Power capping is widely used in cloud datacenters to mitigate power over-provisioning problem, thus improve datacenter capacity and cut off their operation cost. However, inappropriate or aggressive power capping may lead to performance degradation of applications (especially latency-sensitive ones), and there are few effective methods that can accurately evaluate and control such negative impact caused by aggressive power capping. In this paper, we proposeFine-Grained Differential Method(FGD) to quantitatively analyze how inappropriate power capping degrades the performance of latency-sensitive applications. By using FGD, we can minimize the provisioned power for each server by setting a precise power budget according to application’sService Level Agreement(SLA). And we further proposePrecise Power Capping(PPCapping) which is designed to increase the datacenter capacity with a fixed power supply by means of FGD. Our research also provides an insight of precise tradeoff between applications’ SLAs and datacenter capacity. We verify FGD and PPCapping by using real world traces from Tencent’s datecenter with 25,328 servers. The experimental results show that FGD can accurately analyze the impact of power capping on the performance of latency-sensitive applications, and PPCapping can effectively increase datacenter capacity compared with the typical power provisioning strategy.

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm.

Taking full advantages of two cloud-based techniques, i.e., cloud radio access network (C-RAN) and mobile cloud computing (MCC), mobile operators will be able to provide the good service to the mobile user as well as increasing their revenue. This paper aims to minimize the mobile operator's cost while at the same time, meet the task time constraints of the mobile users. In particular, we assume that the mobile cloud first completes the tasks for the mobile user and then transmits the results back to the users through C-RAN. Joint cost-effective resource allocation is proposed between MCC and C-RAN and simulation results confirm that the proposed cost minimization and resource allocation solution outperforms nonoptimal solutions.

Cloud elasticity allows users to dynamically allocate resources for their applications to adapt with the fluctuant demand. But allocating right amount of resources at right time to handle the bursting workload is still challenging. Most practical auto scaling approaches allocate resources in horizontal or vertical manner while the horizontal scaling usually causes considerable overhead and extra cost for short-term bursting workload. Vertical scaling is lightweight and timely but lacks scalability and capacity guarantee in public cloud. In order to find an accurate and cost-effective auto scaling method, we propose a hybrid auto scaling solution called HybridScaler which combines long-term predictive horizontal scaling and timely reactive vertical scaling properly. Specially, our method is based on a resource-pressure model which can provide suitable amount of resources matching the changing workload. We implement our prototype in an OpenStack private cloud. We evaluate the effectiveness and efficiency of HybridScaler by comparing with mainstream auto scaling methods. HybridScaler decreases 16-39% average response time and 34-50% SLO violation rate than both static threshold-based scheme and prediction-based scheme. Meanwhile, it uses less instance-hours than static threshold-based scaling method and keeps CPU utilization almost between 60% and 70% which can avoid significant resource waste and SLO violation in the other methods.

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile users' devices to provide better quality of service (QoS). But the power consumption has become skyrocketing for MSP as it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MCC separately while less work had considered the integration of C-RAN with MCC. In this paper, we present a unifying framework for optimizing the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MCC to minimize the power consumption of MSP while still guaranteeing the QoS for mobile users. Our objective is to maximize the profit of MSP. To achieve this objective, we first formulate the resource scheduling issue as a stochastic problem and then propose a Resource onlIne sCHeduling (RICH) algorithm using Lyapunov optimization technique to approach a time average profit that is close to the optimum with a diminishing gap (1/V) for MSP while still maintaining strong system stability and low congestion to guarantee the QoS for mobile users. With extensive simulations, we demonstrate that the profit of RICH algorithm is 3.3× (18.4×) higher than that of active (random) algorithm.

Although many pricing schemes in IaaS platform are already proposed with pay-as-you-go and subscription/spot market policy to guarantee service level agreement, it is still inevitable to suffer from wasteful payment because of coarse-grained pricing scheme. In this paper, we investigate an optimized fine-grained and fair pricing scheme. Two tough issues are addressed: (1) the profits of resource providers and customers often contradict mutually; (2) VM-maintenance overhead like startup cost is often too huge to be neglected. Not only can we derive an optimal price in the acceptable price range that satisfies both customers and providers simultaneously, but we also find a best-fit billing cycle to maximize social welfare (i.e., the sum of the cost reductions for all customers and the revenue gained by the provider). We carefully evaluate the proposed optimized fine-grained pricing scheme with two large-scale real-world production traces (one from Grid Workload Archive and the other from Google data center). We compare the new scheme to classic coarse-grained hourly pricing scheme in experiments and find that customers and providers can both benefit from our new approach. The maximum social welfare can be increased up to 72.98 and 48.15 percent with respect to DAS-2 trace and Google trace respectively.