for Green Cloud Computing

Transcription

1 A Vision on VM Consolidation for Green Cloud Computing Huaglory Tianfield PhD, Professor of Distributed Systems Glasgow Caledonian University, United Kingdom

2 Data center architectures Three-tier data center architecture Access, Aggregation, and Core layers Scales to over 10,000 servers 8-way ECMP load balancing ECMP - Equal-Cost Multipath Routing

3 Powering Cloud Infrastructure Modern data centers, operating under the Cloud computing model, are hosting a variety of applications ranging from those that run for a few seconds (e.g., serving requests of web applications such as e-commerce and social networks portals) to those that run for longer periods of time (e.g., simulations or large dataset processing). However, Cloud Data Centers consume excessive amount of energy: According to McKinsey report on Revolutionizing Data Center Energy Efficiency : A typical data center consumes as much energy as 25,000 households. The total energy bill for data centers in 2010 was over $11 billion and energy costs in a typical data center doubles every five years.

4 Data Center Power Consumption Currently it is estimated that servers consume 0.5% of the world s total electricity usage. Closer to 1.2% when data center systems are factored into the equation. Server energy demand doubles every 4-6 years. This results in large amounts of CO 2 produced by burning fossil fuels. What if we could reduce the energy used with minimal performance impact?

5 Challenges of Cloud Infrastructure - On-demand resource provisioning in response to time-varying workloads - Data centers are expensive to maintain, High energy costs and huge carbon footprints - Lowering the energy usage of data centers is a complex issue

6 Challenges of Cloud Infrastructure (ct d) - Cloud resources need to be allocated not only to satisfy SLA/QoS, but also to reduce energy usage. - Rising energy cost is a potential threat as it increases the Total cost of ownership (TCO) & Return on Investment (ROI) of Cloud infrastructure set up by providers.

7 Why energy is important? Increased computing demand Data centers are rapidly growing Consume 10 to 100 times more energy per square foot than a typical office building Energy cost dynamics Energy accounts for 10% of data center operational expenses (OPEX) and can rise to 50% in the next few years Accompanying cooling system costs $2-$5 million per year

8 Distribution of data center energy consumption Cooling system 45% IT Equipment 40% Internal Power distribution 15%

9 Where Does the Power Go? Power Consumption in the Datacenter Server/Storage 50% Computer Rm. AC 34% Conversion 7% Network 7% Lighting 2% Compute resources and particularly servers are at the heart of a complex, evolving system! Source: APC

10 Techniques to Improve Energy Efficiency of Data Centers IT Infrastructure Improvements Servers and Storages Network Equipment Power Distribution Smart Cooling and Thermal Management Power Management Techniques Provisioning Consolidation Virtualization Others

11 Green Data Centers A key metric to evaluate how green a data center is Power Usage Efficiency (PUE) PUE process process cool Data Center Efficiency (DCE) P P P DCE P IT Equipment P Data Center A good DCE is A reasonable DCE target is 0.5 C. Belady, The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE,Whitepaper, [Online] Google-Data Center Efficiency, [Online] html, accessed in Oct

12 VIRTUALIZATION AS THE ANSWER Performance-isolated platforms, called virtual machines (VMs), allow resources (e.g., CPU, memory) to be shared on a single server Enables consolidation of online services onto fewer servers Increases per-server utilization and mitigates server sprawl Technique Selectively turn off core components to increase remaining unit efficiency Deploy virtualization for existing and new demand Efficiency Impact 3-5% 25-30% Enables on-demand computing, a provisioning model where resources are dynamically provisioned as per workload demand Implement free cooling 0-15% Introduce greener and more power efficient servers 10-20% McKinsey & Co. Report:

13 What is Cloud Computing? Computing may someday be organized as a public utility just as the telephone system is a public utility... The computer utility could become the basis of a new and important industry. John McCarthy, 1961 Cloud computing is a largescale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet. Ian Foster, 2008

14 Cloud Computing: Service Delivery Models X as a service (XaaS) Cloud computing infrastructure of business enterprises Software as a Service (SaaS) e.g., web services, multimedia, etc. Platform as a Service (PaaS) e.g., software framework, storage, etc. Infrastructure as a Service (IaaS) e.g., Virtual machine

15 Cloud Computing: Deployment Models Various deployment models exist Public clouds serve through the Internet backbone and operate based on the pay-as-you-go fashion Private clouds are dedicated to an organization where the files and tasks are hosted within the corresponding organization alternatively: managed alal

16 Cloud Computing: Deployment Models Various deployment models exist (ct d ) Community clouds enable several organizations to access a shared pool of cloud services forming a community of a special interest Hybrid clouds are a combination of the public, private and the community clouds with the objective of overcoming the limitations of each model

17 Virtual Machine (VM) is a software artifact that executes other software as if it was running on a physical resource directly. Typically uses a Hypervisor or Virtual Machine Monitor (VMM) which abstracts the hardware from an Operating System Virtualization

18 Features of Clouds Cloud Computing Scalable Enhanced Quality of Service (QoS) Specialized and Customized Cost Effective Simplified User Interface

19 NIST Cloud Computing Reference Architecture (CCRA) 2.0 txt

20 Virtualisation Physical Resources Problem Description Quality, Application- Specific Workload Characterization... Broker (Service Managem ent) Effective Power Management Quality, Application- Specific Workload Characterization How can we use and coordinate the workload knowledge of Guest VMs to perform more effective power management, all without sacrificing the benefits of Virtualization?

21 Power consumption Models for data center Servers Energy Model Ppeak Pfixed memory modules, disks, I/O resources CPU 1 Idle server consumes about 66% of the peak load for all CPU frequencies Server load Fmin CPU Frequency Fmax

22 Models for data center Switches Energy Model Chassis ~ 36% Linecards ~ 53% Port transceivers ~ 11%

23 Green Cloud: performance energy efficiency As energy costs are increasing while availability dwindles, there is a need to shift focus from optimising data center resource management for pure performance alone to optimising for energy efficiency while maintaining high service level performance. Green Cloud computing is a model that achieves not only efficient processing and utilisation of computing infrastructure, but also minimise energy consumption.

24 To conserve energy There are a number of areas to explore in order to conserve energy within a Cloud environment. Schedule VMs to conserve energy. Management of both VMs and underlying infrastructure. Minimize operating inefficiencies for non-essential tasks. Optimize data center design.

25 Green Cloud Framework Virtual Machine Controls Data Center Design Scheduling Management Server & Rack Design Air Cond. & Recirculation Power Aware Thermal Aware VM Image Design Migration Dynamic Shutdown

26 Carbon Efficient Green Policy (CEGP) Collect resource requests from user and resource site information such as VMs, carbon emission rate, DCiE, CPU power efficiency Sort jobs based on deadline Sort resource sites based on carbon footprint: Carbon Emission Datacenter Efficiency Energy Efficiency of VM Schedule greedily the most urgent deadline jobs on the most power efficient resource site.

27 At market level: Service Broker Cloud Datacenter A End User Cloud Datacenter B LAN and Gateway router (Network Devices) VM and Storage (Server) Cloud Computing Internet Service Provider Routers Air Conditioning, and Chiller (Cooling Devices) UPS, PDU, lighting (Electrical Devices) Internet Cloud Datacenter C Datacenter Service Broker

28 Power-aware scheduling of VMs Physical machines have different processor speed Adjustable, e.g., DVFS - Dynamic voltage/frequency scaling Type of work Monitor VM status to adjust processor speed Allocate new VMs to servers having the required speed, according to the performance requirement Weakness: the correlation between performance and energy reduction is not certain

29 Watts VM scheduling on Multi-core Systems There is a nonlinear relationship between the number of processes used and power consumption We can schedule VMs to take advantage of this relationship in order to conserve power Number of Processing Cores Power consumption curve on an Intel Core i7 920 Server (4 cores, 8 virtual cores with Hyperthreading)

30 Power-aware Scheduling Schedule as many VMs at once on a multi-core node. Greedy scheduling algorithm Keep track of cores on a given node Match VM requirements with node capacity

31 VM Management Monitor Cloud usage and workload. When workload decreases: Live migrate VMs to more utilized nodes. Shut down unused nodes. When workload increases: Use Wake-on-LAN (WOL)/wake on WAN to start up waiting nodes. Schedule new VMs to new nodes.

32 Dynamic VM Consolidation User User User VM provisioning SLA negotiation Application requests Global Market resource level broker managers Virtual Machines and users applications Consumer, scientific and business applications Virtualization layer (VMMs, local resources managers) Pool of physical computer nodes Power On Power Off

33 Three Sub-Problems When to migrate VMs? Model of varying workloads Host overload detection algorithms Host underload detection algorithms Which VMs to migrate? VM selection algorithms Where to migrate VMs? VM placement algorithms

34 Open Challenges - Ability to transfer VMs between physical nodes using live migration - Minimal number of nodes according to current resource requirements - Performance degradation and, thus result in SLA violation

35 Energy-Aware Data Centre Resource Allocation - The problem of VM allocation can be divided in two parts admission of new requests for VM optimization of current allocation of VM - To solve it we apply modification of the Best Fit Decreasing algorithm - The complexity depends on the number of VMs that have to be allocated and the number of hosts select VMs that need to be migrated chosen VMs are placed on hosts - The first heuristic is Single Threshold

36 VM consolidation Determine the VMs to be migrated Sort all VMs in decreasing order of current utilization Allocate each VM to a host based on a policy of least increasing power consumption, and Reducing performance degradation Minimizaiton of migrations Highest potential growth Random choice

37 Consolidation Algorithm GOAL: find minimal allocation of workload to servers Potential Solution: Add a Performance Constraint! Heuristic: Use both dimensions of the bin to the fullest.

38 Consolidation Algorithm Step-by-Step Determine optimal point via data from graphs Use simple bin-packing heuristic in this case max of the sum of Euclidean distance If we can allocate, do it! If we can t open up a new server and reallocate with same heuristic

39 Simple Example Server A: Current Stats: 30% CPU, 30% Disk Optimal Stats: 80% CPU, 50% Disk Server B: Current Stats: 40% CPU, 10% Disk Optimal Stats: 80% CPU, 50% Disk NEW JOB: REQUIRES: 10% CPU, 10% Disk c (A_after)+ c (B-orig) c (A_orig)+ c (B-after)

40 Application of machine learning technique For the same VM consolidation problem Use machine learning techniques to reduce the performance degradation Predict SLA/customer satisfaction level of each job before moving them across servers In general, predictors can be learned for optimizing server power and reducing performance impact

41 Placement (Scheduling) Optimal mapping of VMs to physical hosts in a data center (cloud) across multiple clouds When? Federation and bursting Multi-cloud service deployment Third-party broker scenarios Admission of new service, upon elasticity, hardware failure, workload consolidation, periodically Optimal? Service Provider perspective Performance (hosts, VMs), cost, guarantees, non-functional criteria (location, isolation, trust, risk, eco-efficiency, etc.) Infrastructure Provider perspective Provisioning cost, consolidation, isolation, SLA violations, etc.

42 Placement (cont.) Further considerations Historical performance data Benchmarking and application profiling Co-location and (anti)affinity End-user location Data constraints (legislations) Federation (lack of control over remote resources) Dynamicity - providers, prices, performance, workloads, etc. change over time (Live) Migration overhead (end-user) SLAs perspectives 1. All management actions are SLA-driven 2. Placement = SLA refinement 3. SLAs are just another criteria

43 VM Placement Architecture VM resizing + idling DVFS

44 Feedback/closed-loop for ondemand provisioning Resource monitoring Status of host nodes & current VMs, Network bandwidth Sense Predicted varying workloads, Server utilisation levels, Multi-site servers (multi clouds), Optimisation of MD VMCP Heuristic strategies for Plan Enforcement e.g., considering Live migration overheads, Act Long-term policy of host nodes Live migration process, New VMs

45 Things need refining Heterogeneous workloads Heterogeneous host nodes Matching workloads to host nodes Resource monitoring Live migration policy

46 Types of workload Workload CPU, I/O, Memory, network, Allocating same type of workloads to one node might not be appropriate Better to mix different types of workloads Need methods for characterizing the workload types

47 Types of host nodes Host nodes in the data center are possibly heterogeneous in terms of CPU, disk, memory, network. Different energy profile Matching (placing) workloads and host nodes

48 Resource monitoring Energy consumption Node performance Important measures for real-time decisions

49 Optimisation techniques for VM matching/placement Considering many types of workloads, and types of nodes Finding optimal matching/placement is nontrivial Modeling as multi-dimensional bin-packing problem may suffer from uncertain initial conditions

50 Back to Bin-Packing Comparison Not so clear correlation to traditional bin packing problem Would end up with a server that looked something like this if we tried to match it up:

51 Overheads of live migration Migration process itself consumes a large amount of energy and has to take a process Data center may span multiple physical locations Communication overheads Should avoid short-sighted, frequent workload movements smarter policies are needed

52 Optimization Formulations Cost performance tradeoff max N M M B( x i, j ) P( AI ) Mig ( Ao, AI ) i 1 j 1 j 1 Performance benefit Power Migration Cost Cost Minimization with Performance Constraint min M j 1 P( A ) Performance benefit maximization with power constraint max N M i 1 j 1 I Mig ( A o, A B( xi, j ) Mig ( Ao, AI ) I )

53 Example Approach Combinatorial optimization formulations Packing formulations for data centers (MMKP) Multi-dimensional (CPU, memory, disk, network), multi-choice (many physical hosts) Knapsack Problems Policies for load balancing, power saving (consolidation), SLA protection Scalability improvements (fractional 2-approximation) 0-1 integer programming (assignment problems) for multi and federated clouds Optimize service performance and/or cost, with service layout (load balancing), budget, VM configuration, etc., as constraints. Model uncertainty (changing conditions in providers, offers, performance, etc.) and migration overhead Approximations (greedy heuristics) for scalability

54 Swarm Optimisation Multi-dimensional bin-packing problems of energy-aware VM consolidation Conventional mathematical programming techniques suffer from computational inefficiency and local optima Swarm optimisation proves to be more robust, high efficiency Ant colony optimisation Artificial bee colony Particle swarm optimisation Differential evolution

55 Vision Provisioning of computing resources, with SLAs/QoS & energy-efficiency metrics, in a computational (self-optimising) manner Genuinely on-demand