This paper describes how to augment techniques such as Distributed Shared Memory with recent trends on disaggregated Non Volatile Memory in the data centre so that the combination can be used in an edge environment with potentially volatile and mobile resources. This article identifies the main advantages and challenges, and offers an architectural evolution to incorporate recent research trends into production-ready disaggregated edges. We also present two prototypes showing the feasibility of this proposal.
Mobile clients that consume and produce data are abundant in fog environments. Low latency access to this data can only be achieved by storing it in close physical proximity to the clients. Current data store systems fall short as they do not replicate data based on client movement. We propose an approach to predictive replica placement that autonomously and proactively replicates data close to likely client locations.
This paper investigates Nash equilibrium (NE) seeking problems for noncooperative games over multi-players networks with finite bandwidth communication. A distributed quantized algorithm is presented, which consists of local gradient play, distributed decision estimating, and adaptive quantization. Exponential convergence of the algorithm is established, and a relationship between the convergence rate and the bandwidth is quantitatively analyzed. Finally, a simulation of an energy consumption game is presented to validate the proposed results.
Yahya Hassanzadeh-Nazarabadi, Ali Utkan Şahin, Öznur Özkasap
et al.
SkipSim is an offline Skip Graph simulator that enables Skip Graph-based algorithms including blockchains and P2P cloud storage to be simulated while preserving their scalability and decentralized nature. To the best of our knowledge, it is the first Skip Graph simulator that provides several features for experimentation on Skip Graph-based overlay networks. In this demo paper, we present SkipSim features, its architecture, as well as a sample blockchain demo scenario.
Truxen is a Trusted Computing enhanced blockchain that uses Proof of Integrity protocol as the consensus. Proof of Integrity protocol is derived from Trusted Computing and associated Remote Attestations, that can be used to vouch a node's identity and integrity to all of the other nodes in the blockchain network. In this paper we describe how Trusted Computing and Proof of Integrity can be used to enhance blockchain in the areas of mining block, executing transaction and smart contract, protecting sensitive data. Truxen presents a Single Execution Model, that can verify and execute transaction and smart contract in a solo node, consequently enables remote calls to off-chain applications and performs in-deterministic tasks.
We systematically survey the literature on analytically sound multiprocessor real-time locking protocols from 1988 until 2018, covering the following topics: progress mechanisms that prevent the lock-holder preemption problem, spin-lock protocols, binary semaphore protocols, independence-preserving (or fully preemptive) locking protocols, reader-writer and k-exclusion synchronization, support for nested critical sections, and implementation and system-integration aspects. A special focus is placed on the suspension-oblivious and suspension-aware analysis approaches for semaphore protocols, their respective notions of priority inversion, optimality criteria, lower bounds on maximum priority-inversion blocking, and matching asymptotically optimal locking protocols.
In this paper, we present a novel method for constrained cluster size signed spectral clustering which allows us to subdivide large groups of people based on their relationships. In general, signed clustering only requires K hard clusters and does not constrain the cluster sizes. We extend signed clustering to include cluster size constraints. Using an example of seating assignment, we efficiently find groups of people with high social affinity while mitigating awkward social interaction between people who dislike each other.
Claudio Reggiani, Yann-Aël Le Borgne, Gianluca Bontempi
This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation based on Hadoop/Spark, and illustrate its scalability on datasets involving millions of observations or features.
The paper addresses problem of data allocation in two-layer computer storage while taking into account dynamic digraph(s) over computing tasks. The basic version of data file allocation on parallel hard magnetic disks is considered as special bin packing model. Two problems of the allocation solution reconfiguration (restructuring) are suggested: (i) one-stage restructuring model, (ii) multistage restructuring models. Solving schemes are based on simplified heuristics. Numerical examples illustrate problems and solving schemes.
In this paper we present a deterministic parallel algorithm solving the multiple selection problem in congested clique model. In this problem for given set of elements S and a set of ranks $K = \{k_1 , k_2 , ..., k_r \}$ we are asking for the $k_i$-th smallest element of $S$ for $1 \leq i \leq r$. The presented algorithm is deterministic, time optimal , and needs $O(\log^*_{r+1} (n))$ communication rounds, where $n$ is the size of the input set, and $r$ is the size of the rank set. This algorithm may be of theoretical interest, as for $r = 1$ (classic selection problem) it gives an improvement in the asymptotic synchronization cost over previous $O(\log \log p)$ communication rounds solution, where $p$ is size of clique.
Several solutions exist for file storage, sharing, and synchronization. Many of them involve a central server, or a collection of servers, that either store the files, or act as a gateway for them to be shared. Some systems take a decentralized approach, wherein interconnected users form a peer-to-peer (P2P) network, and partake in the sharing process: they share the files they possess with others, and can obtain the files owned by other peers. In this paper, we survey various technologies, both cloud-based and P2P-based, that users use to synchronize their files across the network, and discuss their strengths and weaknesses.
Traditional databases have long since reaped the benefits of multidimensional indexes. Numerous proposals in the literature describe multidimensional index designs for P2P systems. However, none of these designs have had real world implementations. Several proposals for P2P multidimensional indexes are reviewed and analyzed. Znet and VBI-tree are the most promising from a technical standpoint. All of the proposed designs assume honest nodes and are thus open to abuse. This is a critical flaw that must be solved before any of the proposed systems can be used.
This paper describes a method for accelerating large scale Artificial Neural Networks (ANN) training using multi-GPUs by reducing the forward and backward passes to matrix multiplication. We propose an out-of-core multi-GPU matrix multiplication and integrate the algorithm with the ANN training. The experiments demonstrate that our matrix multiplication algorithm achieves linear speedup on multiple inhomogeneous GPUs. The full paper of this project can be found at [1].
Development of cloud computing enables to move Big Data in the hybrid cloud services. This requires research of all processing systems and data structures for provide QoS. Due to the fact that there are many bottlenecks requires monitoring and control system when performing a query. The models and optimization criteria for the design of systems in a hybrid cloud infrastructures are created. In this article suggested approaches and the results of this build.
The conditional posterior Cramer-Rao lower bound (PCRLB) is an effective sensor resource management criteria for large, geographically distributed sensor networks. Existing algorithms for distributed computation of the PCRLB (dPCRLB) are based on raw observations leading to significant communication overhead to the estimation mechanism. This letter derives distributed computational techniques for determining the conditional dPCRLB for quantized, decentralized sensor networks (CQ/dPCRLB). Analytical expressions for the CQ/dPCRLB are derived, which are particularly useful for particle filter-based estimators. The CQ/dPCRLB is compared for accuracy with its centralized counterpart through Monte-Carlo simulations.
We present new adaptive format for storing sparse matrices on GPU. We compare it with several other formats including CUSPARSE which is today probably the best choice for processing of sparse matrices on GPU in CUDA. Contrary to CUSPARSE which works with common CSR format, our new format requires conversion. However, multiplication of sparse-matrix and vector is significantly faster for many atrices. We demonstrate it on set of 1 600 matrices and we show for what types of matrices our format is profitable.
Marco Aldinucci, Marco Danelutto, Massimo Torquati
FastFlow is a structured parallel programming framework targeting shared memory multicores. Its layered design and the optimized implementation of the communication mechanisms used to implement the FastFlow streaming networks provided to the application programmer as algorithmic skeletons support the development of efficient fine grain parallel applications. FastFlow is available (open source) at SourceForge (http://sourceforge.net/projects/mc-fastflow/). This work introduces FastFlow programming techniques and points out the different ways used to parallelize existing C/C++ code using FastFlow as a software accelerator. In short: this is a kind of tutorial on FastFlow.