In the resource-rich environment of data centers most failures can quickly failover to redundant resources. In contrast, failure in edge infrastructures with limited resources might require maintenance personnel to drive to the location in order to fix the problem. The operational cost of these "truck rolls" to locations at the edge infrastructure competes with the operational cost incurred by extra space and power needed for redundant resources at the edge. Computational storage devices with network interfaces can act as network-attached storage servers and offer a new design point for storage systems at the edge. In this paper we hypothesize that a system consisting of a larger number of such small "embedded" storage nodes provides higher availability due to a larger number of failure domains while also saving operational cost in terms of space and power. As evidence for our hypothesis, we compared the possibility of data loss between two different types of storage systems: one is constructed with general-purpose servers, and the other one is constructed with embedded storage nodes. Our results show that the storage system constructed with general-purpose servers has 7 to 20 times higher risk of losing data over the storage system constructed with embedded storage devices. We also compare the two alternatives in terms of power and space using the Media-Based Work Unit (MBWU) that we developed in an earlier paper as a reference point.
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Risk-Aware Application Placement in Mobile Edge Computing Systems: A Learning-based Optimization Approach
In this paper, we address the problem of application placement in MEC systems that takes into account the risk of exceeding the energy budget of the edge servers. We formulate the problem as a chance-constrained program, where the objective is to maximize the total quality of service in the system, while keeping the expected risk of exceeding the edge servers' energy budget within an acceptable threshold. We develop a learning-based method to solve the problem which requires a very small execution time for large size instances. We evaluate the performance of the proposed method by conducting an extensive experimental analysis.
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- Award ID(s):
- 1724227
- NSF-PAR ID:
- 10288187
- Date Published:
- Journal Name:
- 2020 IEEE International Conference on Edge Computing (EDGE)
- Page Range / eLocation ID:
- 83 to 90
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/EDGE50951.2020.00020
