A Scalable, Correct Time-stamped Stack

Concurrent data-structures, such as stacks, queues, and deques, often implicitly enforce a total order over elements in their underlying memory layout. However, much of this order is unnecessary: linearizability only requires that elements are ordered if the insert methods ran in sequence. We propose a new approach which uses timestamping to avoid unnecessary ordering. Pairs of elements can be left unordered if their associated insert operations ran concurrently, and order imposed as necessary at the eventual removal. We realise our approach in a new non-blocking datastructure, the TS (timestamped) stack. Using the same approach, we can define corresponding queue and deque datastructures. In experiments on x86, the TS stack outperforms and outscales all its competitors – for example, it outperforms the elimination-backoff stack by factor of two. In our approach, more concurrency translates into less ordering, giving less-contended removal and thus higher performance and scalability. Despite this, the TS stack is linearizable with respect to stack semantics. The weak internal ordering in the TS stack presents a challenge when establishing linearizability: standard techniques such as linearization points work well when there exists a total internal order. We present a new stack theorem, mechanised in Isabelle, which characterises the orderings sufficient to establish stack semantics. By applying our stack theorem, we show that the TS stack is indeed linearizable. Our theorem constitutes a new, generic proof technique for concurrent stacks, and it paves the way for future weakly ordered data-structure designs

The InterMesh Network Architecture

The rapid spread of mobile devices, the emergence of key wireless technologies, and the nomadic user and computing lifestyles on current networks are continuously evolving in synergy. MANETs, WSNs, and WMNs are examples of self-organizing unstructured networks that have their local communication paradigms and are optimized to perform under their particular physical constraints. Wireless Mesh Networks (WMNs) are particularly interesting because of their ability to operate in a pure ad-hoc mode or to include some infrastructural components, making them suitable for a multitude of applications. Inter-networking among the heterogeneous access networks is currently offered by the Internet Protocol (IP). However, the evolution of and the innovation within these networks is greatly hindered by the rigidity of the current Internet implementation and its lag in efficiently supporting flexible unstructured communication paradigms. To broaden the user\u27s innovation space and to efficiently embrace the characteristics of emerging networks, clean-slate architectural approaches are being pursued. In this paper, we propose InterMesh, a novel iner-networking platform for wireless mesh networks. InterMesh enables heterogeneous access networks to converge at novel Persistent Identification and Networking Layer (PINL), providing a seamless service to individual network entities. This paper identifies the key concepts behind the InterMesh network platform, presents an interesting prototype implementation that can coexist with today\u27s Internet while still be able to evolve separately, and discusses some preliminary performance results of the prototype

LIPIcs

The semantics of concurrent data structures is usually given by a sequential specification and a consistency condition. Linearizability is the most popular consistency condition due to its simplicity and general applicability. Nevertheless, for applications that do not require all guarantees offered by linearizability, recent research has focused on improving performance and scalability of concurrent data structures by relaxing their semantics. In this paper, we present local linearizability, a relaxed consistency condition that is applicable to container-type concurrent data structures like pools, queues, and stacks. While linearizability requires that the effect of each operation is observed by all threads at the same time, local linearizability only requires that for each thread T, the effects of its local insertion operations and the effects of those removal operations that remove values inserted by T are observed by all threads at the same time. We investigate theoretical and practical properties of local linearizability and its relationship to many existing consistency conditions. We present a generic implementation method for locally linearizable data structures that uses existing linearizable data structures as building blocks. Our implementations show performance and scalability improvements over the original building blocks and outperform the fastest existing container-type implementations

Monotonically relaxing concurrent data-structure semantics for performance: An efficient 2D design framework

There has been a significant amount of work in the literature proposing semantic relaxation of concurrent data structures for improving scalability and performance. By relaxing the semantics of a data structure, a bigger design space, that allows weaker synchronization and more useful parallelism, is unveiled. Investigating new data structure designs, capable of trading semantics for achieving better performance in a monotonic way, is a major challenge in the area. We algorithmically address this challenge in this paper. We present an efficient, lock-free, concurrent data structure design framework for out-of-order semantic relaxation. Our framework introduces a new two dimensional algorithmic design, that uses multiple instances of a given data structure. The first dimension of our design is the number of data structure instances operations are spread to, in order to benefit from parallelism through disjoint memory access. The second dimension is the number of consecutive operations that try to use the same data structure instance in order to benefit from data locality. Our design can flexibly explore this two-dimensional space to achieve the property of monotonically relaxing concurrent data structure semantics for achieving better throughput performance within a tight deterministic relaxation bound, as we prove in the paper. We show how our framework can instantiate lock-free out-of-order queues, stacks, counters and dequeues. We provide implementations of these relaxed data structures and evaluate their performance and behaviour on two parallel architectures. Experimental evaluation shows that our two-dimensional data structures significantly outperform the respected previous proposed ones with respect to scalability and throughput performance. Moreover, their throughput increases monotonically as relaxation increases