Presentation cloud computing
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This document discusses cloud computing and outlines its key features and research challenges. It defines cloud computing according to NIST, describes common cloud service models (IaaS, PaaS, SaaS), and summarizes the state-of-the-art implementation including distributed file systems like HDFS and frameworks like MapReduce. However, it notes that current technologies are not developed enough and there are many open research challenges around automated provisioning, virtual machine migration, server consolidation, traffic management, data security, software frameworks, storage, and novel cloud architectures.
Presentation cloud computing
- 1. Cloud computing: state-of-the-art and research challenges Qi Zhang, Lu Cheng, Raouf Boutaba Presented by Abhishek Sharma CS 598
- 2. Agenda • Overview • Related technologies • Architecture and design • Key features and characteristics • State-of-the-art implementation • Research challenges
- 3. Overview • NIST definition • Cloud Computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. • Popularized by Eric Schmidt in 2006
- 4. Cloud Service Providers • Two kinds • Infrastructure providers: Manage cloud platforms and lease resources. • Service Providers: Rent resources from one or many infrastructure providers to serve end users.
- 5. Advantages • No up-front investment • Pay-as-you-go model. • Lowering operating cost • Rapid allocation and de-allocation • Highly scalable • Easy to handle rapid increase in service demands • Easy access • Web-based services accessed through different devices • Reduced business risks • Service provider shifts business risks to infrastructure providers
- 6. Related Technologies • Grid Computing • Distributed computing paradigm that coordinates networked resources to achieve a common objective. • Utility Computing • Represents model of providing resources on demand. • Virtualization • Technology that abstracts away details of hardware and provides resources for high-level abstractions. • Autonomic Computing • Computing systems capable of self-management.
- 7. Cloud Computing Architecture • Layered model • Hardware Layer: Responsible for managing the physical resources of the cloud. • Infrastructure layer: Creates pool of resources using virtualization technologies. • Platform Layer: Consists of operating system and application frameworks. • Application Layer: Actual cloud applications.
- 8. • Business model • IaaS: On-demand provisioning of infrastructural resources, usually in terms of VMs. • PaaS: Refers to providing platform layer resources, including OS support and software development framework. • SaaS: Refers to providing on-demand applications over the internet.
- 9. Types of Cloud • Public Clouds • Service providers offer their resources as services to general public. • Private Clouds • Designed for exclusive use by a single organization. • Hybrid Clouds • Facilitate on-demand expansion and contraction(public clouds), but with tighter security and control(private clouds)
- 10. Cloud Computing Characteristics • Multi-tenancy • Shared resource pooling • Ubiquitous network access • Service oriented • Dynamic resource provisioning • Self-organizing • Utility-based pricing
- 11. State-of-the-art Implementation • Architecture • Layered design • Uniform high capacity • Free VM migration • Resiliency or Fault - tolerance • Scalability • Backward capability
- 12. • Distributed file systems • GFS: Provides efficient, reliable access to data. Designed and optimized to run on data centers. Provides extremely high data throughput, low latency. • HDFS: Stores large files across multiple machines. Data stored across multiple geo-servers. Reliability by replicating the data across multiple servers. • Distributed application framework • MapReduce
- 13. Research Challenges • Automated service provisioning • Satisfying service level objectives while minimizing operational costs, resource provisioning decisions must made online. • Virtual Machine Migration • Avoid hotspots. Currently, detecting workload hotspots lacks agility to respond to sudden workload changes
- 14. Server consolidation • Research challenges: Resource congestion •Energy management • Research challenges: Designing energy-efficient data centers •Traffic management • Research challenges: Higher density of links in data centers. Exiting methods compute traffic matrices between few hundreds of data hosts, data center may have thousands. Change in traffic patterns
- 15. • Data Security • Research challenges: Goals – confidentiality and auditability. Remote attestation(for auditability) not sufficient. • Software Framework • Research challenges: VM allocated to each Hadoop/MapReduce node may have heterogeneous characteristics, performance modeling of Hadoop jobs. • Storage technologies • Research challenges: Compatibility issues with legacy file systems and applications. • Novel cloud architectures • Research challenges: High energy expense, high initial investment for constructing data centers.
- 16. Summary • Cloud computing rapidly changing the landscape of information technology. • Despite benefits, current technologies are not developed enough to realize full potential. • Many challenges only starting to receive attention.