• Publications
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CloudScale: elastic resource scaling for multi-tenant cloud systems
TLDR
We present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures with low resource and energy cost. Expand
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AGILE: Elastic Distributed Resource Scaling for Infrastructure-as-a-Service
TLDR
Dynamically adjusting the number of virtual machines (VMs) assigned to a cloud application to keep up with load changes and interference from other uses. Expand
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QoS-assured service composition in managed service overlay networks
TLDR
We present QUEST, a QoS assUred composEable Service infrasTructure, which can provide both QoS assurances under multiple QoS constraints, and load balancing in SON. Expand
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PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems
TLDR
We present a novel Predictive Performance Anomaly Prevention (PREPARE) system that provides automatic performance anomaly prevention for virtualized cloud computing infrastructures. Expand
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Adaptive offloading for pervasive computing
TLDR
We present an adaptive offloading system for pervasive computing, which includes two key parts: a distributed offloading platform and an offloading inference engine. Expand
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UBL: unsupervised behavior learning for predicting performance anomalies in virtualized cloud systems
TLDR
We present an Unsupervised Behavior Learning (UBL) system for IaaS cloud computing infrastructures. Expand
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SecureMR: A Service Integrity Assurance Framework for MapReduce
TLDR
In this paper, we present SecureMR, a practical service integrity assurance framework for MapReduce. Expand
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Towards a distributed platform for resource-constrained devices
TLDR
We propose an approach that allows diverse, resourceconstrained devices to execute the same full version of an application. Expand
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Towards Graph Containment Search and Indexing
TLDR
We use graph fragments such as paths and small subgraphs as indexing features and propose a contrast subgraph-based indexing model for graph search. Expand
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Adaptive offloading inference for delivering applications in pervasive computing environments
TLDR
We have developed an offloading inference engine to adaptively solve two key decision-making problems during runtime offloading: (1) timely triggering of adaptive offloading, and (2) intelligent selection of an application partitioning policy. Expand
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