Performance Isolation in Cloud Storage Systems

Cloud computing enables data centres to provide resource sharing across multiple tenants. This sharing, however, usually comes at a cost in the form of reduced isolation between tenants, which can lead to inconsistent and unpredictable performance. This variability in performance becomes an impediment for clients whose services rely on consistent, responsive performance in cloud environments. The problem is exacerbated for applications that rely on cloud storage systems as performance in these systems is a ffected by disk access times, which often dominate overall request service times for these types of data services. In this thesis we introduce MicroFuge, a new distributed caching and scheduling middleware that provides performance isolation for cloud storage systems. To provide performance isolation, MicroFuge's cache eviction policy is tenant and deadline-aware, which enables the provision of isolation to tenants and ensures that data for queries with more urgent deadlines, which are most likely to be a ffected by competing requests, are less likely to be evicted than data for other queries. MicroFuge also provides simplifi ed, intelligent scheduling in addition to request admission control whose performance model of the underlying storage system will reject requests with deadlines that are unlikely to be satisfi ed. The middleware approach of MicroFuge makes it unique among other systems which provide performance isolation in cloud storage systems. Rather than providing performance isolation for some particular cloud storage system, MicroFuge can be deployed on top of any already deployed storage system without modifying it. Keeping in mind the wide spectrum of cloud storage systems available today, such an approach make MicroFuge very adoptable. In this thesis, we show that MicroFuge can provide signifi cantly better performance isolation between tenants with di fferent latency requirements than Memcached, and with admission control enabled, can ensure that more than certain percentage of requests meet their deadlines

MicroFuge: A Middleware Approach to Providing Performance Isolation in Cloud Storage Systems

Abstract-Most cloud providers improve resource utilization by having multiple tenants share the same resources. However, this comes at the cost of reduced isolation between tenants, which can lead to inconsistent and unpredictable performance. This performance variability is a significant impediment for tenants running services with strict latency deadlines. Providing predictable performance is particularly important for cloud storage systems. The storage system is the performance bottleneck for many cloud-based services and therefore often determines their overall performance characteristics. In this paper, we introduce MicroFuge, a new distributed caching and scheduling middleware that provides performance isolation for cloud storage systems. MicroFuge addresses the performance isolation problem by building an empiricallydriven performance model of the underlying storage system based on measured data. Using this model, MicroFuge reduces deadline misses through adaptive deadline-aware cache eviction, scheduling and load-balancing policies. MicroFuge can also perform early rejection of requests that are unlikely to make their deadlines. Using workloads from the YCSB benchmark on an EC2 deployment, we show that adding MicroFuge to the storage stack substantially reduces the deadline miss rate of a distributed storage system compared to using a deadline oblivious distributed caching middleware such as Memcached

New Mexico State Record, 12-29-1916

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