93 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
SPA : Harnessing Availability in the AWS Spot Market
Amazon Web Services (AWS) offers transient virtual servers at a discounted price as a way to sell unused spare capacity in its data centers. Although transient servers are very appealing as some instances have up to 90% discount, they are not bound to regular availability guarantees as they are opportunistic resources sold on the spot market. In this paper, we present SPA, a framework that remarkably increases the spot instance reliability over time due to insights gained from the analysis of historical data, such as cross-region price variability and intervals between evictions. We implemented the SPA reliability strategy, evaluated them using over one year of historical pricing data from AWS, and found out that we can increase the transient instance lifetime by adding a pricing overhead of 3.5% in the spot price in the best scenario.Peer reviewe
Scheduling Flexible Demand in Cloud Computing Spot Markets
The rapid standardization and specialization of cloud computing services have led to the development of cloud spot markets on which cloud service providers and customers can trade in near real-time. Frequent changes in demand and supply give rise to spot prices that vary throughout the day. Cloud customers often have temporal flexibility to execute their jobs before a specific deadline. In this paper, the authors apply real options analysis (ROA), which is an established valuation method designed to capture the flexibility of action under uncertainty. They adapt and compare multiple discrete-time approaches that enable cloud customers to quantify and exploit the monetary value of their short-term temporal flexibility. The paper contributes to the field by guaranteeing cloud job execution of variable-time requests in a single cloud spot market, whereas existing multi-market strategies may not fulfill requests when outbid. In a broad simulation of scenarios for the use of Amazon EC2 spot instances, the developed approaches exploit the existing savings potential up to 40 percent – a considerable extent. Moreover, the results demonstrate that ROA, which explicitly considers time-of-day-specific spot price patterns, outperforms traditional option pricing models and expectation optimization
On the combination of multi-cloud and network coding for cost-efficient storage in industrial applications
The adoption of both Cyber-Physical Systems (CPSs) and the Internet-of-Things (IoT) has enabled the evolution towards the so-called Industry 4.0. These technologies, together with cloud computing and artificial intelligence, foster new business opportunities. Besides, several industrial applications need immediate decision making and fog computing is emerging as a promising solution to address such requirement. In order to achieve a cost-efficient system, we propose taking advantage from spot instances, a new service offered by cloud providers, which provide resources at lower prices. The main downside of these instances is that they do not ensure service continuity and they might suffer from interruptions. An architecture that combines fog and multi-cloud deployments along with Network Coding (NC) techniques, guarantees the needed fault-tolerance for the cloud environment, and also reduces the required amount of redundant data to provide reliable services. In this paper we analyze how NC can actually help to reduce the storage cost and improve the resource efficiency for industrial applications, based on a multi-cloud infrastructure. The cost analysis has been carried out using both real AWS EC2 spot instance prices and, to complement them, prices obtained from a model based on a finite Markov chain, derived from real measurements. We have analyzed the overall system cost, depending on different parameters, showing that configurations that seek to minimize the storage yield a higher cost reduction, due to the strong impact of storage cost.This work has been partially supported by the Basque Government through the Elkartek program (Grant agreement no. KK-2018/00115), the H2020 research framework of the European Commission under the ELASTIC project (Grant agreement no. 825473), and the Spanish Ministry of Economy and Competitiveness through the CARMEN project (TEC2016-75067-C4-3-R), the ADVICE project (TEC2015-71329-C2-1-R), and the COMONSENS network (TEC2015-69648-REDC)
On the combination of multi-cloud and network coding for cost-efficient storage in industrial applications
The adoption of both Cyber–Physical Systems (CPSs) and the Internet-of-Things (IoT) has
enabled the evolution towards the so-called Industry 4.0. These technologies, together with cloud
computing and artificial intelligence, foster new business opportunities. Besides, several industrial
applications need immediate decision making and fog computing is emerging as a promising solution
to address such requirement. In order to achieve a cost-efficient system, we propose taking advantage
from spot instances, a new service offered by cloud providers, which provide resources at lower prices.
The main downside of these instances is that they do not ensure service continuity and they might
suffer from interruptions. An architecture that combines fog and multi-cloud deployments along with
Network Coding (NC) techniques, guarantees the needed fault-tolerance for the cloud environment,
and also reduces the required amount of redundant data to provide reliable services. In this paper
we analyze how NC can actually help to reduce the storage cost and improve the resource efficiency
for industrial applications, based on a multi-cloud infrastructure. The cost analysis has been carried
out using both real AWS EC2 spot instance prices and, to complement them, prices obtained from
a model based on a finite Markov chain, derived from real measurements. We have analyzed the
overall system cost, depending on different parameters, showing that configurations that seek to
minimize the storage yield a higher cost reduction, due to the strong impact of storage cost
Users’ time preference based stochastic resource allocation in cloud spot market: cloud provider’s perspective
Cloud Computing spot markets have enabled the users to make use of the spare computing capacities of the cloud providers at a relatively cheaper price which in turn has given the providers such as Amazon and Google an opportunity to earn extra money by auctioning-off the underutilized resources. However, resource availability is a problem in the spot market owing to spot-price fluctuations. Ignoring the customer’s preference is one of the potential reasons behind this. In this paper, we propose a time preference (value of service at different points of time) based stochastic integer linear programming model to allocate the cloud resources among the cloud users with a view to maximizing the revenue of cloud providers from the spot-market
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