Bu-Sung Lee

Optimization of Resource Provisioning Cost in Cloud Computing

In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumers future demand and providers resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments.

Optimal virtual machine placement across multiple cloud providers

Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.

Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute Cloud

Amazon Elastic Compute Cloud (EC2) provides a cloud computing service by renting out computational resources to customers (i.e., cloud users). The customers can dynamically provision virtual servers (i.e., computing instances) in EC2, and then the customers are charged by Amazon on a pay-per-use basis. EC2 offers three options to provision virtual servers, i.e., on-demand, reservation, and spot options. Each option has different price and yields different benefit to the customers. Spot price (i.e., price of spot option) could be the cheapest, however, the spot price is fluctuated and even more expensive than the prices of on-demand and reservation options due to supply-and-demand of available resources in EC2. Although the reservation and on-demand options have stable prices, their costs are mostly more expensive than that of spot option. The challenge is how the customers efficiently purchase the provisioning options under uncertainty of price and demand. To address this issue, two virtual server provisioning algorithms are proposed to minimize the provisioning cost for long- and short-term planning. Stochastic programming, robust optimization, and sample-average approximation are applied to obtain the optimal solutions of the algorithms. To evaluate the performance of the algorithms, numerical studies are extensively performed. The results show that the algorithms can significantly reduce the total provisioning cost incurred to customers.

Robust cloud resource provisioning for cloud computing environments

Cloud providers can offer cloud consumers two plans to provision resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, this resource provisioning is challenging due to the uncertainty. For example, consumers demand and providers resource prices can be fluctuated. Moreover, inefficiency of resource provisioning leads to either overprovisioning or underprovisioning problem. In this paper, we propose a robust cloud resource provisioning (RCRP) algorithm to minimize the total resource provisioning cost (i.e., overprovisioning and underprovisioning costs). Various types of uncertainty are considered in the algorithm. To obtain the optimal solution, a robust optimization model is formulated and solved. Numerical studies are extensively performed in which the results show that the solution obtained from the RCRP algorithm achieves both solution-and model-robustness. That is, the total resource provisioning cost is close to the optimality (i.e., solution-robustness), and the overprovisioning and underprovisioning costs are significantly reduced (i.e., model-robustness).

Economic analysis of resource market in cloud computing environment

Cloud computing has been emerged as the flexible, efficient, and economical distributed computing platform to meet the dynamic and random demand from the users. In this paper, we consider cloud computing environment with resource market between private clouds (i.e., buyers) and service providers (i.e., sellers) in public cloud. Economic analysis is proposed for different types of resource markets, i.e., monopoly (single service provider), competitive and cooperative oligopolies (few service providers). We study the optimal strategy for service provider in monopoly market, the Nash equilibria in competitive oligopoly market, and bargaining solution in cooperative oligopoly market. In addition, the decision and condition for service providers to to establish collusion in the oligopoly market are also investigated.

Cooperative Virtual Machine Management in Smart Grid Environment

We focus on the problems of cooperative virtual machine management of cloud users in a smart grid environment. In such an environment, the cloud users can cooperate to share the available computing resources in private cloud and public cloud to reduce the total cost. To achieve an optimal and fair solution, we develop the framework composed of the virtual machine allocation, cost management, and cooperation formation models. The problem is challenging due to the uncertainties (e.g., uncertain power price and unpredictable users demand). Therefore, for the virtual machine allocation, we develop the stochastic programming model to obtain the optimal solutions of virtual machines to be hosted in the local data center, to be hosted on the public cloud servers, or to be migrated to the data centers of other cooperative cloud users. Then, among cooperative cloud users, the cost management is formulated as the coalitional game whose fair share of the total cost is obtained as the Shapley value. Next, given that the cloud users are rational, we formulate the cooperation formation as the network formation game to analyze the stability of the cooperation. In the experiment, we evaluate our proposed framework with real trace data. The results clearly show that the cooperative virtual machine management can achieve the minimum total cost of cloud users compared with expected value and worst case formulations.

Adaptive Power Management for Data Center in Smart Grid Environment

We propose an adaptive power management (APM) algorithm for a data center with an objective to minimize the total cost of power bought from an electrical grid. This APM algorithm is developed for a smart grid environment which is envisioned to be a cooperative, responsive, and economical power system. In particular, APM algorithm takes the spot power price from an electrical grid, the power supply from a renewable power source, and users demand in terms of application workload processing into account when managing the power consumption. Therefore, an APM algorithm is considered to be the demand side management in a smart grid. To obtain an optimal decision of the APM algorithm, an optimization model based on stochastic programming with multi-stage recourse is developed. This optimization model considers various uncertainties and is able to determine the optimal solution for the APM algorithm. The APM algorithm is evaluated by numerical studies. The numerical results clearly show that the APM algorithm can minimize the power cost of a data center.

A Demo Paper: An Analytic Workflow Framework for Green Campus

This paper proposes a multi-tenant workflow framework that allows users to create data analytic workflows whose tasks are efficiently scheduled and distributed in cloud computing environment. We provide a demo of an event room assignment (ERA) as a test application of the framework. The ERA dynamically and automatically assigns registered events (e.g., meetings, classes, conferences, etc.) to available rooms meeting the user requirements such as the event size, purpose, reservation period, etc. The assignment will lead to the energy efficiency with respect to the power usage (e.g., lighting, ventilation, devices, etc.), and the energy savings can be achieved without affecting peoples comfort. We run the ERA with power consumption data (whose size is approximately 50GB) collected from each of over 200 rooms in a building at Dept. of Engineering, Tokyo University. Through the demonstration, we will show that the proposed framework accelerates the speed of data analysis by providing user-friendly workflow composition and parallel processing features utilizing cloud computing technologies.

Joint virtual machine allocation and power portfolio optimization for data centers in smart grid environment

Generally, data centers consume a great amount of electric power which incurs the major operating cost to a data center owner. Currently, smart grid whose one of the important features is the realtime pricing will be implemented by the public utility in a near future such that the owner could also encounter a risk of fluctuating electricity prices (i.e., spot prices) in electricity spot markets. To hedge against such a risk, the owner can sign forward contracts from electricity futures markets. In this paper, a stochastic programming model is formulated to jointly optimize the power cost in the electricity markets and the operating cost of virtual machine allocation in data centers. Numerical studies are performed to evaluate the model. The results clearly show that the proposed model can significantly reduce the cost of operating data centers under uncertainties of demand and power prices.

Capacity planning for data center to support green computing

We propose a data center resource management framework to support green computing. This framework is composed of the power and workload management, and capacity planning schemes. While an action of power and workload management is performed in a short-term basis (e.g., fraction of minute), a decision of capacity planning is made in a long-term basis (e.g., few months). This paper mainly addresses a capacity planning problem. With the power and workload management, we formulate a capacity planning optimization as a stochastic programming model. The solution is the number of servers to be installed/deployed in a data center over multiple periods. The objective of this optimization model is to minimize the long-term cost under workload demand uncertainty. From the performance evaluation, with the proposed optimization model for the capacity planning scheme, the total cost to operate the data center in the long-term basis can be minimized while the job waiting time and job blocking probability are maintained below the target thresholds.