Adaptive Live VM Migration in Share-Nothing IaaS-Clouds with LiveFS

Live migration is a versatile option when it comes to attain load-balancing in IaaS-cloud architectures. Liveness, reliability and conformance to SLAs may all be achieved by moving a VM that creates excessive work from its current physical machine (PM) to a less busy node. Despite its promising features, live migration is an expensive operation in terms of resources. The situation gets further exacerbated when the movement involves PMs working off different file-systems which is often the case in shared-nothing IaaS-cloud infrastructures. In this paper, we suggest an approach that adapts the migration operation based on the I/O activity of the originating-VM. We introduce LiveFS, a FUSE-file system which traps all I/Os and helps determine how to best ship virtual disk segments across PMs in a share-nothing IaaS-cloud. Through prototyping and experimentation, we show that LiveFS can improve the shipment of VMs for diverse types of workloads. In particular, LiveFS succeeds in reducing the Total Migration Time by up to 64% compared to the “pre-copy” live migration technique. Furthermore during migration, we attain up-to 19% less I/O-delay if compared to the “post-copy” livemigration approach

A PROCRUSTEAN APPROACH TO STREAM PROCESSING

The increasing demand for real-time data processing and the constantly growing data volume have contributed to the rapid evolution of Stream Processing Engines (SPEs), which are designed to continuously process data as it arrives. Low operational cost and timely delivery of results are both objectives of paramount importance for SPEs. Given the volatile and uncharted nature of data streams, achieving the aforementioned goals under fixed resources is a challenge. This calls for adaptable SPEs, which can react to fluctuations in processing demands. In the past, three techniques have been developed for improving an SPE’s ability to adapt. Those techniques are classified based on applications’ requirements on exact or approximate results: stream partitioning, and re-partitioning target exact, and load shedding targets approximate processing. Stream partitioning strives to balance load among processors, and previous techniques neglected hidden costs of distributed execution. Load Shedding lowers the accuracy of results by dropping part of the input, and previous techniques did not cope with evolving streams. Stream re-partitioning is used to reconfigure execution while processing takes place, and previous techniques did not fully utilize window semantics. In this dissertation, we put stream processing in a procrustean bed, in terms of the manner and the degree that processing takes place. To this end, we present new approaches, for window-based aggregate operators, which are applicable to both exact and approximate stream processing in modern SPEs. Our stream partitioning, re-partitioning, and load shedding solutions offer improvements in performance and accuracy on real-world data by exploiting the semantics of both data and operations. In addition, we present SPEAr, the design of an SPE that accelerates processing by delivering approximate results with accuracy guarantees and avoiding unnecessary load. Finally, we contribute a hybrid technique, ShedPart, which can further improve load balance and performance of an SPE