Marcus Carvalho

Long-term SLOs for reclaimed cloud computing resources

The elasticity promised by cloud computing does not come for free. Providers need to reserve resources to allow users to scale on demand, and cope with workload variations, which results in low utilization. The current response to this low utilization is to re-sell unused resources with no Service Level Objectives (SLOs) for availability. In this paper, we show how to make some of these reclaimable resources more valuable by providing strong, long-term availability SLOs for them. These SLOs are based on forecasts of how many resources will remain unused during multi-month periods, so users can do capacity planning for their long-running services. By using confidence levels for the predictions, we give service providers control over the risk of violating the availability SLOs, and allow them trade increased risk for more resources to make available. We evaluated our approach using 45 months of workload data from 6 production clusters at Google, and show that 6--17% of the resources can be re-offered with a long-term availability of 98.9% or better. A conservative analysis shows that doing so may increase the profitability of selling reclaimed resources by 22--60%.

Business-driven short-term management of a hybrid IT infrastructure

We consider the problem of managing a hybrid computing infrastructure whose processing elements are comprised of in-house dedicated machines, virtual machines acquired on-demand from a cloud computing provider through short-term reservation contracts, and virtual machines made available by the remote peers of a best-effort peer-to-peer (P2P) grid. Each of these resources has different cost basis and associated quality of service guarantees. The applications that run in this hybrid infrastructure are characterized by a utility function: the utility gained with the completion of an application depends on the time taken to execute it. We take a business-driven approach to manage this infrastructure, aiming at maximizing the profit yielded, that is, the utility produced as a result of the applications that are run minus the cost of the computing resources that are used to run them. We propose a heuristic to be used by a contract planner agent that establishes the contracts with the cloud computing provider to balance the cost of running an application and the utility that is obtained with its execution, with the goal of producing a high overall profit. Our analytical results show that the simple heuristic proposed achieves very high relative efficiency in the use of the hybrid infrastructure. We also demonstrate that the ability to estimate the grid behaviour is an important condition for making contracts that allow such relative efficiency values to be achieved. On the other hand, our simulation results with realistic error predictions show only a modest improvement in the profit achieved by the simple heuristic proposed, when compared to a heuristic that does not consider the grid when planning contracts, but uses it, and another that is completely oblivious to the existence of the grid. This calls for the development of more accurate predictors for the availability of P2P grids, and more elaborated heuristics that can better deal with the several sources of non-determinism present in this hybrid infrastructure.

Predicting the Quality of Service of a Peer-to-Peer Desktop Grid

Peer-to-peer (P2P) desktop grids have been proposed as an economical way to increase the processing capabilities of information technology (IT) infrastructures. In a P2P grid, a peer donates its idle resources to the other peers in the system, and, in exchange, can use the idle resources of other peers when its processing demand surpasses its local computing capacity. Despite their cost-effectiveness, scheduling of processing demands on IT infrastructures that encompass P2P desktop grids is more difficult. At the root of this difficulty is the fact that the quality of the service provided by P2P desktop grids varies significantly over time. The research we report in this paper tackles the problem of estimating the quality of service of P2P desktop grids. We base our study on the OurGrid system, which implements an autonomous incentive mechanism based on reciprocity, called the Network of Favours (NoF). In this paper we propose a model for predicting the quality of service of a P2P desktop grid that uses the NoF incentive mechanism. The model proposed is able to estimate the amount of resources that is available for a peer in the system at future instants of time. We also evaluate the accuracy of the model by running simulation experiments fed with field data. Our results show that in the worst scenario the proposed model is able to predict how much of a given demand for resources a peer is going to obtain from the grid with a mean prediction error of only 7.2%.

Evaluating the impact of planning long-term contracts on the management of a hybrid IT infrastructure

The cloud computing market has emerged as an alternative for the provisioning of resources on a pay-as-you-go basis. This flexibility potentially allows clients of cloud computing solutions to reduce the total cost of ownership of their Information Technology infrastructures. On the other hand, this market-based model is not the only way to reduce costs. Among other solutions proposed, peer-to-peer (P2P) grid computing has been suggested as a way to enable a simpler economy for the trading of idle resources. In this paper, we consider an IT infrastructure which benefits from both of these strategies. In such a hybrid infrastructure, computing power can be obtained from in-house dedicated resources, from resources acquired from cloud computing providers, and from resources received as donations from a P2P grid. We take a business-driven approach to the problem and try to maximise the profit that can be achieved by running applications in this hybrid infrastructure. The execution of applications yields utility, while costs may be incurred when resources are used to run the applications, or even when they sit idle. We assume that resources made available from cloud computing providers can be either reserved in advance, or bought on-demand. We study the impact that long-term contracts established with the cloud computing providers have on the profit achieved. Anticipating the optimal contracts is not possible due to the many uncertainties in the system, which stem from the prediction error on the workload demand, the lack of guarantees on the quality of service of the P2P grid, and fluctuations in the future prices of on-demand resources. However, we show that the judicious planning of long term contracts can lead to profits close to those given by an optimal contract set. In particular, we model the planning problem as an optimisation problem and show that the planning performed by solving this optimization problem is robust to the inherent uncertainties of the system, producing profits that for some scenarios can be more than double those achieved by following some common rule-of-thumb approaches to choosing reservation contracts.

Multi-dimensional admission control and capacity planning for IaaS clouds with multiple service classes

Infrastructure as a Service (IaaS) providers typically offer multiple service classes to deal with the wide variety of users adopting this cloud computing model. In this scenario, IaaS providers need to perform efficient admission control and capacity planning in order to minimize infrastructure costs, while fulfilling the different Service Level Objectives (SLOs) defined for all service classes offered. However, most of the previous work on this field consider a single resource dimension – typically CPU – when making such management decisions. We show that this approach will either increase infrastructure costs due to over-provisioning, or violate SLOs due to lack of capacity for the resource dimensions being ignored. To fill this gap, we propose admission control and capacity planning methods that consider multiple service classes and multiple resource dimensions. Our results show that our admission control method can guarantee a high availability SLO fulfillment in scenarios where both CPU and memory can become the bottleneck resource. Moreover, we show that our capacity planning method can find the minimum capacity required for both CPU and memory to meet SLOs with good accuracy. We also analyze how the load variation on one resource dimension can affect another, highlighting the need to manage resources for multiple dimensions simultaneously.

Capacity planning for IaaS cloud providers offering multiple service classes

Abstract Infrastructure as a Service (IaaS) cloud providers typically offer multiple service classes to satisfy users with different requirements and budgets. Cloud providers are faced with the challenge of estimating the minimum resource capacity required to meet Service Level Objectives (SLOs) defined for all service classes. This paper proposes a capacity planning method that is combined with an admission control mechanism to address this challenge. The capacity planning method uses analytical models to estimate the output of a quota-based admission control mechanism and find the minimum capacity required to meet availability SLOs and admission rate targets for all classes. An evaluation using trace-driven simulations shows that our method estimates the best cloud capacity with a mean relative error of 2.5% with respect to the simulation, compared to a 36% relative error achieved by a single-class baseline method that does not consider admission control mechanisms. Moreover, our method exhibited a high SLO fulfillment for both availability and admission rates, and obtained mean CPU utilization over 91%, while the single-class baseline method had values not greater than 78%.