Scalable Cloud Management with Management Objectives

Transcription

1 Scalable Cloud Management with Management Objectives Rolf Stadler, Fetahi Wuhib School of Electrical Engineering KTH, Royal Institute of Technology, Sweden RMAC Project Meeting, Delft, NL, February 20, 2012

2 Research Interests Effective, low-cost management for large-scale networked systems. Self-X functions in large systems enabled by real-time monitoring. Scalable resource management with management objectives. Network seach; real-time network analytics....

3 Industrial Collaborations Cisco Systems scalable real-time monitoring IBM Research application placement Ericsson, Ireland LTE auto-configuration Ericsson Research EU Ambient Networks controlling trade-offs in monitoring EU 4WARD in-network management EU SAIL scalable resource management

4 Our Objective in RMAC Implement, evaluate and demonstrate a resource management middleware Supporting a range of management objectives (related to fairness, energy consumption, service differentiation, ) that can be dynamically adjusted Enabling dynamic re-allocation Enabling scalability wrt to machine and applications (> )

5 Roadmap for year 1 Implement key components of our framework on KTH testbed Instantiate for management objectives, constraints (initial allocation, re-allocation, demand profiling) Integrate the solution into OpenStack Evaluate efficiency and effectiveness of solution under for different applications and load (interactive apps, data-intensive apps, analytics) Study resource management approaches of other partners and establish collaborations Provide report and demonstration in Q4

6 Roadmap for year 2,3 Extend our framework and implementation to a solution across clouds Intensify collaboration with other partners Perform experiments on larger testbed, beyond KTH

7 Background: Some of our results in Scalable Resource Allocation

8 Motivation Cloud environments are large. Data centers contain > machines, running > tasks/modules. Netcraft finds 365,000 web sites on EC2. Datacenter Knowledge, Rolf Stadler Scalable Resource Management for Large Cloud Environments

9 Design goals and design principles Design goals for resource management system Resource allocation according to management objectives, e.g., Allocate resources fairly among hosted sites; Enable service differentiation; Minimize power consumption of cloud;... Dynamic adaptation to changes in load patterns. Scalable operation (>100,000 servers, modules). Design principles A distributed middleware architecture. Distributed protocols gossip protocols, specifically: A generic protocol for application placement; Instantiations for different management objectives. Rolf Stadler Scalable Resource Management for Large Cloud Environments

10 Management architecture (global view) Management protocols execute in management plane as distributed algorithms. Rolf Stadler Scalable Resource Management for Large Cloud Environments

11 Architecture for resource management (local view) A machine in the cloud. Machine manager and site manager. Rolf Stadler Scalable Resource Management for Large Cloud Environments

12 Protocol GRMP: pseudocode for machine n initialization 1: read ω, γ, Ω, Γ, row n(a); 2: initinstance(); 3: start passive and active threads; active thread 1: for r = 1 to r max do 2: n = choosep eer(); 3: send(n, row n(a)); 4: row n (A) = receive(n ); 5: updatep lacement(n, row n (A)); 6: sleep until end of round; 7: write row n(a); passive thread 1: while true do 2: row n (A) = receive(n ); 3: send(n, row n(a)); 4: updatep lacement(n, row n (A)); Three abstract methods: initinstance(); choosep eer(); updatep lacement(n, row n (A)); The protocol can be run in a synchronous or asynchronous mode. Rolf Stadler Scalable Resource Management for Large Cloud Environments

13 GRMP-P: fairness among sites. GRMP-P: Scalability wrt number of machines and sites (CLF=MLF=0.5). GRMP-Q: fraction of machines on standby. GRMP-Q: Scalability wrt number of machines and sites for LF values of 0.25 and 0.5. Rolf Stadler Scalable Resource Management for Large Cloud Environments

14 Demo: Current State of Prototype

15 Cloud Resource Management with Management Objectives Our goal is to enable dynamic resource allocation of cloud environments with management objectives. A management objective is a policy set by the manager of the resources and specifies a desired property of the resource allocation. Examples: - Balanced load across physical resources by minimizing the maximum load on a server - Fair allocation of physical resources to tenants - Minimal consumption of energy through server consolidation - Minimal consumption of network bandwidth

16 Our Implementation in OpenStack We implement two resource management functions in OpenStack to enable dynamic resource management with management objectives Initial allocation (placement): place new VMs - extend OpenStack s least-cost scheduler with appropriate cost and filter functions Dynamic re-allocation: adapt an an existing allocation in response to events - Currently lacking in OpenStack - implement and integrate GRMP (a gossip-based protocol for dynamic resource allocation) with OpenStack

17 Prototype Setup Management Station VM Start/Stop Commands Cloud Controller Cloud State Zabbix Server OpenStack API Management Objectives Scheduler Compute Node-1 Compute Service GRMP Compute Node-2 Compute Service GRMP Compute Node-8 Compute Service GRMP VM VM VM Hypervisor OS Zabbix Agent VM VM VM Hypervisor OS Zabbix Agent VM VM VM Hypervisor OS Zabbix Agent

18 Current Testbed Hardware 9 Dell PowerEdge servers (24 2GHz / 64GB RAM) FastEthernet switched network for management traffic GB Ethernet switched network for data (e.g. live migration) OS and software Ubuntu KVM OpenStack Diablo Zabbix 1.8.1

19 Demo of Current State of Work Use case: A cloud infrastructure hosts a large number of web sites. Management objective is balanced load. A stream of requests for hosting new sites is served. Sites are removed at the end of their lifetime. Scenario 1: Scheduling new sites Scenario 2: Dynamic re-allocation Scenario 3: Sudden demand increase on a web site Site request rate: One request for a new site every 30 seconds Distribution of site lifetimes: exponential with a mean of 30 minutes Distribution of site flavors: inversely proportional to # vcpus (1-4) VMs run a web application (Joomla) and a demand generator

20 References This presentation is based on: R. Yanggratoke, F. Wuhib, R. Stadler. Gossip-based Resource Allocation for Green Computing in Large Clouds. International Conference on Network and Service Management (CNSM), Paris, France, October 24-28, F. Wuhib, R. Stadler, M. Spreitzer. Gossip-based Resource Management for Cloud Environments. International Conference on Network and Service Management (CNSM), Niagara Falls, Canada, October 25-29, F. Wuhib, R. Stadler, M. Spreitzer. A Gossip-based Protocol for Dynamic Resource Management in Large Cloud Environments. IEEE Transactions on Network and Service Management (TNSM), under review. Rolf Stadler Scalable Resource Management for Large Cloud Environments