Constructing Responsive Web Services Fabio Panzieri. Motivations

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1 Constructing Responsive Web Services Fabio Panzieri Dipartimento di Scienze dell'informazione Mura Anteo Zamboni Bologna Summary Motivations State of the art Design approach Experimental implementations and results Concluding remarks Credits: D. Ingham e S. K. Shrivastava, University of Newcastle upon Tyne (UK) Marco Conti, Enrico Gregori (CNR - CNUCE) Vittorio Ghini, Marco Roccetti () 1 Motivations Success of Web service: QoS provided to users Design and development of middleware services that allow one to construct so-called QoS-aware Web services/applications QoS: QoS: "A set of quality requirements on the collective behavior of one or more objects" (Reference Model for Open Distributed Processing - Part 3: Prescriptive Model", International Standard , ITU - T Recommendation X.903, ITU - ISO, Geneva, 1995) "Set of quantitative and qualitative characteristics of a distributed system necessary to achieve the required functionality of an application" (cf. A. Voegel et al., Distributed Multimedia and QoS: A Survey, IEEE Multimedia, Vol. 2, No. 2, Summer 1995, pp ) Performance related guarantees, e.g., bandwidth, latency (IETF) -> switches/routers set aside resources to meet those guarantees 2

2 User level QoS End-users Information Integrity Specific instance: integrity of hypertext links More advanced services (e.g., e-commerce, CDNs): content generated dynamically via interaction between users and service; application state information must be maintained consistent (e.g., users can t accept that items be lost from their shopping basket) Availability e.g., 24 hrs. service accessible from any part of the globe Timeliness e.g., user response time < 6s (industry standard) Application dependent non-functional properties e.g. security, privacy, anonimity, Availability + Timeliness = Responsiveness ASPs and ISPs: additional class of users of our middleware services 3 ASPs and ISPs QoS requirements Scaleability: service must be able to accommodate an arbitrary number of users Responsiveness service must provide users with acceptable response time, regardless of their number and phisical/geographical location In addition: engineering minimize use of specialized h/w and s/w (i.e., ideally service constructed out of off-the-shelf components, e.g. middleware services) easy to maintain, upgrade,... We concentrate on Availability and Timeliness, i.e. Responsiveness, in Scaleable systems 4

3 Current state Majority of Web sites Read-only services; e.g., Universities (courses, research activity, ), commercial sites (catalogues,...) information changes infrequently relatively small load of requests general purpose WSs used to host services as background tasks concern for QoS provided to users not primary Smaller number of very popular sites offer R/W services (e.g. commercial sites, conference registration sites) experience heavy load must meet user expectation re. QoS to maintain popularity Observation: from NCSA study, 20 rps -> server overload (cf. E.D. Katz, et al., A Scalable HTTP Server: The NCSA Prototype, Comp. Net. and ISDN Sys., 27 (2), pp , Nov. 1994). Situation has improved not as much as requested by the increase of the number of users. 5 Typical Web service Client browser A Client browser B Server Web low speed link INTERNET high speed Link service not available Client browser C server, or gateway has crashed Internet routing problem slow service (i.e. no timeliness) network congestion server overload How can one meet responsiveness requirements? 6

4 Designer bag of tricks Software fault tolerance techniques provide basic elements for solution: caching replication membership services reliable multicast transactions... The challenge implementation using general purpose, low-cost components (e.g. UNIX, commercial middlewares services) 7 S/W fault tolerance S/W fault tolerance techniques consume resources (bandwidth, CPU processing power, storage) E.g. REPLICATION: overheads caused by additional messages among replicas for maintaining consistency LOGGING: overheads caused by additional messages and disc write operations Frequently, FT solutions unacceptably sluggish compared to their non- FT counterpart Crucial issue: to understand constraints under which FT solutions must be developed/deployed 8

5 Proposed solution: use redundant services Client browser Web Server (Replica 1) low speed link INTERNET high speed link Web Server (Replica 2) Web Server (Replica 3) Replicate server a distinct sites Ensure client gets bound to best replica server best: lightly loaded, nearest, lowest latency,... 9 Redundant services Success of this solution depends on: G1: managing replication to maintain mutual concistency among replicas G2: dynamic binding of client to best replica server best replica selection criteria: G1.1: most effective load distribuition among all available replicas G1.2: activation of lowest latency, least congested connection between client and replica 10

6 Replica management problems Replica states must be kept mutually consistent Replica servers must propagate updates very quickly Hard to do without affecting performance particularly, when routing and bandwidth allocation fall outside the designer control, as in current Internet Real, or virtual, partitions in the Internet use of ping allows one to suspect failures -> sites hosting replicas may get inconsistent views of who is up and who is down -> inconsistent update propagation 11 Maintaining replica consistency Strict consistency: update must be propagated from replica server to peer servers in real-time Pros: client always provided with up-to-date information Cons: low performance update operation does not scale Loose consistency: updates propagated in the background Pros: scaleable Consistency among replicas cannot be guaranteed Current techniques of atomic update/multicasting do not provide viable solution, as yet assume asynchronous communications do not scale Multilevel solution: J. Gray, P. Helland, P. O'Neil and D. Shasha, "The dangers of replication and a solution", ACM SIGMOD Record, 25 (2), pp , June

7 Dynamic binding problems Early Web design decision: one-to-one mapping NAME <-> SINGLE COPY of resource i.e., URL contains Internet host name of server machine and name of resource on server Difficult to distribute load across replicas without augmenting browser s capabilities we are looking for solutions that can be implemented using commercial components 13 Dynamic Binding: current solutions Clustering Monitoring Mirroring HTTP REDIRECT DNS round-robin CISCO distributed director QoS based (our approach) Common features: aim to achieving scaleability and responsiveness by augmenting processing power of a site meet objective only partially: may succeed in optimizing service throughput cannot guarantee timeliness as Internet latency is beyond their control 14

8 Clusters client Router/Gateway Workstations INTERNET Shared memory Single server replaced by group of locally distributed servers Client addresses cluster via single IP address of router 15 Router maintains mapping between IP addresses of host servers on LAN and Network Points of Attachment (NPA = MAC address of host server in LAN) implements Network Address Translation (NAT) replaces dynamically IP dst address of incoming pkts with the IP address of a server in cluster, before translation of IP address into NPA; replaces IP src address of outgoing pkts with its own performs translation on a per session basis 16

9 CISCO local director Commercial router implementing NAT intelligent redirection of pkts by monitoring host servers response times, so as to perform load distribution and maximize service throughput if a host fails, its response time = -> that host receives no requests until back to service Pros can perform load distribution/balancing does not affect (i.e. increase) Internet traffic Cons router: single point of failure no control over Internet latency requires ad hoc techniques to redistribute load of faulty servers 17 Monitoring Decentralized approach network s/w of server A configured to respond to both its own IP address and that of a server B A monitors B, via network level ping messages if B fails, A takes over, and responds to requests directed to B 2 machine cluster can be engineered so that A monitors B, and viceversa, in order to tolerate a single machine failure Pros support for dynamic binding does not rely on router Cons servers must be connected by redundant, high bandwidth links run RT OSs so that network protocol processing experience bounded scheduling delays, only no control over Internet latency 18

10 Data distribution: physical partitioning service LAN Web Server 1 Web Server 2 Web Server 3 Web Server 4 Shared SCSI BUS data physically partitioned among Web Servers normal operation: each server has exclusive access to its own primary disc (e.g., WS1 <-> D.1) Server crash: use of monitoring technique each server can take over work of another server in the event of crash, and access crashed server storage via SCSI bus 19 Data distribution: physical partitioning Resource set associated to particular machine (i.e., resource name includes host name ) Pros: ok for both read-only and read-write operations as resources are not replicated no mechanisms are required to maintain data consistency within cluster no special techniques for load distribution required Cons: unpredictability of load distribution most popular server may receive disproportionate load re-distribution of load (i.e. migration of resources between machines) may corrupt intra-site links 20

11 Data distribution: sharing via DFS service network Server Web 1 Server Web 2 Server Web 3 Server Web 4 internal network DFS cluster Two-tiered architecture Web servers share same data set data set distributed across N severs in DFS cluster Each Web server caches in local storage most frequently accessed objects Write op. => updates master copy in DFS (cache consistency mechanisms used to maintain local storage of servers) Availability of DFS: via bus SCSI configuration 21 Data distribution: sharing via DFS Web Srvr crash dealt with redirection of requests (ARP aliasing h/w) Secondary network interface required to support dynamic reconfiguration of MAC address Server crash alternative server gets elected to replace it alternative server must configure its secondary interface with MAC address of crashed server configure its own network software so as to associate IP address of crashed server to MAC address of its secondary interface 22

12 Mirroring Mirroring site replica servers geographically distributed client requested to indicate its geographical location, or select manually preference from a list of URLs usually client selects geographically closer URL, or one wich is reported to have higher performance e.g. EU - NoEs Information Service list of mirror site URLs estimate of time required to transfer a document from each mirror Pros: easy to provide Cons: static (no load balancing, no dinamic binding, no contol over communication delays) 23 HTTP REDIRECT Client connects to main server Server replies with HTTP REDIRECT command to route request to one of N available replica servers Client makes subsequent requests to that server Pros: easy to implement (automatic version of mirroring) can provide dynamic binding Cons makes visible to clients URLs of the replica servers can be stored in hot lists by clients, or search engines, thus defeating subsequent load balancing 24

13 DNS DNS round-robin allows mapping of a host name to one of several IP addresses resolves host name in one address, in round-robin (rr) -> 2 consecutive requests get 2 different addresses Internet Host_id IP address www1.cs.unibo.it www2. cs.unibo.it www3. cs.unibo.it Principal problems service availability in the event of host failures DNS not designed to propagate updates throughout the system quickly Server crash: not possible to update the whole DNS timely -> clients my continue directing their requests to crashed servers until they get service not available hierarchical organization of DNS: may defeat load balancing via rr 25 Hierarchical organization of the DNS Browser interrogates local DNS. If this cannot resolve name, it interrogates higher level DNS. Process continues until name can be resolved by primary DNS. Primary performs round-robin and returns response along the path, to the browser DNSs in path cache responses Next request from browser can be resolved by DNS in path as host name <-> IP address mapping is in its cache Round-robin by primary is bypassed may cause disproportionate load to one server Remedy: TTL associated to DNS cache entry entry removed from cache after TTL expires Problem of TTL tuning the shorter the TTL, the greater the load on the primary DNS Examples: HP main site TTL = 10, Microsoft TTL = 2 hrs. in general TTL = 24 hrs (for not frequently changing info) 26

14 CISCO DISTRIBUTED DIRECTOR selects optimum server for a particular client via use of routing info. inherent in network optimum server found deploying proprietary protocol to query s/w agents running on gateway closest to each server query contains client address agents calculate n. of hops between client and server DD selects server closest to client can operate as DNS acts as primary DNS responds to requests returning a single server address or as HTTP redirector acts as a Web server accepts requests and returns HTTP code temporarily moved to redirect clients to approriate server requires agent s/w running at each site and CISCO gateway systems! 27 QoS-based strategy Assumption: Web replica servers geographically distributed across the Internet (as in fig. before) Clients get bound to their relative most convenient replica server most convenient: replica server providing the client with the shortest URT URT: elapsed time between the generation of a browser request and the rendering of that request to the invoking browser 28

15 1st QoS-based strategy Simple implementation: DNS maintains the addresses of all WS replicas The browser checks the URT each replica can provide it with by sending a dummy request (i.e., a probe message via UDP) The browser selects the replica with the shortest URT Pros Load distribution is effectively performed by each browser No modifications to browser software required Cons Probing overhead may affect URT Dummy request does not capture network traffic and server load variations Early study via simulation (using RESQ simulation tool) to compare proposed strategy with DNS-based and Mirror based early simulation results showed viability of this approach 29 Simulation scenario 4 replicated Web servers located in 4 geographical areas (USA, Europe, Asia, Australia) Area 1 Browsers Browsers Area 2 WS Replica 1 WS Replica 2 Area 1 Network delay Area 2 Network delay Internet Area 3 Network delay Area 4 Network delay WS Replica 3 WS Replica 4 Area 3 Browsers Browsers Area 4 30

16 Simulation model Consecutive queries Number of Web pages per query Size of a Web page Dummy request Independently identically distributed (time interval between queries exponentially distributed) Geometrically distributed (mean = 10 pages) Distributed as typical HTTP traffic [Stevens 96] (mean = 3000 bytes) One 1000-byte page URT estimate: based on multicast, not reliable communication (e.g., IP multicast, UDP) Query/Web pages transmission based on reliable communication (TCP) 31 Network congestion model Two types of delay Intra-area delays: Minimum area RTT = 10 ms Queuing delays in the area routers (we model the most congested router in each area) Inter-area delays: i.i.d. random variables which model the latency between different areas (average = 50 ms) Network latency = intra-area + inter-area delays 32

17 Server model capacity = 200 rps FIFO queue for buffering incoming queries located on high-speed LAN, connected to the Internet via an access router: finite buffer tail-drop discard policy Model captures queuing and transmission delays in router impact of LAN traffic on URT assumed to be negligible 33 Performance comparison methodology Response time experienced by a query (Query RT) examined for the three strategies of interest (DNS-based, Mirror-based, Qos-based): average tail of Query RT distribution In QoS-based strategy the Query RT includes the delay to select the chosen replica 4 classes of experiments Exp1: intra-network congestion. Balanced query generation rate in each area (80% of server capacity), network congestion in Area1 (98% of router capacity) Exp2: heavy load on Area1 server. Query generation rate close to saturation (98% of server capacity) in Area1, balanced network traffic in all areas (80% rotuer utilization in each area) Exp3: symmetric case. All areas experience balanced query generation rate (80% of local server capacity) and network congestion (80% utilization of each router) Exp4: realistic scenario: captures Internet delay diurnal cycle, i.e. in working hours delays are greater than overnight; evening hours exhibit moderate delays in our simulation, the four Areas experience, daily, different loads in different periods of time (early morning, rush hour, late afternoon, night) load configuration in one particular period of the day Area1 Area 2 Area3 Area4 Network Query rate

18 Performance results (summary) Area 1 Server Area 2 Server Area 3 Server Area 4 Server Exp Exp Exp Exp Table 1: Load distribution with the QoS strategy QoS based DNS based Mirror based Min Max Min Max Min Max Exp Exp Exp Exp Table 2: Average Query Response Time per Area (seconds) QoS-based strategy fairer than the others Load distribution takes into account specific scenario Average response times are shorter Note Mirror in Exp4: most unfair, as it redirects no traffic from congested Areas to Area4 35 Implementation (browser-based architecture) Browser Software Server Software HTTP Server Software HTTP WSL OS Interface OS Interface BSL HTTP TCP/IP Server OS Software OS TCP/IP WSL OS Interface TCP/IP HTTP OS assessment most convenient WS replica WSL access to the most convenient WS replica TCP/IP OS OS Interface 36

19 Implementation (proxy-based architecture) Browser Software HTTP OS Interface cache Proxy Server cache miss OS TCP/IP TCP/IP assessment most convenient WS replica access to the most convenient WS replica LD Server Software HTTP Server Software WSL OS Interface HTTP TCP/IP Server OS Software WSL OS Interface TCP/IP HTTP OS WSL OS Interface TCP/IP OS 37 2nd Qos-based strategy C 2 LD : Client-centered Load Distribution Downloading mechanism that minimizes URT makes use of replicated Web servers exploits parallelism inherent in replicated architecture does not bind client browser to single replica involves all active replicas in the retrieval of a given page Adaptive operation monitors replicas performance select those replicas that can provide fragments within specified time interval splits browser s request into more sub-requests for page fragments fetches one or more fragments to each replica fetches larger fragments from faster replicas adapts dynamically fragment sub-requests to replicas, depending on both network latency and replica response time sets internal timeout for each sub requests. If timeout expires before the requested fragment is received, the fragment is fetched from another replica works either on browser or proxy side 38

20 C 2 LD approach (fragment downloading) C 2 LD splits the browser s requests into more sub requests for fragments. When a fragment is received from a replica server, another fragment is requested to that server web replica 1 page fragment 2 web replica 2 page fragment n fragment 1 reassembled page web replica n page fragment m fragment 3 browser site 39 C 2 LD analytical model (1) Monitoring period S: C 2 LD expects to receive the fragments at the end of S given i: index of the replica server r: index of the sub request to replica i T: instant in which C 2 LD receives response for the sub request r-1, from i Si,r* time left before the end of the current monitoring period S at instant T, C 2 LD requires a new fragment to replica i, and expect to receive response at the end of current period S 40

21 C 2 LD analytical model (2) C 2 LD assumes replica i respond to new sub request r, providing Data Rate DRi,r, measured during previous sub request r-1. Given: DR i,r = PS i,r 1 URT i,r 1 expected data rate, where PS size of fragment of sub request r-1 i, r 1 URT response time of request r-1 i, r 1 size of fragment to be requested to replica i, with the sub request r, is i, r = DRi, r Si r PS, Si,r*: time left before the end of the current monitoring period S 41 C 2 LD implementation (skeleton code) /* intercept browser request, and extract URL */ within USD do /* set USD timeout */ /* interrogate DNS for replica IP addresses at URL */ for each_replica do HEAD( ); /* send HEAD request to all replicas to get object size */ URT(i) := ; /* use response time to HEAD to compute replica i URT */ PS(i,1) := ; /* compute 1 st fragment size for replica i */ T_OUT := ; /* compute time out for fragment fetching */ within T_OUT do /* set timeout of length T_OUT */ if not download_done then /* check if page downloaded */ GET ( ); /* get fragment from replica i */ DR i,r :=PS i,r-1 /URT i,r-1 ; /*compute expected Data Rate */ PS i,r :=DR i,r. Si,r * ; /* compute size of next fragment */ else return; od od od 42

22 Implementation notes As replicas may not respond timely, T_OUT associated to each HTTP GET request If timeout expires, replica i is assumed to be unavailable, and placed in stand-by list Requests to replicas in stand-by list redirected to active replicas Replicas in stand-by list probed periodically Replica in stand-by list goes back in use when it responds timely to probe msg 43 C 2 LD Thread Structure Browser GETrequest GETresponse timeout interface thread Timer thread get_document thread C 2 LD monitoring thread.... http_connection thread http_connection thread http_connection thread GET RANGE request GET RANGE response Web Server Replica 1 Web Server Replica2.. Web Server Replica N

23 Experimental measurements Experimental scenario: 4 replica servers (Cesena, Trieste, Newcastle, S.Diego), 1 client (Bologna) 11 different replicated Web service configurations (6 of 2 replicas, 4 of 3 replicas, 1 of 4 replicas) Same file system in each replica Different file size ( Kbytes) used Scope: to compare and contrast C 2 LD with standard HTTP same data set downloaded first with HTTP and then with C 2 LD measurements averaged across a 2 months period 45 Network Traffic Packet loss and RTT: high packet loss rate with Cesena, high delay with S.Diego ping from Bologna to : Cesena Trieste Newcastle S. Diego Packet loss 2%-9% 0% 0% 0%-3% RTT 90% of pkt (msec) RTT minimum (msec) Bandwidth usage (Bologna router): 8 Mbit/s ingoing bandwidth saturated during working hours 46

24 Measures: Availability C 2 LD downloading (any configuration) Objects not retrieved before USD expire standard HTTP downloading from S.Diego Newcastle Trieste Cesena 0% 0.3% 0% 0% 2.83% Average values obtained using different object size (from 50KB to 1MB): C 2 LD always guaranties the downloading within the USD Measures: User Response Time C2LD vs. HTTP (Fastest Replica Server) URT (seconds) nd Fastest Fastest C2LD file size (Kbytes) Average improvement 12.95% (file size>50 KBytes) 48

25 Measures: User Response Time C2LD configurations vs. HTTP (single replica servers) 16 URT (seconds) Kbytes S =500:2000 ms S=San Diego C=Cesena N=NewCastle T=Trieste C 2 LD 0 C+N C+T T+N C+S T+S N+S C+N+T C+N+S C+T+S T+N+S C+T+N+S 49 Measures: User Response Time (URT variation as number of replicas grows) Number of Replica Servers URT improvement provided by C 2 LD with respect to the fastest replica and standard HTTP downloading % 17.2% 21.5% URT decreases as the number of replica servers grows 50

26 Measures: User Response Time (URT variation as number of replicas grows) URT (seconds) replica 2 replicas 3 replicas 4 replicas document size (Kbytes) 51 Measures: User Response Time (URT and monitoring period) URT (seconds) K 500K 200K 100K 50K 30K 10K 3K monitoring period (milliseconds) Best monitoring period: [500, 1000] ms 52

27 Concluding remarks C 2 LD evaluated, via simulation and experiments, as large n. of clients concurrently access the same replicated Web service Results (encouraging) will shortly be available Limitation: C 2 LD works for read-only services Current/future directions: R/W services Peer-to-peer scenario Replica consistency mechanisms over large scale Issues of security Experimental replicated application services (auction service) 53

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