QoS-Aware Service Selection in Geographically Distributed Clouds
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1 WiMAN 2013 QoS-Aware Service Selection in Geographically Distributed Clouds Xin Li *, Jie Wu, and Sanglu Lu * * State Key Laboratory for Novel Software Technology, Nanjing University, China Department of Computer and Information Science, Temple University, USA ICCCN
2 Outline Background and Motivation Problem Statement Our Approach Evaluation 2013/8/5 2
3 Background Cloud service More and more services are accessible with the growth of cloud computing. Functional simplicity It is functional limited for a single service. How can we effectively utilize the plentiful services? Service composition An effective way to utilize the plentiful services, and compensates for the simplicity of single service. 2013/8/5 3
4 Service Composition To accomplish an integrated task, many basic processing units are composed to achieve the complicated job. Service (functional) component The basic processing unit An example of service composition for multimedia delivery 2013/8/5 4
5 Functional Graph functional edge 2013/8/5 5
6 Motivation The opportunity For service provider: rent the existing single services from various clouds, and provide some complex integrated services to users. The challenge Functional equivalent service instances Service instance: an implementation of a service component Same function but different features, e.g. execution time Cost limitation The quality of service instances 2013/8/5 6
7 The Problem Problem description Select a set of service instances such that the composed service offers the best quality under given functional graph. An instantiation for the functional graph 2013/8/5 7
8 The Problem F 1 F 2 F 3 cloud (instance) user We are here! 2013/8/5 8
9 Instantiation of Functional Graph functional component functional edge abstract level concrete level data flow network link service instance 2013/8/5 9
10 Abstract Level & Concrete Level 2013/8/5 10
11 Quality of Service Instance Set How to judge the quality of a set of instances? Quality of an instance set QoS (quality of service) Objective: best quality The minimal expected response time for all users Network model How to measure the network latency between two locations? Network Coordinate System (NCS) 2013/8/5 11
12 Network Coordinate System (NCS) 2013/8/5 12
13 Rethink the Problem Objective Minimize the response time Network latency Execution time However, the response time varies User location For users located on different locations, the network latency varies. Functional path selection Given functional graph, there will be multiple functional paths, the response time varies as the path selection. For some user, there is a probability distribution to determine which path will be selected, according to the context. 2013/8/5 13
14 Delay of Data Flow A data flow is an instantiation of a functional path. The delay of a data flow consists of two parts Network latency For all of the network links (functional edges) Execution time For all service instances (functional component) 2013/8/5 14
15 Case for One Data Flow 2013/8/5 15
16 Response Time (1) 2013/8/5 16
17 Response Time (2) The data flow with minimal delay will be selected to execute the service for given user. 2013/8/5 17
18 Response Time (3) 2013/8/5 18
19 Quality of Service Instance Set minimal. 2013/8/5 19
20 Algorithms Simple case One instance for each functional component General case Multiple instances for each functional component Limitation for the total number of instances 2013/8/5 20
21 Simple Case 2013/8/5 21
22 Simple Case - Algorithm Initial (Terminal) component selection Selected in greedy manner, according to the user distribution. The facility location problem. Source Destination (flow selection) From initial component to terminal component The shortest part problem Each instance has an executing time 2013/8/5 22
23 General Case 2013/8/5 23
24 General Case - Algorithm 2013/8/5 24
25 Evaluation Simulation Setup Random algorithm 2013/8/5 25
26 Result Simple Case 2013/8/5 26
27 Result General Case The voting algorithm and the random algorithm have the same result, when all instances are selected. (red line) 2013/8/5 27
28 Conclusion Service selection A set of service instances with guaranteed quality. Quality of selected instances Network latency Data flow delay Response time Algorithm Simple case General case 2013/8/5 28
29 QoS-Aware Service Selection in Geographically Distributed Clouds Thanks! Xin Li *, Jie Wu, and Sanglu Lu * * State Key Laboratory for Novel Software Technology, Nanjing University, China Department of Computer and Information Science, Temple University, USA ICCCN
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