In the last decade, the landscape of storage hardware and software has changed considerably. Storage hardware has diversified from hard disk drives and solid state drives to include persistent memory (PMEM) devices such as phase change memory (PCM) and Flash-backed DRAM. On the software side, the increasing adoption of cloud services for building and deploying consumer and enterprise applications is driving the use of cloud storage services. Cloud providers have responded by providing a plethora of choices of storage services, each of which have unique performance characteristics and pricing. We argue this variety represents an opportunity for modern storage systems, and it can be leveraged to improve operational costs of the systems.
We propose that storage tiering is an effective technique for balancing operational or de- ployment costs and performance in such modern storage systems. We demonstrate this via three key techniques. First, THMCache, which leverages tiering to conserve the lifetime of PMEM devices, hence saving hardware upgrade costs. Second, CAST, which leverages tiering between multiple types of cloud storage to deliver higher utility (i.e. performance per unit of cost) for cloud tenants. Third, we propose a dynamic pricing scheme for cloud storage services, which leverages tiering to increase the cloud provider's profit or offset their management costs. / Master of Science / Storage and retrival of data is one of the key functions of any computer system. Improvements in hardware and software related to data storage can help computer users store (a) store the data faster, which makes for overall faster performance; and (b) increase the storage capacity, which helps store the increasing amount of data generated by modern computer users. Typically, most computers are equipped with either a hard disk drive (HDD) or, the newer and faster, solid state drive (SSD) for data storage. In the last decade however, the landscape of data storage hardware and software has advanced considerably. On the hardware side, several hardware makers are introducing persistent memory (PMEM) devices, which provide very high speed, high capacity storage at reasonable price points. On the software side, the increasing adoption of cloud services by software developers that are building and operating consumer and enterprise applications is driving the use of cloud storage services. These services allow the developers to store a large amount of data without having to manage any physical hardware, paying for the service on a usage-based pricing structure. However, every application’s speed and capacity needs are not the same; hence, cloud service providers have responded by providing a plethora of choices of storage services, each of which have unique performance characteristics and pricing. We argue this variety represents an opportunity for modern storage systems, and it can be leveraged to improve the operating costs of the systems.
Storage tiering is a classical technique that involves partitioning the stored data and placing each partition in a different storage device. This lets the applications use mulitple devices at once, taking advantage of each’s sterngths and mitigating their weaknesses. We propose that storage tiering is a relevant and effective technique for balancing operational or deployment costs and performance in modern storage systems such as PMEM devices and cloud storage services. We demonstrate this via three key techniques. First, THMCACHE, which leverages tiering between multiple types of storage hardware to conserve the lifetime of PMEM devices, hence saving hardware upgrade costs. Second, CAST, which leverages tiering between multiple types of cloud storage services to deliver higher utility (i.e. performance per unit of cost) for software developers using these services. Third, we propose a dynamic pricing scheme for cloud storage services, which leverages tiering between multiple cloud storage services to increase the cloud service provider’s profit or offset their management costs.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/79142 |
| Date | 19 September 2017 |
| Creators | Iqbal, Muhammad Safdar |
| Contributors | Computer Science, Butt, Ali R., Kafura, Dennis G., Tilevich, Eli |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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