A Demand Based Load Balanced Service Replication Model

Cloud computing allows service users and providers to access the applications, logical resources and files on any computer with ease. A cloud service has three distinct characteristics that differentiate it from traditional hosting. It is sold on demand, typically by the minute or the hour; it is elastic. It is a way to increase capacity or add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software. It not only promises reliable services delivered through next-generation data centers that are built on compute and storage virtualization technologies but also addresses the key issues such as scalability, reliability, fault tolerance and file load balancing. The one way to achieve this is through service replication across different machines coupled with load balancing. Though replication potentially improves fault tolerance, it leads to the problem of ensuring consistency of replicas when certain service is updated or modified. However, fewer replicas also decrease concurrency and the level of service availability. A balanced synchronization between replication mechanism and consistency not only ensures highly reliable and fault tolerant system but also improves system performance significantly. This paper presents a load balancing based service replication model that creates a replica on other servers on the basis of number of service accesses. The simulation results indicate that the proposed model reduces the number of messages exchanged for service replication by 25-55% thus improving the overall system performance significantly. Also in case of CPU load based file replication, it is observed that file access time reduces by 5.56%-7.65%

A dynamic file replication based on CPU load and consistency mechanism in a trusted distributed environment

Pokušalo se predložiti dinamički, kooperativni, pouzdani i sigurni pristup replikaciji datoteke utemeljen na opterećenosti CPU uz konzistenciju među replikama datoteke za distribuirano okruženje. Rezultati simulacije koja se sastoji od 100 potrebnih čvorova, tri servera datoteke i datoteke veličine od 677 KB to 11 MB pokazuju da kada se uzme u obzir opterećenje CPU, prosječno smanjenje vremena potrebnog za popunjavanje datoteke je oko 22,04 ÷ 24,81 %. Tako se optimiziralo opterećenje CPU i smanjilo traženo vrijeme popunjavanja datoteke. Opterećenje CPU smanjuje se za 4,25 ÷ 5,58 %. Rezultati pokazuju da se prosječno kašnjenje upisa (write latency) s predloženim mehanizmom smanjuje za 6,12 % u usporedbi sa Spinnakerovim, a prosječno vrijeme čekanja čitanja (read latency) je 3 puta bolje od Cassandra Quorum Read (CQR). Predložena parcijalna propagacija ažuriranja za održavanje konzistencije datoteke povećava se do 69,67 % u odnosu na vrijeme potrebno za ažuriranje zastarjelih replika. Tako je integritet datoteka i ponašanje zahtijevanih čvorova i servera datoteke zagarantirano za čak manje vremena. Konačno, kroz algebra postupak uspostavljen je odnos između formalnih aspekata jednostavnog modela sigurnosti i sigurnog pouzdanog modela replikacije datoteke zasnovanog na sigurnom pouzdanom opterećenju datoteke.An effort has been made to propose a CPU load based dynamic, cooperative, trust based, and secure file replication approach based along with consistency among file replicas for distributed environment. Simulation results consisting of 100 requesting nodes, three file servers and file size ranging from 677 KB to 11 MB establishes that, when the CPU load is taken into consideration, the average decrease in file request completion time is about 22,04 ÷ 24,81 % thus optimizing the CPU load and minimizing the file request completion time. The CPU load decreases by 4,25 ÷ 5,58 %. Results show that, the average write latency with proposed mechanism decreases by 6,12 % as compared to Spinnaker writes and the average read latency is 3 times better than Cassandra Quorum Read (CQR). The proposed partial update propagation for maintaining file consistency stands to gain up to 69,67 % in terms of time required to update stale replicas. Thus the integrity of files and behaviour of the requesting nodes and file servers is guaranteed within even lesser time. Finally, a relationship between the formal aspects of simple security model and secure reliable CPU load based file replication model is established through process algebra

Recent innovations in mechanical ventilator support

Mechanical ventilation as a means to provide basic lifesaving ventilatory support has grown leaps and bounds in the recent years. The basic modes of ventilation have seen a sea change and in addition other innovative techniques have been developed to prevent lung injury, ease of weaning and improve patient comfort. These modes and techniques though easily available are not adequately utilized for benefits of patient usually due to lack of knowledge about them. This article reviews some of these newer modes and innovations in mechanical ventilatory support

BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling

Named-entity recognition (NER) is one of the primary components in various natural language processing tasks such as relation extraction, information retrieval, question answering, etc. The majority of the research work deals with flat entities. However, it was observed that the entities were often embedded within other entities. Most of the current state-of-the-art models deal with the problem of embedded/nested entity recognition with very complex neural network architectures. In this research work, we proposed to solve the problem of nested named-entity recognition using the transfer-learning approach. For this purpose, different variants of fine-tuned, pretrained, BERT-based language models were used for the problem using the joint-labeling modeling technique. Two nested named-entity-recognition datasets, i.e., GENIA and GermEval 2014, were used for the experiment, with four and two levels of annotation, respectively. Also, the experiments were performed on the JNLPBA dataset, which has flat annotation. The performance of the above models was measured using F1-score metrics, commonly used as the standard metrics to evaluate the performance of named-entity-recognition models. In addition, the performance of the proposed approach was compared with the conditional random field and the Bi-LSTM-CRF model. It was found that the fine-tuned, pretrained, BERT-based models outperformed the other models significantly without requiring any external resources or feature extraction. The results of the proposed models were compared with various other existing approaches. The best-performing BERT-based model achieved F1-scores of 74.38, 85.29, and 80.68 for the GENIA, GermEval 2014, and JNLPBA datasets, respectively. It was found that the transfer learning (i.e., pretrained BERT models after fine-tuning) based approach for the nested named-entity-recognition task could perform well and is a more generalized approach in comparison to many of the existing approaches