Response Time Of Requests Research Articles

Improved Scheme for Data Aggregation of Distributed Oracle for Intelligent Internet of Things.

Oracle is a data supply mechanism that provides real-world data for blockchain. It serves as a bridge between blockchain and the IoT world, playing a crucial role in solving problems such as data sharing and device management in the IoT field. The main challenge at this stage is determining how to achieve data privacy protection in distributed Oracle machines to safeguard the value hidden in data on the blockchain. In this paper, we propose an improved scheme for distributed Oracle data aggregation based on Paillier encryption algorithm, which achieves end-to-end data privacy protection from devices to users. To address the issue of dishonest distributed Oracle machines running out of funds, we have designed an algorithm called PICA (Paillier-based InChain Aggregation). Based on the aggregation on the Chainlink chain and the Paillier encryption algorithm, random numbers are introduced to avoid the problem of dishonest Oracle machines running out of funds. We use the traffic coverage method to solve the problem of exposed request paths in distributed Oracle machines. Simulation and experimental results show that in small and medium-sized IoT application scenarios with 10,000 data nodes, each additional false request in a single request will result in a delay of about 2 s in data acquisition and can achieve a request response time of 20 s. The proposed method can achieve user data privacy protection.

A hybrid Bi-level management framework for caching and communication in Edge-AI enabled IoT

The proliferation of IoT devices has led to a surge in network traffic, resulting in higher energy usage and response delays. In-network caching has emerged as a viable solution to address this issue. However, caching IoT data faces two key challenges: the transient nature of IoT content and the unknown spatiotemporal content popularity. Additionally, the use of a global view on dynamic IoT networks is problematic due to the high communication overhead involved. To tackle these challenges, this paper presents an adaptive management approach that jointly optimizes caching and communication in IoT networks using a novel bi-level control method called BC3. The approach employs two types of controllers: a global ILP-based optimal controller for long-term timeslots and local learning-based controllers for short-term timeslots. The long-term controller periodically establishes a global cache policy for the network and sends specific cache rules to each edge server. The local controller at each edge server solves the joint problem of bandwidth allocation and cache adaptation using deep reinforcement learning (DRL) technique. The main objective is to minimize energy consumption and system response time with utilizing the global and local observations. Experimental results demonstrate that the proposed approach increases cache hit rate by approximately 12% and uses 11% less energy compared to the other methods. Increasing the cache hit rate can lead to a reduction in about 17% in response time for user requests. Our bi-level control approach offers a promising solution for leveraging the network's global visibility while balancing communication overhead (as energy consumption) against system performance. Additionally, the proposed method has the lowest cache eviction, around 19% lower than the lowest eviction of the other comparison methods. The eviction metric is a metric to evaluate the effectiveness of adaptive caching approach designed for transient data.

Proactive real-time scheduling method for apron service vehicles based on mixed strategies

Due to the uncertainty of flight arrival and departure times, the scheduling of apron vehicles in airports has always faced significant challenges. This paper considers the real-time scheduling problem of apron vehicles under dynamic requests, establishing a new parking rule to allow apron vehicles to dynamically adjust parking areas. In order to maximize service level and minimize operational costs, this paper proposes a proactive real-time scheduling method for apron vehicles based on mixed strategies. Through strategies such as flexible waiting and dynamic relocation, this paper responds proactively to future requests. The proposed strategies are validated through implementation in large and medium-sized scale cases at Beijing Capital International Airport and Nanjing Lukou International Airport in China, respectively. Numerical results indicate that the proposed scheduling method is effective and applicable. The flexible waiting strategy significantly reduces vehicle travel distance, while the dynamic relocation strategy is significant in shortening request response times.

Integrating human factors engineering into your pediatric radiology practice.

Human factors engineering involves the study and development of methods aimed at enhancing performance, improving safety, and optimizing user satisfaction. The focus of human factors engineering encompasses the design of work environments and an understanding of human mental processes to prevent errors. In this review, we summarize the history, applications, and impacts of human factors engineering on the healthcare field. To illustrate these applications and impacts, we provide several examples of how successful integration of a human factors engineer in our pediatric radiology department has positively impacted various projects. The successful integration of human factors engineering expertise has contributed to projects including improving response times for portable radiography requests, deploying COVID-19 response resources, informing the redesign of scheduling workflows, and implementation of a virtual ergonomics program for remote workers. In sum, the integration of human factors engineering insight into our department has resulted in tangible benefits and has also positioned us as proactive contributors to broader hospital-wide improvements.

Hybrid Elastic Scaling Strategy for Container Cloud based on Load Prediction and Reinforcement Learning

To harness the advantages of both proactive and responsive scaling, adapting to various workload scenarios, this paper introduces a container hybrid scaling strategy called HyPredRL, rooted in load prediction and reinforcement learning. Within the proactive scaling module RL-PM, a load prediction model, MSC-LSTM, predict workloads and, in conjunction with current workload states, leverages reinforcement learning agents for intelligent scaling decisions. The responsive scaling strategy, SLA-HPA, enhances Kubernetes’ native scaling strategy, which primarily considers resource utilization, by incorporating response time metrics. Ultimately, a hybrid scaling controller is designed, applying the principles of “rapid scaling out” and “balanced conflicts” to coordinate proactive and responsive scaling. Experimental results demonstrate that HyPredRL outperforms existing methods in SLA violation rate, resource utilization, and request response time, effectively improving application performance and scalability.

Load Balancing using Weight Based scheme in AWS

This paper presents research on load balancing methods for cloud computing platforms. Load balancing is an important element of cloud systems since it allows for optimal resource utilization and consistent performance levels. The study investigates several load balancing technologies and evaluates their effectiveness in coping with the dynamic and heterogeneous nature of cloud workloads. The study conducts experiments and analyses to discover significant parameters influencing load balancing performance, such as workload allocation, server capacity, and network latency. With the weight-based technique, different servers are dynamically given requests based on their weights, which are determined by the processing capacities of the servers. By distributing the work proportionately, this approach aims to decrease user request response times and avoid server overload. The effectiveness of the proposed approach is evaluated via simulations, demonstrating its potential to improve system performance and scalability relative to traditional load balancing methods. Generally speaking, this paper presents an optimized load distribution approach that raises the efficiency and stability of cloud computing systems. The findings enable to improve load balancing methods matched to the particular requirements and issues of cloud computing, eventually enhancing system scalability, reliability, and user

Unauthorized Microphone Access Restraint Based on User Behavior Perception in Mobile Devices

Microphone has been widely integrated into mobile devices to provide physical basis for human-device voice interaction. However, the microphone may be spitefully invoked by malicious <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mobile applications</i> (apps) with arousing security and privacy concerns. In this work, to explore the issue of illegal microphone access, we develop spiteful apps through native and injection development to access the microphone viciously on a series of mobile devices. The results demonstrate that baleful apps could enable the microphone arbitrarily without any hint. To combat the unauthorized microphone access behavior, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">microphone illegal access detection</i> (MicDet) scheme by constructing a request-response time model using the Unix time stamps of voice icon touched and microphone invoked. Through conducting numerical analysis and hypothesis testing to effectively verify the request-response pattern of app's normal access, we detect illegal access by analyzing whether the touch operation matches the normal pattern. For friendly user experience, we design an intuitive floating window to alert users by displaying the name of the app that illegally accessed the microphone once the illegal behavior is detected. Finally, we apply our scheme to different mobile devices and test several apps, the experimental results show that the MicDet scheme achieves a high detection accuracy.

Simulation Experiment and Teaching Research of a Land-Based Ship Engine Room

In order to make up for the shortage of scientific research experiments and teaching in land-based engine room laboratory, a simulation system is designed and developed using B/S mode to simulate scientific research experiments, teaching and operation processes, and designed algorithms to simulate the propulsion characteristics of the main propulsion diesel engine. A large number of experiments were carried out, and the relationship curve between output power and fuel consumption rate of the main engine was obtained in the laboratory. Apache JMeter was used to test the pressure of the simulation system deployed in Dalian Maritime University Cloud. It is found that under the test conditions, the average response time of sample request is 330 ms, which is far superior the design standard of 2 s. There is no abnormal response to all requests, and all indicators fully meet the requirements of the project team. The simulation system fills the gap in the application of panoramic interactive technology in Marine engineering education, and provides a reference for the exploration of "intelligent +" experiment and teaching mode in maritime university laboratories.

Data conversion control of virtual network devices in cloud computing: A deep reinforcement learning approach

As the response time of virtual space task requests is affected by different data conversion processes, it becomes more serious to solve the load imbalance problem between the underlying nodes and the underlying data links. The traditional least square method needs to distinguish each node for the remapping of the original virtual space. After the trajectories in different spaces and times are mapped again, it is easier to cause new resource bottlenecks. Aiming at the necessary virtual space remapping cycle of cloud service nodes, this paper selects the nodes with the highest upper and lower limits of load rate according to the dynamic critical value of each node's load rate. It migrates part of the virtual network space on the overloaded service nodes to the non-important nodes with a lower load rate, which is to avoid the gradual formation of new resource bottlenecks after remapping. The data transformation model based on reinforcement learning can further improve the acceptance rate of virtual space requests and reduce the computational overhead caused by space remapping. The experiments compare the performance evaluation of different algorithm models on the simulation platform data conversion tasks, including complex instructions test, virtual network performance test, average remapping link overhead, and root mean square error of the underlying node load rate test. The test results indicate that the calculation accuracy of the proposed method is 7.25% higher than other comparison methods, and the computational overhead is reduced by 15.22%. The overall performance is improved by 10.74%. It is verified that the technology in this paper improves the data conversion control method of virtual network devices in the cloud computing environment, and has practical significance for virtual network remapping and data services in cloud computing.

Noah: Reinforcement-Learning-Based Rate Limiter for Microservices in Large-Scale E-Commerce Services.

Modern large-scale online service providers typically deploy microservices into containers to achieve flexible service management. One critical problem in such containerbased microservice architectures is to control the arrival rate of requests in the containers to avoid containers from being overloaded. In this article, we present our experience of rate limit for the containers in Alibaba, one of the largest e-commerce services in the world. Given the highly diverse characteristics of containers in Alibaba, we point out that the existing rate limit mechanisms cannot meet our demand. Thus, we design Noah, a dynamic rate limiter that can automatically adapt to the specific characteristic of each container without human efforts. The key idea of Noah is to use deep reinforcement learning (DRL) that automatically infers the most suitable configuration for each container. To fully embrace the advantages of DRL in our context, Noah addresses two technical challenges. First, Noah uses a lightweight system monitoring mechanism to collect container status. In this way, it minimizes the monitoring overhead while ensuring a timely reaction to system load changes. Second, Noah injects synthetic extreme data when training its models. Thus, its model gains knowledge on unseen special events and hence remains highly available in extreme scenarios. To guarantee model convergence with the injected training data, Noah adopts task-specific curriculum learning to train the model from normal data to extreme data gradually. Noah has been deployed in the production of Alibaba for two years, serving more than 50 000 containers and around 300 types of microservice applications. Experimental results show that Noah can well adapt to three common scenarios in the production environment. It effectively achieves better system availability and shorter request response time compared with four state-of-the-art rate limiters.

Achieving Revocable Attribute Group-Based Encryption for Mobile Cloud Data: A Multi-Proxy Assisted Approach

Although proxy-assisted revocable attribute-based encryption provides fine-grained privacy protection and reduces the decryption cost of data users, it inherits several disadvantages in outsourcing decryption. The decryption capability in outsourcing decryption is divided into two parts: the outsourcing transformation key and the user decryption key. The proxy server utilizes transformation keys to transform the ciphertext which can only be decrypted by the users who generated the transformation key. When multiple users with the same attributes request outsourcing transformation, the proxy server must do numerous transformation operations, increasing the computing overhead of the proxy server and the user request response time. To address the aforementioned issues, we propose a multi-proxy assisted revocable attribute group-based encryption (MP-RAGBE) scheme. We form a user group out of users who have the same attributes and send the transformation keys directly to the proxy server. Users in the same group can decrypt the transformed ciphertext, and only a small number of transformation keys need to be updated when revocation occurs. The security and experimental analyses show that the proposed scheme meets the defined security requirements while significantly reducing user request response time, the overhead of transformation key generation, transmission, and ciphertext transformation.

A delayed eviction caching replacement strategy with unified standard for edge servers

The current explosion of user traffic compels Internet service providers to cache contents at edge servers, so as to reduce the response time of user requests. To deal with such vast amounts of content in edge servers with limited storage, an efficient caching replacement strategy is necessary, consisting of admission and eviction processes. Facing such a large number of user requests and limited storage of edge servers, traditional caching replacement strategies have encountered two major problems. Firstly, they conduct eviction process immediately after cache miss, which ignores the fetching time, during which there may still be multiple access requests to the evicted contents. Secondly, most existing solutions treat admission process and eviction process separately, with different standards, some contents might be fetched and evicted back and forth, seriously affecting user experience. To solve these two problems and improve caching performance, we design two caching modules, Delayed-Eviction and Unified-Standard, and integrate them together into Adele framework. By conducting extensive simulations using real traces, we demonstrate that Adele can improve the hit rate by 31% compared with the state-of-the-art solutions.

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ~81-99% simulation accuracy in approximating the average response time of incoming requests and ~16-1200 times speed-up factor for the simulation.

ProScale: Proactive Autoscaling for Microservice With Time-Varying Workload at the Edge

Deploying microservice instances on the edge device close to end users can provide on-site processing thus reducing request response time. Each microservice has multiple instances that can process requests in parallel. To achieve high processing efficiency, the number of these instances is scaled according to the workload, which is also known as autoscaling. Previous studies of microservice autoscaling in the edge computing environment lack in-depth consideration of time-varying workload, they assume that the workload of each microservice always depends on that of its upstream. However, through an analysis of Alibaba's microservice trace with hundreds of millions of records, we find that the assumption is impractical thus hurting autoscaling effectiveness. To solve this problem, we propose ProScale, a prediction-driven proactive autoscaling framework for microservices at the edge. ProScale proactively forecasts the workload for each individual microservice per timeslot. Then it utilizes an efficient online algorithm to leverage the predicting results to determine the instance number for each microservice jointly with making placement decisions. For each microservice instance deployed on the edge device, ProScale handles burst requests using a designed offloading strategy. In addition, ProScale can also balance the load for multiple instances of each microservice. Extensive trace-driven experiments show that ProScale has great scalability. It can reduce average response time by 96.7% and resource usage by 96.5% compared with existing strategies and designed baselines.

The Effect of Grhasia Sadewa on the Response Time of Requests for People with Mental Disorders Evacuation at Grhasia Hospital Yogyakarta

Background: The prevalence of severe mental disorders in Indonesia have increased, as well as in the Special Region of Yogyakarta. Healthy Indonesia Program Data with a Family Approach shows that only 46% of people with mental disorders in Yogyakarta receive care and treatment. Grhasia Hospital as a hospital with mental health specialization owned by the Special Region of Yogyakarta Government carried out an innovation based on digital technology which was named Grhasia Ready to Evacuate People with Mental Disorders (Grhasia Sadewa). Grhasia Sadewa is expected to facilitate access to referrals and reduce the length of the people with mental disorders evacuation process that has been carried out so far. Purpose: Determine the effect of Grhasia Sadewa on the response time of requests for people with mental disorders evacuation at Grhasia Hospital Yogyakarta. Methods: This study uses a quasi-experimental posttest-only design with a control group. The research participants were 64 users of people with mental disorders evacuation services at Grhasia Hospital in 2021-2022. Data were obtained secondary from service documentation at Grhasia Hospital. Data analysis using the Mann-Whitney test. Results: The mean response time to requests for evacuation for people with mental disorders in the group not using the Grhasia Sadewa application was 4924.60 minutes, while the mean for the group using the Grhasia Sadewa application was 373.43 minutes. The results of the Mann-Whitney Test show p value = 0.00 (less than 0.05) so it can be concluded that there is an effect of Grhasia Sadewa on the response time of requests for people with mental disorders evacuation at Grhasia Hospital Yogyakarta. Conclusion: The response time for evacuation requests for people with mental disorders is faster after Grhasia Sadewa is applied. Grhasia Sadewa needs to be socialized to the community and health centers in the Special Region of Yogyakarta to facilitate access to mental health services for the community.

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.

Accelerated Information Dissemination for Replica Selection in Distributed Key-Value Store Systems

In distributed key-value stores, multiple replica servers are always available for each key-value access operation when the eventual consistency model is employed. Accordingly, the completion times of the key-value access operations generated by an end-user request at different servers may be of great difference, especially when the replica servers are heterogeneous and have time-varying performance. Accordingly, the replica selection algorithm is crucial to cut the response time of end-user requests. The main challenge of making replica selection for each light-weighted key-value access operation is to timely know the status of replica serves. Recently, the adaptive replica selection algorithm C3 suggests guiding the replica selection with the piggybacked information of replica server in the returned “value”. Although C3 has good performance, the poor timeliness of feedback information makes a large performance gap between C3 and the ideal replica selection algorithm. To narrow this gap, the Accelerated Information Dissemination (AID) mechanism is proposed in this paper. Specifically, AID removes the bottleneck of information dissemination at the “slow” servers by letting both “client” and “replica server” store the records about the status of replica servers and both “key” and “value” piggyback multiple records. AID is implemented in Cassandra and evaluated by experiments and large scale simulations. The results show AID can significantly improve the timeliness of feedback information, especially when the number of nodes is large. Accordingly, AID helps C3 to greatly reduce the latency.

Sensitivity analysis of a single server finite-source retrial queueing system with two-way communication and catastrophic breakdown using simulation

In this paper, a finite-source retrial queueing system with twoway communication is investigated with the help of a simulation program of own. If a randomly arriving request from the finite-source finds the single server idle its service starts immediately, otherwise it joins an orbit from where it generates retrial/repeated calls after a random time. To increase the utilization of the server when it becomes idle after a random time an outgoing request is called for service from an infinity source. Upon its arrival if the server is busy, it goes to a buffer and when the server becomes idle again its service starts immediately. requests arriving from the finite-source and orbit are referred to as primary or incoming ones while requests called from the infinite source are referred to as secondary or outgoing requests, respectively. The service times of the primary and secondary requests are supposed to be random variables having different distributions. However, randomly catastrophic failures may happen to all the requests in the system, that is from the orbit, the service unit, and the buffer. In this case, the primary requests return to the finite-source, and the secondary ones are lost. The operation of the system is restored after a random time. Until the restoration is finished no arrivals and service take place in the system. All the above-mentioned times are supposed to be independent random variables. The novelty of this paper is to perform a sensitivity analysis of the failure and restoration/repair times on the main characteristics to illustrate the effect of different distributions having the same average and variance value. Our aim is to determine the distribution of the number of requests in the system, the average response time of an arbitrary primary request without successful service, also the average response time of an arbitrary and successfully served primary request, the total utilization of the service unit, or the probability that a primary request leaves the system without successful service because of a catastrophic event. Results are illustrated graphically obtained by our simulation program.

A Cloud-based Mobile Privacy Protection System with Efficient Cache Mechanism

People increasingly rely on their mobile devices and use them to store a lot of data. Some of the data are personal and private, whose leakage leads to users' privacy harm. Meanwhile, mobile apps and services over-collect users' data due to the coarse-grained access control approach utilized by the mobile operating system. We propose a cloud-based approach to provide fine-grained access control toward data requests. We add privacy level, as a new metadata, to data and manage the storage using different policies correspondingly. However, the proposed approach leads to performance decreases because of the extra communication cost. We also introduce a novel cache mechanism to eliminate the extra cost by storing non-private and popular data on the mobile device. As part of our cache mechanism, we design a user-preference-based ordering method along with the principle of locality to determine how popular some data are. We also design a configurable refresh policy to improve the overall performance. Finally, we evaluate our approach using a real phone in a simulated environment. The results show that our approach can keep the response time of all data requests within a reasonable range and the cache mechanism can further improve the performance.

State Space Model and Queuing Network Based Cloud Resource Provisioning for Meshed Web Systems

Functions provided by Web applications are increasingly diverse which make their structures complicated and meshed. Cloud computing platforms provide elastic computing capacities for these meshed Web systems to guarantee Service Level Agreement (SLA). Though workloads of meshed Web systems usually change steadily and periodically in total, sometimes there are sudden fluctuations. In this paper, a hybrid State-space-model-and-Queuing-network based Feedback control method (SQF) is developed for auto-scaling Virtual Machines (VMs) allocated to each tier of meshed Web systems. For the case with workloads changing steadily, a State-space-model based static Feedback Control method (SFC) is proposed in SQF to stabilize request response times near the reference time. For unsteadily changing workloads, a Queuing-network based multi-tier collaborative Feedback Control method (QFC) is proposed for effectively eliminating bottlenecks. QFC builds a control system for each tier individually and uses the queuing network to measure the interaction relationships among different tiers. Experimental results show that QFC is able to improve the efficiency of eliminating bottlenecks (decreasing upper-limit SLA violation ratios by 31.99% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 56.52%) with similar or a little bit high VM rental costs compared to existing methods while SFC obtains more stable response times for requests with reasonable additional costs.