Kurt Vanmechelen

Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads

With the recent emergence of public cloud offerings, surge computing –outsourcing tasks from an internal data center to a cloud provider in times of heavy load– has become more accessible to a wide range of consumers. Deciding which workloads to outsource to what cloud provider in such a setting, however, is far from trivial. The objective of this decision is to maximize the utilization of the internal data center and to minimize the cost of running the outsourced tasks in the cloud, while fulfilling the applications’ quality of service constraints. We examine this optimization problem in a multi-provider hybrid cloud setting with deadline-constrained and preemptible but non-provider-migratable workloads that are characterized by memory, CPU and data transmission requirements. Linear programming is a general technique to tackle such an optimization problem. At present, it is however unclear whether this technique is suitable for the problem at hand and what the performance implications of its use are. We therefore analyze and propose a binary integer program formulation of the scheduling problem and evaluate the computational costs of this technique with respect to the problem’s key parameters. We found out that this approach results in a tractable solution for scheduling applications in the public cloud, but that the same method becomes much less feasible in a hybrid cloud setting due to very high solve time variances.

Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds

Cloud computing has found broad acceptance in both industry and research, with public cloud offerings now often used in conjunction with privately owned infrastructure. Technical aspects such as the impact of network latency, bandwidth constraints, data confidentiality and security, as well as economic aspects such as sunk costs and price uncertainty are key drivers towards the adoption of such a hybrid cloud model. The use of hybrid clouds introduces the need to determine which workloads are to be outsourced, and to what cloud provider. These decisions should minimize the cost of running a partition of the total workload on one or multiple public cloud providers while taking into account the application requirements such as deadline constraints and data requirements. The variety of cost factors, pricing models and cloud provider offerings to consider, further calls for an automated scheduling approach in hybrid clouds. In this work, we tackle this problem by proposing a set of algorithms to cost-efficiently schedule the deadline-constrained bag-of-tasks applications on both public cloud providers and private infrastructure. Our algorithms take into account both computational and data transfer costs as well as network bandwidth constraints. We evaluate their performance in a realistic setting with respect to cost savings, deadlines met and computational efficiency, and investigate the impact of errors in runtime estimates on these performance metrics.

Economics of Grids, Clouds, Systems, and Services

The paper focuses the attention to different business models and intended strategic aims of the firms providing Software-as-a-Service (SaaS). SaaS vendors have been said to challenge the business practices of the existing vendors providing proprietary or customer-specific solutions. The current studies on the topic have shown that SaaS is different from preceding software business models, but consider and emphasize SaaS business model as an invariable configuration. This case study compares two SaaS firms with different backgrounds and reveals characteristics of two very different SaaS business models. The findings indicate that along with SaaS vendors providing only standard software applications and focusing on cost efficiency, there are vendors who provide more specialized software applications and complement the SaaS offering with services required by larger customers.

Combining Futures and Spot Markets: A Hybrid Market Approach to Economic Grid Resource Management

Economic forms of resource management in which users can express their valuations for service, offer new possibilities for optimizing resource allocations in Grids. If users are to correctly express these valuations, quality of service guarantees need to be given with respect to the turnaround time of their workloads. Market mechanisms that support bidding and allocations in future time are crucial for delivering such guarantees. To deal with the significant delays that these mechanisms introduce in the allocation process, we present a hybrid market approach in which a low-latency spot market coexists with a higher latency futures market. Based on simulated market scenarios, we show how this combination can significantly increase the total value realized by the Grid infrastructure. We also demonstrate how providers can react to price dynamics in such a hybrid market setting.

Scalability of Grid Simulators: An Evaluation

Due to the distributed nature of resources in grids that cover multiple administrative domains, grid resource management cannot be optimally implemented using traditional approaches. In order to investigate new grid resource management systems, researchers utilize simulators which allows them to efficiently evaluate new algorithms on a large scale. We have developed the Grid Economics Simulator (GES) in support of research into grid resource management in general and economic grid resource management in particular. This paper compares GES to SimGrid and GridSim, two established grid simulation frameworks. We demonstrate that GES compares favourably to the other frameworks in terms of scalability, runtime performance and memory requirements. We explain how these differences are related to the simulation paradigm and the threading model used in each simulator.

PRICING SUBSTITUTABLE GRID RESOURCES USING COMMODITY MARKET MODELS

Enhancing Grid technology with market models for trading resources, is a promising step for Grids to become open systems that allow for user-centric service provisioning. This paper introduces a market model for trading substitutable Grid resources in a commodity market. We develop a pricing scheme and evaluate the market mechanisms through simulation. We show that the resource market achieves price stability and correctness, allocative eciency and fairness.

A Simulation Framework for Studying Economic Resource Management in Grids

Economic principles are increasingly being regarded as a way to address conflicting user requirements, to improve the effectiveness of grid resource management systems, and to deliver incentives for providers to join virtual organizations. Because economic resource management mechanisms can encourage grid participants to reveal the true valuations of their jobs and resources, the system becomes capable of making better scheduling decisions. A lot of exploratory research into different market mechanisms for grids is ongoing. Since it is impractical to conduct analysis of novel mechanisms on operational grids, most of this research is being carried out using simulation. This paper presents the Grid Economics Simulator (GES) in support of such research. The key design goals of the framework are enabling a wide variety of economic and non-economic forms of resource management while simultaneously supporting distributed execution of simulations and exhibiting good scalability properties.

Revenue Maximization with Optimal Capacity Control in Infrastructure as a Service Cloud Markets

Infrastructure-as-a-Service cloud providers offer diverse purchasing options and pricing plans, namely on-demand, reservation, and spot market plans. This allows them to efficiently target a variety of customer groups with distinct preferences and to generate more revenue accordingly. An important consequence of this diversification however, is that it introduces a non-trivial optimization problem related to the allocation of the providers available data center capacity to each pricing plan. The complexity of the problem follows from the different levels of revenue generated per unit of capacity sold, and the different commitments consumers and providers make when resources are allocated under a given plan. In this work, we address a novel problem of maximizing revenue through an optimization of capacity allocation to each pricing plan by means of admission control for reservation contracts, in a setting where aforementioned plans are jointly offered to customers. We devise both an optimal algorithm based on a stochastic dynamic programming formulation and two heuristics that trade-off optimality and computational complexity. Our evaluation, which relies on an adaptation of a large-scale real-world workload trace of Google, shows that our algorithms can significantly increase revenue compared to an allocation without capacity control given that sufficient resource contention is present in the system. In addition, we show that our heuristics effectively allow for online decision making and quantify the revenue loss caused by the assumptions made to render the optimization problem tractable.

Economic Grid Resource Management for CPU Bound Applications with Hard Deadlines

The introduction of economic principles in grid resource management provides an interesting avenue for efficiently addressing the problem of conflicting user requirements. In shared computing infrastructures such as grids, such conflicting requirements are prevalent and stem from the selfish actions users follow when formulating their service requests. We develop and analyze both a centralized and a decentralized algorithm for economic resource management in the context of consumer requests for CPU bound applications with deadline-based QoS requirements and non- migratable workloads. A comparison with an algorithm recently proposed in the literature is presented with a focus on performance in terms of realized consumer value. We establish that our algorithms perform well and that they compare favorably to existing approaches.

Conservative Distributed Discrete Event Simulation on Amazon EC2

A discrete-event simulators ability to distribute the execution of a simulation model allows one to deal with the memory limitations of a single computational resource, and thereby increase the scale or level of detail at which models can be studied. In addition, distribution has the potential to reduce the round trip time of a simulation by incorporating multiple computational cores into the simulations execution. However, such gains can be voided by the overhead that time synchronization protocols introduce. These protocols are required to prevent the occurrence of causality errors during a parallel execution of a simulation. The overhead depends on the protocol, characteristics of the simulation model, and the architecture of the computational resources used. Recently, infrastructure-as-a-service offerings in cloud computing have introduced flexibility in acquiring computational resources on a pay-as-you-go basis. At present, it is unclear to what extent these offerings are suited for the distributed execution of discrete-event simulations, and how the characteristics of different resource types impact the runtime performance of distributed simulations. In this paper we investigate this issue, and assess the performance of different conservative time synchronization protocols on a range of cloud resource types that are currently available on Amazon EC2.