Scheduling for Workflows with Security-Sensitive Intermediate Data by Selective Tasks Duplication in Clouds

Chen, Huangke; Zhu, Xiaomin; Qiu, Dishan; Liu, Ling; Du, Z.

doi:10.1109/tpds.2017.2678507

Cited by 105 publications

(52 citation statements)

References 36 publications

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“…If no preplaced datasets are taken into account, then our problem can be reduced to the non‐uniform graph partition problem. In addition, as high quality security services are increasingly critical for processing workflow applications, data sensitivity constraints were considered in the works of Wen et al and Chen et al Wen et al regarded that cloud customers' sensitive data are restricted to being processed by specific trusted data centers and studied how to schedule workflow with data privacy protection constraints, while minimizing both monetary cost and execution time for workflow applications . Chen et al considered the security requirements of the intermediate data, and studied the performance impact of encryption time of intermediate data on the start of subsequent workflow tasks .…”

Section: Related Workmentioning

confidence: 99%

“…In addition, as high quality security services are increasingly critical for processing workflow applications, data sensitivity constraints were considered in the works of Wen et al and Chen et al Wen et al regarded that cloud customers' sensitive data are restricted to being processed by specific trusted data centers and studied how to schedule workflow with data privacy protection constraints, while minimizing both monetary cost and execution time for workflow applications . Chen et al considered the security requirements of the intermediate data, and studied the performance impact of encryption time of intermediate data on the start of subsequent workflow tasks . However, since we regard that the owner of the scientific workflow application trusts these geo‐distributed data centers for its execution, which is common in large scientific collaborations, we thus leave the concern of data privacy or security out of the scope of this paper.…”

Section: Related Workmentioning

confidence: 99%

“…and studied the performance impact of encryption time of intermediate data on the start of subsequent workflow tasks. 36 However, since we regard that the owner of the scientific workflow application trusts these geo-distributed data centers for its execution, which is common in large scientific collaborations, 37 we thus leave the concern of data privacy or security out of the scope of this paper.…”

Section: Workflow Data and Task Co-scheduling In Geo-distributed Cloumentioning

confidence: 99%

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Graph partition–based data and task co‐scheduling of scientific workflow in geo‐distributed datacenters

Zhang

Chen

Zhan

et al. 2019

Concurrency and Computation

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Summary Most large‐scale scientific workflows take place in multiple collaborative datacenters for access to community‐wide resources, while adhering to each datacenter's non‐uniform resource limits. However, moving both initial input datasets with predetermined locations and intermediate datasets needing placement decisions across geo‐distributed datacenters hinders efficient execution of large‐scale data‐intensive scientific workflows. Thus, scientific workflow's data and task co‐scheduling deal with situations such as pre‐placed initial input datasets, placement of intermediate datasets and each datacenter's non‐uniform computation and storage constraint, while minimizing the cross‐datacenter data transfer. Since this scheduling problem is known to be NP‐hard, here, we propose a novel approach, based on the multilevel graph coarsening and uncoarsening framework, together with a specialized hybrid genetic algorithm having distinctive graph partition driven features of repair and local improvement, for scheduling data‐intensive scientific workflows in geo‐distributed datacenters and optimizing the cross‐datacenter data transfer volume. Extensive simulations, based on four real‐world workflow traces, show that our algorithm significantly reduces the overall geo‐distributed data transfer and demonstrate its effectiveness.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Workflow Data and Task Co-scheduling In Geo-distributed Cloumentioning

confidence: 99%

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Graph partition–based data and task co‐scheduling of scientific workflow in geo‐distributed datacenters

Zhang

Chen

Zhan

et al. 2019

Concurrency and Computation

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“…50,51 For work-flow applications, a number of work-flow scheduling strategies have been proposed to deploy them to cloud. [52][53][54][55][56][57] In these methods, they often assumed that the running time of each subtask are available before scheduling and remain unchangeable, and a subtask can start execution as soon as it arrives. These strategies tried to minimize the completion time of the task by adjusting the arrival time of subtasks.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

Yao

Chen

et al. 2019

Concurrency and Computation

Self Cite

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Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs.Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification-based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP-based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang-scheduling-based, Makespan-based, and Max-Min-based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication-sensitive PADS applications, the advantage of ERPA is more significant. KEYWORDS execution revivification, parallel and distributed simulation, performance prediction, resource allocation INTRODUCTIONWith the development of simulation technology, the scale of parallel and distributed simulation (PADS) applications is expanding, and the demand for computing power increases as well. Currently, the parallel platforms or clusters are widely used to improve the execution efficiency, but the high cost of setting up, operating, and maintaining the basic hardware and software infrastructures severely limits the proliferation of simulation technology. The emergence of cloud computing paradigm, which receives considerable atttention in both academic and industry, 1 makes it possible to run PADS applications with reasonable cost. 2,3 In clouds, customers can access unlimited resources (eg, VMs) on demand at a ''pay-as-you-go'' model, and just pays for their actual use. Therefore, deploying PADS applications in clouds is attractive.In PADS, simulation entities are mapped on different machines/cores, and their states are modified by executing events at discrete time.These entities communicate with each other by sending and receiving time-stamped messages. During parallel execution, these entities are synchronized periodically to yield the same result as the sequential execution. As a result, the execution performance goes down. Due to the frequent synchronizations among entities, the running time of PADS is determined by the slowest entities. Besides, the network latency among entities can place a great influence on the runni...

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“…They investigate the security and privacy necessities and allegations, and also studied existing systems, and in the end develop architecture of a secure manager for cloud-based healthcarerelated data management and exchange. H.Chen.et.al [17] develops a model which focuses SecuritySensitive Intermediate Data by using Selective Tasks Duplication in Cloud environments. With the extensive consumption of cloud computing in numerous business enterprises as well as science and engineering domains, high quality safety services are increasingly critical for giving out workflow applications with confidential transitional data.…”

Section: Related Workmentioning

confidence: 99%

An enhanced security tree to secure cloud data

Renu¹,

Veni²

2017

IJET

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The Cloud computing services and security issues are growing exponentially with time. All the CSPs provide utmost security but the issues still exist. Number of technologies and methods are emerged and futile day by day. In order to overcome this situation, we have also proposed a data storage security system using a binary tree approach. Entire services of the binary tree are provided by a Trusted Third Party (TTP) .TTP is a government or reputed organization which facilitates to protect user data from unauthorized access and disclosure. The security services are designed and implemented by the TTP and are executed at the user side. Data classification, Data Encryption and Data Storage are the three vital stages of the security services. An automated file classifier classify unorganized files into four different categories such as Sensitive, Private, Protected and Public. Applied cryptographic techniques are used for data encryption. File splitting and multiple cloud storage techniques are used for data outsourcing which reduces security risks considerably. This technique offers file protection even when the CSPs compromise.

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Scheduling for Workflows with Security-Sensitive Intermediate Data by Selective Tasks Duplication in Clouds

Cited by 105 publications

References 36 publications

Graph partition–based data and task co‐scheduling of scientific workflow in geo‐distributed datacenters

Graph partition–based data and task co‐scheduling of scientific workflow in geo‐distributed datacenters

An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

An enhanced security tree to secure cloud data

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