Microservices' architecture is getting attention in the academic community and the industry, and mostly is compared with monolithic architecture. Plenty of the results of these research papers contradict each other regarding the performance of these architectures. Therefore, these two architectures are compared in this paper, and some specific configurations of microservices' applications are evaluated as well in the term of service discovery. Monolithic architecture in concurrency testing showed better performance in throughput by 6% when compared to microservices architecture. The load testing scenario did not present significant difference between the two architectures. Furthermore, a third test comparing microservices applications built with different service discovery technologies such as Consul and Eureka showed that applications with Consul presented better results in terms of throughput.

No abstract

Many companies are migrating from monolithic architectures to microservice architectures, and they need to decompose their applications in order to create a microservices application. Therefore, the need comes for an approach that helps software architects in the decomposition process. This paper presents a new approach for decomposing monolithic application to a microservices application through analyzing the application programming interface. Our proposed decomposition methodology uses word embedding models to obtain word representations using operation names, as well as, using a hierarchical clustering algorithm to group similar operation names together in order to get suitable microservices. Also, using grid search method to find the optimal parameter values for Affinity Propagation algorithm, which was used for clustering, as well as using silhouette coefficient score to compare the performance of the clustering parameters. The decomposition approach that was introduced in this research consists of the OpenAPI specifications as an input, then extracts the operation names from the specifications and converts them into average word embedding using fastText model. Lastly the decomposition approach is grouping these operation names using Affinity Propagation algorithm. The proposed methodology presented promising results with a precision of 0.84, recall of 0.78 and F-Measure of 0.81.

The advancement of the Internet has depleted every single extraordinary location studied by the current IPv4 convention viably. Subsequently, IPv6 has been created because of the anticipated long haul interest for locations by Internet surfers. This new IP adaptation has a 128-piece address length, which is four times that of IPv4. As both conventions have distinctive organizations and practices, endeavors are expected to guarantee they can speak with each other specifically. Some move instruments have been studied by IETF to allow IPv4 and IPv6 systems to coincide before entire Internet is dependant on an IPv6 system. The movement from IPv4 to IPv6 must be actualized hub by hub by utilizing auto-design methodology to dispose of the need to arrange IPv6 has physically. I. INTRODUCTION IPv6 (Internet Process version 6, also called IP Next Technology, or IPng) has been produced by the Internet Executive Task Power (IETF) to beat the shortcomings in today's IPv4. For example, IPv6 permits 128-little address measures, some four times that of IPv4[3]. It is envisaged that this protocol will meet the demand for addresses for a long time. Furthermore, IPv6 has other features that are designed to provide more reliable services, such as stateless address auto-configuration, a simplified header format to reduce the expense of packet bandwidth and handling, built-in security, and better support for quality of service requirements. The existing Internet is mainly predicated on IPv4, which was described in 1981 at the same time when developers cannot imagine the range of addresses required by the web today. INTERNET PROTOCOL VERSION 4 (IPv4) : Internet Protocol version 4 (IPv4) is the fourth version of the web Protocol (IP) which is the first version of the protocol to be widely deployed. IPv4 is the most widely deployed Internet Layer protocol still. It runs on the 32 bit addressing and permits 4,294,967,296 unique addresses. Despite the fact that the name appears to imply that it is the fourth generation of the main element Internet Protocol, version 4 of IP was the first that was trusted in modern TCP/IP[2]. It provides the essential datagram delivery capacities after which most of TCP/IP functions and they have proven its quality used over an interval greater than two decades. INTERNET PROTOCOL VERSION 6 (IPv6) : Web Protocol rendition 6 (IPv6) is an adaptation of the Internet Protocol (IP) planned to succeed IPv4. IPv6 is studied by IETF to furnish the Internet with bigger location space and better execution [4]. It gives a huge location space. It has an exceptionally improved header. After full usage of IPv6, each host can specifically achieve different hosts obviously with constraints like firewall, associations strategy etc.[2]. Makes bury portion correspondence conceivable in light of the fact that it underpins both stateful and stateless auto arrangement method of its host gadgets. High-transfer speed mixed media and adaptation to non-critical failure applications are the center of the significant objective of IPv6...

Microservices are becoming a more popular software architecture among companies and developers. Therefore, there is a need to develop methods for quantifying the process of measuring the quality of microservices design. This paper has created a novel set of metrics for microservices architecture applications. The proposed metrics are the Service Granularity Metric “SGM”, the Lack of Cohesion Metric “LCOM”, and the Number of Operations “NOO”. The proposed metrics measure the granularity, cohesion, and complexity of individual microservices through analyzing the application programming interface “API”. Using these metrics, it is possible to evaluate the overall quality of the design of microservices applications. The proposed metrics were measured on 5 applications with different sizes and business cases. This research found that the value for the SGM metric needs to be between 0.2 and 0.6. Besides, the value of LCOM metric for a microservice needs to be between 0 and 0.8 with less than ten operations per microservice. These findings can be applied in the decomposition process of monolithic applications as well.