8 research outputs found
Cloud Index Tracking: Enabling Predictable Costs in Cloud Spot Markets
Cloud spot markets rent VMs for a variable price that is typically much lower
than the price of on-demand VMs, which makes them attractive for a wide range
of large-scale applications. However, applications that run on spot VMs suffer
from cost uncertainty, since spot prices fluctuate, in part, based on supply,
demand, or both. The difficulty in predicting spot prices affects users and
applications: the former cannot effectively plan their IT expenditures, while
the latter cannot infer the availability and performance of spot VMs, which are
a function of their variable price. To address the problem, we use properties
of cloud infrastructure and workloads to show that prices become more stable
and predictable as they are aggregated together. We leverage this observation
to define an aggregate index price for spot VMs that serves as a reference for
what users should expect to pay. We show that, even when the spot prices for
individual VMs are volatile, the index price remains stable and predictable. We
then introduce cloud index tracking: a migration policy that tracks the index
price to ensure applications running on spot VMs incur a predictable cost by
migrating to a new spot VM if the current VM's price significantly deviates
from the index price.Comment: ACM Symposium on Cloud Computing 201
Cloud-scale VM Deflation for Running Interactive Applications On Transient Servers
Transient computing has become popular in public cloud environments for
running delay-insensitive batch and data processing applications at low cost.
Since transient cloud servers can be revoked at any time by the cloud provider,
they are considered unsuitable for running interactive application such as web
services. In this paper, we present VM deflation as an alternative mechanism to
server preemption for reclaiming resources from transient cloud servers under
resource pressure. Using real traces from top-tier cloud providers, we show the
feasibility of using VM deflation as a resource reclamation mechanism for
interactive applications in public clouds. We show how current hypervisor
mechanisms can be used to implement VM deflation and present cluster deflation
policies for resource management of transient and on-demand cloud VMs.
Experimental evaluation of our deflation system on a Linux cluster shows that
microservice-based applications can be deflated by up to 50\% with negligible
performance overhead. Our cluster-level deflation policies allow overcommitment
levels as high as 50\%, with less than a 1\% decrease in application
throughput, and can enable cloud platforms to increase revenue by 30\%.Comment: To appear at ACM HPDC 202
Strategic behavior and revenue management of cloud services with reservation-based preemption of customer instances
Cloud computing is a multi billion dollar industry, based around outsourcing the provisioning and maintenance of computing resources. In particular, Infrastructure as a Service (IaaS) enables customers to purchase virtual machines in order to run arbitrary software. IaaS customers are given the option to purchase priority access, while providers choose whether customers are preempted based on priority level. The customer decision is based on their tolerance for preemption. However, this decision is a reaction to the provider choice of preemption policy and cost to purchase priority.
In this work, a non-cooperative game is developed for an IaaS system offering resource reservations. An unobservable queue with priorities is used to model customer arrivals and service. Customers receive a potential priority from the provider, and choose between purchasing a reservation for that priority and accepting the lowest priority for no additional cost. Customers select the option which minimizes their total cost of waiting. This decision is based purely on statistics, as customers cannot communicate with each other.
This work presents the impact of the provider preemption policy choice on the cost customers will pay for a reserved instance. A provider may implement a policy in which no customers are preempted (NP); a policy in which all customers are subject to preemption (PR); or a policy in which only the customers not making reservations are subject to preemption (HPR). It is shown that only the service load impacts the equilibrium possibilities in the NP and PR policies, but that the service variance is also a factor under the HPR policy. These factors impact the equilibrium possibilities associated to a given reservation cost.
This work shows that the cost leading to a given equilibrium is greater under the HPR policy than under the NP or PR policies, implying greater incentive to purchase reservations. From this it is proven that a provider maximizes their potential revenue from customer reservations under an HPR policy. It is shown that this holds in general and under the constraint that the reservation cost must correspond to a unique equilibrium.2020-06-03T00:00:00