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Resource discovery enables applications deployed in heterogeneous large-scale distributed systems to find resources that meet QoS requirements. In particular, most applications need resource requirements to be satisfied simultaneously for multiple resources (such as CPU, memory and network bandwidth). Due to dynamism in many large-scale systems, providing statistical guarantees on such requirements is important to avoid application failures and overheads. However, existing techniques either provide guarantees only for individual resources, or take a static or memoryless approach along multiple dimensions. The authors present HiDRA, a scalable resource discovery technique providing statistical guarantees for resource requirements spanning multiple dimensions simultaneously.
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