59,348 research outputs found
CloudScope: diagnosing and managing performance interference in multi-tenant clouds
© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%
Extending Eventually Consistent Cloud Databases for Enforcing Numeric Invariants
Geo-replicated databases often operate under the principle of eventual
consistency to offer high-availability with low latency on a simple key/value
store abstraction. Recently, some have adopted commutative data types to
provide seamless reconciliation for special purpose data types, such as
counters. Despite this, the inability to enforce numeric invariants across all
replicas still remains a key shortcoming of relying on the limited guarantees
of eventual consistency storage. We present a new replicated data type, called
bounded counter, which adds support for numeric invariants to eventually
consistent geo-replicated databases. We describe how this can be implemented on
top of existing cloud stores without modifying them, using Riak as an example.
Our approach adapts ideas from escrow transactions to devise a solution that is
decentralized, fault-tolerant and fast. Our evaluation shows much lower latency
and better scalability than the traditional approach of using strong
consistency to enforce numeric invariants, thus alleviating the tension between
consistency and availability
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