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Project Presentation Students: Yan Michalevsky Asaf Cidon Supervisors: Alexander Shraer Assoc. Prof. Idit Keidar
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Users should not blindly “trust the cloud” Users should have tools that guarantee integrity and consistency for data they store in online storage systems Utilize research on untrusted storage for real-world mass- market applications
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Many providers now offer wide-variety of online data storage services, a.k.a. clouds: ◦ Software Collaboration ◦ Social Networking ◦ Online Archiving ◦ Document Repositories ◦ Open Source Development
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Large corporations and SMEs have started utilizing Clouds in order to save IT infrastructure costs Online IT service providers also provide cloud-enabled products ◦ SalesForce.com ◦ Oracle ◦ SAP
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Multiple risks in releasing data to online platforms: ◦ Loss of privacy ◦ Lack of availability ◦ Data corruption ◦ Data inconsistencies between multiple clients ◦ Exposure to malicious online attacks ◦ Unauthorized access
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Byzantine fault-tolerant (BFT) replication protocol ◦ Capable of guaranteeing integrity & consistency If used by a storage provider ◦ Need at least 2/3 correct servers on the cloud But most servers located in the same place, run the same system guaranteeing that servers fail independently would be very costly ◦ Require high communication overhead Providers prefer weaker consistency ◦ Cloud providers do not currently implement BFT protocols ◦ Most importantly: clients would still need tools to check provider behavior and should not simply “trust the cloud” If used by the client ◦ Store data on multiple (at least 4) storage providers costly to the client
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Store a hash of the data locally ◦ Allows clients to verify server responses ◦ Use hash-trees when volume of data is large (store locally the root of the hash-tree) Proofs of Retrievability (PORs) ◦ Allows a client to verify that the server stores its data without reading all the data What about multiple clients?
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Strong consistency (atomicity, sequential consistency) ◦ not possible, assuming we want clients to be able to execute operations independently of each other ◦ Server can hide latest write from readers ◦ Server can split its brain (each client thinks he executes in isolation)
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Fork-Consistency Advantages: Linearizable view of a sub-sequence of the execution After the server lies, if clients see new information from each other, they detect inconsistency Disadvantages Blocking, even when server is correct
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A weaker consistency was devised in order to enable a wait-free protocol. Example: ◦ In Fork-Consistency, if C1 crashed during w1, C2 would be blocked while conducting r2 (even though the server was telling the truth!)
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A common cloud-computing scenario: ◦ Multiple clients working concurrently ◦ Using a commodity online storage provider (e.g. Amazon S3, Salesforce.com Force) Our goal: ◦ Integrity and eventual consistency for client data ◦ Detection of inconsistencies / service failures
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Concept: ◦ An untrusted verification node, can quickly detect inconsistencies when it is not faulty ◦ Clients communicate directly when they suspect that storage or verifier is faulty ◦ Clients can use any unmodified commodity cloud storage provider
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Features: ◦ Operations are conducted optimistically wait- free ◦ Clients can go offline, new clients can join ◦ All clients receive failure notifications when consistency breach is detected ◦ Operations eventually become consistent clients know up to which point the data is consistent with other clients
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Learn about distributed computing research Implement client-to-client communication Deploy verification node on Google Web Apps Assist in writing the article
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E-mail based Authenticated (using GNUPG) A client communicates directly with another: ◦ To request and send status updates, if no recent activity of that client was seen on storage ◦ When an inconsistencies is detected
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Verification node was initially deployed on Google Web Apps and used HTTP to communicate with Clients We realized that Google Web Apps is not appropriate for hosting the verification node ◦ The verification node creates a sequence of client requests ◦ Maintaining such sequence with Google Apps is very costly Instead, verifier was deployed on a regular server in MIT
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We wrote the initial versions of the Introduction and Related Works sections Contributed to the System Design and Deployment and Implementation sections Article was submitted to Eurosys2010 in October 23 rd 2009
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Too many to list on this slide “Trusting the Cloud”, C. Cachin, I. Keidar, A. Shraer, 2009 “Towards a Cloud Computing Research Agenda”, K. Birman, G. Chockler, R. van Renesse, 2009 “Salesforce hits its stride”, J. Hempel, Fortune Magazine, http://money.cnn.com/2009/02/16/technology/he mpel_salesforce.fortune/index.htm http://money.cnn.com/2009/02/16/technology/he mpel_salesforce.fortune/index.htm “Fail-Aware Untrusted Storage”, C. Cachin, I. Keidar, A. Shraer, 2009 Additional references included in article citations section
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