Oren’s Blog

CSG Winter 2017 – Recommendations/guides for updating IT skill portfolio

Paul Erickson – Nebraska

Framing the issue – don’t have the skill sets or expertise for a cloud world. Run, grow transform – on-prem, traditional focus on “run” – how to change a working environment and shift investment/resources (how do you change an engine while the car is running?)?

What skill sets are we missing? Process management (granting/revoking; provisioning; integration; authorizations/permissions, vendor coordination, managing interdependencies); Integration; Product/Service management; Client relationship management.

People who contributed in the past might not have the skills to take us into the future. How do we offer them opportunities to grow that honor their contributions and allow them to grow? Adapt and evolve in an environment of continuous change.

Identify ideal employee skills. Help those who are great technologists make the transition.

Denise Cunningham – Columbia

Head of HR for technology division.

One reason people resist change is because they focus on what the have to give up instead of what they have to gain. Important to keep this at front of mind.

A framework for Organizational Performance & Change: Burke-Litwin Model


External environment (e.g. the cloud) impacts the organization. The spine of the model – external environment influences leadership, which influences management practice, then work unit climate, then motivation, then individual.

Focus on Work Unit Climate: What it feels like to work here; nature of our interaction with each other; interpersonal relations in the group; what we focus on and consider important.

What factors influence Work Unit Climate? Leadership and management practices. Work unit climate is the most direct factor in performance.

There’s a learning climate or a performance climate. Learning: emphasis on improving skills and abilities; stresses process and learning; motivated to increase competence and change. Performance: emphasis is on demonstrating skills; stresses outcomes and results; people are afraid to make mistakes or change.

Goals: Learning: quality, trying new things original ideas; effort. Performance: following standard procedures; high performance standards; getting task done on time.

Feedback: Learning climate: supportive/coaching role; improving work quality; two-way feedback, questions encouraged. Performance climate: evaluative role; level of competence compared to other employees, one-way feedback, questions discouraged.

When implementing change employees want to hear about it from their manager.

There is no correlation between strong individual contributors and leaders.

Changing organizational culture can take 12-18 months. Or two years in higher education. Can’t do it at all without leadership being a part of it.

When people say they’re going to get in trouble, that can be a rationale for not changing. How do you make sure new staff don’t become part of a dysfunctional culture. Ask questions at hiring about the core values. Zappo’s does this well. Build the values into the performance appraisal.




CSG Winter 2017 – Cloud ERP Workshop

Stanford University – Cloud Transformations – Bruce Vincent

Why Cloud and Why now? Earthquake danger; campus space; quick provisioning; easy scalability; new features and functions more quickly

Vision for Stanford UIT cloud transformation program: Starting to behave like an enterprise. Shift most of service portfolio to cloud. A lot of self-examination – assessment of organization and staff. Refactoring of skills.

Trends and areas of importance: Cloud  – requires standards, process changes, amended roles; Automation – not just for efficiency – requires API integration; IAM – federated and social identities, post-password era nearing for SSO; Security – stop using address based access control; Strategic placement of strong tech staff in key positions; timescale of cloud ignores our annual cycles.

Challenges regarding cloud deployments: Business processes tightly coupled within SaaS products, e.g. ServiceNow and Salesforce; Tracking our assets which increasingly exist in disparate XaaS products; Representing the interrelationships between cloud assets; Not using our own domain namespace in URLs.

Trying to make ServiceNow the system of record about assets – need to integrate it with the automation of spinning instances up and down in the cloud.

Cloud ERP – Governance and Cloud ERP – Jim Phelps, Washington

UW going live with Workday in July. Migrating from old mainframe system and distributed business processes and systems. Business process change is difficult. Built an integrated service center (ISC) with 4 tiers of help.

Integrated Governance Model:  across business domains; equal voice from campus; linking business and technology; strategic, transformative, efficient…

Governance Design: Approach – set strategic direction; build roadmap; govern change – built out RACI diagram.

“Central” vs “Campus” change requests – set up a rubric for evaluating: governance should review and approve major changes.

Need for a common structured change request: help desk requests and structured change requests should be easily rerouted to each others’ queues.

Governance seats (proposed): 7 people – small and nimble, but representative of campus diversity.

Focus of governance group needs to be delivering greatest value for the whole university and leading transformational change of HR/P domains. Members must bring a transformational and strategic vision to the table. They must drive continuous change and improvements over time.

Next challenge: transition planning and execution – balancing implementation governance with ISC governance throughout transition – need to have a clear definition of stabilization.

Next steps: determine role of new EVP in RACI; Align with vision of executive director of ISC; provost to formally instantiate ISC governance; develop and implement transition plan; turn into operational processes

UMN ERP Governance – Sharon Ramallo

Went live with 9.2 Peoplesoft on 4/20/2015 – no issues at go-live!

Implemented governance process and continue to operate governance

Process: Planning, Budgeting; Refine; Execution; Refine

  • Executive Oversight Committee – Chair: VP Finance. Members: VP OIT, HR, Vice Provost
  • Operational Administrative Steering Committee: Char: Sr. Dir App Dev;
  • Administrative Computing Steering Committee – people who run the operational teams
  • Change Approval Board

Their CAB process builds a calendar in ServiceNow.

USC Experience in the Cloud – Steve O’Donnell

Current admin systems  – Kuali KFS/Coeus, custom SIS (Mainframe), Lawson, Workday, Cognos

Staffing and skill modernization: Burden of support shifts from an IT knowledge base to more of a business knowledge base – in terms of accountability and knowledge.  IT skill still required for integrations, complex reporting, etc. USC staffing and skill requirements disrupted.

Challenges: Who drives the roadmap and support? IT Ownership vs. business ownership; Central vs. Decentralized; Attrition in legacy system support staff. At risk skills: legacy programmers, data center, platform support, analysts supporting individual areas.

Mitigation: establishing clear vision for system ownership and support; restructure existing support org; repurpose by offering re-tooling/training; Opportunity for less experienced resources – leverage recent grads, get fresh thinking; fellowship/internships to help augment teams.

Business Process Engineering – USC Use cases

Kuali Deployment: Don’t disrupt campus operations. No business process changes. Easier to implement, but no big bang.

Workday HCM/Payroll: Use delivered business process as starting point. Engaged folks from central business, without enough input from campus at large. Frustrating for academics. Workday as a design partner was challenging. Make change management core from beginning – real lever is conversations with campus partners. Sketch future state impact early and consult with individual areas.

Current Approach – FIN pre-implementation investment

Demonstrations & Data gathering (requirements gathering): Sep – Nov. Led by Deloitte consultants; cover each administrative area; work team identifies USC requirements; Community reviews and provides feedback. Use the services folks, not the sales folks.

Workshops (develop requirements)- Nov – Feb. Led by USC business analysts, supported by Deloitte; Work teams further clarify requirements and identify how USC will use Workday; Community reviews draft and provides feedback

Playbacks (configure): March – May. Co-led by consultants and business analysts; Workday configured to execute high-level USC business requirements; Audience includes central and department-level users

Outcomes: Requirements catalog; application fit-gap; blueprint for new chart of accounts; future business process concepts; impacts on other enterprise systems; data conversation requirements; deployment scope, support model

CIO Panel – John Board; Bill Clebsch; Virginia Evans; Ron Kraemer; Kelli Trosvig

Cloud – ready for prime time ERP or not? Bill – approaching cautiously, we don’t know if these are the ultimate golden handcuffs. How do we get out of the SaaS vendors when we need to? Peoplesoft HR implementation has 6,000 customizations and a user community that is very used to being coddled to keep their processes. ERP is towards the bottom of the list for cloud.

Virginia – ERP was at the bottom of list, but business transformation and merger of medical center and physicians with university HR drove reconsideration. Eventually everything will be in the cloud.

John – ERP firmly at the bottom of the list.

Kelli – at Washington were not ready for the implementation they took on – trusted that they could keep quirky business processes, but that wasn’t the case. Took a lot of expenditure of political capital. Everyone around the table thought it was all about other people changing. Very difficult to get large institutions onto SaaS solutions because the business processes are so inflexible. Natural tendency is to stick with what you know – many people in our institutions have never worked anywhere else. Probably easier at smaller or more top-down institutions.

Ron – Should ask is higher-ed ready for prime time ERP or not? We keep trying to fix the flower when it fails to bloom. People changing ERPs are doing it because they have to – data center might be dying, cobol programmers might be done. Try to spend time fixing the ecosystem. Stop fixing the damn flower.

Kelli – it’s about how you do systemic change, not at a theoretical level.

Bill – what problem are we trying to solve? Need to be clear when we go into implementations. At Stanford want to get rid of data centers -space at too much of a premium, too hard to get permits, etc.

John – there’s an opportunity to be trusted to advise on system issues, integration, etc.

Kelli & Ron – The financial models of cap-ex vs. op-ex is a critical success factor.

Ron – separating pre-sales versions from reality is critical. That’s where we can play an important role.

John – we have massive intellectual expertise on campus, but we’ve done a terrible job of leveraging our information to help make the campus work better. We’ve got the data, but we haven’t been using it well.

Bernie – we need to start with rationalizing our university businesses before we tackle the ERP.

Ron – incumbent on us to tell a story to the Presidents. When ND looks at moving Ellucian they think what if they can stop running things that require infrastructure and licenses on campus? Positions us better than we are today. Epiphany over the last 6 months: We have to start telling stories – we can’t just pretend we know the right things to do. Let’s start gathering stories and sharing them.

Kitty – Part of the story is about the junk we have right now. The leaders don’t necessarily know how bad the business processes and proliferation of services are.

CSG Winter 2017 – New Models for Supporting the Academic Enterprise

How do we tie IT Strategic Plan to Teaching & Learning Mission?

Can IT move beyond its traditional role to expand its presence in and support for the academic enterprise?

Marin Stanek – UC Boulder

New IT strategic plan – the first one to focus on the academic mission.

Evolving role of IT – from being the fixer to a focuser. Creating new systems and services. Evolving to listening to campus, leading to further evolution to competence. We have the capacity to understand multiple agendas, and focus on overarching mission.

Focus on students – analytics, retention, etc. A rising rhetoric. Chancellor goal – increase grad rate from 68% to 80% in four years.

Went from a strategic plan with 20-some chapters to one that has the meat in four pages – it’s all about students. Small changes turn into larger results. Utilized LMS to put content first for student welcome. Brought innovative classroom techniques to administrative purpose.

Retention: Large Lecture redesign. Packed lecture hall with mediocre technology experiences. Identified 30 gateway courses that are strong predictor of student success. IT redesign team is engaged. Look at analysis and data to enhance the learning experience and student engagement. E-Bio class – 20% of students take this class. Held a design thinking challenge to understand student behaviors. Discovered that the TA plays a pivotal role in student success. How quickly TAs responded to student questions was the critical issue.

Strategy on a Page / Strategy, It’s Personal – Tom Lewis & Phil Reid, University of Washington

Example: When things go sideways – initiatives get started with no clear goals or clear points of contact. End result – still planning for the plan after 1.5 years. (names scrubbed to protect the innocent).

Strategic goal – strategy on a page. A way to articulate value and for partners to understand and align. Three columns: Change drivers; Initiatives; Outcomes.

Ideas –

Supporting the Academic Enterprise in New Ways: Ben Maddox, NYU

The teaching & learning mission is rife with … opportunity

Case Study 1: all politics are local – learning analytics exploration:

context: Hosted university-wide event to gauge interest (standing room only); distributed instructional technology team; no learning analytics data steward; new leadership (president, provost, CIO)

Identified willing partner to build vocabulary around learning analytics that make sense to faculty; Developed working group and business case; built a site.

Challenge: learning analytics is a sprawling, undefined space. Sudden moves in the space freak people out. Local interests may not transfer to broader needs.

Merits: academic sponsorship; justification for dedicated FTE; credibility through local partnership; leverages standing governance structure to define broader needs.

Strategic Support for Education from IT at Duke – John Board

25% of all Duke students take assembly language-based intro to computer architecture. 40% of all students take intermediate programming (and over half are women). Falure to persuade many under-represented students to go further. Teaching very large classes of 220 a semester is not in the ethos of ECE and CompSci. The Modest Disagreement: Programming should fun to draw people into field, vs. programming classes should train people to be “real” programmers. Standard curriculum instills almost no practical systems knowledge. Faculty are looking to IT to help remedy this. Most of the knowledge of real computing is in IT! Can be used to improve skill set of students who are going to be in the field in the real world. IT developed courses for students to take extra-curricularly in developing code.

Advice: don’t have separate advisory groups for admin and academic IT – it’s all connected.

Strategic planning process: 25 faculty and even more staff from central and distributed IT units) populating 7 working groups: living and learning; research computing support; communications and infrastructure; IT security; administrative and business systems; support models, procurement and licensing; mobile and web

Many recommendations: help people use tech more effectively; prov; support innovation in research and education

Under innovation, relevant points: support the evolving computing needs of our researchers; improve Duke’s competency in data analytics;

Technology engagement center: Windowless telephone with bunker has been transformed into bank of 3d printers. Co-lab with app developers, creating APIs, video production operations; mini courses in many topics; hardware hacking (arduino, sensors, IoT); research computing – led to graduates who wanted to donate specifically to IT

What are the merits and challenges of integrated models, where IT partners with units that support instructional spaces, pedagogy, and assessment, to provide unified instructional support to campus?

Phil Reid: Why unified T&L support, and why IT?

Goal – promote and support innovation in teaching and learning

Barrier: faculty motivation to change (and you can’t blame them – incentives aren’t aligned)

Ideas to overcome barrier:

  • inspirational leaders in novel pedagogy
  • better student learning outcomes
  • improved efficiency
  • disruptive technology

Instructional systems are the “ERP” of teaching and learning

Improving the student experience

Improving the faculty experience

What faculty want is one stop shopping – pedagogy, technology, classrooms, assessment/measurement – they want the Genius Bar

Marin Stanek – How do we bring people together?

There are simple tools that seem like magic to campus. Eg. tap into IT project management discipline for transformative academic projects. Advantages: creates structure; sets expectations for timelines, resources and responsibilities of the partnering department; executive sponsorship help momentum, buy-in and hand-off of initiatives. The IT project portfolio now has a preponderance of initiatives for teaching and learning.

Example – Pathway to Space (a new minor in Aerospace, designed to pull in non-engineering majors). Utilized project portfolio process: project definitions/charter doc; schedule, budget, timeline; exec sponsorship, watch warning signs; change management process; communicate! transparency & updates; crossing the chasm – handing off the creating or build it into the team

Ben Maddox: Running the Governance Gauntlet

Context: university-wide service pilot for instructional tech support; added 10 new instructional technologists based at the schools (“a distributed model, centrally convened”); added instructional tech committee to standing governance structure; new role (joint to IT & Provost) convenes monthly meeting; group sets and recommends shared service model.

Challenge: requires increased coordination and strong sponsorship. For schools that were less resourced, there was Provost support, with management from central IT.

Deans had to write proposals to Provost to ask for the instructional support.

Jenn Stringer (Berkeley) – Academic Innovation Studio (AIS): A Collaborative Service Model

Faculty was getting “no, but” instead of “yes, and”

Space + Partners + Commitment + Trust = AIS (no unit names included). Open to every faculty, instructor, etc.

2k sq ft of space. 4 partners deliver service: research IT; Ed Tech Services; Center for Teaching & Learning; Library; Collaborative Services (google, box, etc).

Commitment is key – part was not branding as IT space. It’s faculty space. Everybody was at table to design space. f2f time – built trust.

Oren Sreebny – Central IT and the University Innovation Sector


Marin –

Challenge: No clear career path for research computing profesiionals

No formal educational track; reward system missing; lmited career path

Solution: Create MA in research computing and a formal collaboration between Research Computing & the Libraries. Develop and advance data science and digital scholarship through discovery & reuse

Certificate in Cybersecurity

Challenge: further develop Cybersecurity track utilizing existing interdisciplinary telecom program. Use existing grad school structure to minimize admin hurdles. Tap into existing courses to create certificate program.

Staff member was teaching a course at another university – there was no clear reward program for him to teach on campus. Story in unfolding, requires tenacity from professionals, but requires incentive structure, and need to happen at speed to keep momentum.

Ben – Supporting Teaching & Learning by TEaching

Consultations for teaching and learning with technology increased by 60%-plus. Center for Advancement of Teaching had no tech curriculum. New Inst. Tech Groups that had lots of instructional experience. Faculty Collaborators value team members with teaching experience. Appetite for Share.

Created online interactive tutorials for T&L Services. Center for Advancement of Teaching uses Instructional Tech Teams to new Tech-oriented curriculum; Provost agreed to sponsor 2 University-wide events per year. Made schools aware that staff were interested in teaching opportunities.

Evan – Duke – Technology classes at Co-Lab

Co-Lab is a technology innovation incubator to encourage students. Started with challenges, but weren’t as effective as they’d hoped. Flipped it around to ask for ideas first. Turned it into more of a grants program, but a persistent problem is that they didn’t have as many students with development skills as they thought. Roots program – teach Python, HTML, Web Development, etc. https://colab.duke.edu/roots – Taught by IT professionals. Faculty began to notice – told them that students were less technical than they used to be. Worked with faculty to develop an intro to Linux course that they use as an informal prerequisite. Going to do a git class for a Physics course.

Duke Digital Initiative – innovation funding for faculty. Over 20 proposals from faculty, funded 10 of them. Why IT? Who else knows how to program a drone, take 360 degree video, and put it on a web site?

A Day in the life of Rob Fatland, Cloud Czar – Tom Lewis

Cloud and Data Research Computing – originated out of UW E-Science institute. Out and about on campus every day, looking for researchers to help. Build – Test – Share

Success stories: ORCA Transit Data – patterns of how people commute. Digital curation at the library – LIDAR data. Genomics – cut cost per genome from $60 – $15 w/help from AWS. Democratizing data and software: cloud plus GitHub plus software carpentry workshops.

Supporting the continuum of research computing – Oren


Data for Researchers – Jenn

Providing learning data to researchers from learning records store. Data warehouse for the interactivity data from your learning systems. Things you mine to get information on student success. Berkeley has a billion records from 2.5 years of data from LMS. Researchers want to mine the data to get insights into how people learn. Most data governance organizations are not thinking about this kind of data at all. There are standards around this data – two competing: xAPI, Caliper.

Take log data and convert into standardized statements – pushing for vendors to hand data over in that format. Canvas doesn’t  (yet) so UCB has to convert.

Learning Record Store: AWS based Learning Record Store; Multi-tenant LRS that can support multiple institutions; Scalability and cost; Faster deployments – lower dev/ops overhead; Lambda architecture which encompasses both Batch and real-time interaction. Have an API for researchers who go through proper approval process to get de-identified data.

Are we telling students what we do with their data? They’ve created an agency dashboard for students (not in production yet). Allows students to opt-in or out of use of their data (where appropriate). Lots of discussion of data ownership, but regardless, they want transparency and agency.

UC Learning Data Privacy Principles: pulled together leaders from across the UC system. Working to draft principles. Something to point procurement and vendors to.

Learning Data Recommended Practices – been circulating them, taking to committees, etc to socialize and increase awareness.

John – Using infrastructure for faculty researh

There are faculty who want to use the infrastructure for research. NSF did us a favor with the first round of CCNIE proposals – thinking about SDN in particular. Insisted PI had to be the University CIO. Unexpected benefit was to have regular meetings on progress. Regular conversation on new opportunities for cyber infrastructure grants. IT staff get opportunities to have time bought out to work on interesting problems. Faculty develop respect for the expertise of IT. OIT thinking about hiring a full-time grant writer on the staff.

Cloud Billing Challnges

Bob Flynn, Indiana University

Microsoft Azure – the challenges. Plsses – Account management; Identity management; Networking; Security management; Incident Response.

Minus – Billing. Hvae to make a pre-commit for your enrollment ($100/month) Everything that happens at your campus later is on the same bill. Enrollment owner pays that. First user that burns the $1200 gets it for free (unless they figure out a way to rebill). Ongoing usage – Does central IT (or Procurement) have to do rebilling? How does the account holder track their usage? Azure marketplace purchases sent to enrollment admin, not the one using them. There are issues with research and education credits. The solution? Started with Resource Groups and tags. Limited to 15 tages per resource group, and not all Azure tools are resource group ready. Notifications come to subscription owner. Started looking at allowing users to have their own subscription. VNet Peering allows you to centrally manage the campus network connection. PO number added to subscription name. Bell Techlogix pulling PO # via API – they’re building a portal for account owners and set alerts at PO thresholds.

Nicole Rawleigh, Cornell University

Have 65 accounts under AWS billing. In August 2015 they manually billed four financial accounts. Sept 2016 billed 45 accounts for 65 AWS accounts. Separating internal CIT costs from external units. Switched to doing multiple financial system edocs created manually. One consolidated bill, but also can have multiple other bills/credits. Credits are applied manually to accounts. Going to automation! API between AWS and Kuali Financial. Batch job runs once a month. Outstanding Challenges: Invoice attachment (they use CloudCheckr so users can see invoice charges), making sure that resources are correctly tagged; one financial edoc per financial system account, not per AWS account; Batch error report is hard to deal with; automates consolidated bill only.

Erik Lundberg, University of Washington

Using DLT / Net+ for AWS. DLT provides a great biling front-end. Individual AWS accounts are associated with separate POs and they get invoiced and paid directly. People can create a blank PO on their university budget. Invoicing is all electronic and automatic (through Ariba). Next steps – get AWS Educate and research grants covered under the DLT contract.


Cloud Forum 2016 – Research In The Cloud

Daniel Fink from Cornell – Computational Ecology and Conservation using Microsoft Azure to draw insights from citizen science data.

Statistician by training. Citizen science and crowd sourced data.

Lab of Ornithology: Mission – to interpret and conserve the earth’s biological diversity through research, education, and citizen science focused on birds.

Why birds? They are living dinosaurs! > 10k species in all environments. Very adaptable and intelligent. Sensitive environmental indicators. Indian Vulture – 30 million in 1990, virtually extinct today. Most easily observed, counted, and studied of all widespread animal groups.

Ebird. Global bird monitoring project- citizen science for people to report what they see and interact with data. 300k people have participated, still experiencing huge growth.

Taking the observation data and turning it into scientific information. Undestanding distribution, abundance, and movements of organisms.

Data visualizations: http://ebird.org/content/ebird/occurrence/

Data – want to know every observation everywhere, with very fine geographic resolution. Computationally fill gaps in observations, and reduce noise and bias in data using models.

Species distribution modeling has become a big thing in ecology. Link populations and environment – learn where species are seen more often or not. Link ebird data with remote sensing (satellite) data. Machine learning can build models. Scaling to continental scale presents problems. Species can use completely different sets of habitats in different places, making it hard to assemble broad models.

SpatioTemopral Exploratory Model (STEM) – Divid (partition extent int regions, train & predict models within regions, then Recombine. Works well, but computationally expensive. On premise on species in North America, fit 10-30k models, 6k CPU hours, 420 hours wall clock (12 nodes, 144 CPUs). Can’t scale – also dealing with growing number of observations in Ebird – 30% /year. Also moving to larger spatial extents.

Cloud requirements: on-demand: reliably provision resources. Open Source software: Linux, hadoop, R. Sufficient CPU & RAM to reduce wall clock time. System that can scale in the future. Started shifting workload about 1.5 years ago. Using Map Reduce and Spark has been key, but isn’t a typical research computation tool.

In Azure: Using HD Insight  and Microsoft R Server – 5k CPU hours, 3 hours wall clock.

Applications – Where populations are, When they are there, What habitats are they in?

Participated in State of North America’s Birds 2016 study. Magnolia Warbler – wanted to summarize abundance in different seasons. Entire population concentrates in a single area in Central America in the winter that is a tenth the size of the breeding environment – poses a risk factor. Then looked to see if the same is true of 21 other species. Still see immense concentration in forested areas of Central America – Yucatan, Guatemala, Honduras. First time there is information to quantify risk assessment. Looking at assessing for climate change and land use.

50 species of water birds using the Pacific Flyway. Concentration point in the California Central Valley, which has had a huge amount of wetlands historically, but now there’s less than 5% of what there was. BirdReturns – Nature Conservancy project for Dynamic Land Conservation. Worked with rice growers in Sacramento River Valley to determine what time of year will be most critical for those populations. The limit is water cover on the land. There’s an opportunity to ask farmers to add water to their patties a little earlier in the spring and later in the fall, through cash incentives. Rice farmers submit bids, TNS selects bids based on abundance estimates (most birds per habitat per dollar). Thy’ve put 36k additional acres of habitat since 2014.

Quantifying habitat uses. Populations use different habitats in different seasons. Seeing a comprehensive picture of that is new and very interesting. Surprising observation of a wood thrushes using cities as a habitat during fall migrations. Is it a fluke caused  by observation bias? Is it common across multiple species?

Compare habitat use of resident species vs. migratory species. Looked at 20 neotropical migrants, and 10 resident species. Found residents have pretty consistent habitat use, but migrants seasonal differences, showing a real association with urban areas on the fall. Two interpretations: 1) cities might contribute important refuges for migrant species or, 2) cities are attracting species but are ecological traps without enough resources. Collaborators are setting up studies to see. Hypothesis that they are attracted to lights.

Heath Pardoe from NYU School of Medicine – Cloud-based neuroimaging data analysis in epilepsy using Amazon Web Services.

Comprehensive Epilepsy Center at NYU is a tertiary center, after local physician and local hospital. Epilepsy is the primary seizure disorder (defined by having two unprovoked seizures in their lifetime). Many different causes and outcomes. Figuring out the cause is a primary goal. There are medications and therapies. Only known cure is surgery, removing a part of the brain. MRI plays a very big role in pre-surgical assessment. Ketogenic diet is quite effective in reducing seizures in children. Implanting electrodes can be effective, zapping when a seizure is likely to control brain activity. Research ongoing on use of medical marijuana to treat seizures. Medication works well in 2/3 of people, but 1/3 will continue to have seizures. First step is to take a MRI scan and find the lesions.  Radiologists evaluate MRI scans to identify lesions.

Human Epilepsy Project – study people as soon as they’re diagnosed with epilepsy to develop biomarkers for epilepsy outcomes, progression, and treatment response. Tracking patients 3-7 years after diagnosis. Image at diagnosis and three years. Maintain a daily seizure diary on iOS device.Take genomics and detailed clinical phenotyping. 27 epilepsy centers across US, Canada, Australia, and Europe.

Analyzing images to detect brain changes over time. Parallel processing of MRI scans. Using StarCluster to create a cluster of EC2 machines (from 20-200) (load balances and manages nodes and turns them off when not used). Occasionally utilize compute optimized EC2 instances for computationally demanding tasks. Recently developed an MRI-based predictive modeling system using OpenCPU and R.

Have a local server in office running x2go server that people connect to from workstations. From that server upload to EC2 cluster.  More than 10 million data points in a MRI scan. Cortical Surface Modelling delineates different types of brain matter. Then you can measure properties to discriminate changes. To compare different patients you need to normalize, by computationally inflating brains like a balloon – called coregistration.

There are more advanced types of imaging.

Some studies done with these techniques: Using MRI to predict postsurgical memory changes.  Brain structure changes with antiepileptic medication use.

Work going on – image analysis as web service: age prediction using MRI. Your brain shrinks as you age. If there’s a big difference between your neurologic age and your chronological age, that can be indicative of poor brain health.

Difficulty of reproducing results is an issue in this field. Usually developed models sit on grad student’s computer never to be run again. Heath developed a web service running on EC2 that can be called to run model consistently.

Cloud Forum 2016 – Cornell’s BI move to the cloud

Jeff Christen – Cornell

Source Systems – PeopleSoft, Kuali, WOrkday, Longview. Dimensional data marts: finance, student, contributor relations, research admin. BI Tools – OBIEE and Tableau

They do data replication and staging of data for the warehouses. Nightly eplication to stage -> ETL -> Data Marts

Why replication/stage? Consistent view of data for ETL processing, protects production source systems; tuning for ETL performance.

Started journey to cloud 2 years ago. Were using Oracle streams – high maintenance, but met some needs. Oracle purchased a more robust tool and de-supported Streams. ETL tools challenge – were using Cognos Data Manager for 90% of their work, but IBM didn’t continue to support it. Replaced it with WhereScape RED, but requires rewriting jobs.  Apps were already moving off-premise. WorkDay for HR/Payroll, PeopleSoft to AT&T hosting; Kuali financials moving to AWS. Launched pilot project to answer “what would it take to run data warehouse environment in AWS?”

Small pilot – Kuali warehouse in AWS. Which existing tools will work? Desire to use AWS services such as RDS where possible; Testing of both user query performance and ETL performance.

Why Oracle RDS and not Redshift? Approximately 80% of the Kuali DW is operational reporting. Needs fine-grained security at the database level; A lot of PL/SQL in the current environment; Currently exploring Redshift for non-sensitive high volume data

Some re-architecting: Oracle Streams not supported with Oracle RDS (used Attunity). Oracle Enterprise Manager scheduler not supported with Oracle RDS – using Jenkins (so beautiful and simple); No access to OS on RDS databases – installed Data Manager on separate Linux EC2 instance; Using WhereScape to call Data Manager from the RDS database.

Need to be more efficient. On premise the KDW had two physical servers. Found some inefficiencies in ETL code and some dashboard queries were masked by large servers. Prioritization of ETL code conversion by long running areas helped get AWS within nightly batch window. Some updates made to dashboards to improve performance or offer better filter options. Hired database tuning consultant (2wk) to help with Oracle tuning.

Testing and User Perception. Started with internal unit testing. Internal query execution time comparisons between on premise and AWS. User testing of dashboards on premise versus AWS. Repoint of production OBIEE financial dashboards to AWS for a day (x3). Some queries came back faster, some slower. Went through optimization and tuning to get it comparable across the board.

Cutover to AWS. Cutover Sept. 8. Redirected all non-OBIEE ODBC client traffic in October. Agreed to keep the on premise KDW loading in parallel for two month end closings as a fall back.

Next Steps. Parallel Research Admin Mart already in AWS – expect cutover by end of CY. Need more progress on ETL conversion before moving student and contributor marts. Continue Big Data / non-traditional data investigation (Cloudera on AWS). Redshift for large non-sensitive data sets.

Lessons learned: Off premise hosting does not equal Cloud technology. Often hard to get data out of SaaS apps.

Cloud Forum 2016 – Lightning Rounds #2

Cloud VDI – Bob Winding (Notre Dame)

Use cases they looked at:

  • Classes that need locally installed software
  • Application delivery instead of high-end lab machines
  • Workstations for researchers wher the whole project is in the cloud
    • NIST 800-171, ITAR, etc
    • Heavyweight, graphics and processing-intensive work

Looked at: Workspaces (AWS); Microsoft RDP and RDP Gateway, Fra.me, Ericom Blaze and Ericom Connect

Performance is everything – did tests with PTC Creo, Siemens NX10, and Solidworks. Set up test environment in Oregon. Nobody in central IT knew how to operate the software. Found in almost every case that the remote setup was beating the local desktop performance. In some cases, local environment crashed under load, but in AWS loaded in under 2 minutes. (G2X.large).

Researchers observed that they can transfer

Cloud Governance – Do You Need a CLoud Center of Excellence? Laura Babson (Arizona)

a group that leads an organization in an area of focus

Establish best practices, governance, and frameworks for an organization

Applications vs Operations – what do you about tagging, automation, monitoring, security, etc. Don’t want to end up with different ops solutions for different applications.

CoE can help streamline decision making. CCoE can make decision if funding isn’t required, or make a recommendation to a budget committee if funding is required.

Recent decision making: Account strategy – how many and where to put each workload? Campus to Cloud Connectivity’ Monitoring; Tagging policy

Can help with communication and engagement across the organization

AWS CloudFront – Gerard Shockley (Boston U)

What is a CDN? geographically dispersed low latency, high bandwidth solution for distributing http and https.

Terminology: Distribution (rules that control how cloudfront will access and deliver content); Origin (where the content lives)

Only works with publicly visible infrastructure at AWS

Easy to get metrics and drill down into specifics

DevOps != DevOps – Orrie Gartner (Colorado)

Brought a new data center online 3 years ago to consolidate IT across campus, built a private cloud

Ops and Devs teams work close together, automating everything, fine with accepting higher risks, building strong relations between teams, performing continuous integration and deployments.

Didn’t go well this summer moving to the public cloud – lack of understanding of vision and goals from other silos.

Ensure the entire enterprise strives for the same end goal, communicates that goal

Created a vision and articulated cloud strategy. 6 phase roadmap to to public cloud, includes embracing DevOps culture. Line in strategic plan – encourages every team to articulate how they will embrace DevOps concepts.

Educate Up. Educate Laterally. Educate Down.

Change is not easy – changing culture in the organization. Prosci ADKAR – model embraced for making organizational change. Small steps, like encouraging process folks to use Jira, the same tool used by the devs and ops folks.

Us versus Them – a View From the Information Security Bleachers- David McCartney (Ohio State)

Security is not the enemy – they’re scared, unaware, and unprepared for the cloud.

Scared – “how can we stop you?”

Unaware – why move? what kind of data? what security is needed (vs. what you think you need)? what did we do to deserve this?

Unprepared – How do current security services expand? What do you mean “no agent”? Logging? Auditing? Access management? Vulnerability scans? incident response? What about regulatory and framework requirements?

Model Us + Them – Embrace security, buy them booze.

Engage security early, sell the opportunity to do something new and exciting, provide options for training and guidance.

MCloud: From Enable to Integrate – Mark Personett (Michigan)

MCloud is an umbrella service. Strictly IaaS – currently offering AWS, but might mean others later

First iteration launched in 2014 – access to UM enterprise agreement, optional consolidated billing; data egress waiver; M Cloud Consulting Service

Working on launching M Cloud AWS Integrate: provisioning – private network space, shibboleth integration, etc; Guardrails – security best practices, common logging, reporting, etc; Foundational services in AWS – AD, Shib, Kerb, DNS, etc; Site to Site VPN services.

Azure Remote App – Troy Igney (Washington U in St. Louis)

two core requirements when enrollment in second year CS class spiked. Needed Visual Studio. New computers too expsensive. On prem VDI – too expensive. Off Prem VDI – Azure Remote App.

Goal – deliver consistent development environment across a range of BYOD devices.

Challenges: Support an entire class’s logons at once. Required Micsrosoft off-menu configuration.

Advantages – template once and deploy, capacity costs based on current enrollment – dynamically adjust for enrollment changes.

Largest RemoteApp deployment directly supporting classroom delivery.

Microsoft dropped RemoteApp in favor of Citrix virtualization technologies.

Lots of lessons learned supporting remote VDI

Adopting Cloud-Friendly Architecture for On-Premise Services – Eric Westfall (Indiana)

Indiana primarily on premise with an increasing amount of SaaS. Have newer data centers and heavy investment in VMWare. Inevitable to get to hybrid environment, but in the meantime working to be prepared – “cloud-ready” app architecture.

12 factor principles
Stateless Architecture
Object Storage (using S3 API in on-prem solutions)
Non-Relational databases

Facilitating DevOps culture

Containerization – investing heavily in Docker. Adopting Docker Data Center

Hope it will allow to take advantage of existing infrastructure investments. Give dev and ops staff opportunities to experiment with cloud services. Allow modernization of app architecture and deliver practices. Prepare for inevitable future.

Cloud Initiative and Research – Steve Kwak (Northwestern)

Cloud Governance – October 2015. IT Directors from the schools and enterprise IT. Hired a consultant to help develop governance.

Cloud Architecture and COnsulting Team – April 2016 – 5 initial team members. set up initial environments at AWS and Azure. Worked through billing and accounts, and providing consulting.

Running cloud days and “open mic” sessions with AWS .

Research environments – 3 centrally managed – HPC (heavy upfront investment for dedicated compute, always a queue); Social Science cluster (aging infrastructure, limited support); Research data storage (separate storage from HPC). Looking to burst HPC to the cloud and move the other two.

Genomics pilot in AWS. Hire on a 3rd party team to put architecture together.

HPC Environment -working on targeting specific workloads in cloud with scheduler, and figure out bursting.

Controlled Approach to Research Computing in AWS – Paul Peterson (Emory)

Mindset of security team – need a similar set of controls in cloud as on-premise. This is quite challenging.

Started working to build Research Cloud. Collected 24 use cases and put them in three categories, divided into 2 VPC types. Worked with AWS professional services to build out VPCs. Pilot started this summer, going to end of year.

Type1 VPC- one availability zone, no Internet gateway – access only through Emory. Single sign-on with Shib.

Tpe2 has two availability zones, and an Internet gateway.

Goal of project team is to make requests for VPCs easy. Automation is key.

Generate VPC service. Created an inventory of accounts, LDS groups, Exchange distribution lists, and CIDR ranges.

Service gets next available account, adds admins to LDS group, creates SAML provider, Creates account alias, selects cloudfront template, get next available CIDR range Creates stack, compute subnets for account. Takes less than 5 minutes.

We Demand, On-Demand: Berkeley Analaytics Environments, VDI and the Cloud – Bill Allison (Berkeley)

Central IT budgets getting cut 10% year-over-year.

VDI use cases have been mostly around desktop pps, not research. Funded a pilot through December. User and use-case driven (faculty oriented) – need to tell story from a faculty perspective. Research IT group is like field workers, mst with PhDs.

Analytics Environment on Demand – not a change in the way you compute, at least on the surface. Use the skills you know already. Creating an abstraction layer.

Art of Letting Go – Relationship advice for dev and ops in the cloud – Bryan Hopkins (Penn)

Team lead for cloud app dev team. Cloud First program – replace homegrown frameworks with off the fhelf frameworks; replace waterfall with agile; replace monliths with integrations and composed apps

Three things we’ve learned so far: 1. Have a clear try-and-scrap phase in R&D – give it leeway. 2. Accept that interests and traditional roles will collide. Dev team can help with platform tasks, ops team can help with dev. Everyone cares about Jenkins. Bring them together. 3. Let go of notions of perfection and clean lines. Off-the-shelf means you get what’s on the shelf.