Response Time Of Requests Research Articles

High Performance IoT Cloud Computing Framework Using Pub/Sub Techniques

The Internet of Things is attracting attention as a solution to rural sustainability crises, such as slowing income, exports, and growth rates due to the aging of industries. To develop a high-performance IoT platform, we designed and implemented an IoT cloud platform using pub/sub technologies. This design reduces the difficulty of overhead for management and communication, despite the harsh IoT environment. In this study, we achieved high performance by applying the pub/sub platform with two different characteristics. As the size and frequency of data acquired from IoT nodes increase, we improved performance through MQTT and Kafka protocols and multiple server architecture. MQTT was applied for fast processing of small data, and Kafka was applied for reliable processing of large data. We also mounted various sensors and actuators to measure the data of growth for each device using the protocol. For example, DHT11, MAX30102, WK-ADB-K07-19, SG-90, and so on. As a result of performance evaluation, the MQTT Kafka platform implemented in this research was found to be effective for use in environments where network bandwidth is limited or a large amount of data is continuously transmitted and received. We realized the performance as follows: the response time for user requests was measured to be within 100 ms on average, data transmission order verification for more than 13 million requests, data processing performance per second on an average of 113,134.89 record/s, and 64,313 requests per second were performed for requests that occurred simultaneously from multiple clients.

A priority queue-based telemonitoring system for automatic diagnosis of heart diseases in integrated fog computing environments.

Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients' response time.This paper, proposes a new performance model by integrating fog computing, priority queues and certainty theory into the Edge computing devices and validating it by analyzing heart disease patients' conditions in clinical decision support systems (CDSS). In this model, a Certainty Factor (CF) value is assigned to each symptom of heart disease. When one or more symptoms show an abnormal value, the patient's condition will be evaluated using CF values in the fog layer. In the fog layer, requests are categorized in different priority queues before arriving into the system. The results demonstrate that network usage, latency, and response time of patients' requests are respectively improved by 25.55%, 42.92%, and 34.28% compared to the cloud model. Prioritizing patient requests with respect to CF values in the CDSS provides higher system Quality of Service (QoS) and patients' response time.

Quick Generation of SSD Performance Models Using Machine Learning

Increasing usage of <i>Solid-State Drives</i> (SSDs) has greatly boosted the performance of storage backends. SSDs perform many internal processes such as out-of-place writes, wear-leveling, and garbage collection. These operations are complex and not well documented which make it difficult to create accurate SSD simulators. Our survey indicates that aside from complex configuration, available SSD simulators do not support both <i>sync</i> and <i>discard</i> requests. Past performance models also ignore the long term effect of I/O requests on SSD performance, which has been demonstrated to be significant. In this article, we utilize a methodology based on machine learning that extracts history-aware features at low cost to train SSD performance models that predict request response times. A key goal of our work is to achieve real-time or near-real time feature extraction and to achieve practical training times so our work can be considered as part of solutions that perform online or periodical characterization such as adaptive storage algorithms. Thus, we extract features from individual read, write, <i>sync</i> , and <i>discard</i> I/O requests and use structures such as exponentially decaying counters to track past activity using <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula> memory and processing cost. To make our methodology accessible and usable in real-world online scenarios, we focus on machine learning models that can be trained quickly on a single machine. To massively reduce processing and memory cost, we utilize feature selection to reduce feature count by up to 63%, allowing a feature extraction rate of 313,000 requests per second using a single thread. Our dataset contains 580M requests taken from 35 workloads. We experiment with three families of machine learning models, a) decision trees, b) ensemble methods utilizing decision trees, and c) <i>Feedforward Neural Networks</i> (FNN). Based on these experiments, FNN achieves an average <inline-formula><tex-math notation="LaTeX">$R^2$</tex-math></inline-formula> score of 0.72 compared to 0.61 and 0.45 for the Random Forest and Bagging, respectively, where <inline-formula><tex-math notation="LaTeX">$R^2 \in (-\inf, 1)$</tex-math></inline-formula> of 1 indicates a perfect fit. However, while the random forest model has lower accuracy, it uses general processing hardware and can be trained much faster, making it viable for use in online scenarios.

Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud service providers to achieve high-quality services with lower operation cost and higher application resilience. In multi-cloud, cloud services are widely distributed at different locations with differentiated prices. Therefore, Web application providers face the challenge to select proper cloud services for application replication and deployment with the aim of minimizing the deployment cost. Meanwhile, the deployed application replicas must satisfy the constraint on request response time to maintain the quality of user experience. To meet the two major requirements, this article studies a new problem of Web application replication and deployment in multi-cloud (WARDMC) that jointly considers both the cost minimization and constraints on average response time, including particularly request processing time and network latency. To address the problem, we develop a new approach named MCApp. MCApp combines iterative mixed integer linear programming with domain-tailored large neighborhood search to optimize both application replicas deployment and user requests dispatching. Extensive experiments using the real-world datasets demonstrate that MCApp significantly outperforms several recently proposed approaches.

GearWheels: A Software Tool to Support User Experiments on Gesture Input with Wearable Devices

We introduce GearWheels, a software tool for studies about gesture input with wearables, including smartwatches, rings, and glasses. GearWheels features an event-based asynchronous software architecture design implemented exclusively with web standards, communications protocols, and data formats, which makes it flexible to support many wearables via HTTP and WebSocket communications. GearWheels differentiates from prior software tools for gesture acquisition, elicitation, recognition, and analysis with its web-based, wearable-oriented, experiment-centered architecture design. We demonstrate GearWheels with a device affixed to the index finger, wrist, and the temple of a pair of glasses to illustrate touch stroke-gesture and motion-gesture input acquisition. We also perform a technical evaluation of GearWheels in the form of a simulation experiment, and report the request-response time performance of the software components of GearWheels with off-the-shelf wearables. We release GearWheels as open source software to assist researchers and practitioners in implementing studies about gesture input with wearables.

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

One of the most critical concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the virtual machine placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of Internet of Things (IoT) tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into NP-hard issues. This study proposes a multi-objective virtual machine placement to jointly minimize energy costs and scheduling. After presenting a modified Memetic algorithm, we compare its performance with baseline methods and state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, service-level agreement violations, and the total response time of IoT requests.

CloudSimSFC: Simulating Service Function chains in Multi-Domain Service Networks

Service Function Chain (SFC) is widely adopted in Multi-domain Service Networks (MDSN) to enforce network policies on customer traffic. Great effort has been devoted to the research of SFC deployment strategies. In this context, simulators are helpful to the design and evaluation of those strategies. A good SFC simulator should be accurate, comprehensive and user-friendly for SFC simulation. However, existing simulators fail to satisfy the requirement due to two major drawbacks: First, they overlook the resource heterogeneity and performance instability of the MDSN environment. Second, their performance models for service functions are too simple to contain the important features such as traffic changing effect and packet queue. To overcome these drawbacks, we propose CloudSimSFC — a new SFC simulator that (1) simulates the performance fluctuations and server failure/recovery events in MDSN environment to align with the performance instability of real-world systems, (2) elaborates the modeling of service functions by incorporating the computation components such as CPU and queue, and the traffic changing effect which happens during packet processing, (3) employs scenario abstraction to simplify the definition of new (heterogeneous) simulation scenarios and supports standard service metrics like request–response time. With these features, CloudSimSFC can be used to simulate SFC run-time performance and evaluate deployment strategies. We introduce the system architecture of CloudSimSFC to explain the simulation principle. We conduct extensive experiments to evaluate the simulation accuracy of CloudSimSFC by comparing it with an SFC prototype. Experimental results confirm that CloudSimSFC achieves 95%+ accuracy in all the evaluation scenarios. We also evaluate the simulation running time and analyze the overhead caused by the proposed simulation features. Finally, we demonstrate the primary usage of CloudSimSFC with case studies.

Development of Web Caching Replacement in Internet Service Based on GDSF

This paper presents a policy to replace the cached web content named GDSF-EXT based on GDSF by adding an extensible cache located in the network device. In processing, these web contents will be retrieved instead of having to search in other devices on the same network layer or at a higher network layer. That helps to reduce the response time of the user's request. The proposed algorithm is compared to GDSF original to evaluate the performance of the network

Load Balancing Based on Closed Loop Control Theory (LBBCLCT): A Software Defined Networking (SDN) powered server load balancing system based on closed loop control theory

AbstractThis article comprises work concerned with proposing a scheme for intercepting network traffic and directs that traffic to servers in Software defined network model. Several studies have explored the effects of average response time of user requests with different load balancing algorithms. Server load balancing is a well‐recognized issue which necessitates a better approach by doing deeper understanding of the network parameters. The implications of the mean response time of client requests are investigated in this article by having a closer look on changing the important network parameter namely probing time (time to investigate the server farm in order to gain access to server data) using the concept of closed loop control theory. Our LBBCLCT proves to be effectively distributes the user requests to the most appropriate server by timely changing the server even when the traffic exhibits inconsistency. The results reveal that LBBCLCT outperforms other methods such as round robin, LBBSRT, and SD‐WLB in terms of processing the client requests with a reduced mean server response time. LBBCLCT showed the improvement of average response time by 46.2%, 25.6%, and 43.59% over round robin, LBBSRT, and SDWLB respectively.

Proposed Framework for Official Document Sharing and Verification in E-government Environment Based on Blockchain Technology

Progression in Computer networks and emerging of new technologies in this field helps to find out new protocols and frameworks that provides new computer network-based services. E-government services, a modernized version of conventional government, are created through the steady evolution of technology in addition to the growing need of societies for numerous services. Government services are deeply related to citizens’ daily lives; therefore, it is important to evolve with technological developments—it is necessary to move from the traditional methods of managing government work to cutting-edge technical approaches that improve the effectiveness of government systems for providing services to citizens. Blockchain technology is among the modern technologies highly suitable for developing digital governance services, and its technological ability to sustain information stability is vital in digital governance systems since it improves integrity and transparency measures while preventing corruption. In this study, computer networking protocols are built to form a peer-to-peer network framework for managing official documents. using blockchain technology was built to illustrate how any element of government work may be developed using it. The suggested framework comprises the addition of a new official document, and the verification of an existing document. The system was created in socket programming using Java and tested the response times for many simultaneous requests. The system was tested using transactions per second (throughput) measurement. The result showed that the proposed system processed 200 document verification transactions within 50 seconds. In addition, the test of the proposed system presented the time required for document retrieval—about three seconds to answer 100 document retrieval transactions. Furthermore, the results of throughput were compared to the results of the same measurement of some popular applications such as bitcoin. And the result of the proposed system was within the average value of output throughput of the other compared applications.

Ontology-Based IoT Healthcare Systems (IHS) for Senior Citizens

Rapid incremental growth in population causes the virulence of infectious diseases worldwide. Due to this, health hazards with population growth raise pollution in the air, water, and soil and affect the immunity of individuals. To handle the situation, reliable and easy to reach healthcare services are required. The proliferation of connected technologies along with the Internet of Things (IoT) is providing modern healthcare with extensive care. All-pervading IoT technology gaining a very much attraction nowadays. This paper presents a brief about the E-Health Care System along with its framework. This attempt also presents the ontology approach as data produced by healthcare applications is vast and unstructured which needs to be organized in proper format with a smooth flow of data and also results in less request-response time. Further, this paper discusses the impact of the disease on senior citizens in the current scenario.

Online VNF Chaining and Predictive Scheduling: Optimality and Trade-Offs

For NFV systems, the key design space includes the function chaining for network requests and the resource scheduling for servers. The problem is challenging since NFV systems usually require multiple (often conflicting) design objectives and the computational efficiency of real-time decision making with limited information. Furthermore, the benefits of predictive scheduling to NFV systems still remain unexplored. In this article, we propose POSCARS, an efficient predictive and online service chaining and resource scheduling scheme that achieves tunable trade-offs among various system metrics with stability guarantee. Through a careful choice of granularity in system modeling, we acquire a better understanding of the trade-offs in our design space. By a non-trivial transformation, we decouple the complex optimization problem into a series of online sub-problems to achieve the optimality with only limited information. By employing randomized load balancing techniques, we propose three variants of POSCARS to reduce the overheads of decision making. Theoretical analysis and simulations show that POSCARS and its variants require only mild-value of future information to achieve near-optimal system cost with an ultra-low request response time.

Design of remote operation and maintenance platform of substation DC system for field defect handling

The current remote operation and maintenance platform of substation DC system mainly relies on manual use of each independent monitoring device to collect and transmit data for remote operation and maintenance management, which is difficult to realize intelligent operation of substation and also leads to slow response and poor processing performance of operation and maintenance platform. In order to optimize the above-mentioned problems, the remote operation and maintenance platform of substation DC system for on-site defect processing is studied and designed. Based on the framework of the operation and maintenance platform, a combination of balanced and unbalanced bridge-based principle is used to monitor the DC insulation status. The JSON protocol is used to realize the data interaction between the operation and maintenance platform and the management terminals, and to design the DC system defect processing scheme. The Floyd shortest path algorithm is used to plan the operation and maintenance inspection strategy of the platform to achieve fast operation and maintenance of the DC system. The platform performance test results show that the performance index of the designed operation and maintenance platform is significantly improved, and the average request response time is shortened by 28.1%, and the operation and maintenance management effect is better.

REST API performance comparison of web applications based on JavaScript programming frameworks

The aim of the work was to compare two technologies for creating server applications based on the JavaScript programming language. For the purposes of the research, two test applications were created. The first one was built on the basis of the Express programming framework and the second one on the basis of the Hapi framework. The client part of both applications was prepared using the React library. The client and server parts communicated with each other by means of REST API – the universal HTTP interface. The client application sent requests to the server application which then performed basic operations on the MongoDB basis and returned the result. As part of the work, an experiment consisting of four scenarios was developed. In each scenario, a different type of data was taken into consideration: a string of characters, an array, an object and an array of objects. The research focused on the efficiency aspect – measuring the response time of requests during GET, POST, PUT and DELETE operations. The tests were performed on two computers and the measurements were made in two ways: using a single code embedded in test applications and using the Postman tool. The obtained results, after averaging and analyzing them allowed for the conclusion that the Express framework proved to be more efficient than Hapi due to the shorter response time of requests. Only in the scenario where operations with large datasets were performed was the response time of requests at a similar level.

QoS Promotion in Energy-Efficient Datacenters Through Peak Load Scheduling

To build energy-efficient datacenters, one widely used way is to dynamically manipulate the quantity of available hardware resources on demand. However, when bursty workloads appear, additional time overhead is required for resource gearing, thus incurring a performance degradation. To explore this problem, we present an intelligible analysis on the request handing in a VM. From the analysis, we find that, when workloads are overloaded (i.e., peak loads appear), the number of QoS guaranteed requests can be greatly increased by deferring the scheduling of a few requests. Inspired by this finding, we propose a Peak Load Scheduling Control (PLSC) method to promote the Quality of Service (QoS) of peak loads for modern energy-efficient datacenters. However, peak loads are usually difficult to identify. To overcome this difficulty, PLSC tracks the number of requests residing in a VM by leveraging a two-tier request queue maintained by it. When the number exceeds the capability of the VM, it means that peak loads appear. In this case, PLSC adds some delay-tolerant requests to the secondary queue. The scheduling of requests in the secondary queue is controlled with a lower priority than that of requests in the primary queue. Sequentially, with critical requests maintained in the primary queue, PLSC shortens the response time of critical requests. In addition, PLSC expands the number of QoS-guaranteed requests. Comprehensive experiments are conducted to attest the effectiveness of PLSC, by simulating a typical energy-efficient datacenter. The experimental results show that PLSC significantly promotes the QoS of workloads with a negligible impact on energy saving.

FLEX-IoT: Secure and Resource-Efficient Network Boot System for Flexible-IoT Platform.

In an internet of things (IoT) platform with a copious number of IoT devices and active variation of operational purpose, IoT devices should be able to dynamically change their system images to play various roles. However, the employment of such features in an IoT platform is hindered by several factors. Firstly, the trivial file transfer protocol (TFTP), which is generally used for network boot, has major security vulnerabilities. Secondly, there is an excessive demand for the server during the network boot, since there are numerous IoT devices requesting system images according to the variation of their roles, which exerts a heavy network overhead on the server. To tackle these challenges, we propose a system termed FLEX-IoT. The proposed system maintains a FLEX-IoT orchestrater which uses an IoT platform operation schedule to flexibly operate the IoT devices in the platform. The IoT platform operation schedule contains the schedules of all the IoT devices on the platform, and the FLEX-IoT orchestrater employs this schedule to flexibly change the mode of system image transfer at each moment. FLEX-IoT consists of a secure TFTP service, which is fully compatible with the conventional TFTP, and a resource-efficient file transfer method (adaptive transfer) to streamline the system performance of the server. The proposed secure TFTP service comprises of a file access control and attacker deception technique. The file access control verifies the identity of the legitimate IoT devices based on the hash chain shared between the IoT device and the server. FLEX-IoT provides security to the TFTP for a flexible IoT platform and minimizes the response time for network boot requests based on adaptive transfer. The proposed system was found to significantly increase the attack-resistance of TFTP with little additional overhead. In addition, the simulation results show that the volume of transferred system images on the server decreased by 27% on average, when using the proposed system.

Planning Fog networks for time-critical IoT requests

The massive growth of the Internet of Things (IoT) applications and the challenges of Cloud computing have increased the importance of Fog networks for timely processing the requests from delay-sensitive applications. A Fog network provides local aggregation, analysis, and processing of IoT requests that may or may not be time-critical. One of the major issues of Fog is its capacity planning considering the traffic load of time-critical requests. The response time can be huge if a time-critical request is processed on Cloud. The response time of a time-critical request can be big on the Fog layer if it is not prioritized. Hence, there is a need to handle the time-critical traffic on a priority basis at the Fog layer. In this paper, a priority queuing model with preemption has been proposed considering the mixed types of requests at the Fog layer. The proposed approach determines the required number of Fog nodes in order to satisfy the desired Quality of Service (QoS) requirements of IoT requests. The proposed mechanism is evaluated through simulations using the iFogSim simulator. The work can be used in the capacity planning of Fog networks.

Modeling of information interconnection in a computer control system of moving objects

The subject of research is a pool of widgets, which provides a user interface in a computer control system for moving objects. The object of research is the process of information exchange between the components of a computer system, which are directly involved in the control of moving objects. The task of allocating resources for a pool of widgets that provide operational control of moving objects is critical. As the number of elements increases in the pool of widgets, the competition for the resources of the computer system increases significantly, respectively, the queues for the most demanded resources grow. Therefore, it is necessary to reallocate resources between requests in such a way that the priorities of requests are taken into account, and the response time of operational requests does not exceed the standard time. Such distribution cannot be realized without a model that would show the parameters of all information flows between the components, which is the purpose of this article. In doing so, the model must consider the characteristics of the pool of widgets that support the user interface. Research results. To achieve this goal, several particular tasks were solved. First, the main sets were identified that will be involved in modeling information interconnections between software components that service requests for user interface widgets in a computer system environment. Then, a diagram of informational interconnections between these sets was developed. Further, the parameters of the requests, which were initiated by the widgets, of the applications, and the fragments of the widget query service database were determined and formalized. All this made it possible to form a model of information interconnections using a tuple of obtained vectors, sets, and Boolean matrices. In the proposed mathematical model, many limitations are imposed on the elements of this tuple, associated with the characteristic features of the control module for the user interface of the computer system. Besides, the limitations associated with the functioning of the basic network of the computer system are taken into account. In the final model, Boolean matrices affecting resource allocation defined as model variables. A fast algorithm for determining the space of admissible distributions is proposed. Conclusions. The developed mathematical model determines the parameters of information flows between the components of the computer system, which are involved in the control of moving objects. This model will allow for the operational reallocation of resources between requests using the proposed fast algorithm.

Machine Translation Utilizing the Frequent-Item Set Concept.

In this paper, we introduce new concepts in the machine translation paradigm. We treat the corpus as a database of frequent word sets. A translation request triggers association rules joining phrases present in the source language, and phrases present in the target language. It has to be noted that a sequential scan of the corpus for such phrases will increase the response time in an unexpected manner. We introduce the pre-processing of the bilingual corpus through proposing a data structure called Corpus-Trie (CT) that renders a bilingual parallel corpus in a compact data structure representing frequent data items sets. We also present algorithms which utilize the CT to respond to translation requests and explore novel techniques in exhaustive experiments. Experiments were performed on specific language pairs, although the proposed method is not restricted to any specific language. Moreover, the proposed Corpus-Trie can be extended from bilingual corpora to accommodate multi-language corpora. Experiments indicated that the response time of a translation request is logarithmic to the count of unrepeated phrases in the original bilingual corpus (and thus, the Corpus-Trie size). In practical situations, 5–20% of the log of the number of the nodes have to be visited. The experimental results indicate that the BLEU score for the proposed CT system increases with the size of the number of phrases in the CT, for both English-Arabic and English-French translations. The proposed CT system was demonstrated to be better than both Omega-T and Apertium in quality of translation from a corpus size exceeding 1,600,000 phrases for English-Arabic translation, and 300,000 phrases for English-French translation.

Secure and Efficient Authentication Scheme in IoT Environments

Optimization of resource consumption and decreasing the response time of authentication requests is an immense urgent requirement for supporting the scalability of resources in IoT environments. The existing research attempts to design lightweight authentication protocols to address these issues. However, the schemes proposed in the literature are lacking in the creation of a lightweight (i.e., low computing, communication, and storage cost) and secure architecture. IoT devices in existing approaches consume high electricity and computing power, despite the fact that IoT devices have limited power and computing capabilities. Furthermore, the existing approaches lead to an increase in the burden on storage memory and also create heavy traffic on a communication channel, increasing the response time of device authentication requests. To overcome these limitations, we propose a novel lightweight and secure architecture that uses crypto-modules, which optimize the usage of one-way hash functions, elliptic-curve cryptography, and an exclusive-or operation. We demonstrate the proposed scheme’s security strength using informal security analysis and verified it by considering the widely used automated validation of internet security protocol application (AVISPA) and the ProVerif tool. The result shows that the proposed scheme is effective against active and passive security attacks and satisfies secure design. Moreover, we calculate the proposed scheme’s working cost by implementing it using a widely accepted standard pairing-based cryptography (PBC) library on embedded devices. The implementation proves that the proposed scheme is lightweight and reduces computation time by 0.933 ms, communication cost by 1408 bits, and storage cost by 384 bits, and removes the existing gaps.