Abstract Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scientific and non-commercial providers can maximize their infrastructure utilization. Traditionally, batch systems have allowed data centers to fill their resources as much as possible by using backfilling and similar techniques. However, in an IaaS cloud, where virtual machines are supposed to live indefinitely, or at least as long as the user is able to pay for them, these policies are not easily implementable. In this work we present a new scheduling algorithm for IaaS providers that is able to support preemptible instances, that can be stopped by higher priority requests without introducing large modifications in the current cloud schedulers. This scheduler enables the implementation of new cloud usage and payment models that allow more efficient usage of the resources and potential new revenue sources for commercial providers. We also study the correctness and the performance overhead of the proposed scheduler against existing solutions.
The resource provisioning from cloud computing infrastructures using Spot Instances or similar mechanisms has been addressed profusely in the scientific literature in the last years @cite_10 . However, the vast majority of this work has been done from the users' perspective when using and consuming Spot Instances @cite_31 and few works tackle the problem from the resource provider standpoint.
@cite_31 @cite_10
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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. 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. 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. 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.