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2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
ISBN: 978-1-5386-5815-4
pp: 311-320
ABSTRACT
With server consolidation, latency-critical and batch workloads are collocated on the same physical servers. The resource manager dynamically allocates the hardware resources to the workloads to maximize the overall throughput while providing the service-level objective (SLO) guarantees for the latency-critical workloads. As the hardware resources are dynamically allocated across the workloads on the same physical server, information leakage can be established, making them vulnerable to micro-architectural side-channel attacks. Despite extensive prior works, it remains unexplored to investigate the efficient design and implementation of the dynamic resource management system that maximizes resource efficiency without compromising the SLO and security guarantees. To bridge this gap, this work proposes SDCP, secure and dynamic core and cache partitioning for safe and efficient server consolidation. In line with the state-of-the-art dynamic server consolidation techniques, SDCP dynamically allocates the hardware resources (i.e., cores and caches) to maximize the resource utilization with the SLO guarantees. In contrast to the existing techniques, however, SDCP dynamically sanitizes the hardware resources to ensure that no micro-architectural side channel is established between different security domains. Our experimental results demonstrate that SDCP provides high resource sanitization quality, incurs small performance overheads, and achieves high resource efficiency with the SLO and security guarantees.
INDEX TERMS
cache storage, computer centres, cryptography, file servers, network servers, resource allocation, virtualisation
CITATION

M. Han, S. Yu and W. Baek, "Secure and Dynamic Core and Cache Partitioning for Safe and Efficient Server Consolidation," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 311-320.
doi:10.1109/CCGRID.2018.00046
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