Speculative client execution in deferred update replication

Many database replication protocols are based on the deferred update replication technique. In deferred update replication, transactions are executed by a single server, and certified and possibly committed by every server. Thus, servers must store a full copy of the database. This assumption is detrimental to performance since servers may not be able to cache the entire database in main memory. This paper introduces RAM-DUR, a variation of deferred update replication whereby transaction execution is in-memory only. RAM-DUR's key insight is a sophisticated distributed cache mechanism that provides high performance and strong consistency without the limitations of existing solutions (e.g., no single server must have enough memory to cache the entire database). In addition to presenting RAM-DUR, we detail its implementation, and provide an extensive analysis of its performance.

Most operations in database (DB) is based on the deferred update replication technique, especially in distributed environments. In deferred update replication, transactio ns are executed on a single host and broadcast to other hosts at commit time. Upon delivering a transaction, each host confirms it to ensure a globally serializable execution. The mechanism is subject to CAP theorem in order to ensure data consistency and availability. This paper proposes a dependable cache query mechanism in distributed relational database (DRDB). The key insight of the cache query mechanism provides high performance, strong consistency and availability for mass connection systems. In addition to presenting the cache query mechanism, we elaborate its implementation, and provide an experiment and analysis of its performance.

Through the ongoing trend in graph technologies due to the massive growth of linked data produced by social networks graph databases gained popularity. Replication, a common approach to increase availability in databases, is also used by diverse graph database solutions. Few approaches implementing fault-tolerance in graph databases have been proposed yet.This paper considers deferred update replication using atomic broadcast in order to implement fault-tolerance in distributed graph databases. The main contribution of this paper is a deferred update algorithm adapted to graph databases offering a more scalable and faster solution, showing a performance advantage of over 30\% compared to existing approaches.

Speculative plan execution for information gathering

Hierarchical consensus protocols are one of the most promising approaches considered to improve the performance and scalability of blockchain-based peer-to-peer energy trading (P2P ET) systems. They allow a network to be divided into multiple clusters where each cluster can independently run its own local consensus on local transactions (i.e., transactions that belong to a single cluster) and also cooperate with other clusters for global transactions (i.e., transactions that affect more than one cluster). However, current solutions do not allow the parallel processing of local and global transactions, leading to low throughput and high latency. Moreover, they impose an additional burden on the leader node by assigning to it the responsibility for both local and global consensus. This paper proposes a hierarchical concurrent optimistic blockchain consensus protocol, called HiCoOB, which includes an optimistic local consensus and a deterministic global consensus that enables the concurrent execution of local and global transactions. Additionally, the local and the global consensus are coordinated by two distinct leaders, thereby reducing the overhead on the local leader node. HiCoOB also provides a synchronization method between local and global consensus to avoid the double spending of energy or money that arises from interdependent client transactions. Experimental results indicate that the proposed protocol achieves 87% higher throughput than the existing systems. Furthermore, it reduces the waiting time of the local transactions by a factor of 5x. Finally, HiCoOB has 10 times less overhead on the local leader node in terms of message exchange when compared to state-of-the-art systems.

Task stragglers dramatically impede parallel job execution of data-intensive computing in Cloud Datacenters Due to the uneven distribution of input data resulted from heterogeneous data nodes, resource contention situations, and network configurations, it causes delay failures due to the violation of job completion time. However, data-intensive computing frameworks, such as MapReduce or Hadoop, employ a mechanism called speculative execution to deal with the straggler issue, speculative execution provide limited effectiveness because in many cases straggler identification occurs too late within a job lifecycle. Identifying the straggler and the timing of identifying it is very important for Straggler mitigation in Data-intensive cloud computing. Speculative execution method is a widely adopted as a straggler identification and mitigation scheme but it has certain inherent limitations. In this paper, we strive to make Hadoop more efficient in cloud environments. We present Progress and Feedback based Speculative Execution Algorithm (PFSE), a new Straggler identification scheme to identify the straggler MapReduce tasks based on the feedback information received from completed tasks beside the progress of the currently processing task, our extensive simulation shows that PFSE can outperform the dynamic scheduling techniques like Self-Learning MapReduce scheduler (SLM) and LATE. PFSE can assist in enhancing straggler Identification and mitigation for tolerating late-timing failures within data intensive cloud computing.

Uncountable loops (such as while loops in C) and if-conditions are some of the most common constructs in programming. While-loops are widely used to determine the convergence in linear algebra algorithms or goal finding problems from graph algorithms, to name a few. In general while-loops are used whenever the loop iteration space, the number of iterations a loop executes is unknown. Usually in while-loops, the execution of the next iteration is decided inside the current loop iteration (i.e. the execution of iteration i depends on the values computed in iteration i-1). This precludes their parallel execution in today's ubiquitous multi-core architectures. In this paper a technique to speculatively create parallel tasks from the next iterations before the current one completes is proposed. If consecutive loop-iterations are only control dependent, then multiple iterations can be executed simultaneously; later in the execution path, the runtime system will decide to either commit the results of such speculatively executed iterations or undo the changes made by them. Data dependences within or between non-speculative and speculative work are honored to guarantee correctness. The proposed technique is implemented in SMPSs, a task-based dataflow programming model for shared-memory multiprocessor architectures. The approach is evaluated on a set of applications from graph algorithms and linear algebra. Results are promising with an average increase in the speedup of 1.2x with 16 threads when compared to non speculative execution of the applications. The increase in the speedup is significant, since the performance gain is achieved over an already parallelized version of the benchmarks.

Many techniques for parallelizing a sequentially coded program have been developed and put to practical use, but there are many cases in which program codes cannot be parallelized because it is impossible to assure that their parallel execution does not violate the data dependencies in the program. To parallelize such programs, we have previously proposed speculative memory (SM). With SM, programmers can specify the parallel and speculative execution of threads explicitly in their programs. The SM system manages the memory data that are speculatively read or written by the threads running in parallel. When the system detects inconsistent memory accesses, it recovers the computational state of the program and restarts the execution. If such inconsistencies are not encountered often, we can expect the total execution time of the program to be shorter. In this paper we present a software implementation of SM (SSM). We have developed an SM library by using POSIX threads. In spite of large overheads that are essentially unavoidable in a software implementation, our SSM system can shorten the execution time of a program that compilers cannot parallelize automatically.

Ethereum is an emerging distributed computing platform that supports a decentralized replicated virtual machine at a large scale. Transactions in Ethereum are specified in smart contracts, disseminated through broadcast, accepted into the chain of blocks, and then executed on each node. In this new Dissemination-Consensus-Execution (DiCE) paradigm, the time interval between when a transaction is known (during the dissemination phase) to when the transaction is executed (after the consensus phase) offers a window of opportunity to accelerate transaction processing through speculative execution. However, the traditional speculative execution, which hinges on the ability to predict the future accurately, is inadequate because of DiCE's many-future nature. Forerunner proposes a novel constraint-based approach for speculative execution on Ethereum. In contrast to the traditional approach of predicting a single future and demanding it to be perfectly accurate, Forerunner speculates on multiple futures and can leverage speculative results based on imperfect predictions whenever certain constraints are satisfied. Under these constraints, a transaction execution is substantially accelerated through a novel multi-trace program specialization enhanced by a new form of memoization. The fully implemented Forerunner is evaluated as a node connected to the worldwide Ethereum network. When processing 13 million transactions live in real time, Forerunner achieves an effective average speedup of 8.39x on the transactions that it hears during the dissemination phase, which accounts for 95.71% of all the transactions. The end-to-end speedup over all the transactions is 6.06x. The code and data sets are publicly available.

Scalability is one of the main problems limiting blockchain applications. Most recent research on blockchain scalability has focused on improving the consensus layer, but the most advanced consensus protocols can only reach a few thousand transactions per second(tps), which is far below the capacity of typical distributed databases. With the use of blockchain sharding technology, the time overhead corresponding to the execution layer and the consensus layer in the blockchain is gradually reduced. Additionally, improving the efficiency of transaction execution could incentivize nodes to actively verify transactions and enhance blockchain security. In this paper, we propose a Sharding Based Parallel Execution of Block Transactions (SPEx-Tran) framework in blockchain, which transforms the sequential execution of intra-block transactions into parallel execution to improve the performance of blockchain. In addition, based on the characteristics of blockchain sharding transactions, we further propose a Blockchain Transaction Scheduling (BCTS) algorithm to improve the performance of blockchain by optimizing the parallel execution process of transactions. In the BCTS algorithm, the mechanism of local multi-processors sharing the Conflict Transactions cache Table (CTT) simplifies the verification process of conflicting transactions, thus improving the blockchain performance. Experimental results show that the throughput of blockchain using the SPExTran framework and BCTS algorithm is significantly higher than that of typical sharding technologies.

This paper presents a new parallelization model, called coarse-grained thread pipelining, for exploiting speculative coarse-grained parallelism from general-purpose application programs in shared-memory multiprocessor systems. This parallelization model, which is based on the fine-grained thread pipelining model proposed for the superthreaded architecture, allows concurrent execution of loop iterations in a pipelined fashion with runtime data-dependence checking and control speculation. The speculative execution combined with the runtime dependence checking allows the parallelization of a variety of program constructs that cannot be parallelized with existing runtime parallelization algorithms. The pipelined execution of loop iterations in this new technique results in lower parallelization overhead than in other existing techniques. We evaluated the performance of this new model using some real applications and a synthetic benchmark. These experiments show that programs with a sufficiently large grain size compared to the parallelization overhead obtain significant speedup using this model. The results from the synthetic benchmark provide a means for estimating the performance that can be obtained from application programs that will be parallelized with this model. The library routines developed for this thread pipelining model are also useful for evaluating the correctness of the codes generated by the superthreaded compiler and in debugging and verifying the simulator for the superthreaded processor.

ReduxSTM: Optimizing STM designs for Irregular Applications

This paper presents new achievements on the automatic mapping of abstract algorithms, written in imperative software programming languages, to custom computing machines. The reconfigurable hardware element of the target architecture consists of one field-programmable gate array coupled with one or more memories. The compilation flow exposes operation- and functional-level parallelism, and speculative execution. Such expositions are efficiently represented in a hierarchical model. In order to take full advantage of such representation, the scheduling scope is significantly improved by merging basic blocks at loop boundaries and by considering the parallel execution of exposed concurrent loops. The paper describes the scheduling technique, shows a study on the impact of the merge operation, and reveals the improvements achieved when the exposed parallelism is fully satisfied.KeywordsParallel ExecutionCompilation TechniqueSpeculative ExecutionAbstract AlgorithmTemporal PartitioningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This article presents Pot, a system that leverages the concept of preordered transactions to achieve deterministic multithreaded execution of programs that use Transactional Memory. Preordered transactions eliminate the root cause of nondeterminism in transactional execution: they provide the illusion of executing in a deterministic serial order, unlike traditional transactions that appear to execute in a nondeterministic order that can change from execution to execution. Pot uses a new concurrency control protocol that exploits the serialization order to distinguish between fast and speculative transaction execution modes in order to mitigate the overhead of imposing a deterministic order. We build two Pot prototypes: one using STM and another using off-the-shelf HTM. To the best of our knowledge, Pot enables deterministic execution of programs using off-the-shelf HTM for the first time. An experimental evaluation shows that Pot achieves deterministic execution of TM programs with low overhead, sometimes even outperforming nondeterministic executions, and clearly outperforming the state of the art.

In state-of-the-art Software Transactional Memory (STM) systems, threads carry out the execution of transactions as non-interruptible tasks. Hence, a thread can react to the injection of a higher priority transactional task and take care of its processing only at the end of the currently executed transaction. In this article we pursue a paradigm shift where the execution of an in-memory transaction is carried out as a preemptable task, so that a thread can start processing a higher priority transactional task before finalizing its current transaction. We achieve this goal in an application-transparent manner, by only relying on Operating System facilities we include in our preemptive STM architecture. With our approach we are able to re-evaluate CPU assignment across transactions along a same thread every few tens of microseconds. This is mandatory for an effective priority-aware architecture given the typically finer-grain nature of in-memory transactions compared to their counterpart in database systems. We integrated our preemptive STM architecture with the TinySTM package, and released it as open source. We also provide the results of an experimental assessment of our proposal based on running a port of the TPC-C benchmark to the STM environment.