SABA: A security-aware and budget-aware workflow scheduling strategy in clouds

Zeng, Lingfang; Veeravalli, Bharadwaj; Li, Xiaorong

doi:10.1016/j.jpdc.2014.09.002

Cited by 103 publications

(58 citation statements)

References 39 publications

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“…Considering real-time systems that are security-critical, [17] provisions security services to optimize performance, where the scheduling of jobs is combined with the allocation of security services. [18] takes an approach that is both security-aware and budgetaware. [19] introduces the idea of firewall-style SECaaS providers, similarly [20] introduces an API called FlowTap to provide a security policy enforcement and monitoring infrastructure for network traffic.…”

Section: Related Workmentioning

confidence: 99%

“…The constraint in (16) ensures that the mixture of prepaid and on-demand security services are sufficient to cover incoming packets n aω ‡ . The constraints in (17) and (18) are similar to that in (12), ensuring that only one insurer is bought from under present or future premiums per application. Finally, we also minimize the cost of the claim stage.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The matrix M is trimmed by removing the constraints that obviously violate TU, for example, those with coefficients not equal to −1, 0, or 1. The resultant matrix, M can then be divided into two disjoint subsets, one containing the rows corresponding to constraints (12), (15), (17), and (24), and the other containing (11), (16), and (18). It is clear from these constraints that the conditions for unimodularity given in [38] are satisfied for the matrix, M , and that it will also hold for every square, non-singular submatrix, which is the criterion for unimodularity to be 'total'.…”

Section: Total Unimodularity Of Constraint Matrixmentioning

confidence: 99%

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A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

Chase

Niyato

Wang

et al. 2019

IEEE Trans. Dependable and Secure Comput.

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Abstract-As computing services are increasingly cloud-based, corporations are investing in cloud-based security measures. The Security-asa-Service (SECaaS) paradigm allows customers to outsource security to the cloud, through the payment of a subscription fee. However, no security system is bulletproof, and even one successful attack can result in the loss of data and revenue worth millions of dollars. To guard against this eventuality, customers may also purchase cyber insurance to receive recompense in the case of loss. To achieve cost effectiveness, it is necessary to balance provisioning of security and insurance, even when future costs and risks are uncertain. To this end, we introduce a stochastic optimization model to optimally provision security and insurance services in the cloud. Since the model we design is a mixed integer problem, we also introduce a partial Lagrange multiplier algorithm that takes advantage of the total unimodularity property to find the solution in polynomial time. We also apply sensitivity analysis to find the exact tolerance of decision variables to parameter changes. We show the effectiveness of these techniques using numerical results based on real attack data to demonstrate a realistic testing environment, and find that security and insurance are interdependent.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Total Unimodularity Of Constraint Matrixmentioning

confidence: 99%

See 1 more Smart Citation

A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

Chase

Niyato

Wang

et al. 2019

IEEE Trans. Dependable and Secure Comput.

View full text Add to dashboard Cite

show abstract

“…Also [20] introduced two auto-scaling mechanisms to solve the resource allocation problem for unpredicted workflow jobs in a cost-efficient way. [27] introduced a workflow scheduling in Clouds solutions with security and cost considerations. [2] provides guidelines and analysis to understand cost optimization in scientific workflow scheduling by surveying existing approaches in Cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Budget-Aware Scheduling Algorithms for Scientific Workflows with Stochastic Task Weights on Heterogeneous IaaS Cloud Platforms

Caniou

Caron

Chang

et al. 2018

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Abstract-This paper introduces several budget-aware algorithms to deploy scientific workflows on IaaS Cloud platforms, where users can request Virtual Machines (VMs) of different types, each with specific cost and speed parameters. We use a realistic application/platform model with stochastic task weights, and VMs communicating through a datacenter. We extend two wellknown algorithms, MIN-MIN and HEFT, and make scheduling decisions based upon machine availability and available budget. During the mapping process, the budget-aware algorithms make conservative assumptions to avoid exceeding the initial budget; we further improve our results with refined versions that aim at re-scheduling some tasks onto faster VMs, thereby spending any budget fraction leftover by the first allocation. These refined variants are much more time-consuming than the former algorithms, so there is a trade-off to find in terms of scalability. We report an extensive set of simulations with workflows from the Pegasus benchmark suite. Most of the time our budget-aware algorithms succeed in achieving efficient makespans while enforcing the given budget, despite (i) the uncertainty in task weights and (ii) the heterogeneity of VMs in both cost and speed values.

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“…SABA algorithm [35] focuses on minimizing the makespan of a scientific workflow with user's security constraint under budget constraints as well as security in multi-cloud environment. They proposed two types of data sets, immovable and movable datasets.…”

Section: Security-aware and Budget-aware (Saba)mentioning

confidence: 99%

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

Chakravarthi

Vijayakumar

2018

IJECE

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In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers.

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SABA: A security-aware and budget-aware workflow scheduling strategy in clouds

Cited by 103 publications

References 39 publications

A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

Budget-Aware Scheduling Algorithms for Scientific Workflows with Stochastic Task Weights on Heterogeneous IaaS Cloud Platforms

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

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