Maximizing server utilization while meeting critical SLAs via weight-based collocation management

Servers in most data centers are often underutilized due to concerns about SLA violations that may result from resource contention as server utilization increases. This low utilization means that neither the capital investment in the servers nor the power consumed is being used as effectively as it could be. In this paper, we present a novel method for managing the collocation of critical (e.g., user interactive) and non-critical (e.g., batch) workloads on virtualized multicore servers. Unlike previous cap-based solutions, our approach improves server utilization while meeting the SLAs of critical workloads by prioritizing resource access using Linux cgroups weights. Extensive experimental results suggest that the proposed work conserving collocation method is able to utilize a server to nearly 100% while keeping the performance loss of critical workloads within the specified limits.

[1]  Azer Bestavros,et al.  Colocation as a Service: Strategic and Operational Services for Cloud Colocation , 2010, 2010 Ninth IEEE International Symposium on Network Computing and Applications.

[2]  Jordi Torres,et al.  Enabling Resource Sharing between Transactional and Batch Workloads Using Dynamic Application Placement , 2008, Middleware.

[3]  Hyong S. Kim,et al.  SageShift: Managing SLAs for highly consolidated cloud , 2012, 2012 Proceedings IEEE INFOCOM.

[4]  Martin Arlitt,et al.  Improving the efficiency of information collection and analysis in widely-used IT applications , 2011, ICPE '11.

[5]  Xiaoyun Zhu,et al.  1000 Islands: Integrated Capacity and Workload Management for the Next Generation Data Center , 2008, 2008 International Conference on Autonomic Computing.

[6]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines , 2009, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines.

[7]  Mahadev Satyanarayanan,et al.  Quantifying interactive user experience on thin clients , 2006, Computer.

[8]  Prashant J. Shenoy,et al.  Profiling and Modeling Resource Usage of Virtualized Applications , 2008, Middleware.

[9]  Asser N. Tantawi,et al.  An analytical model for multi-tier internet services and its applications , 2005, SIGMETRICS '05.

[10]  Guillaume Pierre,et al.  Wikipedia workload analysis for decentralized hosting , 2009, Comput. Networks.

[11]  Kevin Skadron,et al.  Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations , 2011, 2011 44th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[12]  Peter A. Dinda,et al.  VSched: Mixing Batch And Interactive Virtual Machines Using Periodic Real-time Scheduling , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[13]  Bernd Freisleben,et al.  Utility-based resource allocation for virtual machines in Cloud computing , 2011, 2011 IEEE Symposium on Computers and Communications (ISCC).

[14]  Aman Kansal,et al.  Q-clouds: managing performance interference effects for QoS-aware clouds , 2010, EuroSys '10.

[15]  Manish Marwah,et al.  Minimizing data center SLA violations and power consumption via hybrid resource provisioning , 2011, 2011 International Green Computing Conference and Workshops.

[16]  Jerome A. Rolia,et al.  A capacity management service for resource pools , 2005, WOSP '05.

[17]  Zhenhua Liu,et al.  Towards the design and operation of net-zero energy data centers , 2012, 13th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[18]  Adit Ranadive,et al.  Active CoordinaTion (ACT) - toward effectively managing virtualized multicore clouds , 2008, 2008 IEEE International Conference on Cluster Computing.

[19]  Martin F. Arlitt,et al.  Facebook Meets the Virtualized Enterprise , 2008, 2008 12th International IEEE Enterprise Distributed Object Computing Conference.

[20]  Xiaoyun Zhu,et al.  AppRAISE: application-level performance management in virtualized server environments , 2009, IEEE Transactions on Network and Service Management.

[21]  Kenji Funaoka,et al.  Work-Conserving Optimal Real-Time Scheduling on Multiprocessors , 2008, 2008 Euromicro Conference on Real-Time Systems.

[22]  Kai Li,et al.  The PARSEC benchmark suite: Characterization and architectural implications , 2008, 2008 International Conference on Parallel Architectures and Compilation Techniques (PACT).

[23]  Simon A. Dobson,et al.  A Fine-Grained Model for Adaptive On-Demand Provisioning of CPU Shares in Data Centers , 2008, IWSOS.

[24]  Hyong S. Kim,et al.  How to tame your VMs: an automated control system for virtualized services , 2010 .

[25]  Jordi Torres,et al.  GreenSlot: Scheduling energy consumption in green datacenters , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[26]  Yixin Diao,et al.  Closed loop performance management for service delivery systems , 2012, 2012 IEEE Network Operations and Management Symposium.

[27]  Diwakar Krishnamurthy,et al.  Web workload generation challenges – an empirical investigation , 2012, Softw. Pract. Exp..

[28]  Arun Venkataramani,et al.  Black-box and Gray-box Strategies for Virtual Machine Migration , 2007, NSDI.