DONAR Decentralized Server Selection for Cloud Services

B96B02016 生化科技四 張煥基 B97901184 電機三 姜慧如

2011.06.21

Introduction

The trend toward geographically-diverse server placement will only continue and increasingly include smaller enterprises, with the success of cloud-computing platforms like Amazon AWS .

These services all need an effective way to direct clients across the wide area to an appropriate service location (or “replica”).

說文解字： Replica Selection

Geo-replicated services need an effective way to direct client requests to a particular location, based on performance, load, and cost.

Replication Selection 兩大主流

central coordination distributed heuristics(DONAR)

優點(1) reliability(2) security

同時解決(1) client performance(2) server load

缺點

(1) single point of failure

(2) attractive target for attackers

(3) overhead

(4) less responsive to sudden changes

(5) scalability limitations

(1) nodes over-react based on their own local information

(2) the system does not balance replica load effectively

Replica-Selection System 必備特質

[1] Expressive

Customers should have a sufficiently expressive interface to specify policies based on (some combination of) (1) performance, (2)replica load, and (3) server and bandwidth costs.

[2] ReliableThe system should offer reliable service to clients, as well as stable storage of customer policy and replica configuration data.

[3] AccurateClient requests should be directed to the service replicas as accurately as possible, based on the customer’s replica-selection policy.

[4] ResponsiveThe replica-selection system should respond quickly to changing client demands and customer policies without introducing instability.

[5] Flexible The nodes should support a variety of replica-selection mechanisms

[6] SecureOnly the customer, or another authorized party,should be able to create or change its selection policies.

本篇主角： DONAR

This paper presents DONAR, a distributed system that can offload the burden of replica selection, while providing these services with a sufficiently expressive interface for specifying mapping policies.

1.2 Decentralized Replica-Selection System

mapping node 的任務

(1) direct its clients

(2)adapt to changing conditions

Roadmapsection

2Simple and expressive interface for customer policies

section 3

Stable, efficient, and accurate distributed replica-selection algorithm

section 4

Scalable, secure, reliable, and flexible prototype system

section 5

Experiments in Section 5 evaluate both our distributed algorithm operating at scale and a small-scale deployment of our prototype system

section 6

compares DONAR to related work

section 7

concludes

2.1 Customer GoalsCustomers use DONAR to optimally pair clients with service replicas

minimize the network latency

balance load across all replicas

billing costs

2.2 Application Programming Interface

create a DONAR services = create ()

add a replica instance i = add (s, repl, ttl)   time-to-live period (ttl)

set split weight set (s, i, wi, εi)

set bandwidth cap set (s, i, Bi)

match a client-replica pairmatch (s, clnt, i)

prefer a particular replica preference (s, clnt, i)

remove a replica instanceremove (s, i)

Roadmapsection

2Simple and expressive interface for customer policies

section 3

Stable, efficient, and accurate distributed replica-selection algorithm

section 4

Scalable, secure, reliable, and flexible prototype system

section 5

Experiments in Section 5 evaluate both our distributed algorithm operating at scale and a small-scale deployment of our prototype system

section 6

compares DONAR to related work

section 7

concludes

3.1 Global Replica-Selection Problem

若想提高網路效能，就得以 accurate load distribution 為代價。

Our goal is to minimize this performance penalty

3.2 Distributed Mapping Service

每個 mapping node 各有其負責的 clients

The node maps the client to a replica , and returns the result to that client.

3.2 Distributed Mapping Service

所有 clients 的 traffic, node n 所佔的比例

mapping node n 所有的 traffic 中，從 client c 而來的比例

所有從 client c 而來，經過 mapping node n 的 traffic ， 流入 replica i 的比例 , i.e., ∑i Rnci = 1

3.3 Decentralized Selection Algorithm

optimization decomposition : 藉由 algorithmic iterations, 讓 local decisions converge to the global optimum.

global performance local client performance

The optimization of local performance.

每一個 mapping node 以 client population ＆ replica 上的 load term 來 optimizes local performance

the unit price of violating the constraint.

True proportion of requests directed to replica i

local replica selection

某特定 mapping node n, 將其所負責之所有 clients 的 traffic 引到 replicas 所需的 performance penalty.

loadn = load, n∀；超出預期流量的罰款。

( 以一個 mapping node 的視野看世界 )

The core components of the algorithm are the local updates by each mapping node, and the periodic updates of replica prices.

overhead

centralized solution

distributed solution

Each node needs to share its mapping decisions of size and each replica’s price λi needs to be known by each node. This implies messages, each of size computational complexity is of size

每個 mapping node 會有 |N-1| 個 mapping node 的鄰居。他們會告訴此 mapping node 個與replica mapping 有關的消息。

個 replicas 會告訴每一個mapping node 他們的 price

DONAR’s system design

DONAR’s system design

Distribution optimization---tracking requests geographically

A group of similarly

located end-hosts.

Distributed Optimization--Tracking Requests Geographically

Distributed Optimization--Exponentially weighted moving average

Distributed Optimization--Known cost assumption

DONAR’s System Design

Decomposed Local ProblemFor Some Node (n*)

DONAR Algorithm

DONAR Algorithm

DONAR Algorithm

DONAR Algorithm

DONAR Algorithm

Better!

DONAR’s System Design

Protocol-level mechanisms forWide-area replica selection

Data Retrieving Steps

Data Retrieving Steps(cont.)

DONAR’s System Design

Secure Registration and Dynamic Updates

DONAR’s System Design

Distributed Data Storage

CRAQ(Chain Replication with Apportioned Queries)

“while maintaining the strong consistency properties of chain replication, provides lower latency and higher throughput for read operations by supporting apportioned ( 分攤 ) queries: that is, dividing read operations over all nodes in a chain, as opposed to requiring that they all be handled by a single primary node.”

DONAR’s System Design

IP Anycast

Software Architecture

Results: DONAR Curbs Volatility

Results: DONAR Minimizes Distance

Conclusions

Thank You