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Resource Allocation in Cloud Computing PowerPoint Presentation
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Resource Allocation in Cloud Computing

Resource Allocation in Cloud Computing

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Resource Allocation in Cloud Computing

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  1. Resource Allocation in Cloud Computing

  2. Resource Allocation • How to maximize resources in order to most effectively provide cloud computing services Resource Allocation in Cloud Computing

  3. Presentation Plan • Distributed Clouds and their Challenges • IaaS • Resource Allocation based on pre-known demands • On the fly Resource Allocation • SLA • Resource Pricing and Equilibrium Allocation Policy • Questions Resource Allocation in Cloud Computing

  4. Papers Used • Debraj:2. D. Ardagna, M. Trubian, and L. Zhang, “SLA based resource allocation policies in autonomic environments,” Journal of Parallel and Distributed Computing, vol. 67, 2007, pp. 259-270.9.Domain based resource management by Dongwan Shin and HakanAkkan (Secure Computing Laboratory ,Department of Computer Science and Engineering, New Mexico Tech, Socorro, NM 87801 Kiran:1. W. Daniel, “Exploiting dynamic resource allocation for efficient parallel data processing in the cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, 2011, pp. 985-997.3. S. Di and C.-L. Wang, “Dynamic optimization of multiattribute resource allocation in self organizing clouds,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, 2013,Patricia:4. P. T. Endo, A. V. de Almeida Palhares, N. N. Pereira, G. E. Goncalves, D. Sadok, J. Kelner, B. Melander, and J. E. Mangs, "Resource allocation for distributed cloud: concepts and research challenges," Network, IEEE, vol. 25, pp. 42-46.6. T. Fei, Magoule, x, and F. s, “Resource pricing and equilibrium allocation policy in cloud computing,” Computer and Information Technology, 2010, pp. 195-202.8. T. Dillon, C. Wu, and E. Chang, “Cloud Computing: Issues and Challenges,” IEEE Int'l. Conf. Advanced Info. Networking and Apps., 2010, pp. 27-33.Prasanna:5. G. Wei, A. Vasilakos, Y. Zheng, and N. Xiong, “A game-theoretic method of fair resource allocation for cloud computing services,” Supercomputing, vol. 54, 2010, pp. 252-269.7. Yazir, Y.O.; Matthews, C.; Farahbod, R.; Neville, S.; Guitouni, A.; Ganti, S.; Coady, Y.; , "Dynamic Resource Allocation in Computing Clouds Using Distributed Multiple Criteria Decision Analysis," Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on , vol., no., pp.91-98, 5-10 July 2010doi: 10.1109/CLOUD.2010.66 Resource Allocation in Cloud Computing

  5. Resource Allocation in Cloud Computing Distributed Clouds and Challenges - Patricia

  6. Resource Allocation in Cloud Computing Current Cloud Computing Providers • Large, consolidated data centers • Problems • Resource over-provisioning • Heat dissipation/temperature control • Greater average distance to users

  7. Resource Allocation in Cloud Computing Distributed Clouds

  8. Resource Allocation in Cloud Computing Distributed Clouds: Benefits • Scalable services • On demand usage • Pay as you go/business model • Geodiversity

  9. Resource Allocation in Cloud Computing Distributed Clouds: Challenges

  10. Resource Allocation in Cloud Computing Resource Modeling How can the infrastructural resources be represented? • Optimization algorithms depend on it • Must take into account both physical and virtual capabilities • Two Main Challenges • Granularity of Resource Description • Interoperability

  11. Resource Allocation in Cloud Computing Granularity of Resource Description • How much detail should be used to describe cloud resources? • Less details = less time spent optimizing • More details = more flexibility/leverage of resources

  12. Resource Allocation in Cloud Computing Interoperability How do you communicate between different/external clouds? • Vertical – Applications should interact in a standard way regardless of underlying cloud • Horizontal – Different clouds should be able to interact with one another • Benefit: “borrowing” resources from one another

  13. Resource Allocation in Cloud Computing Resource Offering & Treatment • Not necessarily offered using the same metrics as internal modeling • Example: Internally see 100 individual GB of memory, externally offer one 100 GB chunk • Generic • Serve as many apps as possible • Easy for users to deal with (while still maximizing utilization)

  14. Resource Allocation in Cloud Computing Resource Offering & Treatment • Must be able to control all resources in cloud • Currently implemented by each provider individually • Need common protocol for resource reservations in heterogeneous distributed clouds • Example criteria • Topology • Jurisdiction • Node proximity • Application interaction • Scalability

  15. Resource Allocation in Cloud Computing Resource Discovery and Monitoring • Resource Discovery – provider has to find appropriate resources • Must minimize inter domain traffic • Cannot let new overhead impact QoS

  16. Resource Allocation in Cloud Computing Resource Discovery and Monitoring • Resource Monitoring • Balancing act between amount of control overhead and the frequency of refreshing • Two main categories • Active – nodes autonomously send to central authority • Passive – 1+ entities request info from nodes • Either/Both categories are valid, just have to keep them in sync with one another

  17. Resource Allocation in Cloud Computing Resource Selection & Optimization How do you choose which entities to use? • A priori – allocates optimal resources first • A posteri – allocates decent resource first then improves

  18. Resource Allocation in Cloud Computing Conclusion • Many challenges... many opportunities

  19. Resource Allocation in Cloud Computing Cloud Computing Today • Same concepts apply

  20. Infrastructure as a Service • (IaaS) • Domain based resource allocation in IaaS • - Debraj

  21. Introduction • Iaasprovides users with infrastructure services such as computation and data storage, heavily dependent upon virtualization techniques. • Current IaaS service providers have adopted a user-based service model which creates lack of support for scalable management of users and resources as well as lack of flexible pricing model • A domain-based framework has been proposed for provisioning and managing users and resources

  22. Current Scenario

  23. New Approach

  24. Approach • Iaas components • Virtual resources • Uniquely identifiable cloud users • Role based access control (RBAC) domain • RBAC components • U = U x D represents the set of domain users associated with domains, and U x ¢ represents the set of cloud users not associated with any domain. P represents the set of permissions to use virtualized resources associated domains • - R and S represents the set of roles and sessions, respectively. • UA, PA, and RH, representing the relation of user-to-role assignment, permission-to-role assignment, and role hierarchy, respectively. RH is partial order on R, written as ::5.

  25. Approach contd. • user: S → U represents a function mapping each session Si to the single user. • - roles: S → 2R represents a function mapping Si to a set of roles, where roles is subset of {rl(Ǝr' ≥ r)[(user(Si), r') E UAl} and Si has permissions UrEroles(s;) {pI (Ǝr" ≤ 5 r) [(p,r") E PAl}.

  26. Approach, design and Implementation • Domain based Management and Delegations • Management thorugh delegation of the administrative functions • Allocation of virtualized resources to each domain

  27. IAAS Conclusion • A novel approach to managing visualized resources in cloud computing by introducing the notion of domain and injecting a role-based security policy support into IaaS service model. • The approach provides benefits such as domain optimized resource management , domain based advanced policy support based on role, domain based security log analysis and better pricing model. • Proof of Concept prototype has been implemented by modifying an existing IaaS framework called Eucalyptus.

  28. Resource Allocation based • on pre-known demands • - Kiran

  29. Parallel data processing in the cloud • Parallel processing framework • Mostly in clusters • Now deployed in IaaS • Flexible resource allocation in IaaS • On-demand, pay as you go • Is it utilized? (Our Focus)

  30. Opportunities Required? - Scheduler knows cloud environment - Job description specify dependencies Can we use MapReduce? - Not suitable - Can’t shut down idle VMs. Why?

  31. Challenges in Cloud • Opaque data locality • Where is the data stored? • Topology aware scheduling • Infer topologies in IaaS? • Unclear if it works • Network Virtualization • Makes life difficult • Any solutions? • Yes! Nephele…

  32. Nephele • Data processing framework for cloud • Master-Worker Pattern • Cloud Controller (CC) • Manage resources • Job Manager (JM) • Master • Task Manager (TM) • Worker • Persistent Storage (Amazon S3) • Store intermediate data

  33. Input Job • Modeled as DAG (Job Graph) • Multiple input, multiple output • Explicit communication path • Job graph • Simple: Task & relationships • No explicit parallelization • No mapping of Task -> Instance • Submit Job graph to JM

  34. Nephele Job processing • Convert Job graph -> Execution graph • 2 levels of details: • Abstract: • Task-level • Instance allocation/de-allocation • Concrete: • Fine-grained • Map subtasks -> instances • Execution stages • Group vertex (A vertex in Job graph) • Stage start -> allocate instances • Send subtasks to TM • Store intermediate results to persistent storage

  35. Nephele Job • Channel types • Network (same stage, different VMs) • In-memory (same stage, same VM) • File (across stages, persistent storage) • Dynamic Resource allocation • Delete instances no longer required • Allocate instances based on job requirements • Reuse instance from previous stage • Is the “scheduler” really “dynamic”? • Dynamic resource allocation only • Job is fixed throughout the execution

  36. Verdict • First to exploit dynamic resource allocation by IaaS • Current focus= minimize processing time or cost • Improves resource utilization => less cost • Savings in cost & time marginal, but grow by size of input data set • Open Issues (What can you do more?) • Adapt to resource overload or underutilization during job execution automatically

  37. Dynamic optimization of multiattribute resource allocation in Self-Organizing Clouds • VM technology and Resource Multiplexing • Self Organizing Cloud(SOC) • Large number of desktop computers • P2P Network • Each participant is Provider & Consumer • Autonomous location of nodes • 2 Issues (Our focus) • Multi-attribute range query • Optimize execution time by determining optimal share of resources

  38. Key Notations Used in SOC Model

  39. Problem Formulation • Simple Monetary model to analyze economic implications • Total payment is given by-> • where Δt is execution time of tij on ps • Given submitted task tij with designated e(tij) and w(tij) • 1. Efficiently locate a qualified node • In large peer-to-peer network • Controlled message delivery • 2. Optimize tij‘s execution time ->

  40. Optimal Resource Allocation • Lemma 1.The optimal allocation (denoted by r*(tij)) exists iff Inequalities (6) and (7) are met • Proof: Read on your own  • Assume the qualified node ps that satisfies above inequalities can be found by resource discovery protocol (To be discussed later) • More math in the paper to calculate the optimization function • Out of the scope of our discussion

  41. Optimal Resource Allocation • - a(tij) is a “firm” bound – r(tij) cannot be greater than a(tij) • - e(tij) is a “soft” bound – r(tij) can be lower than e(tij) because e(tij) is just an estimate by user • - To optimize resource allocation without exhausting all possible combinations, relax (5) to (11) • - Let denote a subset of • resource attributes π • - C be budget • - CO-STEP( ,C) computes optimal • resource vector for tij w.r.t

  42. Optimal Proportional-Share Allocation • Dynamic Optimal Proportional Share (DOPS) • Resource Allocation method • Leverages Proportional Share Model (PSM) • 2 Main procedures (handlers) • Slice Handler • Event Handler • Performed by a node’s VMM • Slice Handler • Activated periodically • Equally scale resource allocation to tasks • So each task can acquire additional resources proportional to their demand • Event Handler • Activated only on Task arrival and completion • Redistributes resource

  43. DOPS – Slice Handler

  44. DOPS – Event Handler

  45. Pointer-Gossiping CAN (PG-CAN) • Resource Discovery Protocol • Content Addressable Network (CAN) as DHT Overlay • Duty Node (responsible for a multidimensional range zone)

  46. Pointer-Gossiping CAN (PG-CAN) • Periodically propagate state-update to duty node (flooding) • Inefficient • Search area may be too large • Improvement • Periodically diffuse a few “pointer” messages to distant nodes (like breadcrumbs) • In negative direction • 2k hops where k = 0, 1, 2,… • Two ways • Spreading • Hopping • Hopping is better

  47. Pointer Gossiping

  48. Pointer Sender (Hopping Manner)

  49. Pointer Relay (Hopping Manner)

  50. Resource/Range Query