• Software is eating the world, need to release features faster (and more of them) than your competitor • Mobile devices, constraint devices, IoT • Distributed applications
int socket(int domain, int type, int protocol); int bind(int sockfd, struct sockaddr *my_addr, int addrlen); int connect(int sockfd, struct sockaddr *serv_addr, int addrlen); int listen(int sockfd, int backlog); ping netstat ethtool traceroute ifconfig
14 PHYSICAL (x86) VIRTUAL HYPERSCALE MAINFRAME SERVER VIRTUAL MACHINE PARTITION (LPAR) UNIT OF INTERACTION " ERP, CRM, PRODUCTIVITY, MAIL & WEB SERVER " LINUX, WINDOWS " DATA / TRANSACTION PROCESSING " UNIX, IBM OS/360 DEFINITIVE APPS AND OS " ERP, CRM, PRODUCTIVITY, MAIL & WEB SERVER " HYPERVISOR + GUEST OS " BIG DATA, INTERNET OF THINGS, MOBILE APPS " ??? ??? DATACENTER " BIG DATA, INTERNET OF THINGS, MOBILE APPS " DATACENTER OPERATING SYSTEM (DCOS)
APPROACH 15 • Many “snowflakes” • Management nightmare • Lengthy cycles to deploy code • Low utilization Mesosphere DCOS DCOS APPROACH Container Apps (All) Big Data Analytics (All) PaaS (All) Stateful Service (All) • High performance and resource isolation • Easy scalability and multi-tenancy • Fault tolerant and highly available • Highly efficient with highest utilization proven in mission- critical deployments on-prem or in the cloud TRADITIONAL APPROACH PaaS 1 Container App 1 Big Data Analytics 1 Big Data Analytics 2 PaaS 2 Container App 2 Stateful Service 1 Stateful Service 2 workload portability
• Run stateless services (Web server, app server, etc.) and Big Data services like Kafka, Spark, or Cassandra together on one cluster • Dynamic partitioning of your cluster, depending on your business requirements • Increased utilization (10% → 80%++)
• To tackle SDDC challenges, a new approach is necessary: a distributed operating system • Portable workloads across environments (on-premises/hybrid/public cloud) are possible • Container operations is becoming mainstream