Reliable Provisioning of Spot Instances for Compute-intensive Applications

Cloud computing providers are now offering their unused resources for leasing in the spot market, which has been considered the first step towards a full-fledged market economy for computational resources. Spot instances are virtual machines (VMs) available at lower prices than their standard on-demand counterparts. These VMs will run for as long as the current price is lower than the maximum bid price users are willing to pay per hour. Spot instances have been increasingly used for executing compute-intensive applications. In spite of an apparent economical advantage, due to an intermittent nature of biddable resources, application execution times may be prolonged or they may not finish at all. This paper proposes a resource allocation strategy that addresses the problem of running compute-intensive jobs on a pool of intermittent virtual machines, while also aiming to run applications in a fast and economical way. To mitigate potential unavailability periods, a multifaceted fault-aware resource provisioning policy is proposed. Our solution employs price and runtime estimation mechanisms, as well as three fault tolerance techniques, namely checkpointing, task duplication and migration. We evaluate our strategies using trace-driven simulations, which take as input real price variation traces, as well as an application trace from the Parallel Workload Archive. Our results demonstrate the effectiveness of executing applications on spot instances, respecting QoS constraints, despite occasional failures.Comment: 8 pages, 4 figure

Resource Provisioning Exploiting Cost and Performance Diversity within IaaS Cloud Providers

IaaS platforms such as Amazon EC2 allow clients access to massive computational power in the form of instances. Amazon hosts three different instance purchasing options, each with its own SLA covering pricing and availability. Amazon also offers access to a number of geographical regions, zones, and instance types to select from. In this thesis, the problem of utilizing Spot and On-Demand instances is analyzed and two approaches are presented in order to exploit the cost and performance diversity among different instance types and availability zones, and among the Spot markets they represent. We first develop RAMP, a framework designed to calculate the expected profit of using a specific Spot or On-Demand instance through an evaluation of instance reliability. RAMP is extended to develop RAMC-DC, a framework designed to allocate the most cost effective instance through strategies that facilitate interchangeability of instances among short jobs, reliability of instances among long jobs, and a comparison of the estimated costs of possible allocations. RAMC-DC achieves fault tolerance through comparisons of the price dynamics across instance types and availability zones, and through an examination of three basic checkpointing methods. Evaluations demonstrate that both frameworks take a large step toward low-volatility, high cost-efficiency resource provisioning. While achieving early-termination rates as low as 2.2%, RAMP can completely offset the total cost when charging the user just 17.5% of the On-Demand price. Moreover, the increases in profit resulting from relatively small additional charges to users are notably high, i.e., 100% profit compared to the resource provisioning cost with 35% of the equivalent On-Demand price. RAMC-DC can maintain deadline breaches below 1.8% of all jobs, achieve both early-termination and deadline breach rates as low as 0.5% of all jobs, and lowers total costs by between 80% and 87% compared to using only On-Demand instances

Resource Provisioning Exploiting Cost and Performance Diversity within IaaS Cloud Providers

IaaS platforms such as Amazon EC2 allow clients access to massive computational power in the form of instances. Amazon hosts three different instance purchasing options, each with its own SLA covering pricing and availability. Amazon also offers access to a number of geographical regions, zones, and instance types to select from. In this thesis, the problem of utilizing Spot and On-Demand instances is analyzed and two approaches are presented in order to exploit the cost and performance diversity among different instance types and availability zones, and among the Spot markets they represent. We first develop RAMP, a framework designed to calculate the expected profit of using a specific Spot or On-Demand instance through an evaluation of instance reliability. RAMP is extended to develop RAMC-DC, a framework designed to allocate the most cost effective instance through strategies that facilitate interchangeability of instances among short jobs, reliability of instances among long jobs, and a comparison of the estimated costs of possible allocations. RAMC-DC achieves fault tolerance through comparisons of the price dynamics across instance types and availability zones, and through an examination of three basic checkpointing methods. Evaluations demonstrate that both frameworks take a large step toward low-volatility, high cost-efficiency resource provisioning. While achieving early-termination rates as low as 2.2%, RAMP can completely offset the total cost when charging the user just 17.5% of the On-Demand price. Moreover, the increases in profit resulting from relatively small additional charges to users are notably high, i.e., 100% profit compared to the resource provisioning cost with 35% of the equivalent On-Demand price. RAMC-DC can maintain deadline breaches below 1.8% of all jobs, achieve both early-termination and deadline breach rates as low as 0.5% of all jobs, and lowers total costs by between 80% and 87% compared to using only On-Demand instances

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

Provisioning Spot Market Cloud Resources to Create Cost-effective Virtual Clusters

Infrastructure-as-a-Service providers are offering their unused resources in the form of variable-priced virtual machines (VMs), known as "spot instances", at prices significantly lower than their standard fixed-priced resources. To lease spot instances, users specify a maximum price they are willing to pay per hour and VMs will run only when the current price is lower than the user's bid. This paper proposes a resource allocation policy that addresses the problem of running deadline-constrained compute-intensive jobs on a pool of composed solely of spot instances, while exploiting variations in price and performance to run applications in a fast and economical way. Our policy relies on job runtime estimations to decide what are the best types of VMs to run each job and when jobs should run. Several estimation methods are evaluated and compared, using trace-based simulations, which take real price variation traces obtained from Amazon Web Services as input, as well as an application trace from the Parallel Workload Archive. Results demonstrate the effectiveness of running computational jobs on spot instances, at a fraction (up to 60% lower) of the price that would normally cost on fixed priced resources.Comment: 14 pages, 4 figures, 11th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP-11); Lecture Notes in Computer Science, Vol. 7016, 201

AWS PredSpot: Machine Learning for Predicting the Price of Spot Instances in AWS Cloud

Elastic Cloud Compute (EC2) is one of the most well-known services provided by Amazon for provisioning cloud computing resources, also known as instances. Besides the classical on-demand scheme, where users purchase compute capacity at a fixed cost, EC2 supports so-called spot instances, which are offered following a bidding scheme, where users can save up to 90% of the cost of the on-demand instance. EC2 spot instances can be a useful alternative for attaining an important reduction in infrastructure cost, but designing bidding policies can be a difficult task, since bidding under their cost will either prevent users from provisioning instances or losing those that they already own. Towards this extent, accurate forecasting of spot instance prices can be of an outstanding interest for designing working bidding policies. In this paper, we propose the use of different machine learning techniques to estimate the future price of EC2 spot instances. These include linear, ridge and lasso regressions, multilayer perceptrons, K-nearest neighbors, extra trees and random forests. The obtained performance varies significantly between instances types, and root mean squared errors ranges between values very close to zero up to values over 60 in some of the most expensive instances. Still, we can see that for most of the instances, forecasting performance is remarkably good, encouraging further research in this field of study

Cost and fault-tolerant aware resource management for scientific workflows using hybrid instances on clouds

Cloud service providers are offering computing resources at a reasonable price as a pay-per-use model. Further, cloud service providers have also introduced different pricing models like spot, blockspot and spotfleet instances that are cost effective and user’s have to go through the bidding to balance the reliability and monetary costs. Henceforth, Scientific Workflows (SWf) that are used to model applications of high throughput, computation and complex large-scale data analysis are significantly adopting these computing resources. Nevertheless, spot instances are terminated when the market spot price exceeds the users bid price. Moreover, failures are inevitable in such a large distributed systems and often pose a challenge to design a fault-tolerant scheduling algorithm for SWf. This paper presents an efficient, low-cost and fault-tolerant scheduling algorithm and a bidding strategy to minimize the