Comparison of Service Description and Composition for Complex 3-tier Cloud-based Services

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Wesley Goodwin
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1 Date: Place: Dalian, China Comparison of Service Description and Composition for Complex 3-tier Cloud-based Services Asia Pacific Advanced Network (APAN) Cloud Working Group Meeting 2017 Moonjoong Kang and JongWon Kim Networked Computing Systems Laboratory (NetCS Lab) School of Electrical Engineering and Computer Science (EECS) Gwangju Institute of Science and Technology (GIST) Gwangju, South Korea

2 Microservice Architecture With the advent of cloud-first computing era, the trend is changing : Monolithic Architecture à Microservice Architecture Dedicated Cluster à Cloud-based Shared Infrastructure Microservices architecture (MSA) Divides whole service into stitched multiple component services, which may called with a term "function" Dynamic agility, easy and flexible maintenance, cost effectiveness due to shared resource pooling Service composition for MSA-based application services is done by Service Function Chaining (SFC)

3 Step-by-Step SFC-based Service Description & Composition Functions ID, Codes, Configs, Resource (box) req. Service Function & Resource Graph 0 Prerequisite: Service Description 1 Resource allocation 2 Resource Inter-connect 3 Function Deployment 4 Function Stitching

4 SFC-based Approaches for Service Description & Composition Container-based MSA-SFC Web-based SaaS OpenStack Heat-based HOT(Heat Orchestration Template)-SFC Web-based and cloud infrastructure-integrated SaaS IETF(Internet Engineering Task Force) NSH(Network Service Header)-SFC Network infrastructure-focused SaaS

5 SFC-based Approaches for Service Description & Composition Type Container-based MSA-SFC OpenStack Heat-based HOT-SFC IETF NSH-SFC Application services Web-based SaaS Web-based and cloud infrastructure-integrated SaaS Network infrastructure-focused SaaS Function Abstraction Fully abstracted functions only May include non-abstract functions 1-to-1 matched to specific machines Encapsulation Transparent to application services All functions need explicit NSH encapsulation or service function proxy Flow tracking Not available NSH includes whole path info for packets Service function stitching No implementation required for high availability Needs its own implementation for high availability No implementation required for high availability

6 Container-based MSA-SFC We chose Container-based MSA-SFC for deeper performance comparison among them Reason Transparent encapsulation to services Services become independent to the SFC approach beneath it Automated function stitching via KV-based storage No service level implementation required for High Availability Scaling, Load-balancing is provided at SFC tool level

7 1 Resource allocation Container-based MSA-SFC Service Description & Composition 2 Resource Inter-connect 3 Function Deployment 4 Function Stitching Docker Swarm Kubernetes 5x Scaled Protected Zone Cloud Resource Pool 3x Scaled 3x Scaled Service Function Chaining Request 3x Scaled 0 Service Description Protected Zone Firewall Web App DB

8 Docker Swarm & Kubernetes Both are Container-based MSA-SFC approach Service Description String-based functions ID ID-based function dependency representation Required or optional resource amount Allocate resources as Docker Container Dedicated and isolated overlay network to interconnect resources Key-Value storage to manage function stitching and high availability such as scaling, load balancing

9 Difference between Docker Swarm & Kubernetes Docker Swarm Tightly integrated with extended Docker engine All internal functions integrated into Docker engine Provides its own native overlay network driver Kubernetes Only uses Docker Container aspect from Docker engine Loosely Wrapping it with other open-source SW Even Kubernetes itself runs in Docker containers Needs 3rd party network addon to work properly

10 Experiment Environment and Configuration 1 Manager VM + 2 Worker VM Site 3 Site 2 Each VM configuration : - 4 vcpu cores - 8GB RAM - 40GB Storage Site 1 Sock shop Service Kubernetes Sock shop Service Docker Swarm Load test simulating 3 user, requests Collecting resource metrics Primary Cloud Each site contains a box with: - Xeon E5 2640v3-24GB DDR4 ECC RAM - 400GB Intel 750 NVMe SSD Secondary Cloud 1 control box (same with primary cloud s) 4 compute box with: - Xeon D GB DDR4 ECC RAM - 500GB Samsung SSD

11 Experiment Environment and Configuration Kubernetes and Docker Swarm is separated to each different sites to minimize cross-interference Container Networking Kubernetes : Weave Net network addon Docker Swarm : Native network driver Used Sock Shop Microservice from Weaveworks Complex MSA with 14 functions 1 Web, 2 App function is scaled to 3x for Load Balancing Firewall function protecting other functions 2 Load test VMs 6 users, requests to both composed services from each tool at the same time Collected metrics via Intel Snap agent in VMs Compute/Stoarge/Networking resource usages CPU/RAM Utilization, Ethernet, Stoarge IO Stored in Time-Series InfluxDB

Experiment Results Manager CPU Util % Worker CPU Util % Manager RAM Util % Worker RAM Util % Manager Net Traffic Worker Net Traffic Manager Disk I/O Worker Disk I/O Tool Node Type Value CPU Active %

12 Experiment Results Manager CPU Util % Worker CPU Util % Manager RAM Util % Worker RAM Util % Manager Net Traffic Worker Net Traffic Manager Disk I/O Worker Disk I/O Tool Node Type Value CPU Active % RAM Util. % Ethernet Bytes Disk Bytes Received Sent Read Written Docker Swarm Manager Worker μ 0.86 % % σ μ % % σ μ 4.67 % % Manager σ Kubernetes μ % % Worker σ μ = Average, σ = Standard Deviation. CPU% Scale : 1 Core Full Util. set to 100%, RAM% Scale : A VM s Full Util. set to 100%.

13 Experiment Results Overall resource usage shows Docker Swarm is more effective than Kubernetes for the same workload Notable differences On manager node, Docker Swarm uses 14.68% more RAM than Kubernetes On worker node, Kubernetes shows about 30.22MB higher than Docker Swarm with high deviation Docker Swarm shows almost no bytes written

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