اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA Platform Architecture for Multi-Tenant Blockchain-Based Systems
91
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Blockchain has attracted a broad range of interests from start-ups, enterprises and governments to build next generation applications in a decentralized manner. Similar to cloud platforms, a single blockchain-based system may need to serve multiple tenants simultaneously. However, design of multi-tenant blockchain-based systems is challenging to architects in terms of data and performance isolation, as well as scalability. First, tenants must not be able to read other tenants data and tenants with potentially higher workload should not affect read/write performance of other tenants. Second, multi-tenant blockchain-based systems usually require both scalability for each individual tenant and scalability with number of tenants. Therefore, in this paper, we propose a scalable platform architecture for multi-tenant blockchain-based systems to ensure data integrity while maintaining data privacy and performance isolation. In the proposed architecture, each tenant has an individual permissioned blockchain to maintain their own data and smart contracts. All tenant chains are anchored into a main chain, in a way that minimizes cost and load overheads. The proposed architecture has been implemented in a proof-of-concept prototype with our industry partner, Laava ID Pty Ltd (Laava). We evaluate our proposal in a three-fold way: fulfilment of the identified requirements, qualitative comparison with design alternatives, and quantitative analysis. The evaluation results show that the proposed architecture can achieve data integrity, performance isolation, data privacy, configuration flexibility, availability, cost efficiency and scalability.
قيم البحث
اقرأ أيضاً
Massive amounts of multimedia data (i.e., text, audio, video, graphics and animation) are being generated everyday. Conventionally, multimedia data are managed by the platforms maintained by multimedia service providers, which are generally designed
using centralised architecture. However, such centralised architecture may lead to a single point of failure and disputes over royalties or other rights. It is hard to ensure the data integrity and track fulfilment of obligations listed on the copyright agreement. To tackle these issues, in this paper, we present a blockchain-based platform architecture for multimedia data management. We adopt self-sovereign identity for identity management and design a multi-level capability-based mechanism for access control. We implement a proof-of-concept prototype using the proposed approach and evaluate it using a use case. The results show that the proposed approach is feasible and has scalable performance.
Self-sovereign identity (SSI) is considered to be a killer application of blockchain. However, there is a lack of systematic architecture designs for blockchain-based SSI systems to support methodical development. An aspect of such gap is demonstrate
d in current solutions, which are considered coarse grained and may increase data security risks. In this paper, we first identify the lifecycles of three major SSI objects (i.e., key, identifier, and credential) and present fine-grained design patterns critical for application development. These patterns are associated with particular state transitions, providing a systematic view of system interactions and serving as a guidance for effective use of these patterns. Further, we present an SSI platform architecture, which advocates the notion of Design-Pattern-as-a-Service. Each design pattern serves as an API by wrapping the respective pattern code to ease application development and improve scalability and security. We implement a prototype and evaluate it on feasibility and scalability.
Blockchain is an innovative distributed ledger technology which has attracted a wide range of interests for building the next generation of applications to address lack-of-trust issues in business. Blockchain as a service (BaaS) is a promising soluti
on to improve the productivity of blockchain application development. However, existing BaaS deployment solutions are mostly vendor-locked: they are either bound to a cloud provider or a blockchain platform. In addition to deployment, design and implementation of blockchain-based applications is a hard task requiring deep expertise. Therefore, this paper presents a unified blockchain as a service platform (uBaaS) to support both design and deployment of blockchain-based applications. The services in uBaaS include deployment as a service, design pattern as a service and auxiliary services. In uBaaS, deployment as a service is platform agnostic, which can avoid lock-in to specific cloud platforms, while design pattern as a service applies design patterns for data management and smart contract design to address the scalability and security issues of blockchain. The proposed solutions are evaluated using a real-world quality tracing use case in terms of feasibility and scalability.
Deep learning (DL) is becoming increasingly popular in several application domains and has made several new application features involving computer vision, speech recognition and synthesis, self-driving automobiles, drug design, etc. feasible and acc
urate. As a result, large scale on-premise and cloud-hosted deep learning platforms have become essential infrastructure in many organizations. These systems accept, schedule, manage and execute DL training jobs at scale. This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM. We describe how our design balances dependability with scalability, elasticity, flexibility and efficiency. We examine FfDL qualitatively through a retrospective look at the lessons learned from building, operating, and supporting FfDL; and quantitatively through a detailed empirical evaluation of FfDL, including the overheads introduced by the platform for various deep learning models, the load and performance observed in a real case study using FfDL within our organization, the frequency of various faults observed including unanticipated faults, and experiments demonstrating the benefits of various scheduling policies. FfDL has been open-sourced.
Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a ple
thora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
