Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instance and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51 when spot and on-demand instances are considered and of up to 43.74 when self-owned instances are considered, compared to previously proposed or intuitive policies.
Similar to this paper and @cite_16 @cite_18 , executing deadline-constrained jobs cost-effectively in IaaS clouds is also studied in @cite_8 @cite_12 . In particular, Zafer characterize the evolution of spot prices by a Markov model and propose an optimal bidding strategy to utilize spot instances to complete a serial or parallel job by some deadline @cite_8 . Yao study the problem of utilizing reserved and on-demand instances to complete online batch jobs by their deadlines and formulate it as integer programming problems; then heuristic algorithms are proposed to give approximate solutions @cite_12 .
@cite_18 @cite_16 @cite_12 @cite_8
1463932917 2282197438 2051390526 2061849602
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. A method for adaptively allocating resources to a plurality of jobs. The method comprises selecting a first policy from a plurality of policies for a first job in the plurality of jobs by using a policy selection mechanism, allocating at least one resource to the first job in accordance with the first policy, and in response to completion of the first job, updating the policy selection mechanism to obtain an updated policy selection mechanism by using at least one processor. Updating the policy selection mechanism comprises evaluating the performance of the first policy with respect to the first job by calculating a value of a metric of utility for the first policy based on conditions associated with execution of the first job and updating the policy selection mechanism based on the calculated value and a delay of execution of the first job. Many web service providers use commercial cloud computing infrastructures like Amazon for flexible and reliable service deployment. For these web service providers, the cost of cloud computing usage becomes a big part of their IT department cost. Facing the diverse pricing models including on-demand, reserved, and spot instance, it is difficult for web service providers to optimize their cost. This paper introduces a new cloud brokerage service to help web service providers to minimize their cloud computing cost for deadline-constrained batch jobs, which have been a significant workload in web services. Our cloud brokerage service associates each batch job with deadline, and always tries to use cheaper reserved instances for computation to maintain a minimum cost. We achieve this with the following two steps: (1) given a set of jobs' specifications, determine the scheduling of jobs, (2) given the scheduling and pricing options, find an optimal instance renting strategy. We prove that both problems in two steps are computation intractable, and propose approximation algorithms for them. Trace-based evaluation shows that our cloud brokerage service can reduce up to 57 of the cloud computing cost. 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.