Calyam, Prasad

Debroy, Saptarshi

Joshi, Trupti

English

Clemson University: TigerPrints

Multi-Cloud Performance and Security-driven Brokering forBioinformatics WorkflowsPIs: Saptarshi Debroy, Prasad Calyam, Trupti JoshiAffiliations: The City University of New York, University of Missouri-ColumbiaData-intensive science applications, such as bioinformatics often require specialized compute/network-ing/storage resources that are not always available locally on-site and need to use compute resources in remotecloud domains for processing which in tern requires high speed data transfer. Thus, researchers are increas-ingly adopting federated multi-cloud infrastructures (e.g., CyVerse, XSEDE) to support compute-intensiveor data-intensive science collaborations. Adoption of such infrastructures are facilitated by Software-definedNetworking (SDN) enabled campus Science DMZs for friction-less data movement and Federated Identityand Access Management (IAM) that enables campus researchers to reserve and seamlessly access local andremote cloud resources.Figure 1: End-to-end lifecycle stages of a data-intensive application with dynamic security requirementsusing federated multi-cloud resources from domains with diverse resource policy specifications.Allocation of such federated multi-cloud resources is typically based on applications’ performance con-siderations (e.g., data throughput, execution time). However, such one-dimensional resource brokering failsto consider scenarios where applications’ security requirements across different life-cycle stages (Low, Mod-erate, and High) contradict with remote domains’ diverse security policies (ranging from very strict to veryrelaxed) as shown in Figure 1. It is a difficult proposition for users (especially when using complex workflows)to guess how to select options available within federated multi-cloud resources in a manner that overcomesbottlenecks such as resource capacity limitations, network bandwidth, security posture or cost factors at thevarious resource domains. Without a systematic framework and standardized tools, performance-securityconflicts for applications and inefficient/expensive resource usage scenarios occur that are undesirable froma user perspective.In our National Science Foundation (NSF) funded project (Grant no. OAC-1827177) project, we areinvestigating the need for security aware resource brokering over traditional one-dimensional resource allo-cation for multi-cloud data collaboration. We are addressing the lack of knowledge about: a) individualdomain’s security policies, b) how that translates to security assurance of the applications, and c) natureof performance and security trade-offs - that can cause performance-security conflicts for applications andinefficient/expensive resource usage (Figure 1).In particular, we consider the implementations of high-throughput cloud-based bioinformatics data anal-ysis workflows in the SoyKB science gateway developed for soybean and other related organisms. Theseworkflows provide biological users with an avenue to analyze their in-house generated datasets (in terabytesper month) using multi-step workflows and conduct analysis in high performance computing environmentsthat support the necessary security levels to handle Health Insurance Portability and Accountability Act(HIPPA) compliance. For example, a complex PGen workflow is used to efficiently facilitate analysis oflarge-scale next generation sequencing (NGS) data for genomic variations. Whereas a comparatively simplerRNA-Seq analysis workflow is used to perform quantization of gene expression from transcriptomics dataand statistical analysis to discover differential expressed gen/isoform between experimental groups.1Figure 2: Security-aware resource brokering middleware services with MCPS Broker and its underlyingcomponents interactionCurrently, we are developing a security-aware resource brokering middleware framework, i.e., the MCPS(Multi-Cloud Performance and Security) Broker, within federated multi-cloud systems to allocate applicationresources by satisfying their performance and security requirements. The architecture of the MCPS Brokeris demonstrated in Figure 2 workflow within SoyKB science gateway environment.(a) Performance comparison (b) Security evaluationFigure 3: Brokering scheme comparisonOur initial testbed implementation on GENI infrastructure is approximately based on the real computingcenters used for SoyKB workflows: a local University of Missouri (MU) domain, as well as remote clouddomains, such as Texas Advanced Computing Center (TACC), and Information Sciences Institute (ISI).Here workflows are sent from the MU domain users through the MCPS Broker, which decides whether theworkflows are processed locally at MU or remotely at TACC or ISI based on the global and local algorithmoutcomes discussed with the results ultimately sent to CyVerse upon processing. Figure 3(a) shows thatfor different data sizes, MCPS brokering performs almost as good as ‘only performance-driven brokering’in terms of choosing domains for processing that optimize total execution time. The security compliancecomparison results are shown in Figure 3(b) that uses NIST based risk assessment method. The figure showsthat the overall risk of different threat events (I to V) are similar for ‘only security-driven brokering’ andour proposed ‘MCPS brokering’ as these schemes almost always choose ISI or TACC over MU regardlessof the formers’ resource availability. In the next phase, we are planning to scale up our implementation ofMCPS Broker and case study evaluation with workflows on KBCommons. This will demonstrate the benefitsof our proposed middleware in ensuring both performance optimization and security compliance for SoyKBand other workflows in KBCommons.2

Multi-Cloud Performance and Security-driven  Brokering for Bioinformatics Workflows (White Paper)

https://tigerprints.clemson.edu/cgi/viewcontent.cgi?article=1061&amp;context=hugedata

Multi-Cloud Performance and Security-driven Brokering for Bioinformatics Workflows (White Paper)

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