Multi-criteria Scheduling on Clouds Yacine KESSACI

Size: px

Start display at page:

Download "Multi-criteria Scheduling on Clouds Yacine KESSACI"

Roy Gallagher
6 years ago
Views:

1 Multi-criteria Scheduling on Clouds Yacine KESSACI Dolphin Project Team, Université Lille 1, LIFL-CNRS, Inria Lille-Nord Europe 1

2 About me Sessional lecturer and junior researcher at CNRS/LIFL/Inria Lille Nord Europe and Polytech lille (Université Lille1) - Attaché temporaire d enseignement et de recherche (ATER) Ph.D. student at Dolphin (LIFL/CNRS/Inria Lille Nord Europe) (Supervisors Nouredine Melab and El-Ghazali Talbi), defended on November 28 th 2013 (Highest Honors). Topic: Multi-criteria Scheduling on Clouds Master s degree in computer science at Université Lille 1 (France) Specialty : complex system and algorithmic - (Rank 2 nd ) Internship with Dolphin (LIFL/CNRS/INRIA Lille-Nord Europe) Topic : Multi-criteria Energy-aware Scheduling of Parallel Tasks with Precedence Constraints Yacine KESSACI Multi-criteria Scheduling on Clouds 2

3 I. Scientific context Outline II. III. Contributions 1. Service-level scheduling 2. Task-level scheduling 3. VM-level scheduling Conclusion and Future Works Yacine KESSACI Multi-criteria Scheduling on Clouds 3

more unified computing resources based on service-level agreements.

4 What is a Cloud? Cloud computing is A type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements. [Buyya et al, 2008] offering services services = infrastructures (IAAS) Cloud Source: Google Yacine KESSACI Multi-criteria Scheduling on Clouds 4

5 Cloud context: IAAS in (Multi-) Cloud Multi-cloud Meta-view Cloud Broker Cloud Broker Cloud Broker Broker-view Cloud 1 Cloud 2 Cloud n Cloud Manager-view Yacine KESSACI Multi-criteria Scheduling on Clouds 5

6 Scheduling Scheduling in IAAS (Multi-) Cloud Multi-cloud Meta-view Cloud Broker Cloud Broker Cloud Broker Broker-view Cloud 1 Cloud 2 Cloud n Cloud Manager-view Yacine KESSACI Multi-criteria Scheduling on Clouds 6

to cloud type Public cloud (VM instances) Multi-cloud (data centers)

7 Scheduling vs Cloud Type (Cont. ) Service requests VM templates Applications (Tasks) Scheduling according to cloud type Public cloud (VM instances) Multi-cloud (data centers) Private cloud (hosts) Set of requests Bijective Set of IAAS clouds Yacine KESSACI Multi-criteria Scheduling on Clouds 7

8 Scheduling Problem Formulation Objective1 ( Objective2... Constraint2... ( Constraint1 (( N J i j N J i j (( N J i j N J i j Metric1 ij Metric 2 ij Metric 3 Metric 4 x x ij ij ij ij x x ) Time ) Time ij ) Time) Constraint ij ) Time) Constraint Cloud Conflicting Objectives value1 value2 J j x ij 1 for each i,1 i N Yacine KESSACI Multi-criteria Scheduling on Clouds 8

9 Optimization Problem Formulation Multi-Objective Energy CO 2 Profit Quality of Service Performance Resolution approaches Lexicographic Aggregation Pareto Min f( x) Const. ( f ( x), f ( x),..., f ( x)) Real time scheduling may be time intensive (NP-hard) 1 x S f 2 2 n Dominated solution Pareto solution f 1 n 2 Yacine KESSACI Multi-criteria Scheduling on Clouds 9

10 Metaheuristics Exact methods Optimal solutions Exponential complexity Heuristic methods High quality solutions Reasonable time Metaheuristics = Generic heuristics Yacine KESSACI Multi-criteria Scheduling on Clouds 10

11 Challenging Issues and Objectives State of the art: Critical analysis Basic schedulers based on simple heuristics Almost no Pareto-based multi-objective solving algorithms Very few realistic experiments (Simulation-oriented experiments) Rarely considering all the cloud stack levels Contributions Revisiting scheduling for clouds from different points of view: Modeling: multi-objective modeling, improvement of some metrics Design: Pareto-based multi-objective metaheuristics Implementation: Cloud manager embedded implementation and validation through realistic experiments Considering different levels of the cloud stack Yacine KESSACI Multi-criteria Scheduling on Clouds 11

12 I. Scientific context Outline II. Contributions 1. Service-level scheduling Energy-aware 2. Task-level scheduling III. 3. VM-level scheduling Conclusion and Future Works Yacine KESSACI Multi-criteria Scheduling on Clouds 12

Source: Koomey 2007 Energy: 42% of the total data center budget.

13 Motivations Electricity doubled over the period 2005 to 2010 in worldwide. 2% of CO2 emissions. Source: Koomey 2007 Energy: 42% of the total data center budget. A multi-objective modeling is required! Source: Hamilton 2009 Yacine KESSACI Multi-criteria Scheduling on Clouds 13

14 Cloud Model: Cloud Federation / Multi-Cloud Cloud 2 Geographically distributed IAAS Cloud Cloud n Cloud 3 Cloud 1 Yacine KESSACI Multi-criteria Scheduling on Clouds 14

Multi-Cloud Characteristics Geographically distributed cloud, single provider 3 continents (America, Europe et Oceania) CO2 rate Department Of Energy (DOE) Electricity price Energy Information

15 Multi-Cloud Characteristics Geographically distributed cloud, single provider 3 continents (America, Europe et Oceania) CO2 rate Department Of Energy (DOE) Electricity price Energy Information Administration (EIA) Other parameters Statistics on other works [Wang et al, 2008] or synthetic data generation Co2 Rate Electricity price Max Frequency Location COP Rate Nb processor ( kg/kw h) ($/kw h) (Ghz) New York, USA Pennsylvania, USA California, USA Ohio, USA North Carolina, USA Texas, USA France Australia Yacine KESSACI Multi-criteria Scheduling on Clouds 15

16 Service Model Flow of services (HPC) over meta-scheduler. Service j as a triplet: HPC Services (e j, n j, d j ) Cloud (IAAS) Service requests e j execution time of the service (booking) n j number of reserved processors d j deadline of the service Multi-cloud (data centers) Yacine KESSACI Multi-criteria Scheduling on Clouds 16

Service-level Scheduling (Meta scheduler) Solution Optimal service assignment based on metrics and with constraints satisfaction Scheduling approach Multi-objective genetic algorithm meta-scheduler

17 Service-level Scheduling (Meta scheduler) Solution Optimal service assignment based on metrics and with constraints satisfaction Scheduling approach Multi-objective genetic algorithm meta-scheduler (MSCF) Cloud 2 Geographically distributed IAAS Cloud Cloud n Clients Cloud 1 Service (e.g. HPC) assignment Service (e.g. HPC) application Meta-scheduler (MSCF) Yacine KESSACI Multi-criteria Scheduling on Clouds 17

18 Problem Modeling Solution encoding Scheduling J services on N clouds (NP hard) considering the constraints Deadline (strong constraint): no delays Atomic services Objectives Service (HPC application) Satisfying maximum client s requests Cloud to which the service (HPC application) is assigned by optimizing simultaneously (energy consumption, CO 2 footprint, profit) Yacine KESSACI Multi-criteria Scheduling on Clouds 18

Model: Metrics for Objectives Energy Minimizing the energy consumption Computation energy (E c ) Cloud cooling energy (E h ) E c ij ( f ) n i i 3 ij j e ji (CMOS

19 Model: Metrics for Objectives Energy Minimizing the energy consumption Computation energy (E c ) Cloud cooling energy (E h ) E c ij ( f ) n i i 3 ij j e ji (CMOS architecture) Source: Google E h c E COP E total E c E h CO2 Minimizing the amount of CO 2 emission CO2 Etotal CO2Rate Source: Google Yacine KESSACI Multi-criteria Scheduling on Clouds 19

charged to the client Electrical energy price Profit ij n j e ij p c ( p e i E ij ) n j number of

20 Model: Metrics for Objectives (Cont. ) Profit Optimizing the profit = maximizing the provider s earnings at each meta-scheduling phase Price charged to the client Electrical energy price Profit ij n j e ij p c ( p e i E ij ) n j number of processors e ij execution time of the service p c client s price per hour p ie electricity price E ij consumed energy Source: Google Yacine KESSACI Multi-criteria Scheduling on Clouds 20

Algorithm: MSCF Meta-Scheduler Flow of HPC services Init population MO-GA Each scheduling cycle External Pareto archive Selection of a solution according to user s parameters (Meta-selection) Vector

21 Algorithm: MSCF Meta-Scheduler Flow of HPC services Init population MO-GA Each scheduling cycle External Pareto archive Selection of a solution according to user s parameters (Meta-selection) Vector Clouds state saving Y. Kessaci, N. Melab, E-G. Talbi. A Pareto-based GA for Scheduling HPC Applications on Distributed Cloud Infrastructures. International Conference on High Performance Computing and Simulation (HPCS), Istanbul, Turkey, Yacine KESSACI Multi-criteria Scheduling on Clouds 21

22 Pareto set f 1 f f f v f f v f f v Vector v 3 favors objective f 2 Vector v 1 favors objective f 1 v 2 v 3 v 1 Algorithm: Meta-Selection Mechanism Yacine KESSACI Multi-criteria Scheduling on Clouds 22 Y. Kessaci, N. Melab and E-G. Talbi, A Pareto-based Metaheuristic for Scheduling HPC Applications on a Geographically Distributed cloud Federation, Cluster Computing Journal 16(3): , 2012.

23 Experimentation MSCF parameters MO-GA parameters Comparison to other approaches Problem not treated in the literature with a multi-objective Pareto approach Compared with heuristic that maximizes the number of scheduled services (consolidation) Yacine KESSACI Multi-criteria Scheduling on Clouds 23

Results Instance LLNL Instance RICC Specific selection (vector orientation) Increase in the efficiency (normal arrival rates) Decrease in the efficiency (high arrival

24 Results Instance LLNL Instance RICC Specific selection (vector orientation) Increase in the efficiency (normal arrival rates) Decrease in the efficiency (high arrival rates) Using neutral selection (Average) delays the problem Solution: Adapt the selection policies to improve solutions Yacine KESSACI Multi-criteria Scheduling on Clouds 24

25 Results (Cont. ) Criteria Energy consumption Green house gas emissions Provider s profit Number of scheduled services LLNL instance 26% 25.9% 1.8% 2.2% RICC instance 29.4% 26.3% 3.6% 3% Yacine KESSACI Multi-criteria Scheduling on Clouds 25

26 I. Scientific context Outline II. Contributions 1. Service-level scheduling 2. Task-level scheduling Satisfaction-aware III. 3. VM-level scheduling Conclusion and Future Works Yacine KESSACI Multi-criteria Scheduling on Clouds 26

Motivations Providers have a full control over performances and prices of their services Source: AWS Mandatory choice between price and performance

27 Motivations Providers have a full control over performances and prices of their services Source: AWS Mandatory choice between price and performance VM spots and high price fluctuation Source: AWS for Asia (Singapore) for a representative week Yacine KESSACI Multi-criteria Scheduling on Clouds 27

Cloud Brokering Characteristics Instance A Instance B Instance C Source: http://www.virtu-all.

28 Cloud Brokering Characteristics Instance A Instance B Instance C Source: Yacine KESSACI Multi-criteria Scheduling on Clouds 28

Task model Broker receives flows of tasks Each task is a quadruplet ( size,,, ) S rate size execution time of the task Applications (Tasks) S rate minimum satisfaction

29 Task model Broker receives flows of tasks Each task is a quadruplet ( size,,, ) S rate size execution time of the task Applications (Tasks) S rate minimum satisfaction rate needed by the client α advantage to the costs β advantage to performances (response time) Public cloud (VM instances) Yacine KESSACI Multi-criteria Scheduling on Clouds 29

Proposal of spots (VM instances with different specifications) Clients Scheduler (MOGA-CB) IAAS Cloud Cloud

30 Task-level Scheduling Solution Optimal assignment of the tasks over VMs instances using metrics Scheduling approach Multi-objective Genetic Algorithm for Cloud Brokering (MOGA-CB) Reserving the most interesting spots Proposal of spots (VM instances with different specifications) Clients Scheduler (MOGA-CB) IAAS Cloud Cloud broker VM n VM 2 VM 1 Tasks Task assignment First tier Second tier Third tier Yacine KESSACI Multi-criteria Scheduling on Clouds 30

31 Problem Modeling Solution encoding Assigning dynamically J tasks on N VM instances (NP hard) Task Objectives Maximizing client s satisfaction minimizing primary objectives response time of the tasks and their costs Maximizing broker s profit VM instance to which the task (index) is assigned Yacine KESSACI Multi-criteria Scheduling on Clouds 31

Model: Metrics for Objectives Satisfaction of the client Primary objectives: response time and costs of the tasks Minimization Total Task Cost (p) N J cost i j j rpt ij price i Minimization Total

32 Model: Metrics for Objectives Satisfaction of the client Primary objectives: response time and costs of the tasks Minimization Total Task Cost (p) N J cost i j j rpt ij price i Minimization Total Response Time (t) N i J j currenttim e arrivaltime j rpt ij Satisfaction (p,t)= S max α x p β x t Profit of the broker ( S max Satisfacti on Srate t) profit Yacine KESSACI Multi-criteria Scheduling on Clouds 32

33 Algorithm: MOGA-CB Scheduler Flow of tasks A scheduling round Instances prices recovering Init population MOGA-CB Algorithm External Pareto archive Selection of a solution according to the client s parameters Reinjection of the unfinished tasks Tasks updating Y. Kessaci, N. Melab and E-G. Talbi, A Pareto-based Genetic Algorithm for Optimized assignment of VM Requests on a Cloud Brokering Environment, Internationnal IEEE Congress on Evolutionary Computation (CEC), June Cancun, Mexico, Yacine KESSACI Multi-criteria Scheduling on Clouds 33

34 Service price Service price Algorithm: Pareto Selection Process Satisfaction according to Same satisfaction value & 1 8 Selected solution Satisfaction for one instance 1 1 Satisfaction for another instance Classical solving 9 1 Selected solution (0,0) Response time Pareto satisfaction values 1 8 Selected solution Pareto front for one instance Pareto solving 1 Selected solution Selected solution (0,0) Response time Yacine KESSACI Multi-criteria Scheduling on Clouds 34

35 Experimentation Experiment Parameters Tasks requirements VM instances Comparison Problem not addressed in the literature with a multi-objective Pareto approach Experimental study has been conducted to analyze the behavior of our approach according to different parameters. Yacine KESSACI Multi-criteria Scheduling on Clouds 35

36 Results (Pareto analysis) Uselessness of alpha and beta parameters simplification of the model Tradeoff between cost and response time objectives according to (α/β) parameters Yacine KESSACI Multi-criteria Scheduling on Clouds 36

37 Disappointment value Results (Behavior analysis) 45 Broker s profit inversely proportional to the instance price Preserve the client s satisfaction Average Spot Cost Average Response Time Broker Profit Scheduling rounds Satisfaction Disappointment Average spot cost Average response time Scheduling rounds Satisfaction more impacted by response time than by cost More significant variation of the tasks response time: Depending on both type of instance and tasks load Yacine KESSACI Multi-criteria Scheduling on Clouds 37

38 Results (Time analysis) Time (sec) Computation Time timeper per Iteration time interval Time intervals Scheduler s time processing Time interval 30 seconds in average MOGA-CB scheduler < 30 seconds Yacine KESSACI Multi-criteria Scheduling on Clouds 38

39 I. Scientific context Outline II. Contributions 1. Service-level scheduling 2. Task-level scheduling 3. VM-level scheduling III. Performance-aware Conclusion and Future Works Yacine KESSACI Multi-criteria Scheduling on Clouds 39

40 Motivations Colossal energy consumption of physical machines Using consolidation for energy reduction purposes Increase in VM response time according to the background utilization Decrease in VM performance Due to high memory needs, high rate of CPU cache misses Source: Verma 2008 Yacine KESSACI Multi-criteria Scheduling on Clouds 40

41 VM-level Scheduling (Global view) Users OpenNebula Cloud Manager Schedulers (EMLS-ONC /EMLS-ONC-MO) IAAS Cloud Host n Host 2 Host 1 VM requests (templates) VM assignement Yacine KESSACI Multi-criteria Scheduling on Clouds 41

42 Virtual Machine Model Template files with VM information Number of CPU Memory need Disk capacity Time reservation VMs are triplets VM templates ( e j, n j, m j ) e j execution time of the VM (booking) n j number of reserved processors m j required memory Private cloud (hosts) Yacine KESSACI Multi-criteria Scheduling on Clouds 42

VM-level Scheduling in a Cloud Manager Scheduling approach Energy aware single-objective Multi-start Local Search for OpenNebula Cloud manager (EMLS-ONC) Multi-Objective energy and performance aware

43 VM-level Scheduling in a Cloud Manager Scheduling approach Energy aware single-objective Multi-start Local Search for OpenNebula Cloud manager (EMLS-ONC) Multi-Objective energy and performance aware Multi-start Local Search for OpenNebula Cloud manager (EMLS-ONC-MO) Users IaaS Cloud OpenNebula Interface Cloud Manager (OpenNebula Core) KVM Xen VMware Physical Computing Resources Default Scheduler Embedding EMLS-ONC/ EMLS-ONC-MO Schedulers Yacine KESSACI Multi-criteria Scheduling on Clouds 43

44 Problem Modeling Solution encoding Assigning VM instances to physical machines VM id Host on which the VM will be assigned OpenNebula pretreatment constraints Vector of potential hosts for each VM VM s IDs not in a sequential order Y. Kessaci, N. Melab, E-G. Talbi. An Energy-aware Multi-start Local Search Heuristic for Scheduling VMs on the OpenNebula Cloud Distribution. International Conference on High Performance Computing and Simulation (HPCS), Spain, Madrid, Yacine KESSACI Multi-criteria Scheduling on Clouds 44

required Performance Minimizing the response time of the VMs to maximize their performance Memory related to VM performance [Verma et al, 2008].

45 Model: Metrics for Objectives Energy Minimizing energy consumption Energy needed for the computation (E c ) Dynamical evolution based on the CPU usage parameter N J c 3 CPU _ usagei Eij ( i i fij ) n j e ji 1 n j const _ value Note : The larger the CPU usage, the more cooling energy required Performance Minimizing the response time of the VMs to maximize their performance Memory related to VM performance [Verma et al, 2008]. N i J j Response_t ime ij Memory j Memory j Mem _ usage i Source: Yacine KESSACI Multi-criteria Scheduling on Clouds 45

Algorithm: EMLS-ONC/EMLS-ONC-MO Each scheduling cycle Updated list of VMs Filtering out incompatible hosts Affecting compatible

Solution of LS 2 Solution of LS n Best scheduling or Pareto scheduling Dispatching the VMs and updating hosts states according to

Melab, E-G Talbi, A Multi-start Local Search Heuristic for an Energy Efficient VMs Assignment on top of the OpenNebula Cloud

46 Algorithm: EMLS-ONC/EMLS-ONC-MO Each scheduling cycle Updated list of VMs Filtering out incompatible hosts Affecting compatible hosts to each VM Updated list of hosts Launching the Multi-start Launching LS 1 Launching LS 2 Launching LS n Solution of LS 1 Solution of LS 2 Solution of LS n Best scheduling or Pareto scheduling Dispatching the VMs and updating hosts states according to the selected solution n: Min number of hosts and 20 Y. Kessaci, N. Melab, E-G Talbi, A Multi-start Local Search Heuristic for an Energy Efficient VMs Assignment on top of the OpenNebula Cloud Manager, Future Generation Computer Systems Journal, Available online 6 August 2013, ISSN X, Yacine KESSACI Multi-criteria Scheduling on Clouds 46

Neighborhood size Algorithm: Local Search Launching the LS 1 local search Local search LS 1 iterations 2 5 7 0

switch between the hosts of each VM Value of the VM id Value of the cell: representing the host on which the

47 Neighborhood size Algorithm: Local Search Launching the LS 1 local search Local search LS 1 iterations VM s host list The neighborhood operator is a switch between the hosts of each VM Value of the VM id Value of the cell: representing the host on which the VM will be assigned Multi-start example with 4 LS LS1 LS3 LS4 LS2 Yacine KESSACI Multi-criteria Scheduling on Clouds 47

48 Experimentation Comparison scenarios Single objective EMLS-ONC, energy-aware FFD (specific greedy heuristic) and OpenNebula scheduler Pareto EMLS-ONC-MO, energy-aware FFD, memory-aware FFD approach and OpenNebula scheduler EMLS-ONC and OpenNebula scheduler (real deployment - GRID5000 infrastructure) Compared criteria Energy consumption of the infrastructure Performance of the hosted VMs Number of hosted VMs Computation time of schedulers Yacine KESSACI Multi-criteria Scheduling on Clouds 48

49 Parameter Settings VM parameters VM templates deduced from the type of VMs proposed by Amazon EC2 Small instances (1 CPU, 1.7 GBs memory) to extra large ones (8 CPUs, 15 GBs memory) Execution time: 1 to 10 hour(s) Synthetic cloud configuration Nancy Grid5000 site configuration Synthetic experiments Real experiments Yacine KESSACI Multi-criteria Scheduling on Clouds 49

50 Results (Synthetic) Experiments over a configuration of 100VMs/ scheduling cycle and 80 hosts during 20 cycles Energy EMLS-ONC vs Energy-FFD vs OpenNebula (Single-objective) VM Response time EMLS-ONC-MO vs Memory-FFD vs OpenNebula (Multi-objective) Energy EMLS-ONC-MO vs Energy-FFD vs OpenNebula (Multi-objective) Yacine KESSACI Multi-criteria Scheduling on Clouds 50

200 GRID5000 machines Scheduling policy (Consolidation vs EMLS-ONC) Updating frequency of the

51 Results (Realistic) Cumulative results (EMLS-ONC vs OpenNebula scheduler) for 5 VMs/cycle on 50 GRID5000 machines Cumulative results (EMLS-ONC vs OpenNebula scheduler) for 100VMs/cycle on 200 GRID5000 machines Scheduling policy (Consolidation vs EMLS-ONC) Updating frequency of the hosts parameters (>> each scheduling cycle) Yacine KESSACI Multi-criteria Scheduling on Clouds 51

OpenNebula s scheduler and FFD approaches Scheduler s time processing Scheduling cycle 30 seconds

52 Results (Summary) Energy Reduction on average: 26% vs OpenNebula and 25% vs FFD approaches VM Performance Increase on average: 9% vs OpenNebula and equivalent to FFD approaches Number of hosted VMs Equivalent to OpenNebula s scheduler and FFD approaches Scheduler s time processing Scheduling cycle 30 seconds EMLS-ONC/ EMLS-ONC-MO maximum scheduling time 24.3 seconds Yacine KESSACI Multi-criteria Scheduling on Clouds 52

53 I. Scientific context Outline II. Contributions 1. Service-level scheduling 2. Task-level scheduling III. 3. VM-level scheduling Conclusion and Future Works Yacine KESSACI Multi-criteria Scheduling on Clouds 53

Conclusion Revisited metaheuristic schedulers for each level of the cloud Service-level scheduling Exploiting the geographical dispersion Task-level scheduling Managing the price fluctuations of the

54 Conclusion Revisited metaheuristic schedulers for each level of the cloud Service-level scheduling Exploiting the geographical dispersion Task-level scheduling Managing the price fluctuations of the VM instances VM-level scheduling Using the disparity and the differences in the features of a cloud s hosts Metaheuristic-based schedulers fitting cloud constraints Embedded in a Cloud manager Efficient results within the scheduling time slot Pareto-based multi-objective approaches Simplifying the model Pareto selection mechanism for each scheduling level Yacine KESSACI Multi-criteria Scheduling on Clouds 54

objectives give bad results Combining all the different levels of scheduling

55 Perspectives Relationship between the scheduling levels Several conflicting objectives for the whole problem at the different levels More than 3 objectives give bad results Combining all the different levels of scheduling as parts of the same scheduling workflow Yacine KESSACI Multi-criteria Scheduling on Clouds 55

56 Cross Perspectives OSMOSE (Optimisation Simulation MOdeliSation Evolutionnaire) Simulation, modelisation Optimisation and evolutionnary Ex: Combinatorial for weak Schur problem (Monte-Carlo TS, tabu search) Ex: Genetic programming Revisiting Metaheuristics (Single and population based) Already to problem constraint Computation time (Delta evaluation, number of generation ) Hardware and software constraints (Solution encoding, ) Next to the problem specification Landscape Parameters Other specifications Problem similarities with scheduling Harbor area (Logistic problems) Stevedoring (Scheduling) Loading of containers (Bin packing) Interested also by: Simulation, MOdeliSation Yacine KESSACI Multi-criteria Scheduling on Clouds 56

57 Thank you for your attention Yacine KESSACI Multi-criteria Scheduling on Clouds 57

58 Publications International Journals Y. Kessaci, N. Melab & E-G. Talbi, A Multi-start Local Search Heuristic for an Energy Efficient VMs Assignment on top of the OpenNebula Cloud Manager, Future Generation Computer Systems Journal, Y. Kessaci, N. Melab & E-G. Talbi, A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation Cluster Computing Journal, M. Mezmaz, N. Melab, Y. Kessaci, Y. Lee, E-G. Talbi, A. Zomaya & D. Tuyttens, A parallel bi-objective hybrid metaheuristic for energyaware scheduling for cloud computing systems Journal of Parallel and Distributed Computing, International Conferences with selection process and proceedings Y. Kessaci, N. Melab, E-G. Talbi. A Pareto-based Genetic Algorithm for Optimized Assignment of VM Requests on a Cloud Brokering Environment. Internationnal IEEE Congress on Evolutionary Computation (CEC), June Cancun, Mexico Y. Kessaci, N. Melab, E-G. Talbi. An Energy-aware Multi-start Local Search Heuristic for Scheduling VMs on the OpenNebula Cloud Distribution. International Conference on High Performance Computing & Simulation (HPCS), Spain, Madrid, Y. Kessaci, N. Melab, E-G. Talbi. A Pareto-based GA for Scheduling HPC Applications on Distributed Cloud Infrastructures. International Conference on High Performance Computing & Simulation (HPCS), Istanbul, Turkey, M. Mezmaz, Y. Kessaci, Y.C. Lee, N. Melab, E-G. Talbi, A. Zomaya and D. Tuyttens. A Parallel Island-based Hybrid Genetic Algorithm for Precedence-constrained Applications to Minimize Energy Consumption and Makespan. Efficient Grids, Clouds and Clusters Workshop (E2GC2), in conj. with IEEE Grid 2010 conf.,brussels, Oct Book Chapters Y. Kessaci, M. Mezmaz, N. Melab, E-G. Talbi, D. Tuyttens. "Parallel Evolutionary Algorithms for Energy Aware Scheduling". Intelligent Decision Systems in Large-Scale Distributed Environments Series: Studies in Computational Intelligence, Vol. 362 Bouvry et al. (Eds.). (Chapter4), pp Yacine KESSACI Multi-criteria Scheduling on Clouds 58

IBM ICE (Innovation Centre for Education) Welcome to: Unit 1 Overview of delivery models in Cloud Computing. Copyright IBM Corporation

IBM ICE (Innovation Centre for Education) Welcome to: Unit 1 Overview of delivery models in Cloud Computing. Copyright IBM Corporation Welcome to: Unit 1 Overview of delivery models in Cloud Computing 9.1 Unit Objectives After completing this unit, you should be able to: Understand cloud history and cloud computing Describe the anatomy