Server Algorithm Research Articles

Time-Allocation Adaptive Data Rate: An Innovative Time-Managed Algorithm for Enhanced Long-Range Wide-Area Network Performance

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput.

Virtual Network Function Placement for Serving Weighted Services in NFV-Enabled Networks

Network function virtualization (NFV) is a novel network architecture yielding many optimization choices to Internet service providers due to its flexibility. Based on this technique, a network service is constructed from a chain of virtual network functions (VNFs). Although many studies deeply investigate the NFV and propose approaches to address the problem of deploying services in physical networks under different perspectives, they do not consider the importance of services. This issue becomes more critical once the number of network services ever-increasing while the network resources are limited. Here, each service is assigned a weight representing its importance. From this aspect, maximizing the total weight of services accepted to deploy over the physical network is a critical problem and poses many challenges, particularly for networks with limited resources. Therefore, we call this issue the VNF placement for weighted services (VPWS) problem. We then propose three algorithms termed first-fit server algorithm (FSA), best-fit server algorithm (BSA), and service segment-based algorithm (SSBA), respectively, to address the VPWS problem. Eventually, we perform simulations to evaluate the proposed algorithms and factors that affect the VNF placement. The experiment indicates that the total weight yielded by the SSBA outperforms those of the FSA and BSA.

Cloud Key-Value Storage

The paper represents all the stages of designing, architecting, and developing cloud-based key-value storage. This work aims to bring new approaches to distributed data systems. The authors focus on the security and productivity of the project as well as security and maintainability. The authors have studied the use of hash tables in a multi-threaded environment. Architectural approaches and tools have been described. The general structure of the key-value storage server has been presented. The server algorithm has been presented. Our research delves into the intricate nuances of utilizing hash tables in a multi-threaded environment, shedding light on the intricacies and challenges of managing concurrent access to key-value data structures. The authors have explored the trade-offs between lock-free designs and traditional locking approaches.

Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System.

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.

Towards Real-Time Video Caching at Edge Servers: A Cost-Aware Deep Q-Learning Solution

Given the rapid growth of user-generated videos, internet traffic has been heavily dominated by online video streaming. Caching videos on edge servers in close proximity to users has been an effective approach to reduce the backbone traffic and the request response time, as well as to improve the video quality on the user side. Video popularity, however, can be highly dynamic over time. The cost of cache replacement at edge servers, particularly that related to service interruption during replacement, is not yet well understood. This paper presents a novel lightweight video caching algorithm for edge servers, seeking to optimize the hit rate with real-time decisions and minimized cost. Inspired by recent advances in deep Q-learning, our DQN-based online video caching (DQN-OVC) makes effective use of the rich and readily available information from users and networks. We decompose the Q-value function as a product of the video value function and the action function, which significantly reduces the state space. We instantiate the action function for cost-aware caching decisions with low complexity so that the cached videos can be updated continuously and instantly with dynamic video popularity. We used video traces from Tencent, one of the largest online video providers in China, to evaluate the performance of our DQN-OVC and to compare it with state-of-the-art solutions. The results demonstrate that DQN-OVC significantly outperforms the baseline algorithms in the edge caching context.

Communication-, Computation-, and Control-Enabled UAV Mobile Communication Networks

Unmanned-aerial-vehicles (UAVs)-enabled mobile-edge computing (MEC) networks have shown a huge advantage in providing on-demand communication and computation service for the ground users. To reap the benefit of these integrated networks, reducing their energy consumption becomes a key issue, since both UAVs and ground users are energy-limited devices. To address this problem, this article attempts to provide a novel method that optimizes communication, computation, and control (3C), i.e., user association, computational task offloading, and UAVs flight control, to reduce the communication and computation energy consumption. Specifically, in order to find out the appropriate deployment positions for UAV MEC servers to provide on-demand communication and computation service, we propose the concept of the virtual force field (VFF) based on the user statistical distribution model and then devise a coordinated flight control algorithm for UAV MEC servers. After that, the user association and computational task offloading are optimized alternately. Utilizing the optimal transport theory (OTT), we derive the boundary formulation of the optimal user association and develop an iterative algorithm to approach the optimal association boundary. Then, given the user association, the optimal computational task offloading scheme is investigated. The convergence of the proposed iterative algorithm and the alternating optimization algorithm is proved. The complexity of the 3C optimization method is also analyzed. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithm. Comparisons with the benchmark scheme show that the proposed scheme can reduce about 88% energy consumption and also improve energy efficiency performance greatly under the same simulation setups.

LBSMT: Load Balancing Switch Migration Algorithm for Cooperative Communication Intelligent Transportation Systems

We entered an era when the automotive industry is undergoing a digital revolution. Automobiles evolving into automated movable objects are using artificial intelligence capabilities. In contrast, cellular communications networks incorporate emerging technologies, such as SDN (software-defined networking) and NFV (network functions virtualization). Sophisticated software-defined communications networks virtualizes network functions and paving the way for the new design, monitoring, and management strategies. SDN is rising towards the application of load balancing for real time applications due to the heavy load of data on servers. When there is intra-communication between the various switches and domains; migration of switches takes place and the load over servers is imbalanced. An imbalance of the load will increase the response time and decrease the throughput. In intelligent transportation systems (ITS) balance on the servers should be maintained for the network sustainability. To provide a solution for the requirement of ITS, a dynamic QoS-aware load balancing switch migration algorithm (LBSMT) is proposed in this paper. As per the results validated in Python, after the migration LBSWT has improved CPU utilization, memory utilization, throughput and response time over server load, round robin, weighted round robin, LBBSRT and dynamic server algorithms.

Self-Stabilizing and Private Distributed Shared Atomic Memory in Seldomly Fair Message Passing Networks

We study the problem of privately emulating shared memory in message-passing networks. The system includes clients that store and retrieve replicated information on N servers, out of which e are data-corrupting malicious. When a client accesses a data-corrupting malicious server, the data field of that server response might be different from the value it originally stored. However, all other control variables in the server reply and protocol actions are according to the server algorithm. For the coded atomic storage algorithms by Cadambe et al., we present an enhancement that ensures no information leakage and data-corrupting malicious fault-tolerance. We also consider recovery after the occurrence of transient faults that violate the assumptions according to which the system was designed to operate. After their last occurrence, transient faults leave the system in an arbitrary state (while the program code stays intact). We present a self-stabilizing algorithm, which recovers after the occurrence of transient faults. This addition to Cadambe et al. considers asynchronous settings as long as no transient faults occur. The recovery from transient faults that bring the system counters (close) to their maximal values may include the use of a global reset procedure, which requires the system run to be controlled by a fair scheduler. After the recovery period, the safety properties are provided for asynchronous system runs that are not necessarily controlled by fair schedulers. Since the recovery period is bounded and the occurrence of transient faults is extremely rare, we call this design criteria self-stabilization in the presence of seldom fairness. Our self-stabilizing algorithm uses a bounded amount of storage during asynchronous executions (that are not necessarily controlled by fair schedulers). To the best of our knowledge, we are the first to address privacy, data-corrupting malicious behavior, and self-stabilization in the context of emulating atomic shared memory in message-passing systems.

Phylogenetic and Mutation Analysis of the Venezuelan Equine Encephalitis Virus Sequence Isolated in Costa Rica from a Mare with Encephalitis.

Venezuelan Equine Encephalitis virus (VEEV) is an arboviral pathogen in tropical America that causes lethal encephalitis in horses and humans. VEEV is classified into six subtypes (I to VI). Subtype I viruses are divided into epizootic (IAB and IC) and endemic strains (ID and IE) that can produce outbreaks or sporadic diseases, respectively. The objective of this study was to reconstruct the phylogeny and the molecular clock of sequences of VEEV subtype I complex and identify mutations within sequences belonging to epizootic or enzootic subtypes focusing on a sequence isolated from a mare in Costa Rica. Bayesian phylogeny of the VEEV subtype I complex tree with 110 VEEV complete genomes was analyzed. Evidence of positive selection was evaluated with Datamonkey server algorithms. The putative effects of mutations on the 3D protein structure in the Costa Rica sequence were evaluated. The phylogenetic analysis showed that Subtype IE-VEEV diverged earlier than other subtypes, Costa Rican VEEV-IE ancestors came from Nicaragua in 1963 and Guatemala in 1907. Among the observed non-synonymous mutations, only 17 amino acids changed lateral chain groups. Fourteen mutations located in the NSP3, E1, and E2 genes are unique in this sequence, highlighting the importance of E1-E2 genes in VEEV evolution.

A mobile-assisted voice condition analysis system for Parkinson\u2019s disease: assessment of usability conditions

Background and objectiveAutomatic voice condition analysis systems to detect Parkinson’s disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim of this research is to investigate the impact of uncontrolled conditions (realistic acoustic environment and lack of supervision) on the performance of automatic PD detection systems based on speech.MethodsA mobile-assisted voice condition analysis system is proposed to aid in the detection of PD using speech. The system is based on a server–client architecture. In the server, feature extraction and machine learning algorithms are designed and implemented to discriminate subjects with PD from healthy ones. The Android app allows patients to submit phonations and physicians to check the complete record of every patient. Six different machine learning classifiers are applied to compare their performance on two different speech databases. One of them is an in-house database (UEX database), collected under professional supervision by using the same Android-based smartphone in the same room, whereas the other one is an age, sex and health-status balanced subset of mPower study for PD, which provides real-world data. By applying identical methodology, single-database experiments have been performed on each database, and also cross-database tests. Cross-validation has been applied to assess generalization performance and hypothesis tests have been used to report statistically significant differences.ResultsIn the single-database experiments, a best accuracy rate of 0.92 (AUC = 0.98) has been obtained on UEX database, while a considerably lower best accuracy rate of 0.71 (AUC = 0.76) has been achieved using the mPower-based database. The cross-database tests provided very degraded accuracy metrics.ConclusionThe results clearly show the potential of the proposed system as an aid for general practitioners to conduct triage or an additional tool for neurologists to perform diagnosis. However, due to the performance degradation observed using data from mPower study, semi-controlled conditions are encouraged, i.e., voices recorded at home by the patients themselves following a strict recording protocol and control of the information about patients by the medical doctor at charge.

A PROSS-designed extensively mutated estrogen receptor \u03b1 variant displays enhanced thermal stability while retaining native allosteric regulation and structure

Protein stability limitations often hamper the exploration of proteins as drug targets. Here, we show that the application of PROSS server algorithms to the ligand-binding domain of human estrogen receptor alpha (hERα) enabled the development of variant ERPRS* that comprises 24 amino acid substitutions and exhibits multiple improved characteristics. The protein displays enhanced production rates in E. coli, crystallizes readily and its thermal stability is increased significantly by 23 °C. hERα is a nuclear receptor (NR) family member. In NRs, protein function is allosterically regulated by its interplay with small molecule effectors and the interaction with coregulatory proteins. The in-depth characterization of ERPRS* shows that these cooperative effects are fully preserved despite that 10% of all residues were substituted. Crystal structures reveal several salient features, i.e. the introduction of a tyrosine corner in a helix-loop-helix segment and the formation of a novel surface salt bridge network possibly explaining the enhanced thermal stability. ERPRS* shows that prior successes in computational approaches for stabilizing proteins can be extended to proteins with complex allosteric regulatory behaviors as present in NRs. Since NRs including hERα are implicated in multiple diseases, our ERPRS* variant shows significant promise for facilitating the development of novel hERα modulators.

An adaptive, utilization-based approach to schedule real-time tasks for ARM big.LITTLE architectures

ARM big.LITTLE architectures are spreading more and more in the mobile world thanks to their power-saving capabilities due to the use of two ISA-compatible islands, one focusing on energy efficiency and the other one on computational power. This architecture makes the problem of energy-aware task scheduling particularly challenging, due to the number of variables to take into account and the need for having lightweight mechanisms that can be readily computed in an operating system kernel scheduler. This paper presents a novel task scheduler for big.LITTLE platforms, combining the well-known Constant Bandwidth Server algorithm with a power-aware per-job migration policy. This achieves real-time adaptation of the CPU islands' frequencies based on the individual cores' overall utilization, as available in the scheduler thanks to the use of the resource reservation paradigm. Preliminary results obtained by simulations based on modifications to the open-source RTSim tool show that the proposed technique is able to achieve interesting performance/energy trade-offs.

Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

The dockless bike sharing system (DBSS) has been globally adopted as a sustainable transportation system. Due to the robustness and tractability of the closed queuing network (CQN), it is a well-behaved method to model DBSSs. In this paper, we view DBSSs as CQNs and use the mean value analysis (MVA) algorithm to calculate a small size DBSS and the flow equivalent server (FES) algorithm to calculate the larger size DBSS. This is the first time that the FES algorithm is used to study the DBSS, by which the CQN can be divided into different subnetworks. A parking region and its downlink roads are viewed as a subnetwork, so the computation of CQN is reduced greatly. Based on the computation results of the two algorithms, we propose two optimization functions for determining the optimal fleet size and repositioning flow, respectively. At last, we provide numerical experiments to verify the two algorithms and illustrate the optimal fleet size and repositioning flow. This computation framework can also be used to analyze other on-demand transportation networks.

<b>Earthquake Early Warning Using Ocean Bottom Seismic Data for Railways</b>

This article explains processes for improving the railway earthquake early warning system. These processes were introduced to use data transmitted in real time from recently developed ocean bottom seismic networks. Three mechanisms were designed to be able to exploit this data: 1) an algorithm for servers in OBS system base stations; 2) a procedure to allow transmissions between the servers and railway company receivers; and 3) a system built into the receiver to determine whether running trains need to be stopped or not. Confirmation was obtained that the proposed processes were able to reduce the risks to railways from earthquakes, because they are capable of extending the lead time before the arrival of strong seismic motion.

An IoT Based Novel Approach for Prevention of Road Accidents due to Extreme Weather Conditions using L-BOT

As more and more highways are being developed by the government, cities are getting connected and more people are commuting through highways; due to which there are fair chances of increase in the number of accidents on such highways, especially in extreme weather conditions (fog and heavy rain). From the statistics of the Ministry of Road Transport and Highways, India, it can be said that almost 50 people die, and 200 people get injured everyday due to accidents occurring due to fog or heavy rain. There is an urgent need of time to have cost-effective and accurate solutions which avoid/reduce the accidents caused due to fog or heavy rain. Ensuring road safety is utmost important and our solution for preventing road accidents occurring on the highways due to extreme weather conditions will lead the world towards smart and safe transport. This paper discusses various possible approaches to address the said issue with their pros and cons. In this paper, we are proposing a robust, cost-effective and energy-efficient framework for prevention of road accidents on highways due to extreme weather conditions. The proposed framework includes the design of the L-BOT device to get the location of the vehicle and communicate with the server. Getting the location of nearby vehicles using GPS and displaying them on the screen in real time manner can solve the problem to a greater extent with less cost and more accuracy. The proposed framework will be implemented and tested in a real-world environment to measure the performance parameters like delay, location accuracy, power consumption and server algorithm efficiency. KEYWORDS : Network protocols, wireless networks, connected vehicles, location sharing, nearby vehicles, L-Bot (Location Bot), cyberphysical system, GPS (Global Positioning System) Cite this Article Tushar Champaneria, Maitrik Shah, Sunil Jardosh. An IoT Based Novel Approach for Prevention of Road Accidents Due to Extreme Weather Conditions Using L-BOT. Journal of Web Engineering & Technology . 2019; 6(1): 15–24p.

Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks.

The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network.

A Migration Aware Scheduling Technique for Real-Time Aperiodic Tasks Over Multiprocessor Systems

Multi-processor systems consist of more than one processor and are mostly used for computationally intensive applications. Real-time systems are those systems that require completing execution of tasks within a pre-defined deadline. Traditionally, multiprocessor systems are given attention in periodic models, where tasks are executed at regular intervals of time. Gradually, as maturity in a multiprocessor design had increased; their usage has become very common for real-time systems to execute both periodic and aperiodic tasks. As the priority of an aperiodic task is usually but not essentially greater than the priority of a periodic task, they must be completed within the deadline. There is a lot of research works on multiprocessor systems with scheduling of periodic tasks, but the task scheduling is relatively remained unexplored for a mixed workload of both periodic and aperiodic tasks. Moreover, higher energy consumption is another main issue in multiprocessor systems. Although it could be reduced by using the energy-aware scheduling technique, the response time of aperiodic tasks still increases. In the literature, various techniques were suggested to decrease the energy consumption of these systems. However, the study on reducing the response time of aperiodic tasks is limited. In this paper, we propose a scheduling technique that: 1) executes aperiodic tasks at full speed and migrates periodic tasks to other processors if their deadline is earlier than aperiodic tasks-reduces the response time and 2) executes aperiodic tasks with lower speed by identifying appropriate processor speed without affecting the response time-reduces energy consumption. Through simulations, we demonstrate the efficiency of the proposed algorithm and we show that our algorithm also outperforms the well-known total bandwidth server algorithm.

Distributed Cache with Utilizing Squid Proxy Server and LRU Algorithm

<p>In relation to the dissemination of information, the Internet is one of the fastest media to do so. The internet’s presence is growing very swiftly and rapidly, so it has become recognized by people from all walks of life. For that, the people need the appropriate way to maintain effectiveness in the use of the Internet. The following paper describes a study of the distribution of the cache, which is performed by the squid proxy server by creating a storage network design on Linux. Cache documents that are stored in the proxy server will be distributed to another over a network storage server. The process of caching on the proxy server is using the Least Recently Used (LRU) Algorithm. This research was carried out by developing the existing method of caching server process, then it is to be added a unit as a backup storage device for the data that must be erased because of the replacement policy applied to the squid proxy server. This study is looking at how the hit ratio and byte hit ratio after adding the storage server compared to not having a storage server. At the end of this research, it is concluded that the distributed cache processes a hit ratio and byte hit ratio higher than the cache on the current proxy server.</p>

Overview of IO Queuing Algorithm in Distributed Memory

The distributed shared memory system provides a development road for establishment of a large-scale easy-to-use parallel computer, and the improvement of its corresponding algorithm can optimize the distributed shared memory. This paper mainly summarizes the spatial data memory system and parallel processing methods, thus providing the specific theoretical guidance for the parallel spatial data memory and processing. This paper also analyzes and improves the basic distributed shared IO algorithm of the central server algorithm, migration algorithm, read-replication algorithm and full-replication algorithm, and then focuses on discussion of the consistency model of the distributed shared memory, and analyzes the direction of using different consistency models, and points out the shortcomings of traditional methods to store and process massive spatial data, thus providing a theoretical direction for the future improvement of the memory.

BetaCavityWeb: a webserver for molecular voids and channels.

Molecular cavities, which include voids and channels, are critical for molecular function. We present a webserver, BetaCavityWeb, which computes these cavities for a given molecular structure and a given spherical probe, and reports their geometrical properties: volume, boundary area, buried area, etc. The server's algorithms are based on the Voronoi diagram of atoms and its derivative construct: the beta-complex. The correctness of the computed result and computational efficiency are both mathematically guaranteed. BetaCavityWeb is freely accessible at the Voronoi Diagram Research Center (VDRC) (http://voronoi.hanyang.ac.kr/betacavityweb).