90 research outputs found
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
KubeNow: A Cloud Agnostic Platform for Microservice-Oriented Applications
KubeNow is a platform for rapid and continuous deployment of microservice-based applications over cloud infrastructure. Within the field of software engineering, the microservice-based architecture is a methodology in which complex applications are divided into smaller, more narrow services. These services are independently deployable and compatible with each other like building blocks. These blocks can be combined in multiple ways, according to specific use cases. Microservices are designed around a few concepts: they offer a minimal and complete set of features, they are portable and platform independent, they are accessible through language agnostic APIs and they are encouraged to use standard data formats. These characteristics promote separation of concerns, isolation and interoperability, while coupling nicely with test-driven development. Among many others, some well-known companies that build their software around microservices are: Google, Amazon, PayPal Holdings Inc. and Netflix [11]
Accelerating Fair Federated Learning: Adaptive Federated Adam
Federated learning is a distributed and privacy-preserving approach to train
a statistical model collaboratively from decentralized data of different
parties. However, when datasets of participants are not independent and
identically distributed (non-IID), models trained by naive federated algorithms
may be biased towards certain participants, and model performance across
participants is non-uniform. This is known as the fairness problem in federated
learning. In this paper, we formulate fairness-controlled federated learning as
a dynamical multi-objective optimization problem to ensure fair performance
across all participants. To solve the problem efficiently, we study the
convergence and bias of Adam as the server optimizer in federated learning, and
propose Adaptive Federated Adam (AdaFedAdam) to accelerate fair federated
learning with alleviated bias. We validated the effectiveness, Pareto
optimality and robustness of AdaFedAdam in numerical experiments and show that
AdaFedAdam outperforms existing algorithms, providing better convergence and
fairness properties of the federated scheme
Federated Machine Learning for Resource Allocation in Multi-domain Fog Ecosystems
The proliferation of the Internet of Things (IoT) has incentivised extending cloud resources to the edge in what is deemed fog computing. The latter is manifesting as an ecosystem of connected clouds, geo-dispersed and of diverse capacities. In such ecosystem, workload allocation to fog services becomes a non-trivial challenge. Users' demand at the edge is diverse, which does not lend to straightforward resource planning. Conversely, running services at the edge may leverage proximity, but it comes at higher operational cost let alone increasing risk of resource straining. Consequently, there is a need for intelligent yet scalable allocation solutions that counter the adversity of demand, while efficiently distributing load between the edge and farther clouds. Machine learning is increasingly adopted in resource planning. This paper proposes a federated deep reinforcement learning system, based on deep Q-learning network (DQN), for workload distribution in a fog ecosystem. The proposed solution adapts a DQN to optimize local workload allocations, made by single gateways. Federated learning is incorporated to allow multiple gateways in a network to collaboratively build knowledge of users' demand. This is leveraged to establish consensus on the fraction of workload allocated to different fog nodes, using lower data supply and computation resources. System performance is evaluated using realistic demand from Google Cluster Workload Traces 2019. Evaluation results show over 50% reduction in failed allocations when spreading users over larger number of gateways, given fixed number of fog nodes. The results further illustrate the trade-offs between performance and cost under different conditions
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