1,179 research outputs found
Optimal Posted Prices for Online Cloud Resource Allocation
We study online resource allocation in a cloud computing platform, through a
posted pricing mechanism: The cloud provider publishes a unit price for each
resource type, which may vary over time; upon arrival at the cloud system, a
cloud user either takes the current prices, renting resources to execute its
job, or refuses the prices without running its job there. We design pricing
functions based on the current resource utilization ratios, in a wide array of
demand-supply relationships and resource occupation durations, and prove
worst-case competitive ratios of the pricing functions in terms of social
welfare. In the basic case of a single-type, non-recycled resource (i.e.,
allocated resources are not later released for reuse), we prove that our
pricing function design is optimal, in that any other pricing function can only
lead to a worse competitive ratio. Insights obtained from the basic cases are
then used to generalize the pricing functions to more realistic cloud systems
with multiple types of resources, where a job occupies allocated resources for
a number of time slots till completion, upon which time the resources are
returned back to the cloud resource pool
A distributed auctioneer for resource allocation in decentralized systems
In decentralized systems, nodes often need to coordinate to access shared resources in a fair manner. One approach to perform such arbitration is to rely on auction mechanisms. Although there is an extensive literature that studies auctions, most of these works assume the existence of a central, trusted auctioneer. Unfortunately, in fully decentralized systems, where the nodes that need to cooperate operate under separate spheres of control, such central trusted entity may not exist. Notable examples of such decentralized systems include community networks, clouds of clouds, cooperative nano data centres, among others. In this paper, we make theoretical and practical contributions to distribute the role of the auctioneer. From the theoretical perspective, we propose a framework of distributed simulations of the auctioneer that are Nash equilibria resilient to coalitions and asynchrony. From the practical perspective, our protocols leverage the distributed nature of the simulations to parallelise the execution. We have implemented a prototype that instantiates the framework for bandwidth allocation in community networks, and evaluated it in a real distributed setting.Peer ReviewedPostprint (author's final draft
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure
Recent technology advancements in the areas of compute, storage and
networking, along with the increased demand for organizations to cut costs
while remaining responsive to increasing service demands have led to the growth
in the adoption of cloud computing services. Cloud services provide the promise
of improved agility, resiliency, scalability and a lowered Total Cost of
Ownership (TCO). This research introduces a framework for minimizing cost and
maximizing resource utilization by using an Integer Linear Programming (ILP)
approach to optimize the assignment of workloads to servers on Amazon Web
Services (AWS) cloud infrastructure. The model is based on the classical
minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin
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