This book constitutes the refereed proceedings of the IFIP/ACM International Conference on Distributed Systems Platforms, Middleware 2001, held in Heidelberg, Germany, in November 2001. The 20 revised full papers presented were carefully reviewed and selected from a total of 116 submissions. The papers are organized in topical sections on Java, mobility, distributed abstractions, reliability, home and office, scalability, and quality of service.
This book constitutes the refereed proceedings of the IFIP/ACM International Conference on Distributed Systems Platforms, Middleware 2001, held in Hei...
This book constitutes the revised selected papers of the First International Conference on Networked Systems, NETYS 2013, held in Marrakech, Morocco, in May 2013. The 33 papers (17 regular and 16 short papers) presented were carefully reviewed and selected from 74 submissions. They address major topics from theory and practice of networked systems: multi-core architectures, middleware, environments, storage clusters, as well as peer-to-peer, sensor, wireless, and mobile networks.
This book constitutes the revised selected papers of the First International Conference on Networked Systems, NETYS 2013, held in Marrakech, Morocco, ...
Transactional memory (TM) is an appealing paradigm for concurrent programming on shared memory architectures. With a TM, threads of an application communicate, and synchronize their actions, via in-memory transactions. Each transaction can perform any number of operations on shared data, and then either commit or abort. When the transaction commits, the effects of all its operations become immediately visible to other transactions; when it aborts, however, those effects are entirely discarded. Transactions are atomic: programmers get the illusion that every transaction executes all its...
Transactional memory (TM) is an appealing paradigm for concurrent programming on shared memory architectures. With a TM, threads of an application com...
Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust...
Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use ...
