Modern software development abstracts applications from the underlying infrastructure, enabling global-scale deployment with minimal concern about low-level networking details. However, when these infrastructure-agnostic software components need to communicate, they encounter significant networking limitations. This forces developers to either navigate complex, low-level networking constructs to achieve the desired connectivity or give up on truly flexible connectivity and limit their software to static connectivity patterns. In this paper, we focus on the evolving challenges of application connectivity in today's hyper-distributed reality. We propose to model connectivity around the notion of application services and have realized this proposal as ClusterLink, which exposes the app-level APIs for specifying communication policies at a very granular level and implements them efficiently. This paper shares details on ClusterLink design principles, APIs, architecture, and implementation, and shows that ClusterLink outperforms its closest competitor by 2.5x in throughput in a cloud-based experimental setting.

Enterprises often deploy their business applications in multiple clouds as well as in multiple traditional environments. This work focuses on the connectivity aspects of this new way of operating and consuming digital services. We define the related requirements, analyze the challenges, and present ClusterLink, our solution for interconnecting today's and future multi-cloud applications.

Fabric is a modular and extensible open-source system for deploying and operating permissioned blockchains and one of the Hyperledger projects hosted by the Linux Foundation (www.hyperledger.org).

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Distributed Ledger Technology (DLT) is rapidly emerging as a new paradigm for automating complex business processes in secure and decentralised fashion. Currently, however, its wider adoption is hampered by scalability problems [3] rooted in an inherent tension between stringent consistency, security, and robustness requirements on one hand, and growing application demand coupled with high performance expectations on the other. For example, popular peer-to-peer DLTs based on proof-of-work consensus [4] can only improve the transaction throughput by degrading their security and consistency guarantees, which is unacceptable in the enterprise and mission-critical settings.

Reconfigurable state machine replication is an important enabler of elasticity for replicated cloud services, which must be able to dynamically adjust their size as a function of changing load and resource availability. We introduce a new generic framework to allow the reconfigurable state machine implementation to be derived from a collection of arbitrary non-reconfigurable state machines. Our reduction framework follows the black box approach, and does not make any assumptions with respect to its execution environment apart from reliable channels. It allows higher-level services to leverage speculative command execution to ensure uninterrupted progress during the reconfiguration periods as well as in situations where failures prevent the reconfiguration agreement from being reached in a timely fashion. We apply our framework to obtain a reconfigurable speculative state machine from the non-reconfigurable Paxos implementation, and analyze its performance on a realistic distributed testbed. Our results show that our framework incurs negligible overheads in the absence of reconfiguration, and allows steady throughput to be maintained throughout the reconfiguration periods.

A distributed algorithm is adaptive if its performance depends on k, the number of processes that are concurrently active during the algorithm execution (rather than on n, the total number of processes). This paper presents adaptive algorithm for mutual exclusion using only read and write operations.