An In-Switch Architecture for Low-Latency Microservices

In recent time, there is has been a movement away from standard monolithic architecture in cloud and web services towards what is known as a microservice architecture. Microservice architecture decomposes the previous monolithic architecture into multiple independent services called "microservices". Examples of applications that use a microservice architecture include Netflix and Amazon. These applications typically send large numbers of microservice requests, which go through the OSI network layers to establish a client server connection. This trend towards microservices has developed interest by other researchers to make improvements in this field, due to the growing reliance importance on such architectures by consumers. There have been studies regarding the security of these microservices, performance analysis of various applications, and the use of these microservice applications in cloud technology. Any improvements in the speed, security, or organization of such network architecture would be very beneficial of these popular API's, and their user base. This project's objective is to investigate the potential of moving some of the processing that is done for these microservices within a network switch, and as a result the performance at the application level, by alleviating network communication. We formulate a high-level design for an in-switch architecture for low-latency microservice leveraging existing programmable-switches support. We investigate the implementation of NetCache as a microservice in our model and predict a significant latency reduction and subsequent performance increase

EduChain: CIA-Compliant Block-chain forIntelligent Cyber Defense of Microservices inEducation Industry 4.0

This is an accepted manuscript of an article published by IEEE in IEEE Transactions on Industrial Informatics, available online: https://ieeexplore.ieee.org/document/9468408 The accepted version of the publication may differ from the final published version.Massive data handling requirement in education industry 4.0 has attracted interests in the research of microservice architectures due to their scalability, resilience and elasticity characteristics. This development has been challenged by extensive data exchange required by a set of independent microservices tobuilda complete application, which could resultin increasing risksandexposuretothe securityand privacy breaches of the data. It is imperative to see that educational data are highly sensitive, critical for ascertaining educational attainment and facilitating credentials for qualifcation verifcations. This paper puts forward a new proposal of devising a security and privacy-preserving design mechanism of data transactions in educational microservices leveraging the blockchain technology. The design comprises three phases, namely the blockchain framework, data sending-receiving and confdentiality-integrity-availability over a secured platform with each phase having detailed mechanisms for algorithm implementation. The proposal is shown to exhibit favourable performance in terms of time cost of publishing, throughput and latency, and shown to have high surveyacceptance in terms of confdentiality, integrity and availability with approximately 10% improvement from prior blockchain adoption

BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices

Precision Beekeeping, a field of Precision Agriculture, is an apiary management strategy based on monitoring honeybee colonies to promote more sustainable resource usage and maximise productivity. The approach related to Precision Beekeeping is based on methodologies to mitigate the stress associated with human intervention in the colonies and the waste of resources. These goals are achieved by supporting the intervention and managing the beekeeper’s timely and appropriate action at the colony’s level. In recent years, the growth of IoT (Internetof-Things) in Precision Agriculture has spurred several proposals to contribute to the paradigm of Precision Beekeeping, built on different technical concepts and with different production costs. This work proposes and describes an information systems architecture concept named BHiveSense, based on IoT and microservices, and different artefacts to demonstrate its concept: (1) a low-cost COTS (Commercial Off-The-Shelf) hive sensing prototype, (2) a REST backend API, (3) a Web application, and (4) a Mobile application. This project delivers a solution for a more integrated and sustainable beekeeping activity. Our approach stresses that by adopting microservices and a REST architecture, it is possible to deal with long-standing problems concerning interoperability, scalability, agility, and maintenance issues, delivering an efficient beehive monitoring system.info:eu-repo/semantics/publishedVersio

Performance Evaluation Metrics for Cloud, Fog and Edge Computing: A Review, Taxonomy, Benchmarks and Standards for Future Research

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments

Optimal Blind and Adaptive Fog Orchestration under Local Processor Sharing

International audienceThis paper studies the tradeoff between running cost and processing delay in order to optimally orchestrate multiple fog applications. Fog applications process batches of objects' data along chains of containerised microservice modules, which can run either for free on a local fog server or run in cloud at a cost. Processor sharing techniques, in turn, affect the applications' processing delay on a local edge server depending on the number of application modules running on the same server. The fog orchestrator copes with local server congestion by offloading part of computation to the cloud trading off processing delay for a finite budget. Such problem can be described in a convex optimisation framework valid for a large class of processor sharing techniques. The optimal solution is in threshold form and depends solely on the order induced by the marginal delays of N fog applications. This reduces the original multidimensional problem to an unidimensional one which can be solved in O(N 2) by a parallelised search algorithm under complete system information. Finally, an online learning procedure based on a primal-dual stochastic approximation algorithm is designed in order to drive optimal reconfiguration decisions in the dark, by requiring only the unbiased estimation of the marginal delays. Extensive numerical results characterise the structure of the optimal solution, the system performance and the advantage attained with respect to baseline algorithmic solutions