Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

One of many advantages of the cloud is the elasticity, the ability to dynamically acquire or release computing resources in response to demand. However, this elasticity is only meaningful to the cloud users when the acquired Virtual Machines (VMs) can be provisioned in time and be ready to use within the user expectation. The long unexpected VM startup time could result in resource under-provisioning, which will inevitably hurt the application performance. A better understanding of the VM startup time is therefore needed to help cloud users to plan ahead and make in-time resource provisioning decisions. In this paper, we study the startup time of cloud VMs across three real-world cloud providers -- Amazon EC2, Windows Azure and Rackspace. We analyze the relationship between the VM startup time and different factors, such as time of the day, OS image size, instance type, data center location and the number of instances acquired at the same time. We also study the VM startup time of spot instances in EC2, which show a longer waiting time and greater variance compared to on-demand instances.

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A Performance Study on the VM Startup Time in the Cloud

Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined.By analyzing the spot price histories of Amazon’s EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. Our findings suggest that usually prices are not market-driven, as sometimes previously assumed. Rather, they are likely to be generated most of the time at random from within a tight price range via a dynamic hidden reserve price mechanism. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.

/pdf/deconstructing-amazon-ec2-spot-instance-pricing-4sfykd81o9.pdf

Deconstructing Amazon EC2 Spot Instance Pricing

Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined. By analyzing the spot price histories of Amazon's EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. We find that prices are usually not market-driven as sometimes previously assumed. Rather, they are typically generated at random from within a tight price interval via a dynamic hidden reserve price. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.

/pdf/deconstructing-amazon-ec2-spot-instance-pricing-56r6vo8w7i.pdf

Cloud is becoming a dominant computing platform. Naturally, a question that arises is whether we can beat notorious DDoS attacks in a cloud environment. Researchers have demonstrated that the essential issue of DDoS attack and defense is resource competition between defenders and attackers. A cloud usually possesses profound resources and has full control and dynamic allocation capability of its resources. Therefore, cloud offers us the potential to overcome DDoS attacks. However, individual cloud hosted servers are still vulnerable to DDoS attacks if they still run in the traditional way. In this paper, we propose a dynamic resource allocation strategy to counter DDoS attacks against individual cloud customers. When a DDoS attack occurs, we employ the idle resources of the cloud to clone sufficient intrusion prevention servers for the victim in order to quickly filter out attack packets and guarantee the quality of the service for benign users simultaneously. We establish a mathematical model to approximate the needs of our resource investment based on queueing theory. Through careful system analysis and real-world data set experiments, we conclude that we can defeat DDoS attacks in a cloud environment.

Can We Beat DDoS Attacks in Clouds

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.

/pdf/reducing-costs-of-spot-instances-via-checkpointing-in-the-1ni7cjhq3f.pdf

Reducing Costs of Spot Instances via Checkpointing in the Amazon Elastic Compute Cloud

With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. A critical challenge is to determine bid prices that minimize monetary costs for a user while meeting Service Level Agreement (SLA) constraints (for example, sufficient resource availability to complete a computation within a desired deadline). We propose a probabilistic model for the optimization of monetary costs, performance, and reliability, given user and application requirements and dynamic conditions. Using real instance price traces and workload models, we evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.

/pdf/decision-model-for-cloud-computing-under-sla-constraints-493tygsvj7.pdf

Decision Model for Cloud Computing under SLA Constraints

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.

Monetary Cost-Aware Checkpointing and Migration on Amazon Cloud Spot Instances

In large-scale Grid computing environments, providing fault-tolerance is required for both scientific computation and file-sharing to increase their reliability. In previous works, several mechanisms were proposed for the Grids or distributed computing systems. However, some of them used only space redundancy (hardware replication), and others used only time redundancy (checkpointing and rollback). For this reason, the existing mechanisms are inefficient in terms of their resource utilization on the Grids. The main goal of ART is reducing the number of replications by using checkpointing and rollback scheme for each replication. In ART, the minimum number of replications is adaptively selected based on analysis of probability of successful execution within the given deadline and reliability requirement of each task. Our simulation results show that ART can significantly reduce the number of replications and improve scalability compared with existing mechanisms.

/pdf/using-replication-and-checkpointing-for-reliable-task-4r5z9c3ai0.pdf

Using replication and checkpointing for reliable task management in computational Grids

Many large-scale distributed computing applications demand real-time responses by soft deadlines. To enable such real-time task distribution and execution on the volunteer resources, we previously proposed the design of the real-time volunteer computing platform called RT-BOINC. The system gives low O(1) worst-case execution time for task management operations, such as task scheduling, state transitioning, and validation. In this work, we present a full implementation RT-BOINC, adding new features including deadline timer and parameter-based admission control. We evaluate RT-BOINC at large scale using two real-time applications, namely, the games Go and Chess. The results of our case study show that RT-BOINC provides much better performance than the original BOINC in terms of average and worst-case response time, scalability and efficiency.

/pdf/towards-real-time-volunteer-distributed-computing-9qlguiotjj.pdf

Sangho Yi

Papers

Reducing Costs of Spot Instances via Checkpointing in the Amazon Elastic Compute Cloud

Decision Model for Cloud Computing under SLA Constraints

Monetary Cost-Aware Checkpointing and Migration on Amazon Cloud Spot Instances

Using replication and checkpointing for reliable task management in computational Grids

Towards Real-Time, Volunteer Distributed Computing