Application-centric Resource Provisioning for Amazon EC2 Spot Instances

In late 2009, Amazon introduced spot instances to offer their unused resources at lower cost with reduced reliability. Amazon's spot instances allow customers to bid on unused Amazon EC2 capacity and run those instances for as long as their bid exceeds the current spot price. The spot price changes periodically based on supply and demand, and customers whose bids exceed it gain access to the available spot instances. Customers may expect their services at lower cost with spot instances compared to on-demand or reserved. However the reliability is compromised since the instances(IaaS) providing the service(SaaS) may become unavailable at any time without any notice to the customer. Checkpointing and migration schemes are of great use to cope with such situation. In this paper we study various checkpointing schemes that can be used with spot instances. Also we device some algorithms for checkpointing scheme on top of application-centric resource provisioning framework that increase the reliability while reducing the cost significantly

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

A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances

Cloud providers sell their idle capacity on markets through an auction-like mechanism to increase their return on investment. The instances sold in this way are called spot instances. In spite that spot instances are usually 90% cheaper than on-demand instances, they can be terminated by provider when their bidding prices are lower than market prices. Thus, they are largely used to provision fault-tolerant applications only. In this paper, we explore how to utilize spot instances to provision web applications, which are usually considered availability-critical. The idea is to take advantage of differences in price among various types of spot instances to reach both high availability and significant cost saving. We first propose a fault-tolerant model for web applications provisioned by spot instances. Based on that, we devise novel auto-scaling polices for hourly billed cloud markets. We implemented the proposed model and policies both on a simulation testbed for repeatable validation and Amazon EC2. The experiments on the simulation testbed and the real platform against the benchmarks show that the proposed approach can greatly reduce resource cost and still achieve satisfactory Quality of Service (QoS) in terms of response time and availability

A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

Cloud platforms have emerged as a prominent environment to execute high performance computing (HPC) applications providing on-demand resources as well as scalability. They usually offer different classes of Virtual Machines (VMs) which ensure different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs are unused instances available for lower price. Despite the monetary advantages, a spot VM can be terminated, stopped, or hibernated by EC2 at any moment. Using both hibernation-prone spot VMs (for cost sake) and on-demand VMs, we propose in this paper a static scheduling for HPC applications which are composed by independent tasks (bag-of-task) with deadline constraints. However, if a spot VM hibernates and it does not resume within a time which guarantees the application's deadline, a temporal failure takes place. Our scheduling, thus, aims at minimizing monetary costs of bag-of-tasks applications in EC2 cloud, respecting its deadline and avoiding temporal failures. To this end, our algorithm statically creates two scheduling maps: (i) the first one contains, for each task, its starting time and on which VM (i.e., an available spot or on-demand VM with the current lowest price) the task should execute; (ii) the second one contains, for each task allocated on a VM spot in the first map, its starting time and on which on-demand VM it should be executed to meet the application deadline in order to avoid temporal failures. The latter will be used whenever the hibernation period of a spot VM exceeds a time limit. Performance results from simulation with task execution traces, configuration of Amazon EC2 VM classes, and VMs market history confirms the effectiveness of our scheduling and that it tolerates temporal failures

Hybrid spot instance based resource provisioning strategy in dynamic cloud environment

Utilization of resources to the maximum extent in large scale distributed cloud environment is a major challenge due to the nature of cloud. Spot Instances in the Amazon Elastic Compute Cloud (EC2) are provisioned based on highest bid with no guarantee of task completion but incurs the overhead of longer task execution time and price. The paper demonstrates the last partial hour and cost overhead that can be avoided by the proposed strategy of Hybrid Spot Instance. It aims to provide reliable service to the ongoing task so as to complete the execution without abruptly interrupting the long running tasks by redefining the bid price. The strategy also considers that on-demand resource services can be acquired when spot price crosses on-demand price and thereby availing high reliability. This will overcome the overhead involved during checkpointing, restarting and workload migration as in the existing system, leading to efficient resources usage for both the providers and users. Service providers revenue is carefully optimized by eliminating the free issue of last partial hour which is a taxing factor for the provider. Simulation carried out based on real time price of various instances considering heterogenous applications shows that the number of out-of-bid scenarios can be reduced largely which leads to the increased number of task completion. Checkpointing is also minimized maximally due to which the overhead associated with it is reduced. This resource provisioning strategy aims to provide preference to existing customers and the task which are nearing the execution completion

Image Transfer and Storage Cost Aware Brokering Strat. for Multiple Clouds

Cloud Brokering, Resource Allocation, Storage, Data Transfer, SimGrid Cloud BrokerNowadays, Clouds are used for hosting a large range of services. But between different Cloud Service Providers, the pricing model and the price of individual resources are very different. Furthermore hosting a service in one Cloud is the major cause of service outage. To increase resiliency and minimize the monetary cost of running a service, it becomes mandatory to span it between different Clouds. Moreover, due to dynamicity of both the service and Clouds, it could be required to migrate a service at run time. Accordingly, this ability must be integrated into the multi-Cloud resource manager, i.e. the Cloud broker. But, when migrating a VM to a new Cloud Service Provider, the VM disk image must be migrated too. Accordingly, data storage and transfer must be taken into account when choosing if and where an application will be migrated. In this paper, we extend a cost-optimization algorithm to take into account storage costs to approximate the optimal placement of a service. The data storage management consists in taking two decisions: where to upload an image, and keep it on-line during the experiment lifetime or delete it when unused. Although the default approach can be to upload an image on demand and delete it when it is no more used, we demonstrate that by adopting other policies the user can achieve better economical results.De nos jours, les Clouds sont utilisés pour héberger un grand ensemble de services. Mais entre les différents fournisseurs de service Cloud, les modéles de prix et le prix de chaque ressource sont très différents. De plus, héberger un service dans un unique Cloud est une des causes principales d'interruption de service. Pour améliorer la résistance et diminuer le coût monétaire d'une application, il devient obligatoire de la distribuer dans plusieurs Clouds. En outre, à cause de la dynamicité de l'application et des Clouds, il peut être nécessaire de migrer l'application pendant l'exécution. Par conséquence, cette capacité doit être intégrée dans le gestionnaire de ressources multi-Cloud i.e. le Cloud Broker. Mais, quand une VM migre vers un nouveau fournisseur de service Cloud, l'image disque de la VM doit être migrée également. Par conséquence, le stockage et transfert de donnée doivent être pris en compte quand il est choisi si une application doit migrer et où. Dans ce papier, nous étendons un algorithme d'optimisation de coût pour prendre en compte le coût du stockage afin d'approximer le placement optimal d'une application. La gestion du stockage de donnée consiste à devoir prendre 2 décisions: où l'image doit être envoyée et doit-elle être conservée ou supprimée quand elle n'est plus utilisée. Même si l'approche par défaut peut être d'envoyer l'image à la demande et la supprimer quand elle n'est plus utilisée, nous démontrons qu'en adoptant d'autres politiques l'utilisateur peut réussir à atteindre de meilleurs résultats économiques

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

Cloud capacity planning and HSI based optimal resource provisioning

Cloud service providers offer spot instances through highest bidding plans that are at a very economical price compared to other pricing plans, namely on-demand and reservation. The usage of spot instance enables utilization of idle resources and provide service for cost sensitive tasks. However, this approach introduces the problem of cloud capacity allocation to different pricing plans that will have impact on the task completion time. To address these issues and improve the providers revenue, in this paper a capacity planning has been carried out based on the prediction of resource requirements for each of the different resource pricing pools. The paper also presents a solution to overcome the burden faced by the service provider due to the free issue of last hour at the time of out-of-bid situation. Simulation carried out based on capacity planning along with hybrid spot instance using Amazon EC2's price show that the resource utilization is improved across the different resource pricing pools with increased number of task completion and improved provider's revenue. © 2017 IEEE