An Innovative Approach to Achieve Compositionality Efficiently using Multi-Version Object Based Transactional Systems

In the modern era of multicore processors, utilizing cores is a tedious job. Synchronization and communication among processors involve high cost. Software transaction memory systems (STMs) addresses this issues and provide better concurrency in which programmer need not have to worry about consistency issues. Another advantage of STMs is that they facilitate compositionality of concurrent programs with great ease. Different concurrent operations that need to be composed to form a single atomic unit is achieved by encapsulating them in a single transaction. In this paper, we introduce a new STM system as multi-version object based STM (MVOSTM) which is the combination of both of these ideas for harnessing greater concurrency in STMs. As the name suggests MVOSTM, works on a higher level and maintains multiple versions corresponding to each key. We have developed MVOSTM with the unlimited number of versions corresponding to each key. In addition to that, we have developed garbage collection for MVOSTM (MVOSTM-GC) to delete unwanted versions corresponding to the keys to reduce traversal overhead. MVOSTM provides greater concurrency while reducing the number of aborts and it ensures compositionality by making the transactions atomic. Here, we have used MVOSTM for the list and hash-table data structure as list-MVOSTM and HT- MVOSTM. Experimental results of list-MVOSTM outperform almost two to twenty fold speedup than existing state-of-the-art list based STMs (Trans-list, Boosting-list, NOrec-list, list-MVTO, and list-OSTM). HT-MVOSTM shows a significant performance gain of almost two to nineteen times better than existing state-of-the-art hash-table based STMs (ESTM, RWSTMs, HT-MVTO, and HT-OSTM). MVOSTM with list and hash-table shows the least number of aborts among all the existing STM algorithms. MVOSTM satisfies correctness-criteria as opacity.Comment: 35 pages, 23 figure

Achieving Starvation-Freedom with Greater Concurrency in Multi-Version Object-based Transactional Memory Systems

To utilize the multi-core processors properly concurrent programming is needed. Concurrency control is the main challenge while designing a correct and efficient concurrent program. Software Transactional Memory Systems (STMs) provides ease of multithreading to the programmer without worrying about concurrency issues such as deadlock, livelock, priority inversion, etc. Most of the STMs works on read-write operations known as RWSTMs. Some STMs work at high-level operations and ensure greater concurrency than RWSTMs. Such STMs are known as Object-Based STMs (OSTMs). The transactions of OSTMs can return commit or abort. Aborted OSTMs transactions retry. But in the current setting of OSTMs, transactions may starve. So, we proposed a Starvation-Free OSTM (SF-OSTM) which ensures starvation-freedom in object based STM systems while satisfying the correctness criteria as co-opacity. Databases, RWSTMs and OSTMs say that maintaining multiple versions corresponding to each key of transaction reduces the number of aborts and improves the throughput. So, to achieve greater concurrency, we proposed Starvation-Free Multi-Version OSTM (SF-MVOSTM) which ensures starvation-freedom while storing multiple versions corresponding to each key and satisfies the correctness criteria such as local opacity. To show the performance benefits, We implemented three variants of SF-MVOSTM (SF-MVOSTM, SF-MVOSTM-GC and SF-KOSTM) and compared it with state-of-the-art STMs.Comment: 68 pages, 24 figures. arXiv admin note: text overlap with arXiv:1709.0103

Efficient Concurrent Execution of Smart Contracts in Blockchains using Object-based Transactional Memory

This paper proposes an efficient framework to execute Smart Contract Transactions (SCTs) concurrently based on object semantics, using optimistic Single-Version Object-based Software Transactional Memory Systems (SVOSTMs) and Multi-Version OSTMs (MVOSTMs). In our framework, a multi-threaded miner constructs a Block Graph (BG), capturing the object-conflicts relations between SCTs, and stores it in the block. Later, validators re-execute the same SCTs concurrently and deterministically relying on this BG. A malicious miner can modify the BG to harm the blockchain, e.g., to cause double-spending. To identify malicious miners, we propose Smart Multi-threaded Validator (SMV). Experimental analysis shows that the proposed multi-threaded miner and validator achieve significant performance gains over state-of-the-art SCT execution framework.Comment: 49 pages, 26 figures, 11 table

Using Nesting to Push the Limits of Transactional Data Structure Libraries

Transactional data structure libraries (TDSL) combine the ease-of-programming of transactions with the high performance and scalability of custom-tailored concurrent data structures. They can be very efficient thanks to their ability to exploit data structure semantics in order to reduce overhead, aborts, and wasted work compared to general-purpose software transactional memory. However, TDSLs were not previously used for complex use-cases involving long transactions and a variety of data structures. In this paper, we boost the performance and usability of a TDSL, towards allowing it to support complex applications. A key idea is nesting. Nested transactions create checkpoints within a longer transaction, so as to limit the scope of abort, without changing the semantics of the original transaction. We build a Java TDSL with built-in support for nested transactions over a number of data structures. We conduct a case study of a complex network intrusion detection system that invests a significant amount of work to process each packet. Our study shows that our library outperforms publicly available STMs twofold without nesting, and by up to 16x when nesting is used

Brief Announcement: Using Nesting to Push the Limits of Transactional Data Structure Libraries

Transactional data structure libraries (TDSL) combine the ease-of-programming of transactions with the high performance and scalability of custom-tailored concurrent data structures. They can be very efficient thanks to their ability to exploit data structure semantics in order to reduce overhead, aborts, and wasted work compared to general-purpose software transactional memory. However, TDSLs were not previously used for complex use-cases involving long transactions and a variety of data structures. In this paper, we boost the performance and usability of a TDSL, towards allowing it to support complex applications. A key idea is nesting. Nested transactions create checkpoints within a longer transaction, so as to limit the scope of abort, without changing the semantics of the original transaction. We build a Java TDSL with built-in support for nested transactions over a number of data structures. We conduct a case study of a complex network intrusion detection system that invests a significant amount of work to process each packet. Our study shows that our library outperforms publicly available STMs twofold without nesting, and by up to 16x when nesting is used

Speculation in Parallel and Distributed Event Processing Systems

Event stream processing (ESP) applications enable the real-time processing of continuous flows of data. Algorithmic trading, network monitoring, and processing data from sensor networks are good examples of applications that traditionally rely upon ESP systems. In addition, technological advances are resulting in an increasing number of devices that are network enabled, producing information that can be automatically collected and processed. This increasing availability of on-line data motivates the development of new and more sophisticated applications that require low-latency processing of large volumes of data. ESP applications are composed of an acyclic graph of operators that is traversed by the data. Inside each operator, the events can be transformed, aggregated, enriched, or filtered out. Some of these operations depend only on the current input events, such operations are called stateless. Other operations, however, depend not only on the current event, but also on a state built during the processing of previous events. Such operations are, therefore, named stateful. As the number of ESP applications grows, there are increasingly strong requirements, which are often difficult to satisfy. In this dissertation, we address two challenges created by the use of stateful operations in a ESP application: (i) stateful operators can be bottlenecks because they are sensitive to the order of events and cannot be trivially parallelized by replication; and (ii), if failures are to be tolerated, the accumulated state of an stateful operator needs to be saved, saving this state traditionally imposes considerable performance costs. Our approach is to evaluate the use of speculation to address these two issues. For handling ordering and parallelization issues in a stateful operator, we propose a speculative approach that both reduces latency when the operator must wait for the correct ordering of the events and improves throughput when the operation in hand is parallelizable. In addition, our approach does not require that user understand concurrent programming or that he or she needs to consider out-of-order execution when writing the operations. For fault-tolerant applications, traditional approaches have imposed prohibitive performance costs due to pessimistic schemes. We extend such approaches, using speculation to mask the cost of fault tolerance.:1 Introduction 1 1.1 Event stream processing systems ......................... 1 1.2 Running example ................................. 3 1.3 Challenges and contributions ........................... 4 1.4 Outline ...................................... 6 2 Background 7 2.1 Event stream processing ............................. 7 2.1.1 State in operators: Windows and synopses ............................ 8 2.1.2 Types of operators ............................ 12 2.1.3 Our prototype system........................... 13 2.2 Software transactional memory.......................... 18 2.2.1 Overview ................................. 18 2.2.2 Memory operations............................ 19 2.3 Fault tolerance in distributed systems ...................................... 23 2.3.1 Failure model and failure detection ...................................... 23 2.3.2 Recovery semantics............................ 24 2.3.3 Active and passive replication ...................... 24 2.4 Summary ..................................... 26 3 Extending event stream processing systems with speculation 27 3.1 Motivation..................................... 27 3.2 Goals ....................................... 28 3.3 Local versus distributed speculation ....................... 29 3.4 Models and assumptions ............................. 29 3.4.1 Operators................................. 30 3.4.2 Events................................... 30 3.4.3 Failures .................................. 31 4 Local speculation 33 4.1 Overview ..................................... 33 4.2 Requirements ................................... 35 4.2.1 Order ................................... 35 4.2.2 Aborts................................... 37 4.2.3 Optimism control ............................. 38 4.2.4 Notifications ............................... 39 4.3 Applications.................................... 40 4.3.1 Out-of-order processing ......................... 40 4.3.2 Optimistic parallelization......................... 42 4.4 Extensions..................................... 44 4.4.1 Avoiding unnecessary aborts ....................... 44 4.4.2 Making aborts unnecessary........................ 45 4.5 Evaluation..................................... 47 4.5.1 Overhead of speculation ......................... 47 4.5.2 Cost of misspeculation .......................... 50 4.5.3 Out-of-order and parallel processing micro benchmarks ........... 53 4.5.4 Behavior with example operators .................... 57 4.6 Summary ..................................... 60 5 Distributed speculation 63 5.1 Overview ..................................... 63 5.2 Requirements ................................... 64 5.2.1 Speculative events ............................ 64 5.2.2 Speculative accesses ........................... 69 5.2.3 Reliable ordered broadcast with optimistic delivery .................. 72 5.3 Applications .................................... 75 5.3.1 Passive replication and rollback recovery ................................ 75 5.3.2 Active replication ............................. 80 5.4 Extensions ..................................... 82 5.4.1 Active replication and software bugs ..................................... 82 5.4.2 Enabling operators to output multiple events ........................ 87 5.5 Evaluation .................................... 87 5.5.1 Passive replication ............................ 88 5.5.2 Active replication ............................. 88 5.6 Summary ..................................... 93 6 Related work 95 6.1 Event stream processing engines ......................... 95 6.2 Parallelization and optimistic computing ................................ 97 6.2.1 Speculation ................................ 97 6.2.2 Optimistic parallelization ......................... 98 6.2.3 Parallelization in event processing .................................... 99 6.2.4 Speculation in event processing ..................... 99 6.3 Fault tolerance .................................. 100 6.3.1 Passive replication and rollback recovery ............................... 100 6.3.2 Active replication ............................ 101 6.3.3 Fault tolerance in event stream processing systems ............. 103 7 Conclusions 105 7.1 Summary of contributions ............................ 105 7.2 Challenges and future work ............................ 106 Appendices Publications 107 Pseudocode for the consensus protocol 10