As scaling of conventional memory devices has stalled, many high-end computing systems have begun to incorporate alternative memory technologies to meet performance goals. Since these technologies present distinct advantages and tradeoffs compared to conventional DDR* SDRAM, such as higher bandwidth with lower capacity or vice versa, they are typically packaged alongside conventional SDRAM in a heterogeneous memory architecture. To utilize the different types of memory efficiently, new data management strategies are needed to match application usage to the best available memory technology. However, current proposals for managing heterogeneous memories are limited, because they either (1) do not consider high-level application behavior when assigning data to different types of memory or (2) require separate program execution (with a representative input) to collect information about how the application uses memory resources. This work presents a new data management toolset to address the limitations of existing approaches for managing complex memories. It extends the application runtime layer with automated monitoring and management routines that assign application data to the best tier of memory based on previous usage, without any need for source code modification or a separate profiling run. It evaluates this approach on a state-of-the-art server platform with both conventional DDR4 SDRAM and non-volatile Intel Optane DC memory, using both memory-intensive high-performance computing (HPC) applications as well as standard benchmarks. Overall, the results show that this approach improves program performance significantly compared to a standard unguided approach across a variety of workloads and system configurations. The HPC applications exhibit the largest benefits, with speedups ranging from 1.4× to 7× in the best cases. Additionally, we show that this approach achieves similar performance as a comparable offline profiling-based approach after a short startup period, without requiring separate program execution or offline analysis steps.
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CHARIOT: Goal-driven Orchestration Middleware for Resilient IoT Systems
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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- Award ID(s):
- 1528799
- NSF-PAR ID:
- 10054148
- Date Published:
- Journal Name:
- ACM Transactions on Cyber-Physical Systems
- 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/10.1145/3134844
