CATS: linearizability and partition tolerance in scalable and self-organizing key-value stores

Distributed key-value stores provide scalable, fault-tolerant, and self-organizing storage services, but fall short of guaranteeing linearizable consistency in partially synchronous, lossy, partitionable, and dynamic networks, when data is distributed and replicated automatically by the principle of consistent hashing. This paper introduces consistent quorums as a solution for achieving atomic consistency. We present the design and implementation of CATS, a distributed key-value store which uses consistent quorums to guarantee linearizability and partition tolerance in such adverse and dynamic network conditions. CATS is scalable, elastic, and self-organizing; key properties for modern cloud storage middleware. Our system shows that consistency can be achieved with practical performance and modest throughput overhead (5%) for read-intensive workloads

The Simulation Model Partitioning Problem: an Adaptive Solution Based on Self-Clustering (Extended Version)

This paper is about partitioning in parallel and distributed simulation. That means decomposing the simulation model into a numberof components and to properly allocate them on the execution units. An adaptive solution based on self-clustering, that considers both communication reduction and computational load-balancing, is proposed. The implementation of the proposed mechanism is tested using a simulation model that is challenging both in terms of structure and dynamicity. Various configurations of the simulation model and the execution environment have been considered. The obtained performance results are analyzed using a reference cost model. The results demonstrate that the proposed approach is promising and that it can reduce the simulation execution time in both parallel and distributed architectures

Distributed Hybrid Simulation of the Internet of Things and Smart Territories

This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487

Scalable Scientific Computing Algorithms Using MapReduce

Cloud computing systems, like MapReduce and Pregel, provide a scalable and fault tolerant environment for running computations at massive scale. However, these systems are designed primarily for data intensive computational tasks, while a large class of problems in scientific computing and business analytics are computationally intensive (i.e., they require a lot of CPU in addition to I/O). In this thesis, we investigate the use of cloud computing systems, in particular MapReduce, for computationally intensive problems, focusing on two classic problems that arise in scienti c computing and also in analytics: maximum clique and matrix inversion. The key contribution that enables us to e ectively use MapReduce to solve the maximum clique problem on dense graphs is a recursive partitioning method that partitions the graph into several subgraphs of similar size and running time complexity. After partitioning, the maximum cliques of the di erent partitions can be computed independently, and the computation is sped up using a branch and bound method. Our experiments show that our approach leads to good scalability, which is unachievable by other partitioning methods since they result in partitions of di erent sizes and hence lead to load imbalance. Our method is more scalable than an MPI algorithm, and is simpler and more fault tolerant. For the matrix inversion problem, we show that a recursive block LU decomposition allows us to e ectively compute in parallel both the lower triangular (L) and upper triangular (U) matrices using MapReduce. After computing the L and U matrices, their inverses are computed using MapReduce. The inverse of the original matrix, which is the product of the inverses of the L and U matrices, is also obtained using MapReduce. Our technique is the rst matrix inversion technique that uses MapReduce. We show experimentally that our technique has good scalability, and it is simpler and more fault tolerant than MPI implementations such as ScaLAPACK

Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays

In this paper, we discuss on the use of self-organizing protocols to improve the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar approaches are studied, which are based on local knowledge of the nodes' 2nd neighborhood. The first scheme is a simple protocol requiring interactions among nodes and their direct neighbors. The second scheme adds a check on the Edge Clustering Coefficient (ECC), a local measure that allows determining edges connecting different clusters in the network. The performed simulation assessment evaluates these protocols over uniform networks, clustered networks and scale-free networks. Different failure modes are considered. Results demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking and Applications. The final publication is available at Springer via http://dx.doi.org/10.1007/s12083-015-0384-