The Kangaroo Approach to Data Movement on the Grid Jim Basney, Miron Livny, Se-Chang Son, and Douglas Thain Condor Project University of Wisconsin

Outline A Vision of Grid Data Movement Architecture and Example Semantics and Design Necessary Mechanisms The First Hop What Next?

An Old Problem Run programs that make use of CPUs and storage in separate locations. There are basic, working solutions to this problem, but they do not address many of its subleties.

The Problem is Not Trivial Distributed systems are subject to failures that most applications are not designed to handle. “Oops, a router died.” “Oops, the switch is in half-duplex mode.” “Oops, I forgot to start one server.” “Oops, I forgot to update my AFS tokens.” We want to avoid wasting resources (cpu, network, disk) that charge for tenancy. Co-allocation is a common solution, but external factors can get in the way. Co-allocation in and of itself is wasteful! Can’t we overlap I/O and cpu?

Example Compute MachinesWorkstation 1000 Mb/s 1 ms 240 Mb/s 5 ms 10 Mb/s 100 ms 100 Mb/s 1 ms Traffic: Many CPU users on shared network Failures: Long- distance link breaks once per day Policy: local traffic has priority over external traffic during office hours Traffic: Local apps need disk bandwidth, too. Unsynchronized variations

What’s in Our Toolbox? Partial File Transfer: Condor Remote I/O Storage Resource Broker (SRB) (NFS?) Whole file transfer: Globus GASS FTP, GridFTP (AFS?) It’s not just what you move, but when you move it.

A Taxonomy of Existing Systems Whole File Data Movement Systems Off-Line Stage in, execute, stage out. Examples: FTP+telnet, Globus GASS On-Line Fetch files on demand. Get whole file at open, and write out at close. Examples: Globus GASS in app, AFS Partial File Get/put only the fragment immediately needed. Examples: Condor, Storage Resource Broker (SRB) NFS telnet stdout

Offline I/O Benefits: Makes good throughput by pipelining. Co-allocation of cpu and network not needed. Easy to schedule. Drawbacks: Must know needed files in advance. Co-use of cpu and network not possible. Must pull/push whole file, even when only partial is needed.

Online I/O Benefits: Need not know I/O requirements up front. (Some programs compute file names.) Gives user incremental results. (Partial) Only moves what is actually used. Drawbacks: Very difficult to schedule small or un- announced operations. (Partial) Stop-and-wait does not scale to high latency networks.

Problems with Both Error handling GASS, AFS - close fails?!? Condor - disconnect causes rollback The longer the distance, the worse the performance Drop rate is multiplied with each additional link. Latency increases with each link. TCP throughput is limited to the slowest link. Resource allocation Network allocation is done end-to-end. CPU and I/O rarely overlap.

Our Vision A no-futz wide-area data movement system that provides end-to-end reliability, maximizes throughput, and adapts to local conditions and policies. Basic idea: Add buffers. Add a process to oversee.

Our Vision Compute MachinesHome Machine 1000 Mb/s 1 ms 300 Mb/s 5 ms 10 Mb/s 100 ms 100 Mb/s 1 ms RAM

Our Vision: A Grid File System File System File System File System K K K K K K K Data Movement System App Has my data arrived safely?

Our Vision Requirements: Must be “fire-and-forget.” Relieve the application of error handling! Robust wrt to machine and software crashes. (No-futz) Must provide incremental output results. Hide latency from applications by overlapping I/O and cpu. Maximize use of resources (cpu, network, disk) when available, and evacuate same when required.

Our Vision Concessions: No inter-process consistency needed. Increased latency of actual data movement is acceptable.

The First Hop A working test bed that validates the core architecture. Supports applications using standard POSIX operations. Concentrate on write-behind because it doesn’t require speculation. Leave room in the architecture to experiment with read-ahead. Preview of results: Small scale, overlapping is slower. Large scale, overlapping is faster.

Outline A Vision of Grid Data Movement Architecture and Example Necessary Mechanisms Semantics and Design The First Hop What Next?

Architecture Layers Application Adaptation Consistency Transport Example

Architecture Transport Consistency Adaptation open, read, write, close, fsync get, put, push, abort put Application Transport Consistency ack put File System open, read, write, close, fsync Blocking procedure call Non-blocking message put ack put

Transport Layer Interface Send message, query route, query status Semantics Ordering - None (or worse!) Reliability - Likely, but not guaranteed. Duplication - Unlikely, but possible. Performance Uses all available resources (net, mem, disk) to maximize throughput. Subject to local conditions (traffic, failures) and policies (priority, bw limits)

Transport Layer Transport RAM 1 Gb/s 300 Mb/s If output is blocked, then save input to disk until it is full. When output is ready again, read from disk, memory, or input? InOut The freedom to reorder transported blocks may allow us to improve throughput.

Consistency Layer Interface Get block, put block, sync file, abort file Semantics Ordering - Order preserving or not? Reliability - Detects success Duplication - Delivers at most once Performance Must cache dirty blocks until delivered Might cache clean blocks Might speculatively read clean blocks

Consistency Layer Transport Consistency Transport Consistency Sender: Keeps records to detect success, cache writes. Receiver: Keeps records to enforce ordering and supress duplicates.

Adaptation Layer Converts POSIX operations into Kangaroo operations Open O_CREAT, always succeeds Otherwise, checks for existence with a ‘get’ Read = kangaroo get Write = kangaroo put Close = NOP Fsync = kangaroo sync

Example Transport Consistency Adaptation Application Transport Consistency File System Blocking procedure call Non-blocking message OpenWriteClose Put Ack Has ‘file’ arrived? Or: Abort this transfer. Read Get ReadWrite Get

Outline A Vision of Grid Data Movement Architecture and Example Semantics and Design Necessary Mechanisms The First Hop What Next?

Semantics and Design A data movement system is a bridge between file systems. It addresses many of the same issues as file systems: Consistency Committal Ordering Replication

Consistency Single Node A put/get blocks until the local server has atomically accepted it. Multiple processes that are externally synchronized will see a consistent view. Multiple Nodes No guarantees unless you use an explicit sync. This is reasonable in a Grid environment, because most users make use of a wide-area scheduler to partition jobs and data.

Commital Possible meanings of commit: Force this data to the safest medium available. Make these changes visible to others. Make this data safe from a typical crash. Possible implementations in Kangaroo: Push all the way to target, and force to disk (tape?) Push to the target server. Push to the nearest disk.

Commital Safest choice is to implement the most conservative -- push all the way to the server, and force it to disk there. Some applications may want the more relaxed meanings. POSIX only provides one interface: fsync(). Easy solution: implement all three, and provide a flexible binding in the Adaptation layer.

Ordering Does the system commit operations in the same order they were sent? Relaxed -- no ordering Satisifies large majority of apps that do not overlap writes. Interesting case of output log files. Need to wait max TTL before re-using an output file name Strict -- exact ordering, enforced at recvr Increases queue lengths everywhere. Doesn’t burden user with determining if application is safe to relax.

Strict Ordering Algorithm Much like TCP: Sender keeps copies of data blocks until they are acknowledged. Receiver sends cumulative acks and commits unbroken sequences.

Strict Ordering Algorithm But some differences from TCP: No connection semantics. Block ID is (birthday,sequence). Receiver keeps on disk last ack’d ID of all senders it has ever talked to. If sender reboots: Compute the next ID from blocks on disk If none, reset b to current time, s to 0 If receiver reboots: Last recvd ID of all senders is on disk. Garbage problem: fix with a long receiver timeout + reset message causes sender to start over.

Replication Issues We would like to delete data stored at the sender ASAP, but… Do I Trust this Disk? Buffer Storage - Could disappear at any time. Reliable Storage - No deliberate destruction. Reliability is not everything If delivery is highly likely and recomputation is relatively cheap, then losing data is acceptable… but only if delivery failure is detectable! Reliability = More copies. User should be able to configure a range from “most reliable” to “fewest copies.”

Replication Issues End-to-End Argument: Regardless of whatever duplication is done internally for performance or reliability, only the end points can be responsible for ensuring (or detecting) correct delivery. So, the sender must retain a record of what was sent, even if it does not retain the actual data.

Replication Techniques Pass the Buck Hold the Phone Don’t Trust Strangers

Pass the Buck Delete the local copy after a one- hop ack. Requires atomic accept and sync. (Similar to ) KKKK R D DDD

Hold the Phone Sender keeps a copy of local data until the end-to-end ack is received. Midway hops need not immediately flush to disk. KKKK R D D DD

Don’t Trust Strangers If the sender determines the receiver to be reliable, then delete, otherwise hold. KKKK R D D I trust you. No, you are just buffer. D I trust you. D R

Replication Comparison Pass the Buck Evacuates source ASAP. One copy of data. Dirty reads must hop through all nodes. No retry of failures. (Success still likely.) Hold the Phone Evacuates source more slowly. Two copies. Dirty reads always satisfied at source. Sender can retry failures. Don’t Trust Strangers Evacuates source like PTB, but still 2 copies. Dirty reads hop. Retries done midway.

Outline A Vision of Grid Data Movement Architecture and Example Necessary Mechanisms Semantics and Design The First Hop What Next?

Necessary Mechanisms Adaptation Layer Needs a tool for trapping and rerouting an application’s I/O calls without special privileges: Bypass Transport Layer Needs a tool for detecting network conditions and enforcing policies: Cedar

Bypass General-purpose tool for trapping and redirecting standard library procedures. Trap all I/O operations. Those involving Kangaroo are sent to Adaptation layer. Otherwise, execute without modification. Can be applied at run-time to any dynamically-linked program: vi kangaroo://home.cs.wisc.edu/tmp/file grep thain gsiftp://ftp.cs.wisc.edu/etc/passwd gcc -o kangaroo://home/output

Cedar Standard socket abstraction. Enforces limits on how much bandwidth can be consumed across multiple times scales. Also measures congestion and reports to locally-determined manager. Example: If conditions are good, do not exceed 10Mb/s. If there is competition for the link, fall back to no more than 1Mb/s.

Why Limit Bandwidth? Isn’t TCP flow control sufficient? An overloaded receiver can squelch a sender with back-pressure. Competing TCPs will tend to split the available bw equally. No. Three reasons: To enforce local policies on resources consumed by visiting processes. To clamp processes competing for a single resource. To leave some bandwidth available for small- scale unscheduled operations.

Outline A Vision of Grid Data Movement Architecture and Example Semantics and Design Necessary Mechanisms The First Hop What Next?

The First Hop We have implemented a kangaroo testbed which has most of the critical features: Each node runs a kangaroo_server process which accepts messages on TCP and UNIX- domain sockets. Outgoing data is placed into a spool dir in the file system for a kangaroo_mover process to pick it up and send it out. Bypass is used to attach unmodified UNIX applications to a libkangaroo.a which contacts the local server to execute puts and gets.

The First Hop Several important elements are yet to be implemented: Only one sync algorithm push to server but not to disk Only one replication algorithm: hold the phone Consistency layer detects delivery success, but does not timeout and retry. Receiver implements only relaxed ordering. Reads are implemented simply as minimal blocking RPCs to the target server.

Measurements Micro: How fast can an app write output? Plain file Plain file through Kangaroo Kangaroo Mini: How fast can output be moved? Online: Stream from memory to network. Offline: Stage to disk, then write to network. Kangaroo Macro: How fast can we run an event- processing program? Online: Read and write over network. Offline: Stage input, run program, stage output. Kangaroo

Measurements Two types of machines used: Disk>Network (Linux Workstations) 100 Mb/s switched Ethernet 512 MB RAM 10.2 GB Quantum Fireball Plus LM Ultra ATA/66, 7200 RPM, 2MB cache 650 MHz P3 Network>Disk (Linux Cluster Nodes) 100 Mb/s switched Ethernet 1024 MB RAM 9.1 GB IBM 08L8621 Ultra2 Wide SCSI-3, RPM, 4MB cache 2 * 550 MHz P3 Xeon

Macrobenchmark: Event Processing A fair number of standard, but non- Grid-aware, applications look like this: For I=1 to N Read input Compute results Write output

Macrobenchmark: I/O Models INCPUINOUTPUT CPU OUTPUT INCPUINOUTPUT CPU OUTPUTIN CPUIN OUTPUT CPU OUTPUT Online I/O: Offline I/O: Current Kangaroo: IN

Macrobenchmark: Event Processing Synthetic Example: Ten loops of: 1 MB input 15 seconds CPU 100 MB output Results on workstations: Offline: 289 seconds (disk bound) Online: 249 seconds (network bound) Kangaroo: 183 seconds

Summary Micro view: Kangaroo imposes a severe penalty, due to additional memory copies and contention for disk and directory ops. Mini view: Kangaroo is competitive with staging and streaming, depending on the circumstances. Macro view: Kangaroo provides a big win when there is ample opportunity to overlap CPU and I/O.

Outline A Vision of Grid Data Movement Architecture and Example Semantics and Design Necessary Mechanisms The First Hop What Next?

Implementation Details Error Reporting “Where is my data?” Acute failures should leave an error record that can be queried. Chronic failures should trigger . Strict Ordering Read-Ahead

Research Issues Prioritizing Reads over Writes Easy to do at a single node. Hard to synchronize between several. Virtual Memory Need a disk system optimized for read-once, write-once, delete-once. Interaction with CPU scheduling Long delay for input? Start another job. Multi-Hop Staging Probably a win for buffering between mismatched networks. Where is the boundary?

Conclusion We have built a naïve implementation of Kangaroo using existing building blocks. Despite its inefficiencies, the benefits of write-behind can be a big win. Many open research issues!