scholarly journals A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Lili Bo ◽  
Shujuan Jiang
Keyword(s):  
Data Access ◽  
Optimization Method ◽  
Service Performance ◽  
Cloud Environment ◽  
Security Issue ◽  
Concurrent Software ◽  
Concurrent Program ◽  
Cloud Computation ◽  
Distributed Cloud ◽  
Concurrency Bug

The advent of cloud computation and big data applications has enabled data access concurrency to be prevalent in the distributed cloud environment. In the meantime, security issue becomes a critical problem for researchers to consider. Concurrency bug diagnosis service is to analyze concurrent software and then reason about concurrency bugs in them. However, frequent context switches in concurrent program execution traces will inevitably impact the service performance. To optimize the service performance, this paper presents a static constraint-aware method to simplify concurrent program buggy traces. First, taking the original buggy trace as the operation object, we calculate the maximal sound dependence relations based on the constraint models. Then, we iteratively check the dependent constraints and move forward current event to extend thread execution intervals. Finally, we obtain the simplified trace that is equivalent to the original buggy trace. To evaluate our approach, we conduct a set of experiments on 12 widely used Java projects. Experimental results show that our approach outperforms other state-of-the-art approaches in terms of execution time.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

REACHABILITY TESTING: AN APPROACH TO TESTING CONCURRENT SOFTWARE

1995 ◽  
Vol 05 (04) ◽  
pp. 493-510 ◽  
Author(s):  
GWAN-HWAN HWANG ◽  
KUO-CHUNG TAI ◽  
TING-LU HUANG
Keyword(s):  
Empirical Studies ◽  
Concurrent Programs ◽  
Concurrent Software ◽  
Sequential Programs ◽  
Concurrent Program ◽  
Testing Approach ◽  
Reachability Testing ◽  
Deterministic Behavior

Concurrent programs are more difficult to test than sequential programs because of non-deterministic behavior. An execution of a concurrent program non-deterministically exercises a sequence of synchronization events called a synchronization sequence (or SYN-sequence). Non-deterministic testing of a concurrent program P is to execute P with a given input many times in order to exercise distinct SYN-sequences. In this paper, we present a new testing approach called reachability testing. If every execution of P with input X terminates, reachability testing of P with input X derives and executes all possible SYN-sequences of P with input X. We show how to perform reachability testing of concurrent programs using read and write operations. Also, we present results of empirical studies comparing reachability and non-deterministic testing. Our results indicate that reachability testing has advantages over non-deterministic testing.

scholarly journals Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks

2020 ◽  
Vol 9 (9) ◽  
pp. 518
Author(s):  
Qing Zhu ◽  
Meite Chen ◽  
Bin Feng ◽  
Yan Zhou ◽  
Maosu Li ◽  
...  
Keyword(s):  
Flow Model ◽  
Data Flow ◽  
Scheduling Algorithm ◽  
Maximum Flow ◽  
Data Access ◽  
Spatiotemporal Data ◽  
Cloud Environment ◽  
Calculation Algorithm ◽  
Data Scheduling ◽  
First Come First Serve

Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment.

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