Peer-to-Peer Computing: The hype, the hard problems and quest for solutions

1. Peer-to-Peer Computing: The hype, the hard problems and quest for solutions Krishna Kant Ravi Iyer Vijay Tewari Intel Corporation

2. Outline • Section I • Overview of P2P • P2P Framework • Overview of distributed computing frameworks • Additional P2P framework requirements • P2P Middleware • Section II • Taxonomy of P2P applications • Research Issues • Section III • Preliminary Performance Modeling • Conclusion

3. Goals for Section I • Examine the early beginnings of Peer-to-Peer. • Look at some possible definitions of Peer-to-Peer • General idea about the Peer-to-Peer applications and frameworks. • Identify the requirements of Peer-to-Peer applications.

4. Where is “X”? Mediator 1 2 Peer B has it 3 Copying X Peer A Peer B P2P Beginnings • Interest kindled by distributed file-sharing applications • Napster: Mediated digital music swapping. (http://www.napster.com)

5. 1 Where is File X? 1 Where is File (Key) X? 4 C: I have it. 2 Where is File (Key) X? C: I have it. 3 P2P Beginnings • Gnutella: Fully distributed file sharing. (http://gnutella.wego.com) • Freenet Distributed file sharing with anonymity and key based search. (http://freenet.sourceforge.net) Peer B Peer A 5 GET File (Key) X (HTTP) 6 File X Peer D Peer C

6. We had them already! • Using idle CPU cycles on home PCs, e.g., SETI@home • Involves scanning of radio telescope images for extraterrestrial life. • Chunks of data downloaded by home PCs, processed and results returned to the coordinator. • Similar schemes used for other heavy-duty computational problems. • Idle disk and main memory on workstations exploited in a number of network of workstation (NOW) projects. Master Processed Data Raw Data Peer 4 Peer 1 Peer 2 Peer 3 Data Crunching Data Crunching Data Crunching Data Crunching

7. Newer Applications • P2P streaming media distribution • CenterSpan (C-Star Multisource Peer Streaming) • Mediated, Secure P2P platform for distributing digital content. • Partition content and encrypt each segment. Distribute segments amongst peers. Redundant distribution for reliability. • Download segments from local cache, peers or seed servers. • http://www.centerspan.com • vTrails • vtCaster: At stream source. Creates network topology tree based on end users (vtPass client software). • Dynamically optimizes tree. • Content distributed in a tiered manner. • http://www.vtrails.com

8. Newer Applications • P2P Collaboration • Groove (http://www.groove.net) • Real time, small group interaction and collaboration. • Fundamental notion around a “shared space” • Each member of the group owns a copy of the “shared space”. • Changes made to the “shared space” by one member are propagated to each member of the group (Store and forward if some member is offline). • Platform is secure. • PKI for user authentication. • End to end encryption. • Groove components are digitally signed

9. So, what is P2P? • Hype: A new paradigm that can • Unlock vast idle computing power of the Internet, and • Provide unlimited performance scaling. • Skeptic’s view: Nothing new, just distributed computing “re-discovered” or made fashionable. • Reality: Distributed computing on a large scale • No longer limited to a single LAN or a single domain. • Autonomous nodes, no controlling/managing authority. • Heterogeneous nodes intermittently connected via links of varying speed and reliability. • A tentative definition: • A dynamic network (peers can come & go as they please) • No central controlling or managing authority. • A node can act as both as a “client” and as a “server”.

10. P2P Platforms • Legion, University of Virginia, Now owned by “Avaki” Corp. • Globe, Vrije Univ., Netherlands • Globus, Developed by a consortium including Argonne Natl. Lab and USC’s Information Sciences Institute. • JXTA, Open source P2P effort started by Sun Microsystems. • .NET by Microsoft Corp. • WebOS, University of Washington • Magi, Endeavors Technology • Groove

11. Avaki (Legion) • Objective: Wide-area O/S functionality via distributed objects. • Middleware infrastructure for distributed resource sharing in mutually distrustful environment.. • Global O/S services built on top of local O/S *Source: Peer-to-Peer Computing by David Barkai (Intel Press)

12. Avaki (Legion) • Naming: LOID (location Indep. Object Id), current object address & object name • Persistent object space: generalization of file-system (manages files, classes, hosts, etc.) • Communication: RPC like except that the results can be forwarded to the real consumer directly. • Security: RSA keys a part of LOIDs, Encryption, authentication, digesting provided. • Local autonomy: Objects call local O/S services for all management, protection and scheduling. • Active objects: objects represent both processes and methods. • Overall: Comprehensive WAN O/S, but not targeted as a general P2P enabler.

13. Globe • Objective: Another model for WAN O/S. • Distributed passive object model. Processes are separate entities that bind to objects. • Each object consists of 4 subobjects: • Semantics subobject for functionality. • Communication subobject for inter-object communication. • Replication subobject for replica handling including consistency maintenance. • Control subobject for control flow within the object. • Binding to object includes two steps: • Name & location lookup and contact address creation. • Selecting an implementation of the interface. • Overall: Similar to Legion, except that processes and objects are not tightly integrated.

14. Globus • Objective: Grid computing, integration of existing services. • Defines a collection of services, e.g., • Service discovery protocol • Resource location & availability protocol • Resource replication service • Performance monitoring service • Any service can be defined and becomes the part of the “system”. • Higher level services can be built on top of basic ones. • Preserves site autonomy. Existing legacy services can be offered unaltered. • Overall: Excellent reusability. Unconstrained toolbox approach => Very difficult to join two “islands”.

15. JXTA • Objective: A low-level framework to support P2P applications: • Avoids any reference to specific policies or usage models. • Not targeted for any specific language, O/S, runtime environment, or networking model. • All exchanges are XML based. • Base concepts for • Identifiers • Advertisements • Peers • Peer Groups • Pipes • At the highest abstraction defines a set of protocols using the base concepts: • Peer Discovery protocol: Discovery of peers, resources, peer groups etc. • Peer Resolver Protocol • Peer Information Protocol • Peer Membership protocol. • Pipe binding protocol • Peer endpoint protocol.

16. JXTA Source: White Paper on Project JXTA: A Technology Overview by Li Gong

17. Microsoft .NET in the context of P2P • Objective: An enabler of general XML/SOAP based web services. • Message transfer via SOAP (simple object access protocol) over HTTP. • Kerberos based user authentication. • Extensive class library. • Emphasizes global user authentication via passport service (user distinct from the device being used). • Hailstorm supports personal services which can be accessed via SOAP from any entity

18. MAGI • Enabler for collaborative business applications. *Source: Peer-to-Peer Computing by David Barkai (Intel Press)

19. Magi • Magi: Micro-Apache Generic Interface, an extension of Apache project. • Superset of HTTP using • WebDAV: Web distributed authoring & versioning protocol, which provides, locking services, discovery & assignment services, etc. for web documents. • SWAP (simple workflow access protocol) that supports interaction between running services (e.g., notification, monitoring, remote stop/synchronization, etc.) • Intended for servers; client interface is HTTP.

20. WebOS • Objective: WAN O/S that can dynamically push functionality to various nodes depending on loading. • Outgrowth of the Berkeley NOW (network of workstations) project. • Consists of a number of components • Global naming: Mapping a service to multiple nodes, load balancing & failover. • Wide-area file system (with transparent caching and cache coherency). • Security & Authentication w/ fine-grain capability control. • Process control: Support for remote process execution. • Project no longer active, parts of it being used elsewhere. • Overall: Dynamic configurability useful for P2P environment.

21. Groove • Groove (http://www.groove.net) • Real time, small group interaction and collaboration. • Fundamental notion around a “shared space” • Each member of the group owns a copy of the “shared space”. • Changes made to the “shared space” by one member are propagated to each member of the group (Store and forward if some member is offline). • Platform is secure. • PKI for user authentication. • End to end encryption. • Groove components are digitally signed

22. Requirements for P2P Applications • Local autonomy: No control or management by a central authority. • Scalability: Support collaboration of arbitrarily large number of nodes. • Security & Privacy: All accesses are authenticated and authorized. • Fault Tolerance: Assured progress with up to k failures anywhere. • Interoperability: Any peer that follows the protocol can participate irrespective of platform, OS, etc. • Responsiveness: Satisfy the latency expectations of the application. • Non-imposing: Allows machine user full resource usage whenever desired without affecting responsiveness. • Simplicity: Setting up a P2P application or participating in one should require minimum of manual intervention. • Auto-optimization: Ability to dynamically reconfigure the application (no of nodes, functionality, etc.) • Extensibility: Dynamic addition of functionality.

23. P2P Services • Basic. • Network Services. • Naming. • Event and Exception management services. • Storage Services • Metadata services • Security Services • Advanced. • Search and Discovery. • Administrative and Auditing. • File services akin to a virtual file system. • User and group management services. • Resource management services. • Digital Rights management. • Replication and Migration services.

24. Transport and data protocols for interoperability • Common protocols: IP, IPv6, sockets, http, XML, SOAP, . . . • NAT and firewall solutions • Roaming, intermittent connectivity Location Independent Services Sharable Resources Naming, Discovery, Directory Administration, Monitoring Standards Policies Identity, Presence, Community Identity, Presence, Community Identity, Presence, Community Security Security Security Availability Availability Availability Communications Communications Communications From Services to possible Layers • Availability from unreliable components • Replication • Striping • Failover • Guaranteed message queuing • Authorization • Integrity • Privacy • Web of trust • Certification • DRM

25. Location Independent Services Sharable Resources Naming, Discovery, Directory Administration, Monitoring Standards Policies Identity, Presence, Community Identity, Presence, Community Identity, Presence, Community Security Security Security Availability Availability Availability Communications Communications Communications From Services to possible Layers • Local Autonomy • IT allocation of resources • Self administration – reliable whole from unreliable parts • Resource monitoring • Payment tracking • CPU, storage, memory • Bandwidth • I/O devices • Capability discovery • Name space management • Metadata management • Discovery & location of peers, services, resources, users • User / group identity • Authentication • Persistence • Beyond a session • Across multiple devices

26. Questions ???

27. Part 2: Taxonomy & Research Issues • Goals: • To introduce a taxonomy for classifying P2P applications and environments. • To elaborate upon some major research issues.

28. P2P Taxonomy • Consider two types of properties: • Application characteristics • Environmental characteristics • Application Characteristics: • Resource (or data) storage: organized or scattered. • Resource control: organized or scattered. • Resource usage: isolated or collaborative. • Consistency constraints: loose or tight. • QoS constraints: loose (e.g., non real-time), moderate (e.g., online transaction processing). • query/response), or tight (e.g., streaming media).

29. P2P Taxonomy Cont’d • Environmental characteristics: • Network latency: Ranges from uniformly low (e.g., for a high-speed LAN) to highly variable (e.g., for general WAN). • Security concerns: Ranges from low (e.g., corporate intranet) to high (e.g., public WAN). • Scope of failures: Ranges from occasional isolated failures (e.g., a laboratory network of workstations) to network partitioning. • Connectivity: Ranges from always-on (e.g., nodes in a business LAN) to occasional-on (e.g., mobile devices). • Heterogeneity: Ranges from complete homogeneity to complete heterogeneity (in platform, O/S, protocols etc.). • Stability: Ranges from highly stable (i.e., Planned occasional changes/upgrades) to unpredictable. • Convenient to aggregate them as “friendly” and “hostile”.

30. Research Issues • Intelligent caching of search results. • Intelligent object retrieval • Retrieval by properties rather than URL. • Need distributed indexing mechanisms. • Directing searches to more promising and less loaded nodes. • Multiparty synchronization and communication that scales to thousands of nodes. • For home computers: Utilize idle computing resources w/o significant communication requirements. • Unobtrusive use: If the owner wants to use the resources, get out of the way quickly. • Low latency service handoff protocols.

31. Research Issues Cont’d • Distributed load balancing that scales to thousands of geographically distributed nodes. • Stitching traffic from multiple paths to reduce latency or losses for real-time applications. • Access control in a mutually suspicious environment (foreign objects on your machine must protect themselves from you, and you from these objects). • Effective mapping of the application topology to the physical topology. • Architectural features to • Efficiently propagate requests and responses w/o significant CPU involvement • Squelch duplicate, orphaned or very late responses.

32. Additional P2P Issues • Communicating with peers behind NAT devices and firewalls. • Naming and addressing peers that do not have DNS entries. • Coping with intermittent connectivity & presence (e.g., queued transfers). • Authentication of users independent of devices. • Digital rights management. • On demand task migration w/o breaking the application. • Efficient distributed information location and need based content migration. • Scalability to huge number of peers (e.g., 100M): • Peer state management • Discovery and presence management (intermittent connectivity & slow last mile links) • Certificate management and authentication.

33. Part 3: Performance Study Goals: 1. Define a performance model including - Network model - File storage and access model - File caching and propagation model 2. Discuss sample results 3. Discuss Architectural impacts

34. P2P Network Characteristics • Desirable characteristics • Adequate representation of ad hoc nature of the network. • Expected to contain a few special sites (well-known, content rich, substantial resources, etc.) • Heavy-tailed nature of connectivity. • Other Issues • Dynamic changes to the network • Direct modeling not required if rate of change << request rate. • Metadata consistency issues still need to be considered. • Mapping of virtual P2P network on physical network • P2P applications generally don’t pay attention to mapping. • “Virtual links” bet. P2P neighbors are essentially statistically identical. • A better modeling possible, but difficult to calibrate.

35. P2P Node Model • Consider a 3-tier model for nodes • tier-1: Well-known, resource-rich, always on & part of network. • Similar to traditional server nodes (globally known sites in Gnutella) • Henceforth called as distinguished nodes. • tier-2: “Hub” nodes (reasonably resource rich & mostly on) • Contribute storage/files in addition to requesting them. • May join/leave the network, but at time-scale >> req-response time. • Henceforth called as undistinguished nodes. • tier-3: Infrequently connected or primarily “client” functionality • No need to represent these explicitly in the network • Requests/responses from these appear to originate from tier-1/2 nodes that they home on.

36. P2P Network Model • Use a random graph model to represent topology. • Traditional G(n,p) RG model too simplistic. • Use a 2-tier non-uniform model built as follows: • Start with a degree Kd regular graph of Nd dist. Nodes. • Add Nu undistinguished nodes sequentially as follows: • The new node connects to K other nodes. • K: const or an integer-valued RV in range 1..Kmax • Each connection targets an undistinguished node with prob qu (this may not be possible for the first Kmax nodes). • Dist. Node target: uniform distribution over all dist nodes. • Undist. Node target: Zipf(a) over existing undist. nodes. • At most one connection allowed between any pair of nodes. • a controls the decay rate of nodal degree • a=0 => Uniform dist => Very slow decay. Used here for simplicity.

37. Topological properties • Some network properties can be analyzed analytically • Outline of Analysis (see http://kkant.ccwebhost.com/download.htm) • Degree distribution: • Distinguished nodes at level 0, each new node defines a new level. • Pn(l2,l): Prob(level l node has degree n when current level = l2) • Get recurrence eqns for Pn(l2,l) & hence its PGFf(z| l2,l) . • Get avg degree Dat(l2,l) at level l when current level = l2. • Can be adapted for computing the undistinguished degree of a node. • No of nodes reached in h hops: • Rh matrix: Rh(i,j) is prob of reaching level i from level j in exactly h hops. • Compute Rh(i,j) by enumerating all unique paths of length h. • Compute G(l2,h), avg no of nodes reached in h hops starting from a level l2. • Request and response traffic at level l node: • nreqs = No of requests reaching undist. nodes in h hops = 1 + ShG(l2,h), • nresps = 1 + Shh G(l2,h), since resp from h hops away goes thru h nodes. • Nodal utilization & node engineering: • Easy to ensure that nodal utilization do not exceed some limits. • Queuing properties generally intractable; explored via simulation.

38. Sample Results - 100 nodes undist no_of nodes undist resps traf prob hops reached reached /node /node 1 5.9 3.3 4.9 6.1 2 55.2 44.5 103.6 146.5 0.05 3 99.1 85.8 235.2 320.5 4 100 90.0 238.8 328.8 5 100 90.0 238.8 328.8 1 5.9 4.3 4.9 8.4 2 34.3 23.8 61.7 82.3 0.50 3 91.0 73.9 231.7 304.0 4 99.9 89.4 267.5 356.9 5 100 89.6 267.7 357.3 1 5.9 5.3 4.9 10.6 2 28.6 22.6 50.3 73.6 0.95 3 76.7 63.8 194.6 258.4 4 98.5 87.4 281.8 369.2 5 99.7 89.3 287.8 377.2

39. Sample Results - 500 nodes undist no_of nodes undist resps traf prob hops reached reached /node /node 1 6.0 3.6 5.0 6.2 2 243.7 232.7 480.5 711.5 0.05 3 499.7 488.6 1248.4 1737.0 4 500.0 490.0 1249.6 1739.6 1 6.0 4.7 5.0 8.5 2 95.7 84.2 184.3 264.6 0.50 3 483.5 465.1 1347.8 1812.4 4 500.0 490.0 1413.9 1903.9 1 6.0 5.8 5.0 10.7 2 35.1 29.1 63.2 91.7 0.95 3 163.5 137.1 448.3 582.4 4 405.7 367.7 1417.2 1782.7

40. Simulation of Random Graphs • Simulation of Random graph is a hard problem • Model represents a large number of topologies that the actual network might take. • Too many instances to simulate explicitly and then average the results. • Example: 2 dist & 3 undist nodes, each connects to 2 nodes => 6 distinct topologies. • Possible approaches to simulation: • Average case analysis • Model with limited set of instances. • Direct simulation of probabilistic model.

41. Average case analysis • Intended environment • To study performance of an “average” network defined by RG model. • No dynamic changes to the topology possible. • Graph construction • Start with the regular graph of distinguished nodes (as usual). • For adding undist nodes, work with only the avg connectivities Kd & Kufor an incoming node. • Always connect to the existing node with min connectivity. • Kd & Kd can be used successively to handle non-integer Kd values (similarly for Ku). • Characteristics/issues • Simple, only one graph to deal with in simulation. • Gives correct avg reachability and nodal utilizations. • All queuing metrics (including avg response time) are underestimated.

42. Constrained Connectivity • Intended environment • To capture most likely scenarios of connectivity. • Accommodate both static topology an slowly changing topology. • Graph construction and simulation • For the entering level l2 node, analytically estimate Dat(l2,l) at all l. • Allow connection to a level l node only if degree(l) falls in the range (min..max) Dat(l2,l) . • Found that min=0.5 and max=1.5 is quite adequate. • Generate a limited set (~100) instances of the graph. • During simulation, each query randomly selects one instance. • Characteristics/issues • Avoids highly asymmetric topologies => queuing properties are underestimated. • All generated instances are given equal weight. Relative weights can be estimated but very expensive.

43. Probabilistic Graph Emulation • Intended environment • To study overall performance when the topology is defined by the random graph model. • Accommodate fast changing or unstable topologies. • Method: • For each node i, estimate relative prob qij of having an edge to node j  i. • A query coming from node k to node i is sent to node j with prob qij/(1-qik). • This virtual topology for the query is used to return responses as well. • Characteristics/Issues • Method dependent on analytic calculation of edge probabilities to neighbors. • Single simulation automatically visits various instances in the correct proportion. • No explicit control over which instances are visited => Reliable results may take a very long time. • Very expensive and difficult to handle complex operations (e.g., file migration).

44. File Size & access distribution • Using a 2-segment model: • Small sizes: Distribution generally irregular; uniform is a reasonable model. • Pareto tail with decay rate 1<a<2 is quite reasonable. • Adopted distribution: • Uniform dist in the small-size range 400 bytes to 4 KB. • Pareto distribution with a min value of 4KB and mean of 40 KB => a = 1.11. • 40 KB mean is typical for web pages, but too small for MP3 files. • “File category” provides a link between file size and its “popularity”. Needed to model higher access rate of small files. • Chose 9 categories (equally spaced in log domain) 400B, 1.265KB, 4KB, 12.65KB, 40KB, 126.5KB, 400KB, 1.265MB, 4MB, 12.65MB • File access distribution: • Across categories, distribution specified by a discrete mass function: (0.07, 0.14, 0.2018, 0.20, 0.14, 0.098, 0.0686, 0.048, 0.0336) • This increases linearly first and then decays geometrically w/ factor 0.7. • Within each category, assume uniform access distribution.

45. Parameters: File Copies • Each search in a P2P network may result in multiple “hits”. • Need only dist. of hits; precise modeling of search mechanism not needed. • Use file copies for this: • Each file has C copies in the range (1..Cmax) with a given distribution. • A file is now identified by the triplet: (category, file_no, copy_no) where file_no is a unique id (e.g., sequence no) of files in a category. • This allows following capabilities: • Unique searches specified by the file-id triplet. • Non-unique searches specified by (category, file_no). • Replication control and fault-tolerant operation. • File copy parameters: • Distribution may be related to the nature of the file (not considered here). • Separate distributions allowed for files allocated to dist & undist nodes. • Assuming a triangular distribution with Cmax = 20, and mode Cmode= 5 for all nodes => Mean no of copies = 8.667.

46. File Assignment to Nodes • Assignment of copies to nodes: • Assign copies at a fixed distance so as to distribute them evenly across the network. • Apply an offset for each round of copy assignment to avoid bunching up. • Do not assign more than one copy of a file to a node. • Algorithm: loop over all files n_copies = triangular_rv(1, Cmax , Cmode) // Generate random no of copies if ( n_copies > n_nodes ) n_copies = n_nodes; // Don’t allow more copies than nodes distance = n_nodes/n_copies; // Distance for copy allocation offset = 1 + n_nodes/no_files; // If too few files, get an offset to avoid bunching tot_offset = (tot_offset + offset) % n_nodes; node_no = tot_offset; // Node for the assignment of first copy for ( copy_no = 0; copy_no < n_copies; copy_no++) { assign_file( node_no, file_no, size); node_no = (node_no + distance) % n_nodes; // Next node for assignment if ( copy_no < n_copies -1 && node_no == (tot_offset + wraps)% n_nodes) { node_no = (node_no + 1) % n_nodes; wraps++; } } // loop over copies

47. Query Characteristics • Assumptions: • No queries (searches) started from distinguished nodes since these nodes are essentially “servers”. • Identical query arrival process at each undistinguished node. • Arrival process model • An on-off process with identical Pareto distribution for on \& off periods: P(X>x) = (x/T)g for x > T • Assume T=12 secs, and g=1.4 which gives E(X)=30 secs. • Const inter-arrival time of 4 secs during the on-period, no traffic during off period. • Total traffic at a node is superposition of arrivals from all reachable nodes. • Approx. a self-similar process with Hurst parameter H=(3 - g)/2 = 0.8 when no of reachable nodes is large. • Query properties: • Each query specifies a file (category, file_no) w/ given access characteristics. • Shown results do not specify copy_no => Multiple hits possible for each query. • Query percolates for h “hops”. (h=3 can cover 95% of nodes for chosen graph). • If a query arrives at a node more than once, it is not propagated.

48. File Retrieval • Query Response: • Query reaching a node generates found/not found response, which travels backwards along the search path. • Querying node runs a timer Tu; all responses after the timeout are ignored. • Currently no concept of retrying the timed out requests. • Distribution of Tu: Triangular in the range (3,14) secs with mean 8.0 secs. • File retrieval: • Randomly choose one of the positively responding nodes for file retrieval. • Requested file(s) are obtained directly (i.e., do not follow the response path). • Retrieved file may be optionally cached at the requesting node. • File cache flushing • Used as a way of modeling dynamic changes in tier-3 nodes (which are not represented). • A cache flush represents a tier3 user disconnecting and replaced by another statistically identical tier-3 node. • No of cycles before cache flushing: Zipf with min=30, max=120 and a =1.0.

49. Simulation Results

50. Major Observations