Cost reduction bounds of proactive management based on request prediction

International audienceData Centers (DCs) need to manage their servers periodically to meet user demand efficiently. Since the cost of the energy employed to serve the user demand is lower when DC settings (e.g. number of active servers) are done a priori (proactively), there is a great interest in studying different proactive strategies based on predictions of requests. The amount of savings in energy cost that can be achieved depends not only on the selected proactive strategy but also on the statistics of the demand and the predictors used. Despite its importance, due to the complexity of the problem it is difficult to find studies that quantify the savings that can be obtained. The main contribution of this paper is to propose a generic methodology to quantify the possible cost reduction using proactive management based on predictions. Thus, using this method together with past data it is possible to quantify the efficiency of different predictors as well as optimize proactive strategies. In this paper, the cost reduction is evaluated using both ARMA (Auto Regressive Moving Average) and LV (Last Value) predictors. We then apply this methodology to the Google dataset collected over a period of 29 days to evaluate the benefit that can be obtained with those two predictors in the considered DC

Column generation integer programming for allocating jobs with periodic demand variations

International audienceIn the context of service hosting in large-scale datacenters, we consider the problem faced by a provider for allocating services to machines. An analysis of a public Google trace corresponding to the use of a production cluster over a long period shows that long-running services experience demand variations with a periodic (daily) pattern, and that services with such a pattern account for most of the overall CPU demand. This leads to an allocation problem where the classical Bin-Packing issue is augmented with the possibility to co-locate jobs whose peaks occur at different times of the day, which is bound to be more efficient than the usual approach that consist in over-provisioning for the maximum demand. In this paper, we propose a column-generation approach to solving this problem, where the subproblem uses a sophisticated SOCP (Second Order Cone Program) formulation. This allows to explicitely select jobs which benefit from being co-allocated together. Experimental results comparing with theoretical lower bounds and with standard packing heuristics shows that this approach is able to provide very efficient assignments in reasonable time

Cost reduction bounds of proactive management based on request prediction

International audienceData Centers (DCs) need to manage their servers periodically to meet user demand efficiently. Since the cost of the energy employed to serve the user demand is lower when DC settings (e.g. number of active servers) are done a priori (proactively), there is a great interest in studying different proactive strategies based on predictions of requests. The amount of savings in energy cost that can be achieved depends not only on the selected proactive strategy but also on the statistics of the demand and the predictors used. Despite its importance, due to the complexity of the problem it is difficult to find studies that quantify the savings that can be obtained. The main contribution of this paper is to propose a generic methodology to quantify the possible cost reduction using proactive management based on predictions. Thus, using this method together with past data it is possible to quantify the efficiency of different predictors as well as optimize proactive strategies. In this paper, the cost reduction is evaluated using both ARMA (Auto Regressive Moving Average) and LV (Last Value) predictors. We then apply this methodology to the Google dataset collected over a period of 29 days to evaluate the benefit that can be obtained with those two predictors in the considered DC

Analyzing Real Cluster Data for Formulating Allocation Algorithms in Cloud Platforms

A problem commonly faced in Computer Science research is the lack of real usage data that can be used for the validation of algorithms. This situation is particularly true and crucial in Cloud Computing. The privacy of data managed by commercial Cloud infrastructures, together with their massive scale, make them very uncommon to be available to the research community. Due to their scale, when designing resource allocation algorithms for Cloud infrastructures, many assumptions must be made in order to make the problem tractable. This paper provides deep analysis of a cluster data trace recently released by Google and focuses on a number of questions which have not been addressed in previous studies. In particular, we describe the characteristics of job resource usage in terms of dynamics (how it varies with time), of correlation between jobs (identify daily and/or weekly patterns), and correlation inside jobs between the different resources (dependence of memory usage on CPU usage). From this analysis, we propose a way to formalize the allocation problem on such platforms, which encompasses most job features from the trace with a small set of parameters.Simulation de systèmes de prochaine génératio

Multi-attribute demand characterization and layered service pricing

As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage. Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare