information regarding the customers accepted to date with a forecast of expected customers to come that is adapted during the progress of the booking horizon. Thus, future customer requests demand management, i.e. the consequences of future pricing decisions, is anticipated. We approximate the retailer’s vehicle routes and thus delivery costs of expected customers by a dynamic seed-based scheme in which potential seeds’ locations as well as related distance approximations are dynamically adjusted under consideration of the locations of already accepted customers. In a computational study, we compare the approach to established pricing approaches in practice and to the state-of-the-art dynamic pricing policy. We show that our approach constantly yields the highest profit, specifically given a tight capacity level. We further provide implications for practical use. We show that, even for large-scale implementations in a real-time environment, our approach is applicable by using parallel computing and by only periodically recalculating opportunity cost. Even then, our approach leads to very good results.

Joint Stochastic Geometry and Mean Field Game Optimization for Energy-Efficient Proactive Scheduling in Ultra Dense Networks

In this paper, we investigate the energy efficiency performance of optimal proactive scheduling strategies in the context of ultra-dense networks. The network consists of a superposition of homogeneous Poisson point process, whereas the users requests follow a space-time homogeneous point process. The objective is to define the optimal transmission powers at any time, that allows to completely serve every user request, while minimizing the total consumed energy. We also assume the system has predictive knowledge about the future transmission contexts. The problem is cast as a dynamic stochastic game which is hard to solve in ultra-dense networks, due to a complex coupling in the interference term, the large number of elements interacting, as well as uncertainties on the channel dynamics, interference, and future requests. Our contribution first lies in addressing the inherent complexity issue of the optimization, by transitioning into an equivalent and more tractable mean field game. Second, we propose to combine this mathematical framework with elements of stochastic geometry. The numerical simulations provide good insights on notable performance gains in terms of energy efficiency, compared to reference scheduling strategies. Additional simulations harnessing the impact of future knowledge uncertainty on the performance of the proposed strategies are also provided.

Does fragmentation avoidance improve the performance of dynamic spectrum allocation in elastic optical networks?

Most spectrum allocation algorithms in elastic optical networks apply a greedy approach: A new connection is allocated as long as there are enough spectrum slots to accommodate it. Recently, a different approach was proposed. Named Deadlock–Avoidance (DA), it only establishes a new connection if the portion of spectrum left after allocating it is zero (full-link utilization) or is big enough to accommodate future requests. Otherwise, the connection request is blocked as a way to avoid fragmentation. The performance of DA has been evaluated in a single-link scenario, where its performance is not affected by the slot continuity constraint. In this paper, we evaluate for the first time the blocking performance and fragmentation level of DA in a fully dynamic network scenario with different bitrates and number of slots for a single link, a 4-node bus and a mesh topology. The performance was evaluated by simulation, and a lower bound was also derived using a continuous Markov chain model. Results are obtained for DA and three greedy algorithms: First Fit, Exact Fit and First–Last Fit. Results show that DA significantly decreases fragmentation, and thus, it exhibits a much lower blocking due to fragmentation than the greedy algorithms. However, this decrease is compensated by a new type of blocking due to the selective acceptance of connections. As a result, the extra computational complexity of DA does not compensate a gain in performance.

Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN

Since the content delivery unit over Information-Centric Networking (ICN) has shifted from files to the segments of a file named chunks, solely either file-level or chunk-level request probability is insufficient for ICN cache management. In this paper, a Request Expectation Index (RXI) based cache replacement algorithm for streaming content delivery is proposed. In this algorithm, RXI is introduced to serve as a fine-grained and unified estimation criteria of possible future request probability for cached chunks. RXI is customized for streaming content delivery by adopting both file-level and chunk-level request probability and considering the dynamically varied request status at each route as well. Compared to prior work, the proposed algorithm evicts the chunk with the minimum expectation of future request to maintain a high cache utilization. Additionally, simulation results demonstrate that the RXI-based algorithm can remarkably enhance the streaming content delivery performance and can be deployed in complex network scenarios. The proposed results validate that, by taking fine-grained request probability and request status into consideration, the customized in-network caching algorithm can improve the ICN streaming content delivery performance by high cache utilization, fast content delivery, and lower network traffic.

Use of SNOMED CT® and LOINC® to standardize terminology for primary care asthma electronic health records

ABSTRACTObjectives: The burden of asthma ranks among the highest for chronic diseases. Interoperable electronic health records (EHRs) can improve the management of chronic diseases such as asthma by facilitating sharing of data between health care settings along the continuum of care. Terminology such as SNOMED CT® (Systematized Nomenclature of Medicine-Clinical Terms) and LOINC® (Logistical Observation Identifier Names and Codes) are prerequisites for interoperability of EHRs. We sought to determine the extent to which data elements in a validated asthma care map (ACM) are congruent with these terminologies. Methods: A certified asthma educator entered all 169 elements in the ACM into the SNOMED CT® browser. Matched elements were assigned a concept name, an identification number, and classified into a hierarchy. LOINC® terminology was reviewed for asthma-related pulmonary function tests (PFTs). Results: Forty-two percent of the ACM elements were complete matches to existing SNOMED CT® concepts, 24% partial matches, and 34% unmatched. Specific asthma control parameters were either complete (n = 3) or partial (n = 4) matches, but overall “asthma control” was unmatched. There were 92% complete or partial matches for PFT elements to SNOMED CT® and 83% to LOINC®. Conclusions: The majority of ACM elements are congruent with standardized terminology, enabling EHR interoperability. Future requests for new concepts in SNOMED CT® and LOINC® should be pursued for asthma control parameters paramount to evidence-based practice.

Flow cytometry vs optical microscopy in the evaluation of the genotoxic potential of xenobiotic compounds.

It is now recognized that mutational events play a key role in the development of pathological processes like cancer, cardiovascular, and neurodegenerative disease. Therefore, it is crucial to have Genetics Toxicology tests that allow rapid and accurate identification of the mutagenic potential of a xenobiotic. Currently the most widely used technique is the "In vitro mammalian cell micronucleus test" performed by optical microscopy, but some problems have been highlighted, including the number of cells analyzed, the high subjectivity of the reading at the microscope and the long analysis times. The aim of this work was to develop a study protocol, for the automation of the "In vitro mammalian cell micronucleus test", by flow cytometry (FCM) analysis, to overcome the limits that afflict the optical microscopy. The study was conducted on peripheral blood lymphocytes treated with three known clastogens and three known aneugens. The results obtained by the proposed FCM technique compared with those obtained through the validated method, demonstrated that the increase of micronuclei percentage is perfectly comparable between the two methods. This fact, in view of results supported by a high number of cells analyzed and obtained by an accurate and objective reading, with a considerable reduction of the analysis time, can support a future request for validation of the micronucleus analysis by FCM. © 2017 International Clinical Cytometry Society.

Performance linked dynamic cache tuning: A static energy reduction approach in tiled CMPs

Advancement in semiconductor technology increases power density in recent Chip Multi-Processors (CMPs) which significantly increases the leakage energy consumptions of on-chip Last Level Caches (LLCs). Performance linked dynamic tuning in LLC size is a promising option for reducing the cache leakage.This paper reduces static power consumption by dynamically shutting down or turning on cache banks based upon system performance and cache bank usage statistics. Shutting down of a cache bank remaps its future requests to another active bank, called as target bank. The proposed method is evaluated on three different implementation policies, viz (1) The system can decide to shutdown or turn-on some cache banks periodically throughout the process execution. (2) The system allows to shutdown banks initially and once the bank restarting initiates, no more shutdown is permitted further. (3) This policy resizes cache like first policy with some predefined time slices, in which cache cannot be resized.For a 4MB 4 way set associative L2 cache, experimental analysis shows 66% reduction in static energy with 29% gain in Energy Delay Product (EDP) for first strategy; for the second policy, static power is reduced by 59% with 27% savings in EDP. Finally, last policy saves 65% in static power and 30% in EDP with minimal performance penalty.

Improved analysis of the online set cover problem with advice

We study the advice complexity of an online version of the set cover problem. The goal is to quantify the information that online algorithms for this problem need to be supplied with to compute high-quality solutions and to overcome the drawback of not knowing future requests. This concept was successfully applied to many prominent online problems in the past while trying to capture the essence of “what makes an online problem hard.” The online set cover problem was introduced by Alon et al. (2009) [2]: for a ground set of size n and a set family of m subsets of the ground set, we obtain bounds in both n and m. We show that a linear number (with respect to both n and m) of advice bits is both sufficient and necessary to perform optimally. Furthermore, we prove that O((nlog⁡c)/c) bits are sufficient to design a c-competitive online algorithm, and this bound is tight up to a factor of O(log⁡c). We further give upper and lower bounds for achieving c-competitiveness with respect to m. Finally, we analyze the advice complexity of the problem with respect to some natural parameters, i.e., measurable properties of the inputs.

The Same-Day Delivery Problem for Online Purchases

Same-day delivery for online purchases is a recent trend in online retail. We introduce a multi-vehicle dynamic pickup and delivery problem with time constraints that incorporates key features associated with same-day delivery logistics. To make better informed decisions, our solution approach incorporates information about future requests into routing decisions. We also introduce an analytical result that identifies when it is beneficial for vehicles to wait at the depot. We present a wide range of computational experiments that demonstrate the value of our approach. The results show that more requests can be filled when time windows are evenly spread throughout the day compared to when many requests' time windows occur late in the day. However, the anticipation of future requests is most valuable when many requests' time windows occur late in the day. As a result of increased flexibility, experiments also demonstrate that the value of anticipating the future decreases when the number of vehicles or the arrival rate of requests increases. The online appendix is available at https://doi.org/10.1287/trsc.2016.0732 .

ProteinsPlus: a web portal for structure analysis of macromolecules.

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre-processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein–protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus.

A co-evolutionary approach using information about future requests for dynamic vehicle routing problem with soft time windows

This paper presents a technique for integrating information about future customer requests to improve decision making for dynamic vehicle routing. We use a co-evolutionary approach to generate better waiting strategies such that the expected number of late-request customers who are served is maximized. An empirical evaluation of the proposed approach is performed within a previously reported hybrid genetic algorithm for the dynamic vehicle routing problem with time windows. Comparisons with other heuristic methods demonstrate the potential improvement that can be obtained through the application of the proposed approach.

Online Algorithms with Advice

In online scenarios requests arrive over time, and each request must be serviced in an irrevocable manner before the next request arrives. Online algorithms with advice is an area of research where one attempts to measure how much knowledge of future requests is necessary to achieve a given performance level, as defined by the competitive ratio. When this knowledge, the advice, is obtainable, this leads to practical algorithms, called semi-online algorithms. On the other hand, each negative result gives robust results about the limitations of a broad range of semi-online algorithms. This survey explains the models for online algorithms with advice, motivates the study in general, presents some examples of the work that has been carried out, and includes an extensive set of references, organized by problem studied.