Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Hierarchical SLA-based Service Selection for Multi-Cloud Environments

843 vues

Publié le

Cloud computing popularity is growing rapidly and consequently the number of companies offering their services in the form of Software-as-a-Service (SaaS) or Infrastructure-as-a-Service (IaaS) is increasing. The diversity and usage benefits of IaaS offers are encouraging SaaS providers to lease resources from the Cloud instead of operating their own data centers. However, the question remains for them how to, on the one hand, exploit Cloud benefits to gain less maintenance overheads and on the other hand, maximize the satisfactions of customers with a wide range of requirements. The complexity of addressing these issues prevent many SaaS providers to benefit from the Cloud infrastructures. In this paper, we propose HS4MC approach for automatic service selection by considering SLA claims of SaaS providers. The novelty of our approach lies
in the utilization of prospect theory for the service ranking that represents a natural choice for scoring of comparable services due to the users preferences. The HS4MC approach first constructs a set of SLAs based on the given accumulated SaaS provider requirements. Then, it selects a set of services that best fulfills the SLAs. We evaluate our approach in a simulated environment by comparing it with a state-of-the-art utility based algorithm. The evaluation results show that our approach selects services that more effectively satisfy the SLAs.

Publié dans : Formation
  • ⇒ www.WritePaper.info ⇐ is a good website if you’re looking to get your essay written for you. You can also request things like research papers or dissertations. It’s really convenient and helpful.
       Répondre 
    Voulez-vous vraiment ?  Oui  Non
    Votre message apparaîtra ici

Hierarchical SLA-based Service Selection for Multi-Cloud Environments

  1. 1. Hierarchical SLA-based Service Selection for Multi-Cloud Environments Soodeh Farokhi, Foued Jrad, Ivona Brandic and Achim Streit Vienna University of Technology, Austria
  2. 2. Motivation Scenario  SaaS providers potentianl IaaS users • Less maintenance overhead • More customer satisfaction  Multiple Clouds • Various QoSs and pricing models  Service Level Agreement  Multi-Cloud delivery model Problem: SLA-based Multi-Cloud Service selection for the SaaS provider 2 CAD Application UI (1 Large VM) CAD Models (2x 1000 GB Storage) GPU (5 Large VM) CAD App Provider CAD App Customer HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  3. 3. Approach Overview Problem domain • Handling SLA heterogeneity • Maximizing SaaS provider benefits Solution domain • InterCloud-SLA • Service selection by utilizing prospect theory 3HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  4. 4.  Prospect Theory is a behavioral economic theory • by Daniel Kahneman in 1979 (Nobel prize in 2002) • a descriptive model for decision making under uncertainty • based on the potential value of losses and gains rather than the final outcome  Alternative decision making model for the Utility Theory • more realistic in calculating the user satisfaction • psychologically more accurate  First application at Cloud service selection problem What is the Prospect Theory? 4HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  5. 5.  Service Ranking • Calculating the user satisfaction based on Prospect Theory principles  Determining how well a service satisfies a user’s requirements • Satisfaction with a specific service is not the same for different users • The most suitable service for a user is not the one with the best QoS  Modeling user satisfaction as a function of • Service quality • The importance of each QoS parameter Using Prospect Theory 5HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  6. 6. HS4MC Phases and Architecture  Phases • Phase 1: SLA Construction − Meta-SLA − Sub-SLA • Phase 2: Service Selection − Service Selection Algorithm 6 SLA Repository IaaS offers Repository SLA Construction Engine Meta-SLA Sub-SLA Service Selection Engine Service Ranker Composite Service Ranker SaaS provider Requirements Phase 1 Phase 2 2 sub SLA InterCloud SLA Meta SLAsub SLA sub SLA sla sla sla sla Composite Infrastructure Service
  7. 7. Meta-SLA and Sub-SLA 7 Meta-SLA Parameters Functional Contract duration hour Non-functional VM Budget $ per hour Storage Budget $ per month Data-traffic Budget $ per month Availability % Throughput Mbit/ sec Latency ms Reputation 1 to 10 Sub-SLA Parameters Functional Resource type VM or Storage Resource Size S, M, L Number How many? Geo-location Where? Non-functional Availability % Response time sec Reputation 1 to 10 For Multi-Cloud App (Composite infrastructure service) For each component of composite service Non-functional tuple= {Min, Max, Weight, Type} e.g. Availability (%) = {99.5, 100, very important, soft} HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  8. 8. Motivation Scenario (details) CAD application distributed on Multi-Cloud (CAD-as-a-Service) (1) Computation, GPU (VMs) • high performance (2) CAD application UI (VM) • high availability, low response time (3) Cloud storages • geo-location, reputation Aggregated QoS • resource cost, traffic costs, latency 8 meta-SLA CAD Application UI (1 Large VM) sub-SLA (UI) CAD Models (2x 1000 GB Storage) sub- SLA (Storage) GPU (5 Large VM) sub- SLA (Computation) CAD App Customer sub-SLAs meta-SLA HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  9. 9. Service Selection Algorithm (simplified) 9{s4, s10, s2, s3} 1 23 {s8, s4} {s5, s10, s3} Scored lists Step 2 {S2, S3, S4, S10} 1 23 {S4, S8} {S3, S5, S10} Filtered lists Step 1 Available services : {S1, S2, S3, …, S10} Step1 Filtering out unqualified services for each sub-SLA • Hard non-functional constraints • Functional constraints Step 2 Local scoring of services • Sub-SLA values and weights • Sub-scoring based on satisfaction function
  10. 10. Satisfaction Function 10 Availability = {Min, Max, Weight} = {99.5, 100, w1 / w2 / w3} Service1 = {QoS, Value} = {Availability, 99.7} -------------------------------------------------------------------------------- Normalize value 99.7% = 0.4 as normalized value -------------------------------------------------------------------------------- Calculate satisfaction score HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  11. 11. All possible combinations (meta-SLA ranking) ranked 1: {s10, s4,s2} ranked 2: {s5, s4, s4} …. ranked 24: {s3, s8, s3} Service Selection Algorithm (simplified) 11 Step 3 Step 4 Best combination (meta-SLA and sub-SLA ranking) {s5, s4, s4} {s4, s10, s2, s3} 1 23 {s8, s4} {s5, s10, s3} Sorted lists Step 3 Scoring of possible compositions • Meta-SLA values and weights • Aggregated QoS values of selected services • Meta-scoring based on satisfaction function Step 4 Finding the best combination of services • Considering both meta-score and sub-scores Evaluation
  12. 12. Evaluation (1)  Comparing functionality with a utility-based algorithm [Jrad et al., 2013] • Implementing both algorithms • Considering SLA satisfaction as metric  Setup a realistic simulation environment • Simulating 12 commercial IaaS Cloud providers (20 distributed DC) − QoS attributes by using CloudHarmony service (via a Client located at Europe) − Pricing models based on real Cloud providers  Considering the impact of 3 aspects on service selection • Cost, meta-SLA, and sub-SLA 12
  13. 13. Evaluation (2)  Using CAD app scenario for users (3 software editions) • One Meta-SLA for each software edition • Three sub-SLA (UI, Computation, Storage) for each software edition 13 Meta-SLAs Sub-SLAs
  14. 14. Theorotical Comparison Features Utility-based algorithm Prospect-based Algorithm Cost Focused, fixed and predefined impact Flexible impact like other QoSs Weighting From (0,1] dependently for each parameter From (0, 1] independently SLA satisfaction Meta-SLA Both meta-SLA and sub-SLA Hard non-functional - supported Fitting function Separated and predefined for each QoS Unified based on prospect theory, flexible by weight (scalable to support more QoS) 14
  15. 15. Experimental results (1)  Impact of cost on service selection • Minimizing cost is the main goal for the user • 3 executions of both algorithms for each software edition − result of standard edition: 15 HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  16. 16. Experimental results (2)  Impact of meta-SLA parameters on service selection • Availability is as important for the user as cost • One execution of both algorithms − result of enterprise edition setup: 16HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  17. 17. Experimental results (3)  Impact of sub-SLA parameters on service selection • User has specific non-functional requirements for sub-components (UI, Storage) • One execution of both algorithms − result of professional edition setup: 17HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  18. 18. Summary  Proposing for the SaaS providers • Multi-Cloud Service selection • InterCloud-SLA concept (sub-SLA and meta-SLA)  Service Selection Algorithm • Justification of using prospect theory to rank the Cloud services  Evaluation • Tendency of choosing a single Cloud (latency and traffic cost) • The importance of Sub-SLA concept in a Multi-Cloud environment • Cost is not always the most important factor 18HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  19. 19. Future Work  scalability of algorithm (performance evaluation)  Complete InterCloud-SLA construction phase • IEEE InterCloud Working Group (ICWG), InterCloud-SLA  Investigating on Multi-Cloud SLA management steps at runtime • SLA monitoring strategies • SLA violation detection and (pre) reaction • SLA validation and enforcement (developing penalty model)  Related paper • Soodeh Farokhi, “Towards a Multi-Cloud SLA Management Framework”, Doctoral Symposium 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2014), USA, May 26-29, 2014 - accepted. 19HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  20. 20. Thank you for your attention Soodeh Farokhi Distributed System Group, Institute of Information Systems, Vienna University of Technology, Austria soodeh.farokhi@tuwien.ac.at http://www.infosys.tuwien.ac.at/staff/sfarokhi/ 20HS4MC: Hierarchical SLA-based Service Selection for Multi-Cloud Environments (CLOSER 2014)
  21. 21. 21 Backup slides
  22. 22. Multi-Cloud SLA Management FW 22
  23. 23. Theoretical Comparison (2)  Utility-base Algorithm Utility = QoS Scores * Budget – Service Cost  Prospect-based Algorithm Final Score = SS sub-SLAs* Wsub-SLA + SS meta-SLA * Wmeta-SLA 23
  24. 24. Aggregate Functions  Non-functional parameters in the meta-SLA • corresponding aggregation function • To calculate the aggregated values of composite service non-functional parameters based on its constituent services. 24
  25. 25. Latency Calculation  W(Sij) is the connectivity value of each edge in the graph  Lat(Sij) is gathered from the latency matrix • According to the data center geographical location of included services in the composition. 25
  26. 26. Provider profile parameters 26
  27. 27. SLA Specification 27
  28. 28. Utility based algorithm (details)  The utility for each composite service is calculated by subtracting the total service usage costs (include VM, traffic, storage) from its monetized usage benefit.  Monetized usage benefits: multiplying his overall score for the SLAs with his maximal payment for a perfect service  The overall SLA scores (a normalized value between 0 and 1), defined as the sum of the weighted single SLA parameter scores, express the overall user satisfaction for a certain service quality.  SLA score: uses fitting functions to map each SLA metric value to a satisfaction level between 0 and 1. 28
  29. 29. Prospect theory (details)  This theory implies that changes in a specific quality aspects of a service is sensed more by users who have assigned higher weights to those quality parameters.  The satisfaction of user for a service is based on the gains and losses relative to the reference point (normalized value of 0.5) instead of absolutely determined by the normalized QoS parameters of that service.  The satisfaction function should be concave for gains, and convex for losses. 29
  30. 30. What is missing in the related work?  Cloud Service Selection • Maximizing the profit of either the customer or the IaaS provider • Using only single Cloud services • Without throughly investigate SLA challenges 30
  31. 31. Budget Calculation/ Cost Prediction  Budget: willingness of the user’s investment • VM a unit of cost: value of renting a small VM per hour • Storage cost: value of storing1GB of data per month • Data Traffic cost: value of transferring 1GB of data per month − Considering contract duration and the size of possible transferred data 31
  32. 32. Provider Profiles 32
  33. 33. Experimental results (1st and 2nd)  Impact of cost on service selection • Minimizing cost is the main goal for the user • 3 executions of both algorithms for each software edition setup − result of standard edition: 13
  34. 34. Multi-Cloud middleware SLA hierarchy 34 Multi-Cloud SLA SLA SLA SLA SaaS provider customer customer SLA SLA Infrastructure Requirement Sub SLA sub SLA sub SLA Middleware Layer meta SLA InterCloud SLAs SLA SLA SLA SLA IaaS provider Layer IaaS provider SLAs SaaS Provider Layer SaaS provider Infrastructure Requirement

×