Karma: Resource allocation for dynamic demands
The classical max-min fairness algorithm for resource allocation provides many desirable
properties, eg, Pareto efficiency, strategy-proofness and fairness. This paper builds upon the …
properties, eg, Pareto efficiency, strategy-proofness and fairness. This paper builds upon the …
Dynamicmr: A dynamic slot allocation optimization framework for mapreduce clusters
MapReduce is a popular computing paradigm for large-scale data processing in cloud
computing. However, the slot-based MapReduce system (eg, Hadoop MRv1) can suffer from …
computing. However, the slot-based MapReduce system (eg, Hadoop MRv1) can suffer from …
Fair resource allocation for data-intensive computing in the cloud
To address the computing challenge ofbig data', a number of data-intensive computing
frameworks (eg, MapReduce, Dryad, Storm and Spark) have emerged and become popular …
frameworks (eg, MapReduce, Dryad, Storm and Spark) have emerged and become popular …
Astraea: A fair deep learning scheduler for multi-tenant gpu clusters
Modern GPU clusters are designed to support distributed Deep Learning jobs from multiple
tenants concurrently. Each tenant may have varied and dynamic resource demands …
tenants concurrently. Each tenant may have varied and dynamic resource demands …
Reciprocal resource fairness: Towards cooperative multiple-resource fair sharing in iaas clouds
Resource sharing in virtualized environments have been demonstrated significant benefits
to improve application performance and resource/energy efficiency. However, resource …
to improve application performance and resource/energy efficiency. However, resource …
Dynamic proportional sharing: A game-theoretic approach
Sharing computational resources amortizes cost and improves utilization and efficiency.
When agents pool their resources together, each becomes entitled to a portion of the shared …
When agents pool their resources together, each becomes entitled to a portion of the shared …
Dynamic job ordering and slot configurations for MapReduce workloads
MapReduce is a popular parallel computing paradigm for large-scale data processing in
clusters and data centers. A MapReduce workload generally contains a set of jobs, each of …
clusters and data centers. A MapReduce workload generally contains a set of jobs, each of …
Tempo: robust and self-tuning resource management in multi-tenant parallel databases
Multi-tenant database systems have a component called the Resource Manager, or RM that
is responsible for allocating resources to tenants. RMs today do not provide direct support …
is responsible for allocating resources to tenants. RMs today do not provide direct support …
Elastic multi-resource fairness: balancing fairness and efficiency in coupled CPU-GPU architectures
Fairness and efficiency are two important concerns for users in a shared computer system,
and there tends to be a tradeoff between them. Heterogeneous computing poses new …
and there tends to be a tradeoff between them. Heterogeneous computing poses new …
A cost model for IaaS clouds based on virtual machine energy consumption
Cloud Computing has revolutionized the software, platform and infrastructure provisioning.
Infrastructure-as-a-Service (IaaS) providers offer on-demand and configurable Virtual …
Infrastructure-as-a-Service (IaaS) providers offer on-demand and configurable Virtual …