SERVERLESS COMPUTATIONS RESOURCE SCHEDULING BASED ON DATA DEPENDENCY

The serverless computing model is becoming quite widespread. This model allows developers to create flexible and fault tolerant applications with an attractive billing model. The increasing complexity of serverless functions has led to the necessity to use serverless workflows – serverless functions invoking other serverless functions. However, such concept imposes certain requirements on the serverless functions that make distributed computations. The overhead of transferring data between serverless functions can significantly increase the execution time of a program using this approach. One way to reduce overhead is to improve serverless scheduling techniques. This paper discusses an approach to scheduling serverless computations based on data dependency analysis. We propose to divide the problem of planning of the computation of a composite serverless function into three stages. For each stage we provide a description by a mathematical model. We carried out a review of algorithms used to schedule resources by compilers and in parallel computing in multiprocessor systems to determine the best algorithm to implement in a prototype scheduler. For each algorithm, it was specified how it could be used for resource scheduling in serverless platforms. We provide a description of the developed prototype based on the Fission serverless platform. The prototype implements the critical path heuristic. It is shown that the improvements can significantly reduce the execution time up to two times for some types of serverless functions.

A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique

In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we propose a new distributed RST version based on Locality Sensitive Hashing (LSH), named LSH-dRST, for big data feature selection. LSH-dRST uses LSH to match similar features into the same bucket and maps the generated buckets into partitions to enable the splitting of the universe in a more efficient way. More precisely, in this paper, we perform a detailed analysis of the performance of LSH-dRST by comparing it to the standard distributed RST version, which is based on a random partitioning of the universe. We demonstrate that our LSH-dRST is scalable when dealing with large amounts of data. We also demonstrate that LSH-dRST ensures the partitioning of the high dimensional feature search space in a more reliable way; hence better preserving data dependency in the distributed environment and ensuring a lower computational cost.

An Assessment of the Application of Private Aggregation of Ensemble Models to Sensible Data

This paper explores the use of Private Aggregation of Teacher Ensembles (PATE) in a setting where students have their own private data that cannot be revealed as is to the ensemble. We propose a privacy model that introduces a local differentially private mechanism to protect student data. We implemented and analyzed it in case studies from security and health domains, and the result of the experiment was twofold. First, this model does not significantly affecs predictive capabilities, and second, it unveiled interesting issues with the so-called data dependency privacy loss metric, namely, high variance and values.

ECG data dependency for atrial fibrillation detection based on residual networks

Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98–99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53–92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.

Hardware software partitioning using directed acyclic data dependence graph with precedence

n this these we present a system partitioning technique that employs C/C++ as input specification language for hardware/software co-design. The proposed algorithm is able to explore a number of partitioning solutions as compared to other partitioning research. This benefit is obtained by processing data dependency and precedence dependency simultaneously in a new representation called Directed Acyclic Data dependency Graph with Precedence (DADGP). DADGP is an extension of Directed Acyclic Graph (DAG) structure frequently used in the past for partitioning. The DADGP based partitioning algorithm minimizes communication overhead, overall system execution time as well as system cost in terms of hardware area. The algorithm analyzes the DADGP and tries to expose parallelism between processing elements and repeated tasks. The benefits of exposing parallelism with minimum inter PE communication overhead are shown in the experimental results. However, such benefits come with increase in cost due to additional hardware units and their interconnections. DADGP-based partitioning technique is also employed to implement block matching and SOBEL edge detection techniques. Overall, the proposed system partitioning algorithm is fast and powerful enough to handle complicated and large system designs.

Hardware software partitioning using directed acyclic data dependence graph with precedence

n this these we present a system partitioning technique that employs C/C++ as input specification language for hardware/software co-design. The proposed algorithm is able to explore a number of partitioning solutions as compared to other partitioning research. This benefit is obtained by processing data dependency and precedence dependency simultaneously in a new representation called Directed Acyclic Data dependency Graph with Precedence (DADGP). DADGP is an extension of Directed Acyclic Graph (DAG) structure frequently used in the past for partitioning. The DADGP based partitioning algorithm minimizes communication overhead, overall system execution time as well as system cost in terms of hardware area. The algorithm analyzes the DADGP and tries to expose parallelism between processing elements and repeated tasks. The benefits of exposing parallelism with minimum inter PE communication overhead are shown in the experimental results. However, such benefits come with increase in cost due to additional hardware units and their interconnections. DADGP-based partitioning technique is also employed to implement block matching and SOBEL edge detection techniques. Overall, the proposed system partitioning algorithm is fast and powerful enough to handle complicated and large system designs.

Study On ECG Data Dependency For Atrial Fibrillation Detection Based On Residual Networks

Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98–99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53–92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.

CONFUZZIUS: A Data Dependency-Aware Hybrid Fuzzer for Smart Contracts

<div> <div> <p>Smart contracts are Turing-complete programs that are executed across a blockchain. Unlike traditional programs, once deployed, they cannot be modified. As smart contracts carry more value, they become more of an exciting target for attackers. Over the last years, they suffered from exploits costing millions of dollars due to simple programming mistakes. As a result, a variety of tools for detecting bugs have been proposed. Most of these tools rely on symbolic execution, which may yield false positives due to over-approximation. Recently, many fuzzers have been proposed to detect bugs in smart contracts. However, these tend to be more effective in finding shallow bugs and less effective in finding bugs that lie deep in the execution, therefore achieving low code coverage and many false negatives. An alternative that has proven to achieve good results in traditional programs is hybrid fuzzing, a combination of symbolic execution and fuzzing. In this work, we study hybrid fuzzing on smart contracts and present ConFuzzius, the first hybrid fuzzer for smart contracts. ConFuzzius uses evolutionary fuzzing to exercise shallow parts of a smart contract and constraint solving to generate inputs that satisfy complex conditions that prevent evolutionary fuzzing from exploring deeper parts. Moreover, ConFuzzius leverages dynamic data dependency analysis to efficiently generate sequences of transactions that are more likely to result in contract states in which bugs may be hidden. We evaluate the effectiveness of ConFuzzius by comparing it with state-of-the-art symbolic execution tools and fuzzers for smart contracts. Our evaluation on a curated dataset of 128 contracts and a dataset of 21K real-world contracts shows that our hybrid approach detects more bugs than state-of-the-art tools (up to 23%) and that it outperforms existing tools in terms of code coverage (up to 69%). We also demonstrate that data dependency analysis can boost bug detection up to 18%.</p> </div> </div>

CONFUZZIUS: A Data Dependency-Aware Hybrid Fuzzer for Smart Contracts

<div> <div> <p>Smart contracts are Turing-complete programs that are executed across a blockchain. Unlike traditional programs, once deployed, they cannot be modified. As smart contracts carry more value, they become more of an exciting target for attackers. Over the last years, they suffered from exploits costing millions of dollars due to simple programming mistakes. As a result, a variety of tools for detecting bugs have been proposed. Most of these tools rely on symbolic execution, which may yield false positives due to over-approximation. Recently, many fuzzers have been proposed to detect bugs in smart contracts. However, these tend to be more effective in finding shallow bugs and less effective in finding bugs that lie deep in the execution, therefore achieving low code coverage and many false negatives. An alternative that has proven to achieve good results in traditional programs is hybrid fuzzing, a combination of symbolic execution and fuzzing. In this work, we study hybrid fuzzing on smart contracts and present ConFuzzius, the first hybrid fuzzer for smart contracts. ConFuzzius uses evolutionary fuzzing to exercise shallow parts of a smart contract and constraint solving to generate inputs that satisfy complex conditions that prevent evolutionary fuzzing from exploring deeper parts. Moreover, ConFuzzius leverages dynamic data dependency analysis to efficiently generate sequences of transactions that are more likely to result in contract states in which bugs may be hidden. We evaluate the effectiveness of ConFuzzius by comparing it with state-of-the-art symbolic execution tools and fuzzers for smart contracts. Our evaluation on a curated dataset of 128 contracts and a dataset of 21K real-world contracts shows that our hybrid approach detects more bugs than state-of-the-art tools (up to 23%) and that it outperforms existing tools in terms of code coverage (up to 69%). We also demonstrate that data dependency analysis can boost bug detection up to 18%.</p> </div> </div>