Dynamic Placement Research Articles

Genome-wide localization of histone variants in Toxoplasma gondii implicates variant exchange in stage-specific gene expression

BackgroundToxoplasma gondii is a protozoan parasite that differentiates from acute tachyzoite stages to latent bradyzoite forms in response to environmental cues that modify the epigenome. We studied the distribution of the histone variants CenH3, H3.3, H2A.X, H2A.Z and H2B.Z, by genome-wide chromatin immunoprecipitation to understand the role of variant histones in developmental transitions of T. gondii parasites.ResultsH3.3 and H2A.X were detected in telomere and telomere associated sequences, whereas H3.3, H2A.X and CenH3 were enriched in centromeres. Histones H2A.Z and H2B.Z colocalize with the transcriptional activation mark H3K4me3 in promoter regions surrounding the nucleosome-free region upstream of the transcription start site. The H2B.Z/H2A.Z histone pair also localizes to the gene bodies of genes that are silent but poised for activation, including bradyzoite stage-specific genes. The majority of H2A.X and H2A.Z/H2B.Z loci do not overlap, consistent with variant histones demarcating specific functional regions of chromatin. The extent of enrichment of H2A.Z/H2B.Z (and H3.3 and H2A.X) within the entire gene (5’UTR and gene body) reflects the timing of gene expression during the cell cycle, suggesting that dynamic turnover of H2B.Z/H2A.Z occurs during the tachyzoite cell cycle. Thus, the distribution of the variant histone H2A.Z/H2B.Z dimer defines active and developmentally silenced regions of the T. gondii epigenome including genes that are poised for expression.ConclusionsHistone variants mark functional regions of parasite genomes with the dynamic placement of the H2A.Z/H2B.Z dimer implicated as an evolutionarily conserved regulator of parasite and eukaryotic differentiation.

Dynamic placement of multiple controllers based on SDN and allocation of computational resources based on heuristic ant colony algorithm

With the rapid development of the Internet and the explosive growth of network applications, traditional computer networks have ushered in unprecedented challenges and opportunities. To solve the communication delay between controllers and switches and the communication problem between controllers due to link failure in the network, this paper considers the delay between controllers, the delay problem between controllers and switches, and the reliability problem. It proposes a dynamic controller placement method based on delay and load optimization. A multi-objective optimization model based on link failure is constructed, and the multi-objective optimization problem with constraints is solved by improving the controller prevention algorithm with spectral clustering. Meanwhile, this paper proposes a resource allocation method based on task delay and reliability constraints to solve the problem of considerable task completion delay and wasted computational resources due to uneven resource allocation of edge servers. A model based on task latency and dynamic constraints is constructed, and a heuristic ant colony algorithm solves an adaptive allocation scheme for computing resources. The experimental results show that the proposed resource allocation algorithm weighs the delay between controllers, the communication delay between controllers and switches, and the reliability and gives a reasonable controller placement scheme and controller locations. The proposed resource allocation algorithm can optimize computing resources and reduce task completion delay.

Reliability Assurance Dynamic SSC Placement Using Reinforcement Learning

Software-defined networking (SDN) and network function virtualization (NFV) make a network programmable, resulting in a more flexible and agile network. An important and promising application for these two technologies is network security, where they can dynamically chain virtual security functions (VSFs), such as firewalls, intrusion detection systems, and intrusion prevention systems, and thus inspect, monitor, or filter traffic flows in cloud data center networks. In view of the strict delay constraints of security services and the high failure probability of VSFs, we propose the use of a security service chain (SSC) orchestration algorithm that is latency aware with reliability assurance (LARA). This algorithm includes an SSC orchestration module and VSF backup module. We first use a reinforcement learning (RL) based Q-learning algorithm to achieve efficient SSC orchestration and try to reduce the end-to-end delay of services. Then, we measure the importance of the physical nodes carrying the VSF instance and backup VSF according to the node importance of VSF. Extensive simulation results indicate that the LARA algorithm is more effective in reducing delay and ensuring reliability compared with other algorithms.

Dynamic Care Unit Placements Under Unknown Demand with Learning

Dynamic Care Unit Placements Under Unknown Demand with Learning

Gaze-driven placement of items for proactive visual exploration

Recent advances in digital signage technology have improved the ability to visually select specific items within a group. Although this is due to the ability to dynamically update the display of items, the corresponding layout schemes remain a subject of research. This paper explores the sophisticated layout of items by respecting the underlying context of searching for favorite items. Our study begins by formulating the static placement of items as an optimization problem that incorporates aesthetic layout criteria as constraints. This is further extended to accommodate the dynamic placement of items for more proactive visual exploration based on the ongoing search context. Our animated layout is driven by analyzing the distribution of eye gaze through an eye-tracking device, by which we infer how the most attractive items lead to the finally wanted ones. We create a planar layout of items as a context map to establish association rules to dynamically replace existing items with new ones. For this purpose, we extract the set of important topics from a set of annotated texts associated with the items using matrix factorization. We also conduct user studies to evaluate the validity of the design criteria incorporated into both static and dynamic placement of items. After discussing the pros and cons of the proposed approach and possible themes for future research, we conclude this paper.

Adaptive Placement and Routing for Service Function Chains With Service Deadlines

Network Function Virtualization (NFV) pushes the hardware-based network functions to generic servers as software and brings a highly flexible for deployment. The availability of Virtual Machines (VMs) enables the dynamic placement of Virtual Network Functions (VNFs) on demand, and it can reduce a large number of manual configuration processes that increase deployment efficiency. However, some services require more than one VNF to process. Therefore, the network flows need to traverse a set of sequential network functions called Service Function Chain (SFC). How to efficiently route traffic along service function chain and place VNFs in a network under operational constraints is a crucial issue. In this paper, we must overcome two challenges: (1) determining a flow path that traverses suitable network functions in the required order to meet the requirement of services, and (2) considering network loading and other dynamic characteristics when traffic is routed through existing VNFs. Thus, we present methods to solve the routing and placement problems for the service function chain. Our solutions transform the network representation to a virtual layered graph that considers NFV processing latency and allows conventional shortest path algorithms to solve the problem. We are not only pursuing high success rates to serve more flows but also taking into account the execution time of the algorithms.

Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Nowadays, maximizing profits, decreasing operating cost and scheduling tasks are the most important issues of cloud computing with its growing usage. In this regard, one of the challenges in cloud computing is to provide an efficient method to deploy virtual machines on physical machines with the aim of optimizing energy consumption, fair load distribution and task scheduling. The purpose of present study is to provide a method for improving task scheduling through an improved particle swarm optimization algorithm. In the proposed method of present study, selection of a proper objective function has led to balanced workload of virtual machines, decreased time of all tasks as well as maximum utilization of all resources and increased productivity in addition to dynamic placement of virtual machine on physical machine. The results of simulation showed that the proposed method has provided an optimized solution for scheduling tasks, equal allocation of tasks in virtual machines and placement on the appropriate physical machine and less time with an improvement of 0.02 has been spent on the process of outsourcing virtual machines.

Pattern-Based Dynamic Compilation System for CGRAs With Online Configuration Transformation

Prevailing data-intensive applications, such as artificial intelligence and internet of things, demand considerable compute capability. Coarse-grained reconfigurable architectures (CGRAs) can meet this demand via providing abundant compute resources. However, compilation has become an essential problem because the increasing resources need to be orchestrated efficiently. Static compilation is insufficient due to conservative resource allocation and exponentially increasing time cost while state-of-the-art dynamic compilation still performs poorly in both generality and efficiency. This article proposes a dynamic compilation system for CGRAs through online pattern-based configuration transformation, which enables virtualization to improve resource utilization and flexibility. It utilizes statically-generated patterns to straightforwardly determine dynamic placement of registers and operations so that the transformation algorithm has a low complexity. Domain-specific features are extracted by a k-means clustering algorithm to help improve the quality of patterns. The experimental results show that statically compiled applications can be transformed onto arbitrary resources at runtime, reserving 73.5 (22.8-163.3 percent) of the original performance/resource on average, 9.1 (0-52.9 percent) better than the state-of-theart non-general methods.

RETRACTED ARTICLE: Genetic algorithm-based tabu search for optimal energy-aware allocation of data center resources

Cloud computing delivers practical solutions for long-term image archiving systems. Cloud data centers consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, tabu search is a popular algorithm that shows better exploitation in comparison with ABC. In this study, cloud computing environments are detailed with an allocation protocol for efficient energy and resource management. The technique of energy-aware allocation splits data centers (DCs) resources among client applications end routes to enhance energy efficacy of DCs and also achieves anticipated quality of service (QoS) for everyone. Heuristic protocols are exercised for optimizing the distribution of resources to upgrade the efficiency of DC. In the current paper, energy-aware resources allotment technique is employed and optimized in clouds via a new approach called Tabu Job Master (JM). Tabu JM claims the benefits of some variables and also rapid convergence speeds. Results are duly achieved for energy consumption—the count of virtual machines (VMs) migration and also makespan. The results shown by Tabu JM are benchmarked by using genetic algorithm (GA), artificial bee colony (ABC), ABC with crossover and technique of mutation, the basic tabu search techniques, and Tabu Job Master.

Model decomposition via optical delay estimation using the camera tracking in an active noise control system for creation of a movable quiet zone

This article presents an adaptive control system for active local reduction of broadband noise at moving locations, using the camera tracking for path model decomposition to reduce the filter lengths. To enable a dynamic placement of local quiet zones with respect to a moving target, previous works proposed the combina- tion of the remote microphone technique with a camera-based target tracking. The estimated 3-D coordinates are used at run time for a dynamic update of the trans- fer paths models, which correspond to virtual microphones at predetermined loca- tions. Nevertheless, whereas current applications like headrests feature only short distances to the target, the considered ANC system aims at larger ranges for the moving quiet zone, resulting in long impulse responses and high delays in the transfer paths. To reduce the associated model lengths, this work evaluates a decomposed identification stage that takes advantage of the camera-tracking sys- tem by estimating the acoustic path delays optically. In experiments, this method is shown to be superior to signal processing-based approaches like cross-correlation and thus enables an accurate separation of the delays. Eventually, the valida- tion of the broadband ANC performance at a moving microphone shows that the quiet zone could be tracked over a range of 0.8 m, while using only few transfer path models and saving over 100 coefficients due to the proposed decomposition.

Research on Development and Key Technologies of Intelligent Bed-jig System for Ship Sectional Construction

Nowadays, the existing bed-jig is difficult to meet the requirements of modern ship intelligent manufacturing. This is mainly due to the following three problems: one is the redundancy of location arrangement, the other is the difficulty of moving, and the third is the discontinuity of height adjustment. For the segmental construction of ships, this paper puts forward the research and development of intelligent bed-jig system. Breakthroughs have been made in key technologies, including dynamic placement of jig and dynamic adjustment of support height. It also realizes the intelligent decision-making of process data in ship sectional construction. In general, it provides a technical basis for intelligent manufacturing of ship segments.

Towards Dynamic Controller Placement in Software Defined Vehicular Networks.

The emerging SDVN (Software Defined Vehicular Network) paradigm promises to bring flexibility and efficient resource utilization to vehicular networks, enabling the emergence of novel Intelligent Transportation Services. However, as it was initially designed with wired network in mind, applying the SDN paradigm to a vehicular context faces new challenges related to the peculiar characteristics of this network (high node mobility and node density, and the presence of wireless links). In this paper, we focus on one of the critical architectural elements of SDVN, namely, the SDN Controller Placement, and promote the use of dynamic placement methods that take into account the dynamicity of vehicular networks’ topology. We also describe the different approaches towards a dynamic controller placement and also propose an ILP (Integer Linear Programming) based dynamic placement method that adaptively readjusts the number and placement of controllers according to road traffic fluctuations. The proposed method is evaluated using a realistic traffic trace from Luxembourg City. Simulation results show that our approach outperforms the static approach as proposed in the literature.

Dynamic Placement Optimization for Bio-Inspired Self-Repairing Hardware

Bio-inspired self-repairing hardware is a distributed self-adaptive system, characterized by powerful fault-tolerant ability and environment adaptivity. However, it suffers from some difficulties such as large resource consumption and degraded circuit performances. From the viewpoint of cybernetics and computer science, the cellular differentiation and substitution process of bio-inspired self-repairing hardware can be converted into dynamic placement problems on a reconfigurable system. Current systems can only generate some predefined fault-free placements from a finite number of initial placements. The aim of this paper was to achieve high-quality placements from arbitrary initial placements. Based on P systems, an analysis has been made on the limitations of current systems and an improved computing system has been developed to achieve the ergodic property. Its computational power has been verified by a constructive proof. Moreover, centering on the problem how to improve the placement quality on a distributed platform, the optimization model, task allocation, optimization strategy, and membrane optimization algorithm have been designed and developed. The optimization performances were verified and the calculation amount was exhibited by experiments. Finally, it indicated by comparison that the proposed approach would reduce the resource consumption and maintain good circuit performances.

A Dynamical Evolution Approach to High Placement Performance and Low Fault-Tolerant Cost for Bio-Inspired Self-Repairing Hardware

Bio-inspired self-repairing hardware is a reconfigurable system characterized by high fault-tolerant ability. To further increase the placement performance and reduce the fault-tolerant cost, a dynamical evolution approach to multi-objective dynamic placement is developed. A primary challenge for our study is computational complexity. Based on group theory, the computing task was divided on the dimensions of time and space to increase the utilization of online computing resources. By introducing the multi-step placement transformation, a discrete dynamical system model was built and the multi-objective dynamic placement was converted into a stability problem of constrained systems. In order to satisfy the dynamical requirements, an artificial particle system model was built and its evolution laws were verified by dynamic analysis. By simulating the evolution laws, a dynamical evolution algorithm was proposed, which could avoid large-scale iterative calculation used in the conventional optimization search algorithms. Experiments have shown that the dynamical evolution method could offer high placement performance with low fault-tolerant cost. Besides, it also has the advantage of high design flexibility since there is no initial placement constraint.

A real plug-and-play fog: Implementation of service placement in wireless multimedia networks

Initially as an extension of cloud computing, fog computing has been inspiring new ideas about moving computing tasks to the edge of networks. In fog, we often repeat the procedure of placing services because of the geographical distribution of mobile users. We may not expect a fixed demand and supply relationship between users and service providers since users always prefer nearby service with less time delay and transmission consumption. That is, a plug-and-play service mode is what we need in fog. In this paper, we put forward a dynamic placement strategy for fog service to guarantee the normal service provision and optimize the Quality of Service (QoS). The simulation results show that our strategy can achieve better performance under metrics including energy consumption and end-to-end latency. Moreover, we design a real Plug-and-Play Fog (PnPF) based on Raspberry Pi and OpenWrt to provide fog services for wireless multimedia networks.

Dynamic limit order placement strategies: survival analysis with a multiple-spell duration model

This study investigates the multiple events that occur in the life of each limit order by utilising a survival analysis methodology with a multiple-spell duration model. The estimates suggest that the hazard rates of limit order event transitions are determined by a number of factors and their impacts depend on whether the initial order event is a limit order submission, partial execution or revision. The differences in estimates across initial order events increase as exchange latency reduces in recent years. Using a multiple-spell duration model to examine the full spectrum of events that occur in the life of a limit order is thus shown to be informative and essential in modelling dynamic limit order placement strategies.

Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing.

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.

A novel cost-aware algorithm for dynamic task placement problem in a heterogeneous Internet-scale data center

In recent years, data centers have deployed many rapidly growing Internet-scale services. As the size of the data center continues to expand, it not only causes huge energy consumption, but also brings environmental problems such as huge carbon emissions. However, for an Internet-scale data center, it is a challenge to optimize power consumption while meeting the growing demands of Internet-scale applications. Moreover, current data centers usually have heterogeneous servers (with different power consumption and computing capacity), which causes the dynamic task placement problem more complicated. In this work, we first observe two kinds of heterogeneity from the analysis of a public trace that was collected from a Google data center. Considering the demands in energy consumption and performance, we model the dynamic task placement problem in a heterogeneous Internet-scale data center and propose a heuristic cost-aware algorithm to solve it. By simulating and comparing with the other two scheduling algorithms, our proposed heuristic algorithm can gain a well energy saving and keep application performance within acceptable limits.

A Framework for Constructing a Secure Domain of Sensor Nodes.

Application of the Internet of Things (IoT) in some critical areas (e.g., military) is limited mainly due to the lack of robust, secure, and trusted measures needed to ensure the availability, confidentiality, and integrity of information throughout its lifecycle. Considering the mostly limited resources of IoT devices connected by wireless networks and their dynamic placement in unsupervised or even hostile environments, security is a complex and considerable issue. In this paper, a framework which encompasses an approach to integrate some security measures to build a so-called “secure domain of sensors nodes” is proposed. The framework is based on the use of the Trusted Platform Modules (TPMs) in wireless sensor nodes. It encompasses an architecture of sensor nodes, their roles in the domain, and the data structures as well as the developed procedures which could be applied to generate the credentials for the sensor nodes, and subsequently, to build a local trust structure of each node as well as to build a trust relationship between a domain’s nodes. The proposed solution ensures the authentication of sensor nodes and their resistance against unauthorized impact with the hardware/software configuration allowing protection against malware that can infect the software. The usefulness of the presented framework was confirmed experimentally.

DOZ047.119: Five year experience of a tertiary-level referral center in children with anastomotic strictures after esophageal atresia repair

Abstract Background Esophageal atresia (EA) is the most common congenital anomaly of the esophagus. Anastomotic strictures (AS) frequently occur in patients surgically treated for esophageal atresia (EA). The primary aim of this study is to determine the role of esophageal dilations in the management of AS in childhood after reconstruction of EA. Methods A retrospective chart review of patients treated with esophageal dilation for EA was conducted at our tertiary referral center from 2013 to 2017. We included patients treated at our Institution since diagnosis and patients referred from other Institutions. All dilations were performed with Savary-Gilliard polyvinyl bougies. Dilation was performed on an ‘as needed’ basis. Results Eighty-nine patients (68.5% males, 31.5% female) underwent 433 dilations overall in the study period (median: 3, range: 1–36). Type I of EA was 26.1%, Type II was 9.1%, Type III was 64.8%. Associated malformations in 52.8% cases. Six patients received 33 local applications of mitomycin C (MMC) and two of triamcinolone acetate for recurrent stenosis. Four patients developed a preanastomotic diverticulum-treated endoscopically. Nine patients (10.1%) underwent dynamic esophageal stent placement after a median of six dilations. Antireflux surgery was performed in 28 patients (31.5%), Nissen fundoplication in 20 patients, Toupet in 6, and both in 2. Patients treated with antireflux surgery received a mean of 7.1 dilations before surgery, versus 3.9 in patients without antireflux surgery (P = 0.0285, unpaired t test). Seven patients (7.9%), 5 referral, needed major esophageal surgery (4 esophageal reanastomosis and 3 esophagocolonplasty). Patients underwent major surgery received a mean of 19.3 dilations versus 3.6 dilations in no-surgery patients (P = 0.0002, Mann-Whitney U test). Six of 9 stented patients did not require surgery. Perforation was present in 0.4% of 433 dilatations. Conclusions Esophageal dilatation remains the mainstay of treatment for AS after EA repair. Stricture resection with end-to-end anastomosis is the reconstructive option of choice; esophageal substitution is reserved only in cases of intractable stricture. The use of esophageal dynamic stent, MMC, or steroids could help in avoiding or delaying the need of operative stricture resection. Antireflux surgery is mandatory in case of severe pathological gastroesophageal reflux to prevent intractable strictures from developing.