An emerging trend in Internet of Things (IoT) applications is to move the computation (cyber) closer to the
source of the data (physical). This paradigm is often referred to as edge computing. If edge resources are pooled
together they can be used as decentralized shared resources for IoT applications, providing increased capacity
to scale up computations and minimize end-to-end latency. Managing applications on these edge resources is
hard, however, due to their remote, distributed, and (possibly) dynamic nature, which necessitates autonomous
management mechanisms that facilitate application deployment, failure avoidance, failure management, and
incremental updates. To address these needs, we present CHARIOT, which is orchestration middleware capable
of autonomously managing IoT systems consisting of edge resources and applications.
CHARIOT implements a three-layer architecture. The topmost layer comprises a system description
language, the middle layer comprises a persistent data storage layer and the corresponding schema to store
system information, and the bottom layer comprises a management engine that uses information stored
persistently to formulate constraints that encode system properties and requirements, thereby enabling the
use of Satisfiability Modulo Theories (SMT) solvers to compute optimal system (re)configurations dynamically
at runtime. This paper describes the structure and functionality of CHARIOT and evaluates its efficacy as the
basis for a smart parking system case study that uses sensors to manage parking spaces
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DeepPM: Efficient Power Management in Edge Data Centers using Energy Storage
With the rapid development of the Internet of Things (IoT), computational workloads are gradually moving toward the internet edge for low latency. Due to significant workload fluctuations, edge data centers built in distributed locations suffer from resource underutilization and requires capacity underprovisioning to avoid wasting capital investment. The workload fluctuations, however, also make edge data centers more suitable for battery-assisted power management to counter the performance impact due to underprovisioning. In particular, the workload fluctuations allow the battery to be frequently recharged and made available for temporary capacity boosts. But, using batteries can overload the data center cooling system which is designed with a matching capacity of the power system. In this paper, we design a novel power management solution, DeepPM, that exploits the UPS battery and cold air inside the edge data center as energy storage to boost the performance. DeepPM uses deep reinforcement learning (DRL) to learn the data center thermal behavior online in a model-free manner and uses it on-the-fly to determine power allocation for optimum latency performance without overheating the data center. Our evaluation shows that DeepPM can improve latency performance by more than 50% compared to a power capping baseline while the server inlet temperature remains within safe operating limits (e.g., 32°C).
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- NSF-PAR ID:
- 10295689
- Date Published:
- Journal Name:
- 2020 IEEE 13th International Conference on Cloud Computing (CLOUD)
- Page Range / eLocation ID:
- 370 to 379
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CLOUD49709.2020.00058
