Exploiting Cost - Performance Tradeoffs for Modern Cloud Systems By

The trade-off between cost and performance is a fundamental challenge for modern cloud systems. This thesis explores cost-performance tradeoffs for three types of systems that permeate today’s clouds, namely (1) storage, (2) virtualization, and (3) computation. A distributed key-value storage system must choose between the cost of keeping replicas synchronized (consistency) and performance (latency) or read/write operations. A cloud-based disaster recovery system can reduce the cost of managing a group of VMs as a single unit for recovery by implementing this abstraction in software (instead of hardware) at the risk of impacting application availability performance. As another example, run-time performance of graph analytics jobs sharing a multi-tenant cluster can be made better by trading of the cost of replication of the input graph dataset stored in the associated distributed file system. Today cloud system providers have to manually tune the system to meet desired trade-offs. This can be challenging since the optimal trade-off between cost and performance may vary depending on network and workload conditions. Thus our hypothesis is that it is feasible to imbue a wide variety of cloud systems with adaptive and opportunistic mechanisms to efficiently navigate the cost-performance tradeoff space to meet desired tradeoffs. The types of cloud systems considered in this thesis include key-value stores, cloud-based disaster recovery systems, and multi-tenant graph computation engines. Our first contribution, PCAP is an adaptive distributed storage system. The foundation of the PCAP system is a probabilistic variation of the classical CAP theorem, which quantifies the (un-)achievable envelope of probabilistic consistency and latency under different network conditions characterized by a probabilistic partition model. Our PCAP system proposes adaptive mechanisms for tuning control knobs to meet desired consistency-latency tradeoffs expressed in terms in service-level agreements.