Towards a Distributed Platform for Resource-Constrained Devices
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Balancing Performance, Energy, and Quality in Pervasive Computing
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Tactics-based remote execution for mobile computing
Proceedings of the 1st international conference on Mobile systems, applications and services
Leveraging smart phones to reduce mobility footprints
Proceedings of the 7th international conference on Mobile systems, applications, and services
The Case for VM-Based Cloudlets in Mobile Computing
IEEE Pervasive Computing
Securing elastic applications on mobile devices for cloud computing
Proceedings of the 2009 ACM workshop on Cloud computing security
Dynamically partitioning applications between weak devices and clouds
Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond
MAUI: making smartphones last longer with code offload
Proceedings of the 8th international conference on Mobile systems, applications, and services
CODES/ISSS '10 Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
CloneCloud: elastic execution between mobile device and cloud
Proceedings of the sixth conference on Computer systems
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Offloading has emerged as a promising idea to allow handheld devices to access intensive applications without performance or energy costs. This could be particularly useful for enterprises seeking to run line-of-business applications on handhelds. However, we must address two practical roadblocks in order to make offloading amenable for enterprises: (i) ensuring data privacy and the use of trusted offloading resources, and (ii) accommodating offload at scale with diverse handheld objectives and compute resource capabilities. We present the design and implementation of an Enterprise-Centric Offloading System (ECOS) which augments prior offloading proposals to address these issues. ECOS uses a logically central controller to opportunistically leverage diverse compute resources, while tightly controlling where specific applications offload depending on privacy, performance, and energy constraints of users and applications. A wide range of experiments using a real prototype establish the effectiveness of our approach.