1. Proactive computing: Adaptability, autonomic behavior, dependability Gustavo Alonso Computer Science Department Swiss Federal Institute of Technology ETH Zürich alonso@inf.ethz.ch http://www.inf.ethz.ch/department/IS/iks/

2. ©Gustavo Alonso. ETH Zürich.2 Proactive computing CENTRAL TOPIC in DeFINE “The challenge is to build proactive systems that regulate themselves and reduce the involvement of humans, whether these be administrators, operators or end users. Humans can thus concentrate on the main task instead of dedicating unnecessary efforts to tasks that can be performed by computers” (Define documentation 18.11.02)

3. ©Gustavo Alonso. ETH Zürich.3 The All vs. All o Problem: Compute a cross- comparison of the SwissProt v38 protein database. Þ80‘000 entries. ÞAverage sequence length: 400 amino acid residues. Þ1.5 years (cpu time) on a single machine. ÞSeveral months to run just updates (using a medium size cluster). CY2_RHOVI P00083; C YTOCHROME C2 PRECURSOR. R HODOPSEUDOMONAS VIRIDIS. PROKARYOTA; GRACILICUTES; ANOXYPHOTOBACTERIA; PURPLE BACTERIA; RHODOSPIRILLACEAE. PDB; 1CRY; ELECTRON TRANSPORT; PHOTOSYNTHESIS; HEME; SIGNAL; 3DSTRUCTURE. MRKLVFGLFVLAASVAPAAAQDAASGEQVFKQCLVCH SIGPGAKNKVGPVLNGLFGRHSGTIEGFAYSDANKNSGITWT EEVFREYIRDPKAKIPGTKMIFAGVKDEQKVSDLIAYIKQFN ADGSKK 1.6 lengths=97,110 simil=275.3, PAM_dist=85.9883, offsets=5978923,5974267, identity=40.4%, similarity=12.3% QDAASGEQVFKQCLVCHSIGPGAKNKVGPVLNGLFGRHSGTIEGFAYSDANKNS___GITWTEEVFREYIRDPKA_____ |||.:||.|||||!.||...||.|||.|.|:.||.:||..||.||..|.|| |:.||.!..|:.||.| QDAKAGEAVFKQCMTCHR___ADKNMVGPALGGVVGRKAGTAAGFTYSPLNHNSGEAGLVWTADNIINYLNDPNAFLKKF _________KIPGTKMIFAGVKDEQKVSDLIAYI.!. |||.|. :.:||:..|:!||: LTDKGKADQAVGVTKMTFK_LANEQQRKDVVAYL

4. ©Gustavo Alonso. ETH Zürich.4 Proactive vs. manual computing 0 10 20 30 Dec 17Dec 18Dec 19Dec 20Dec 21Dec 22Dec 23Dec 24Dec 25Dec 26Dec 27Dec 28Dec 29Dec 30Dec 31 Jan 01Jan 02Jan 03Jan 04Jan 05Jan 06Jan 07Jan 08Jan 09Jan 10Jan 11Jan 12Jan 13Jan 14Jan 15Jan 16Jan 17Jan 18Jan 19Jan 20Jan 21 0 10 20 30 Number of processors Available processorsRunning jobsSuccesful jobs Failed jobsJobs on CPU Other user needs cluster Disk space shortage Cluster busy with other jobs Server down for maintenance Cluster failure All jobs manually killed

5. ©Gustavo Alonso. ETH Zürich.5 PROPOSAL A RESEARCH AGENDA ON PROACTIVE COMPUTING

6. ©Gustavo Alonso. ETH Zürich.6 The challenge is to do it all Models and instrumentation Flexible, efficient architectures High level representations PROACTIVE COMPUTING

7. ©Gustavo Alonso. ETH Zürich.7 The basis for proactive computing o High level representations of complex systems and computations Þprogramming languages are at the wrong level for doing this Þprocesses, new languages for composition, visual languages o Flexible and efficient architectures in which almost any aspect of the system can be dynamically adapted (beyond typical tuning knobs) Þseparation of concerns and fully modular architectures Þreflective middleware Þdynamic AOP o Models of behavior and adequate instrumentation (which, in fact, is an adaptation) for acquiring the data that will be processed by adaptation algorithms that will use the flexibility of the architecture to dynamically change the system as needed Þbetter understanding and tested models of non-functional properties Þability to cope with complex data sets and algorithms for mining this information both online and offline

8. ©Gustavo Alonso. ETH Zürich.8

9. 9 These are hard problems o A consensus is emerging on what is needed, but many of these goals contradict each other (an improvement here makes life very difficult there): Þlack of models, programming paradigms, and suitable architectures Þautonomic behavior and automatic reconfiguration makes it very complex to understand what is going on (code evolution, instrumentation, monitoring) Þdynamic AOP, reflective middleware, and application extensibility change the nature of the application on the fly making it more difficult to monitor and instrument Þprogramming for composition may actually lead to new programming paradigms where extensibility is an essential part of the language and not a middleware feature (what is better?) Þend to end understanding of the problem is the key but we don’t know how to deal with composite systems

10. ©Gustavo Alonso. ETH Zürich.10 Dependability: autonomic behavior o True dependability encompasses a broad range of issues usually ignored by research: o Advance management support: Þchange almost every aspect of the system at run time Þmonitoring and dynamically alter the configuration of the system Þdecision support mechanisms: what needs to be stopped if machine A is taken off-line? given the current load, what is the best schedule for maintenance of each sub-cluster? o Autonomic behavior: Þautomatic rejuvenation (fast kill and reboot) Þautomatic recovery of complex distributed computations Þadd and remove nodes without stopping computations Þlow level instrumentation for run time awareness model (off line nodes, over loaded nodes, defective networks, load prediction, QoS, etc) Þinstrumentation as a dynamic extension