Energy-aware auto-scaling algorithms for Cassandra virtual data centers

Abstract

Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient autoscaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energyaware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

PASCAL: An architecture for proactive auto-scaling of distributed services

“…Their solution scales a sharded MongoDB cluster reactively, while we operate proactively. Finally, Casalicchio et al proposed an energy-aware autoscaling solution for Cassandra [45] where they evaluate and compare three heuristics under a model similar to ours, with homogeneous machines and assuming a CPU-bound workload. Their results show that horizontal scaling of Cassandra is slow due to the amount of data to move.…”

RHAS: robust hybrid auto-scaling for web applications in cloud computing

“…IaaS feature of cloud computing allows us to scale-up or scale-down the resources on-demand to get the desired response-time for service level agreement (SLA) fulfillment [9]. In a single-tier application, the response time prediction is sufficient to scale the resources [10]. However, the multi-tier applications workload is complex in nature due to the multi-tier architecture and flash traffic [26].…”