Query: The offloading of inference tasks to more capable cloud servers not only enhances the computational
capabilities of these devices but also addresses critical performance metrics such as latency reduction,
energy efficiency, and privacy preservation. The existing literature predominantly focuses on develop-
ing optimal offloading strategies tailored to achieve specific objectives. However, in order to fully
harness the potential of offloadin inference,it is crucial to adopt a holistic approach that intergrates
multiple objectives depending on the characteristics of the offloading scenario. Hence, before we
can proceed to discuss the existing offloading strategies, it is essential to first understand the critical
performance metrics that are considered when designing an effective offloading strategy.
In the context of offloading inference, evaluating effectiveness hinges on several critical performance
metrics, including resource optimization, latency reduction, integrity and privacy preservation, and
energy consumption minimization. Resource optimization aims to maximize the efficiency and
effectiveness of computational resources by strategically determining the allocation and execution of
tasks. this dynamic approach ensures that inferencing task are performed in an optimal paradigm such
that the throughput is maximized while adapts to varying network condition and device workload.
In contrast, latency reduction focuses on minimizing the time required to complete inferencing
requests. This encompasses the entire duration from the initiation of an inference request to the
acquisition of the desired results. Latency can be further dissected into two primary categories:
data transfer latency and processing latency. Data transfer latency involves the delays experienced
during the transmission of data from the source node to the destination node. This includes several
components: weight transfer latency, which pertains to the time taken to transfer model weights;
network latency, which arises from delays due to network congestion or geographical distance; and
intermediate data transfer latency. Additionally, processing latency contributes to the overall task
latency and encompasses computation overhead, as well as scheduling and coordination delays
associated with managing inference tasks. Together, these components contribute to the temporal
challenges associated with offloading inference.
Furthermore, preserving the integrity and privacy of the inference process is a significant objective in
the realm of offloading inference. Given that offloading typically involves transmitting users’ confi-
dential data to untrusted third-party cloud services for processing, ensuring data security throughout
this process is therefore paramount not only in maintaining the user trust but also in complying with
the legal and regulatory requirements. Lastly, energy consumption is a crucial consideration when
discussing offloading inference, particularly in edge devices such as mobile phones and virtual reality
headsets. Minimizing energy consumption not only enhances operational efficiency but also extends
device longevity. The energy consumption associated with offloading can be dissected into various
components: the energy cost incurred from transmitting data through communication channels and
the energy cost required for the inference process itself.
In summary, addressing these performance metrics is essential for optimizing offloading inference
processes in machine learning applications, ensuring that they are efficient, responsive, secure, and
sustainable.
Subject: computer science
Intent: Other