Data Center Load Balancing T106 5840 Seminar Kristian

Data Center Load Balancing T-106. 5840 Seminar Kristian Hartikainen Aalto University, Helsinki, Finland 9. 12. 2015

Load Balancing • Efficient distribution of the workload across the available computing resources – Distributing computation over multiple CPU cores – Distributing network requests across multiple servers – And many others. . . • The goals is efficient resource usage to optimize the desired performance metrics – Maximizing network throughput – Minimizing latency – And many others. . .

Data Center Load Balancing • Load balancing problems arise in several (computing) contexts • Our focus is on the data center load balancing • Data center load balancing also consists of several different levels – Network traffic, CPU inside servers, server racks, server clusters, between data centers • We studied load balancing of network traffic and virtual servers

MOTIVATION

The Free Lunch Is Over • Single threaded preformance have hit the wall • Number of transistors in the microprocessors is still growing Herb Sutter: The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software. Dr. Dobb’s Journal, 30(3), March 2005 (updated graph in August 2009).

Amdahl’s Law • Clear limitations in the speed up gains that parallel programs can achieve • Thus, keeping the resources utilized is challenging https: //en. wikipedia. org/wiki/Amdahl%27 s_law

Towards Data Center Computing • At the same time, the amount of data mobile devices, sensors and data transferred in general, have proliferated • Data center technology and virtualization has been developing rapidly as well • Data centers are becoming larger and more common – Large companies are building their own data centers – Many other companies are moving their computation, storage and operations to cloud

Large Scale Data Centers • Data centers provide many advantages over traditional computing – Economics of scale – Enables use of cheap commodity hardware – Cheap hardware, cooling, electricity, network, etc. . . • However, to efficiently utilize data center resources, and to provide the required performance guarantees, efficient load balancing mechanisms are needed on different levels of the data center

NETWORK TRAFFIC LOAD BALANCING

Network Traffic Load Balancing • Today’s data centers are huge – Hundreds of thousands of servers – Supporting huge amount of services • Big data apps • Web services • High performance computing • Network traffic grows – Both inter and intra data center traffic • Network bandwidth is one of the major bottlenecks in data centers

Data Center Networks • Traditional data center network topologies are single rooted trees • Limited port density (even in the highest-end) switches forces the data center topology to take a form of multi-rooted tree • For example fat-tree or leaf-spine

Data Center Networks • The problem: How to efficiently utilize theoretical bandwidth gains for the multi-rooted design?

Flow Hashing • Most of today’s load balancing mechanisms are based on flow hashing – E. g. Equal-Cost Multi-Path forwarding • Basic idea: split the packet flows randomly across multiple network paths – E. g. by hashing the packet header (e. g. 5 -tuple) • ECMP – Forwarding decisions made hop-by-hop – All the routes are equal cost

Flow Hashing

Flow Hashing • Pros: – Easy to implement – Good performance in ideal system conditions – Packets are automatically kept in order, which is crucial for certain protocols such as TCP. • Cons: – Hashing decisions are purely local – And totally unaware of the congestion state of the system

Congestion Aware Load Balancing • Several proposals of congestion aware load balancing have been made to overcome the problems of hash-based methods • Difficulties: – How to handle packet reordering? – Centralized vs. distributed systems? – How to implement fast system with no specialized hardware? • Couple of examples: Hedera, Presto, CONGA

Hedera: Dynamic Flow Scheduling for Data Center Networks

Flowlets • One of the problems in non-hash-based load balancing mechanisms is packet reordering • Several solutions overcome this problem by doing the load balancing decisions on per-flow basis, instead of per-packet basis • Flowlet is a burst of packets belonging to the same flow, that are separated from other brusts in the same flow by a large enough gap, that splitting them on a separate paths do not cause reordering problem

CONGA: Distributed Congestion-Aware Load Balancing For Datacenters • Distributed load balancing scheme • Maintains the congestion state of each path in the leaf nodes • Congestion information is carried directly in the hardware data plane of the switches (in the VXLAN virtualization overlay headers)

CONGA: Distributed Congestion-Aware Load Balancing For Datacenters • Pros: – High utilization of the network – Reacts fast to congestion – Fairly simple • Cons: – Distributed load balancing systems are often slow – CONGA and many other distributed systems overcome this problem by using customized networking hardware • Makes deployment hard

Presto: Edge-based Load Balancing for Fast Datacenter Networks • Load balancing mechanism implemented in the soft network edge (virtual switches) • Routes the flowlets through the network using round robin algorithm • Solves the problems of hash-based algorithms – Works even in asymmetric topologies – Elephant flows do not cause problems

Presto: Edge-based Load Balancing for Fast Datacenter Networks • Pros: – Deals well with network failures and asymmetry – Fully implemented in the software (~500 lines of code in Open v. Switch and ~900 lines of code in Linux Generic Receive Offload (GRO)) – Thus easy to deploy • Cons: – Too slow compared to HW solutions?

VIRTUAL SERVER LOAD BALANCING

Virtual Server Load Balancing • Another part of data center that needs to be balanced • Goals and methods differ from network load balancing – Goal seems to be more about energy efficiency rather than pure speed ups of scalability

Power Usage of Warehouse Scale Server Figure: L. A. Barroso, J. Clidaras, and U. Hlzle, “The datacenter as a computer: An introduction to the design of warehouse-scale machines, second edition”

Server Load Balancing • Cloud data center servers are often virtualized • Virtual machine migration allows flexible movement of servers between physical hardware • Migration brings over head • When and which virtual machine should be migrated, and where? • How to develop algorithm that scales? • How to cope with heterogeneous allocation policies and different objectives?

Virtual Server Migration • Distributed vs. centralized load balancing – Similarly as with network traffic load balancing • Dynamic vs. Static load balancing • Metrics to make the migration decisions – CPU-, memory-, network usage, etc. . .

Example Load Balancing System [A. Beloglazov and R. Buyya, “Energy efficient resource management in virtualized cloud data centers”] • Decentralized • Three level system architecture – Dispatcher • Distributes requests between global managers – Global Manager • Supervises a set of local managers • Distributes their own local manager data between other global managers – Local manager • Inside each of the physical servers nodes • Responsible for continuous monitoring of the resource utilization

Example Load Balancing System [A. Beloglazov and R. Buyya, “Energy efficient resource management in virtualized cloud data centers”]

EXPERIMENT

Experiment: Simulation of ECMP • Equal-Cost Multi-Path (ECMP) experiment in a small data center • Simulated using Performance Simulation Environment (PSE) – In-house discrete event simulator

Simulation setup Figure: http: //simula. stanford. edu/~alizade/papers/conga-sigcomm 14. pdf

PSE Model

CONCLUSIONS

Conclusions Load balancing is important Load balancing is challenging Experiment is not ready Network traffic load balancing is more about scalability without sacrificing latency or throughput under unexpected network conditions • Server load balancing is more about efficient utilization of the server nodes, to reduce energy consumption • •