Cloud Security and Performance Dr Lee Gillam Department of Computing University of Surrey

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1 Cloud Security and Performance Dr Lee Gillam Department of Computing University of Surrey

2 Motivation 1 Security and performance (resilience) are key Cloud issues... and price. See, for example, the Cloud Circle report:

See, for example, the Cloud Circle report: http://www.

3 Motivation 2 Deperimeterization of any kind presents risks, not least to Intellectual Property 9.2bn for UK OCSIA/Detica report notion of insider assistance vs $300bn recently mooted in US study The National Bureau of Asian Research - literally copy patents from any country and have them filed and granted in China as a utility model patent

2bn for UK OCSIA/Detica report notion of insider assistance vs $300bn recently mooted

4 Motivation 2 Deperimeterization is inherent in Supply Chains and in Cloud. So, who is an insider? (How) Do we control them? How do we detect leaks?

5 Structure Part 1: When perimeters down, information can leak. Policing (monitoring) versus command and control But can you search for what you don t want to expose? Part 2: Are all Clouds (infrastructures) made the same. What can you expect in performance So why should prices be static?

6 PART 1: CLOUD SECURITY

7 General Cloud Security Protect data in transit - Protect data at rest - Design for privacy Protect Cloud credentials (e.g. AWS keys / certificates / login ) Secure your application, O/S etc. Secure the wetware? Adapted from:

Secure the wetware? Adapted from: http://awsmedia.s3.amazonaws.

8 From /var/log/auth.log (ssh open ) Apr 22 21:10:09 ip sshd[14681]: Failed password for invalid user harvey from port ssh2 Apr 22 21:14:54 ip sshd[14683]: Failed password for invalid user hasok from port ssh2 Apr 22 21:19:41 ip sshd[14688]: Failed password for invalid user hassan from port ssh2 Apr 22 22:21:39 ip sshd[14717]: Failed password for invalid user helen from port ssh2 Apr 22 22:26:25 ip sshd[14719]: Failed password for invalid user helena from port ssh2

251 port 38196 ssh2 Apr 22 21:19:41 ip-10-2-34-82 sshd[14688]: Failed password for invalid user hassan from 85.214.234.

9 Cloud Security Privacy Design Strict separation of sensitive data from nonsensitive; encryption of sensitive. See: Reese, p80 et seq. And heterogeneous systems good! Organisation-centric consideration: - customer data? (77m users of the PlayStation) Addresses without names? Credit card numbers without names / CCV?

10 Cloud Security - Tokenization Systematic data substitution generate randomly Credit Card Number: Tokenized Card Number Secure the lookup, use tokenized value everywhere. Cannot derive data from token. Credit Card Number: Encrypted Card Number Azk92l1f#*xz&#Ynv#*. Hashed Card Number 6cba83a85a32ac45be69bb23cee7d06b

tokenized value everywhere. Cannot derive data from token.

11 All well and good.. But what about when you have to share real stuff for people to read?

12 IPCRESS IP Protecting Cloud Services in Supply Chains (IPCRESS) R&D project involving Jaguar Land Rover, GeoLang, Surrey, co-funded by TSB, Innovating in the Cloud see 2pm, Andrew Tyrer People need to see stuff in order to do stuff. Especially relevant in new supply chains. Can you build parts for a car without knowing shape, etc.? Tracking of IP

Andrew Tyrer People need to see stuff in order to do stuff.

13 IPCRESS - Analogies X is a secure system. Y is not; Wetware/meatware bridge: X compromised. And we don t want to connect Y to X to detect leaks The superinjunction (??) problem: How to find out if data in X has been exposed without suggesting the identity of the [Manchester United and Wales footballer / Downton Abbey actor] private search without revealing (or encrypting / hashing) the query

?) problem: How to find out if data in X has been exposed without suggesting the identity of the

14 IPCRESS Doc. never leaves.

15 IPCRESS Can it work? Research prototype evaluated for plagiarism detection (PAN*), first run on AWS Latest precision figure (April 2013): On data that cannot be seen * plagiarism, authorship, and social software misuse

17 IPCRESS - How IPCRESS patterns difficult to reverse engineer hash-like, but highly ambiguous or lossy hash: make patterns public.. and seriously scare the data security specialists! Compact representation efficient Patents filed - US and PCT A kind of search engine (document [passages] [passages] documents), in a federation-like supply chain interaction;

. and seriously scare the data security specialists!

18 IPCRESS and beyond Applicability to other kinds of data new research questions video/audio? And, how fast it can run will depend on the performance of the Cloud systems it runs on

19 PART 2: CLOUD PERFORMANCE

20 Clouds fail Everything fails, all the time. We lose whole datacenters! Those things happen. Vogels, CTO Amazon.com But, failure to meet expectations is very different.

21 Cloud Performance We used micro-benchmarks as a means to an end. Performance of applications predicated on performance of underlying (virtualized) resources We know what the label on the box says, but what kind of present do we actually get? Probability of good/bad performance? Scaling? What measurements are important for whom?

22 Cloud Performance Tested AWS (several regions), Rackspace (UK, US), IBM SmartCloud, OpenStack at Surrey. Different machine types (flavours), and 2 Linux distros Using Bonnie++ and IOZone for disk, LINPACK for CPU flops, STREAM for memory bandwidth, iperf for network bandwidth, MPPTEST for MPI, and a bzip2 application benchmark We want simplicity so the results are understandable, following Gray non-optimized: out of the box Gray, J. (ed.) (1993), The Benchmark Handbook For Database and Transaction Processing Systems. Morgan Kaufmann.

24 Cloud Performance

27 Cloud Performance Quality of Service (QoS) for Service Level Agreements (SLAs) SLAs for Brokers (provider agreements may remain unchanged or barely worth the RoI of even reading) A Cloud Broker has no resources of its own

28 Cloud Broker(s) Opportunity value overall: Gartner, $100bn by 2014 Types [Gartner / NIST] Aggregation: multiples of similar stuff Arbitrage: dynamic pricing (credit scoring cloud providers?) Customization/Intermediation: better view / value add for extant services Integration: simplified view of multiples of different stuff NIST Special Publication types, underlined

29 Cloud Broker(s) As a means to an end? AWS CloudWatch, alarms for various metrics. But, how do predict what to scale up/down to? Cost management QoS parameters in SLAs allows for Cloud Brokers: quality (performance) as differentiator. Fixed price not a dynamic market; performance-based part way there? Probability of, and penalty for, failure. And, so, a market in insurance?

30 An inspiration: Collateralized Debt Obligations (CDOs) Underlying assets CDS, a spread indicates level of risk Potential for default Default correlations important Lower order tranches take losses first Financial CDO Li, B., Gillam, L., and O'Loughlin, J. (2010) Towards Application-Specific Service Level Agreements: Experiments in Clouds and Grids, In Antonopoulos and Gillam (Eds.), Cloud Computing: Principles, Systems and Applications. Springer-Verlag. Li, B., and Gillam, L. (2009), Towards Job-specific Service Level Agreements in the Cloud, Cloud-based Services and Applications, in 5th IEEE e-science International Conference, Oxford, UK.

31 PART 3: CLOSING REMARKS

32 Take away messages Part 1: When perimeters down, information leaks. Protect that which is truly high value Expect it to leak, and think about how to monitor for leaks Part 2: All Clouds (infrastructures) are not the same. Performance can be known after purchase Need performance guarantees, monitoring, and brokers

33 Take away messages The Cloud-qualified are out there: 79 MSc students over 4 years on a module about Cloud Computing Principles, definitions, etc. Relatives in Grids, HPC, P2P, Mainframes Management, Security, Governance AWS, Google App Engine, MapReduce, OpenStack Gillam, L., Li, B. and O Loughlin, J. (2012). Teaching Clouds: Lessons Taught and Lessons Learnt. In Cloud Computing for Teaching and Learning: Strategies for Design and Implementation, IGI.

34 Take away messages Literature is available JoCCASA, articles free to everyone, for ever. A financial brokerage model for cloud computing A constraints-based resource discovery model for multi-provider cloud environments A multi-level security model for partitioning workflows over federated clouds

35 Contact: Qs? The work presented here has been supported in part by the TSB (IPCRESS) and in the recent past by EPSRC, JISC, TSB (KTP), amongst others.

ADDING CLOUD PERFORMANCE TO SERVICE LEVEL AGREEMENTS

ADDING CLOUD PERFORMANCE TO SERVICE LEVEL AGREEMENTS Lee Gillam, Bin Li, and John O'Loughlin Department of Computing, University of Surrey, Guildford, United Kingdom { L.Gillam, B.Li, J.O'Loughlin}@surrey.ac.uk