U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Virtualization in Data Centers Prashant Shenoy

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Data Centers Networked apps run on data centers Data Centers Large clusters of servers Networked storage devices Allocate resources to meet application SLAs Energy costs are large part of operating budget Modern data centers are increasingly virtualized

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Virtualized Data Centers Virtualized data centers Each application runs inside a virtual server One or more VS mapped onto each physical server Application isolation Dynamic resource allocation VM migration Server consolidation

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Research Themes Theme 1: Elastic computing in virtualized clouds Theme 2: Server consolidation and power management Theme 3: Virtualization for High Availability Theme 4: Automated Modeling of Virtualized Data Centers Monitoring large-scale data centers

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Elastic Computing Apps hosted on virtual clouds Dynamic workload fluctuations Elasticity: match resources to application needs Dynamic capacity provisioning Hotspot mitigation VM mechanisms Live migration Fast VM Replicas Sandpiper System [NSDI’07] When, where and how much to allocate? Monitor VM usage from outside Detect hotspots or SLA violations Trigger VM migration / replication

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Consolidation & Energy Management Trend: Number of processor cores per server is increasing Easily run multiple virtual servers per machine Consolidate apps and power down freed-up servers Question: Which apps to co-locate? Memory Buddies: Exploit Content-based page sharing Computer memory fingerprints for each VM Automatically co-locate VMs with “similar” fingerprints to decrease memory footprint Key challenge: large data centers contain hundreds of diverse applications / OS platforms how can we automatically identify consolidation / power saving opportunities? ?

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Cheap High Availability  Goal: Exploit virtualization to build a highly available service  Cheaper than full replication  Needs to tolerate disasters i.e., failure of an entire data center  Exploit VM check-pointing  Asynchronous mirroring of checkpoints and disk state  Router-level failover  Advantage:  Backup VM in doze mode  Less resources  Cheap HA  Overhead is   Can multiplex multiple backup VMs on a cluster

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science VM Modeling and Monitoring Goal: develop models of virtualized applications Predict impact of migrating app to a VM Impact of moving from one server config to another Impact of workload changes Approach: automatic model derivation Use machine learning and statistical techniques to automatically learn models of application behavior Learn dependencies between application workload How does workload at tier i impact tier j ? Learn virtualization overheads Uses regression-based methods, graphical modeling and queuing theory Need to address challenges in large-scale monitoring Adaptive monitoring: dynamically turn sensors on/off

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Summary Virtualization can provide many benefits Focus on mechanisms and policies at server, network level Elasticity in clouds Consolidation and power management High availability Automatic Modeling Downsides of virtualization Increased complexity: one more layer in the stack More “machines” per server -> higher administration cost Security becomes more complex Limited visibility into “LANs” inside a physical server Many open challenges => opportunities for research More at