Cloud platforms offer different types of virtual machines 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 instances while spot instances are unused resources available for a lower price. Despite the monetary advantages, a spot instance can be terminated or hibernated by EC2 at any moment. Using both hibernation-prone spot instances (for cost sake) and on-demand instances, we propose in this paper a static scheduling for applications which are composed of independent tasks (bag-of-task) with deadline constraints. However, if a spot instance 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. Performance results with task execution traces, configuration of Amazon EC2 virtual machines, and EC2 market history confirms the effectiveness of our scheduling and that it tolerates temporal failures.
Some works take into account Amazon spot VMs instance features. In @cite_20 , use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks. Authors of @cite_25 propose to switch to on-demand resources when there is no spot instance available to ensure the desired performance. Using both on-demand and spot VM instances, SpotCheck @cite_8 provides the illusion of an IaaS platform that offers always-available on-demand VMs for a cost near that of spot VMs. Also claiming performance of on-demand VMs, but at a cost near that of the spot market, the authors in @cite_14 present the SpotOn batch service computing, that uses fault-tolerance mechanism to mitigate the impact of spot revocations. To our knowledge no work studies the impact of the new hibernation feature of spot instances on scheduling algorithms.
@cite_14 @cite_25 @cite_20 @cite_8
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Infrastructure-as-a-Service (IaaS) cloud platforms rent resources, in the form of virtual machines (VMs), under a variety of contract terms that offer different levels of risk and cost. For example, users may acquire VMs in the spot market that are often cheap but entail significant risk, since their price varies over time based on market supply and demand and they may terminate at any time if the price rises too high. Currently, users must manage all the risks associated with using spot servers. As a result, conventional wisdom holds that spot servers are only appropriate for delay-tolerant batch applications. In this paper, we propose a derivative cloud platform, called SpotCheck, that transparently manages the risks associated with using spot servers for users. SpotCheck provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports all types of applications, including interactive ones. SpotCheck's design combines the use of nested VMs with live bounded-time migration and novel server pool management policies to maximize availability, while balancing risk and cost. We implement SpotCheck on Amazon's EC2 and show that it i) provides nested VMs to users that are 99.9989 available, ii) achieves nearly 5x cost savings compared to using equivalent types of on-demand VMs, and iii) eliminates any risk of losing VM state. Cloud spot markets enable users to bid for compute resources, such that the cloud platform may revoke them if the market price rises too high. Due to their increased risk, revocable resources in the spot market are often significantly cheaper (by as much as 10×) than the equivalent non-revocable on-demand resources. One way to mitigate spot market risk is to use various fault-tolerance mechanisms, such as checkpointing or replication, to limit the work lost on revocation. However, the additional performance overhead and cost for a particular fault-tolerance mechanism is a complex function of both an application's resource usage and the magnitude and volatility of spot market prices. We present the design of a batch computing service for the spot market, called SpotOn, that automatically selects a spot market and fault-tolerance mechanism to mitigate the impact of spot revocations without requiring application modification. SpotOn's goal is to execute jobs with the performance of on-demand resources, but at a cost near that of the spot market. We implement and evaluate SpotOn in simulation and using a prototype on Amazon's EC2 that packages jobs in Linux Containers. Our simulation results using a job trace from a Google cluster indicate that SpotOn lowers costs by 91.9 compared to using on-demand resources with little impact on performance. Spot virtual-machine (VM) instances, such as Amazon EC2 Spot VMs, are a class of VMs that are purchased through a market mechanism of price-bids submitted by cloud users. Spot VMs can be obtained at substantially lower cost than other VM classes such as Reserved and On-demand instances, but they do not have guaranteed availability since it depends on the submitted price bids and the fluctuating spot VM price. Many applications with large computing requirements but no real-time availability constraints, such as scientific computing, financial modelling and large data analysis, can be carried out at a significantly lower cost using spot VMs. For such jobs, an important question that arises is what should the submitted price bids be so that the computation is completed within a fixed time interval while the cost is minimized. Towards this goal, we model a job as a fixed computation request with a deadline constraint and formulate the problem of designing a dynamic bidding policy that minimizes the average cost of job completion. We obtain analytical and closed-form results for the optimal strategy under a Markov spot price evolution, and then evaluate the performance of the algorithms on the actual spot price history of Amazon EC2 Spot VMs. Cloud spot markets offer virtual machines (VMs) for a dynamic price that is much lower than the fixed price of on-demand VMs. In exchange, spot VMs expose applications to multiple forms of risk, including price risk, or the risk that a VM's price will increase relative to others. Since spot prices vary continuously across hundreds of different types of VMs, flexible applications can mitigate price risk by moving to the VM that currently offers the lowest cost. To enable this flexibility, we present HotSpot, a resource container that "hops" VMs---by dynamically selecting and self-migrating to new VMs---as spot prices change. HotSpot containers define a migration policy that lowers cost by determining when to hop VMs based on the transaction costs (from vacating a VM early and briefly double paying for it) and benefits (the expected cost savings). As a side effect of migrating to minimize cost, HotSpot is also able to reduce the number of revocations without degrading performance. HotSpot is simple and transparent: since it operates at the systems-level on each host VM, users need only run an HotSpot-enabled VM image to use it. We implement a HotSpot prototype on EC2, and evaluate it using job traces from a production Google cluster. We then compare HotSpot to using on-demand VMs and spot VMs (with and without fault-tolerance) in EC2, and show that it is able to lower cost and reduce the number of revocations without degrading performance. 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 check pointing, 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. 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 of spot instances, and customers whose bid exceeds 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. In this paper, we study various checkpointing schemes to increase the reliability over spot instances. Also we devise a novel checkpointing scheme on top of application-centric resource provisioning framework that increases the reliability while reducing the cost significantly. 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 deadlineconstrained 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. Many cost-conscious public cloud workloads ("tenants") are turning to Amazon EC2's spot instances because, on average, these instances offer significantly lower prices (up to 10 times lower) than on-demand and reserved instances of comparable advertized resource capacities. To use spot instances effectively, a tenant must carefully weigh the lower costs of these instances against their poorer availability. Towards this, we empirically study four features of EC2 spot instance operation that a cost-conscious tenant may find useful to model. Using extensive evaluation based on both historical and current spot instance data, we show shortcomings in the state-of-the-art modeling of these features that we overcome. Our analysis reveals many novel properties of spot instance operation some of which offer predictive value while others do not. Using these insights, we design predictors for our features that offer a balance between computational efficiency (allowing for online resource procurement) and cost-efficacy. We explore "case studies" wherein we implement prototypes of dynamic spot instance procurement advised by our predictors for two types of workloads. Compared to the state-of-the-art, our approach achieves (i) comparable cost but much better performance (fewer bid failures) for a latency-sensitive in-memory Memcached cache, and (ii) an additional 18 cost-savings with comparable (if not better than) performance for a delay-tolerant batch workload. Recent years witness the proliferation of Infrastructure-as-a-Service (IaaS) cloud services, which provide on-demand resources (CPU, RAM, disk) in the form of virtual machines (VMs) for hosting applications services of third parties. Given the state-of-the-art IaaS offerings, it is still a problem of fundamental importance how the Application Service Providers (ASPs) should rent VMs from the clouds to serve their application needs, in order to minimize the cost while meeting their job demands over a long run. Cloud providers offer different pricing options to meet computing requirements of a variety of applications. However, the challenge facing an ASP is how these pricing options can be dynamically combined to serve arbitrary demands at the optimal cost. In this paper, we propose an online VM purchasing algorithm based on the Lyapunov optimization technique, for minimizing the long-term-averaged VM rental cost of an ASP with time-varying and delay-tolerant workloads, while bounding the maximum response delay of its jobs. In stark contrast with the existing studies, the proposed algorithm enables an ASP to optimally decide the amount of reserved, on-demand and spot instances to purchase simultaneously. Rigorous analysis shows that our algorithm can achieve a time-averaged resource cost close to the offline optimum. Trace-driven simulations further verify the efficacy of our algorithm. Cloud computing provides an attractive computing paradigm in which computational resources are rented on-demand to users with zero capital and maintenance costs. Cloud providers offer different pricing options to meet computing requirements of a wide variety of applications. An attractive option for batch computing is spot-instances, which allows users to place bids for spare computing instances and rent them at a (often) substantially lower price compared to the fixed on-demand price. However, this raises three main challenges for users: how many instances to rent at any time? what type (on-demand, spot, or both)? and what bid value to use for spot instances? In particular, renting on-demand risks high costs while renting spot instances risks job interruption and delayed completion when the spot market price exceeds the bid. This paper introduces an online learning algorithm for resource allocation to address this fundamental tradeoff between computation cost and performance. Our algorithm dynamically adapts resource allocation by learning from its performance on prior job executions while incorporating history of spot prices and workload characteristics. We provide theoretical bounds on its performance and prove that the average regret of our approach (compared to the best policy in hindsight) vanishes to zero with time. Evaluation on traces from a large datacenter cluster shows that our algorithm outperforms greedy allocation heuristics and quickly converges to a small set of best performing policies. Testing and executing large-scale computational applications in public clouds is becoming prevalent due to cost saving, elasticity, and scalability. However, how to increase the reliability and reduce the cost to run large-scale applications in public clouds is still a big challenge. In this paper, we analyzed the pricing schemes of Amazon Elastic Compute Cloud (EC2) and found the disturbance effect that the price of the spot instances can be heavily affected due to the large number of spot instances required. We proposed a dynamic approach which schedules and runs large-scale computational applications on a dynamic pool of cloud computational instances. We use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks so as to further reduce the cost without significantly increasing the completion time. Our proposed method takes the dynamic pricing of cloud instances into consideration, and it reduces the cost and tolerates the failures for running large-scale applications in public clouds. We conducted experimental tests and an agent based Scalable complex System modeling for Sustainable city (S3) application is used to evaluate the scalability, reliability and cost saving. The results show that our proposed method is robust and highly flexible for researchers and users to further reduce cost in real practice. Cloud computing resources that are equipped with GPU devices are widely used for applications that require extensive parallelism, such as deep learning. When the demand of cloud computing instance is low, the surplus of resources is provided at a lower price in the form of spot instance by AWS EC2. This paper proposes DeepSpotCloud that utilizes GPU-equipped spot instances to run deep learning tasks in a cost efficient and fault-tolerant way. Thorough analysis about spot instance price history logs reveals that GPU spot instances show more dynamic price change pattern than other general types of cloud computing resources. To deal with the price dynamicity of the GPU spot instance, DeepSpotCloud utilizes instances in different regions across continents as a single resource pool. This paper also proposes a task migration heuristic by utilizing a checkpointing mechanism of existing deep learning analysis platform to conduct fast task migration when a running spot instance is interrupted. Extensive experiments using real AWS services prove that the proposed task migration method is effective even in a WAN environment with limited network bandwidth. Comprehensive simulations by replaying AWS EC2 price history logs reveal that DeepSpotCloud can achieve 13 more cost gain than a state-of-the-art interrupt-driven scheduling policy. The prototype of DeepSpotCloud is implemented using various cloud computing services provided by AWS to serve real deep learning tasks. Recently introduced spot instances in the Amazon Elastic Compute Cloud (EC2) offer low resource costs in exchange for reduced reliability; these instances can be revoked abruptly due to price and demand fluctuations. Mechanisms and tools that deal with the cost-reliability tradeoffs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. We study how mechanisms, namely, checkpointing and migration, can be used to minimize the cost and volatility of resource provisioning. Based on the real price history of EC2 spot instances, we compare several adaptive checkpointing schemes in terms of monetary costs and improvement of job completion times. We evaluate schemes that apply predictive methods for spot prices. Furthermore, we also study how work migration can improve task completion in the midst of failures while maintaining low monetary costs. Trace-based simulations show that our schemes can reduce significantly both monetary costs and task completion times of computation on spot instance. Cloud resources are usually priced in multiple markets with different service guarantees. For example, Amazon EC2 prices virtual instances under three pricing schemes -- the subscription option (a.k.a., Reserved Instances), the pay-as-you-go offer (a.k.a., On-Demand Instances), and an auction-like spot market (a.k.a., Spot Instances) -- simultaneously. There arises a new problem of capacity segmentation: how can a provider allocate resources to different categories of pricing schemes, so that the total revenue is maximized? In this paper, we consider an EC2-like pricing scheme with traditional pay-as-you-go pricing augmented by an auction market, where bidders periodically bid for resources and can use the instances for as long as they wish, until the clearing price exceeds their bids. We show that optimal periodic auctions must follow the design of m+1-price auction with seller's reservation price. Theoretical analysis also suggests the connections between periodic auctions and EC2 spot market. Furthermore, we formulate the optimal capacity segmentation strategy as a Markov decision process over some demand prediction window. To mitigate the high computational complexity of the conventional dynamic programming solution, we develop a near-optimal solution that has significantly lower complexity and is shown to asymptotically approach the optimal revenue. In order to keep the costs of operating in-memory storage on the public cloud low, we devise novel ideas and enabling modeling and optimization techniques for combining conventional Amazon EC2 instances with the cheaper spot and burstable instances. Whereas a naturally appealing way of using failure-prone spot instances is to selectively store unpopular ("cold") content, we show that a form of "hot-cold mixing" across regular and spot instances might be more cost-effective. To overcome performance degradation resulting from spot instance revocations, we employ a highly available passive backup using the recently emergent burstable instances. We show how the idiosyncratic resource allocations of burstable instances make them ideal candidates for such a backup. We implement all our ideas in an EC2-based memcached prototype. Using simulations and live experiments on our prototype, we show that (i) our hot-cold mixing, informed by our modeling of spot prices, helps improve cost savings by 50-80 compared to only using regular instances, and (ii) our burstable-based backup helps reduce performance degradation during spot revocation, e.g., the 95 latency during failure recovery improves by 25 compared to a backup based on regular instances. Amazon introduced Spot Instance Market to utilize the idle resources of Amazon Elastic Compute Cloud (EC2) more efficiently. The price of a spot instance changes dynamically according to the current supply and demand for cloud resources. Users can bid for a spot instance and the job request will be granted if the current spot price falls below the bid, whereas the job will be terminated if the spot price exceeds the bid. In this paper, we investigate the problem of designing a bidding strategy from a cloud service broker's perspective, where the cloud service broker accepts job requests from cloud users, and leverages the opportunistic yet less expensive spot instances for computation in order to maximize its own profit. In this context, we propose a profit aware dynamic bidding (PADB) algorithm, which observes the current spot price and selects the bid adaptively to maximize the time average profit of the cloud service broker. We show that our bidding strategy achieves a near-optimal solution, i.e., (1−∈) of the optimal solution to the profit maximization problem, where ∈ can be arbitrarily small. The proposed dynamic bidding algorithm is self-adaptive and requires no a priori statistical knowledge on the distribution of random job sizes from cloud users. Cloud Federation is a recent paradigm that helps Infrastructure as a Service (IaaS) providers to overcome resource limitation during spikes in demand for Virtual Machines (VMs) by outsourcing requests to other federation members. IaaS providers also have the option of terminating spot VMs, i.e, cheaper VMs that can be canceled to free resources for more profitable VM requests. By both approaches, providers can expect to reject less profitable requests. For IaaS providers, pricing and profit are two important factors, in addition to maintaining a high Quality of Service (QoS) and utilization of their resources to remain in the business. For this, a clear understanding of the usage pattern, types of requests, and infrastructure costs are necessary while making decisions to terminate spot VMs, outsourcing or contributing to the federation. In this paper, we propose policies that help in the decision-making process to increase resources utilization and profit. Simulation results indicate that the proposed policies enhance the profit, utilization, and QoS (smaller number of rejected VM requests) in a Cloud federation environment. Recently introduced spot instances in the Amazon Elastic Compute Cloud (EC2) offer lower resource costs in exchange for reduced reliability; these instances can be revoked abruptly due to price and demand fluctuations. Mechanisms and tools that deal with the cost-reliability trade-offs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. We study how one such a mechanism, namely check pointing, can be used to minimize the cost and volatility of resource provisioning. Based on the real price history of EC2 spot instances, we compare several adaptive check pointing schemes in terms of monetary costs and improvement of job completion times. Trace-based simulations show that our approach can reduce significantly both price and the task completion times. Commercial cloud offerings let users allocate compute resources on demand, charging based on reserved time intervals. Users, however, lack guidance for assembling instance pools from different cloud instance types, in order to control completion time and monetary budget. BaTS, our budget-constrained scheduler uses tiny statistical samples of task executions in order to predict completion times (and associated costs) for given bags of tasks, allowing the user to favor either fast execution or low computation budget. BaTS' estimator, however, can not handle variably-priced spot instances appropriately. In this work, we present a new prediction module for BaTS that quickly computes accurate approximations to the Pareto set of mixed on-demand and spot instance pools, based on a genetic algorithm (GA). This new approach allows BaTS to react to changing spot instance prices at runtime by re-configuring the instance pool according to the user's runtime and budget constraints. Simulator-based results show that the GA can approximate the Pareto sets for machine configurations in about 30 seconds time, without noticeable loss of quality, compared to an exact solution, computed offline within 40 to 60 minutes time. Dedicated computing clusters are typically sized based on an expected average workload over a period of years, rather than on peak workloads, which might exist for relatively short times of weeks or months. Recent work has proposed temporarily adding capacity to dedicated clusters during peak periods, by purchasing additional resources from Infrastructure as a Service (IaaS) providers such as Amazon's EC2. In this paper, we consider the economics of purchasing such resources by taking advantage of new opportunities offered for renting virtual infrastructure such as the spot pricing model introduced by Amazon. Furthermore, we define different provisioning policies and investigate the use of spot instances compared to normal instances in terms of cost savings and total breach time of tasks in the queue.