33,666 research outputs found
Dynamic Prioritization and Adaptive Scheduling using Deep Deterministic Policy Gradient for Deploying Microservice-based VNFs
The Network Function Virtualization (NFV)-Resource Allocation (RA) problem is
NP-Hard. Traditional deployment methods revealed the existence of a starvation
problem, which the researchers failed to recognize. Basically, starvation here,
means the longer waiting times and eventual rejection of low-priority services
due to a 'time out'. The contribution of this work is threefold: a) explain the
existence of the starvation problem in the existing methods and their
drawbacks, b) introduce 'Adaptive Scheduling' (AdSch) which is an 'intelligent
scheduling' scheme using a three-factor approach (priority, threshold waiting
time, and reliability), which proves to be more reasonable than traditional
methods solely based on priority, and c) a 'Dynamic Prioritization' (DyPr),
allocation method is also proposed for unseen services and the importance of
macro- and micro-level priority. We presented a zero-touch solution using Deep
Deterministic Policy Gradient (DDPG) for adaptive scheduling and an
online-Ridge Regression (RR) model for dynamic prioritization. The DDPG
successfully identified the 'Beneficial and Starving' services, efficiently
deploying twice as many low-priority services as others, reducing the
starvation problem. Our online-RR model learns the pattern in less than 100
transitions, and the prediction model has an accuracy rate of more than 80%
A Self-adaptive Agent-based System for Cloud Platforms
Cloud computing is a model for enabling on-demand network access to a shared
pool of computing resources, that can be dynamically allocated and released
with minimal effort. However, this task can be complex in highly dynamic
environments with various resources to allocate for an increasing number of
different users requirements. In this work, we propose a Cloud architecture
based on a multi-agent system exhibiting a self-adaptive behavior to address
the dynamic resource allocation. This self-adaptive system follows a MAPE-K
approach to reason and act, according to QoS, Cloud service information, and
propagated run-time information, to detect QoS degradation and make better
resource allocation decisions. We validate our proposed Cloud architecture by
simulation. Results show that it can properly allocate resources to reduce
energy consumption, while satisfying the users demanded QoS
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