Cloud Datacenter Selection Using Service Broker Policies:A Survey

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

Cloud Data Center Selection Using a Modified Differential Evolution

The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector.Cloud data center selection challenges are compounded by ever-increasing users’requests and the number of data centers required to execute these requests.Cloud service broker policy defines cloud data center’s selection,which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution.Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness,and it is well suited for selecting an appropriate cloud data center.This paper presents a modified differential evolution algorithm-based cloud service broker policy for the most appropriate data center selection in the cloud computing environment.The differential evolution algorithm is modified using the proposed new mutation technique ensuring enhanced performance and providing an appropriate selection of data centers.The proposed policy’s superiority in selecting the most suitable data center is evaluated using the CloudAnalyst simulator.The results are compared with the state-of-arts cloud service broker policies.

COVID-19 Automatic Detection Using Deep Learning

The novel coronavirus disease 2019(COVID-19)is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people.The first step to mitigate and control its spread is to identify and isolate the infected people.But,because of the lack of reverse transcription polymerase chain reaction(RT-CPR)tests,it is important to discover suspected COVID-19 cases as early as possible,such as by scan analysis and chest X-ray by radiologists.However,chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case.In this paper,an automated novel detection model of COVID-19 cases is proposed to perform real-time detection of COVID-19 cases.The proposed model consists of three main stages:image segmentation using Harris Hawks optimizer,synthetic image augmentation using an enhanced Wasserstein And Auxiliary Classifier Generative Adversarial Network,and image classification using Conventional Neural Network.Raw chest X-ray images datasets are used to train and test the proposed model.Experiments demonstrate that the proposed model is very efficient in the automatic detection of COVID-19 positive cases.It achieved 99.4%accuracy,99.15%precision,99.35%recall,99.25%F-measure,and 98.5%specificity.

Intrusion Detection Systems Using Blockchain Technology: A Review, Issues and Challenges

Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches.However,the current approaches have challenges in detecting intrusions,which may affect the performance of the overall detection system as well as network performance.For the time being,one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology.Blockchain technology moves in the direction of persistent revolution and change.It is a chain of blocks that covers information and maintains trust between individuals no matter how far apart they are.Recently,blockchain was integrated into intrusion detection systems to enhance their overall performance.Blockchain has also been adopted in health-care,supply chain management,and the Internet of Things.Blockchain uses robust cryptography with private and public keys,and it has numerous properties that have leveraged security’s performance over peer-to-peer networks without the need for a third party.To explore and highlight the importance of integrating blockchain with intrusion detection systems,this paper provides a comprehensive background of intrusion detection systems and blockchain technology.Furthermore,a comprehensive review of emerging intrusion detection systems based on blockchain technology is presented.Finally,this paper suggests important future research directions and trending topics in intrusion detection systems based on blockchain technology.