Priya Sehgal, Kaladhar Voruganti, and Rajesh Sundaram.
In this paper we present an SLO based resource management algorithm that controls the amount of SSD given to a particular workload.
In the past storage vendors used different types of storage depending upon the type of workload. For example, they used Solid State Drives (SSDs) or FC hard disks (HDD) for online transaction, while SATA for archival type workloads. However, recently many storage vendors are designing hybrid SSD/HDD based systems that can satisfy multiple service level objectives (SLOs) of different workloads all placed together in one storage box, at better cost points. The combination is achieved by using SSDs as a read-write cache while HDD as a permanent store. In this paper we present an SLO based resource management algorithm that controls the amount of SSD given to a particular workload. This algorithm solves following problems: 1) it ensures that workloads do not interfere with each other 2) it ensure that we do not overprovision (cost wise) the amount of SSD allocated to a workload to satisfy its SLO (latency requirement) and 3) dynamically adjust SSD allocated in light of changing workload characteristics (i.e., provide only required amount of SSD). We have implemented our algorithm in a prototype Hybrid Store, and have tested its efficacy using many real workloads. Our algorithm satisfies latency SLOs almost always by utilizing close to optimal amount of SSD and saving 6-50% of SSD space compared to the naïve algorithm
In Proceedings of the IEEE Conference on Mass Storage Systems and Technologies 2012 (MSST’12)
- The author’s version of the paper is attached to this posting, please observe the following copyright:
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L.N. Bairavasundaram, G. Soundararajan, V. Mathur, K. Voruganti, and S. Kleiman.
This paper investigates the reasons for slow adoption of SLOs and discusses ideas using SLOs that can truly simplify storage management.
Literature is rife with compelling ideas on simplifying storage management using service-level objectives (SLOs). However, very few of these ideas have actually been productized, despite the fact that many of the original ideas came from industry and were developed more than a decade ago. While many good research ideas do not become products, in this case, we believe that there are important reasons why adoption has been slow.
In Proceedings of the USENIX Workshop on Hot Topics in Storage and File Systems 2011 (HotStorage ’11)
- A copy of the paper is attached to this posting.
- A video of the talk from HotStorage ’11 is also available.
Automated Workload Mapping and SLO Creation
The use of consolidated storage systems requires deep understanding of performance requirements as well as suitable tools to manage such requirements for configuring such systems. As advanced features such as Quality-of-Service (QoS) are introduced, most customers will have difficulties to configure these features. This is mainly due to the fact, that state-of-the-art storage products integrate various kinds of devices and offer customers several interfaces to seamlessly access file system space in their infrastructure. Common features include high availability, overbooking, de-duplication, and even support for configuring priorities for achieving desired levels of Quality-of-Service for individual workloads sharing the same system. This results in multiple QoS criteria to be taken in account and to be optimized in order to guarantee proper operation of the consolidated storage environment.
However, the business interest does not lie in mere storage management, but in fulfilling the requirements of applications and higher-level services, from which it is in general difficult to deduce specific storage QoS parameters. Here, this fellowship proposal is aiming at creating one essential pillar linking business objectives and storage-level QoS parameters through an automated mapping approach. Instead of setting priorities on certain parameters, we propose research and development tasks for describing the desired Quality-of-Service at a higher level (called Service Level Objectives) and automatically map those to storage requirements including the configuration parameters necessary to configure the storage system. This approach simplifies the configuration of the system and also allows administrators more easily to verify whether the target desired QoS level is has been achieved.
This research work will create suitable application models based on workload analyses of typical business applications. As a result, tools will be provided which allow workload modeling on this application level and to automatically create suitable storage QoS/SLO configurations for this workload. The fellowship will, in addition, cover the verification of the models through simulation and testing on real systems. Based on these results, adaption mechanism will be evaluated to dynamically verify whether the workload for the forecasted application setting fits the original modeling and to propose or initiate changes to the QoS/SLO configuration of storage systems.
Feedback Control for Elastic Cloud Storage
Within cloud-based infrastructures, many applications can share a set of storage resources, and each application has its own service level objective that should be satisfied within this environment. As workloads change and applications are started, stopped, or moved, the load placed on the storage system changes. The storage system needs to automatically respond to these load changes by adjusting where data is stored and how it is serviced in order to continue to efficiently meet each application’s SLO.
This project focuses on performance control for storage-intensive workloads in such a cloud environment. Taking a control-theoretic approach, sensors placed throughout the system can monitor the current performance of various subsystems, and actuators can be used to tune the storage system. One goal of the project is to create control solutions and policies that are modular and non-intrusive, minimizing their assumptions about the system’s internal structure and behavior, including other resource management functions.