Utility and Cloud Computing 

Abstract: The advent of cloud computing promises to provide computational resources to customers like public utilities such as water and electricity. To deal with dynamically fluctuating resource demands, market-driven resource allocation has been proposed and recently implemented by public Infrastructure-as-a-Service (IaaS) providers like Amazon EC2. In this environment, cloud resources are offered in distinct types of virtual machines (VMs) and the cloud provider runs an auction-based market for each VM type with the goal of achieving maximum revenue over time. However, as demand for each type of VMs can fluctuate over time, it is necessary to adjust the capacity allocated to each VM type to match the demand in order to maximize total revenue while minimizing the energy cost. In this paper, we consider the case of a single cloud provider and address the question how to best match customer demand in terms of both supply and price in order to maximize the providers revenue and customer satisfactions while minimizing energy cost. In particular, we model this problem as a constrained discrete-time optimal control problem and use Model Predictive Control (MPC) to find its solution. Simulation studies using real cloud workloads indicate that under dynamic workload conditions, our proposed solution achieves higher net income than static allocation strategies and minimizes the average request waiting time.

Abstract: Cloud computing is currently one of the most hyped information technology fields and it has become one of the fastest growing segments of IT. Cloud computing allows us to scale our servers in magnitude and availability in order to provide services to a greater number of end users. Moreover, adopters of the cloud service model are charged based on a pay-per-use basis of the cloud's server and network resources, aka utility computing. With this model, a conventional DDoS attack on server and network resources is transformed in a cloud environment to a new breed of attack that targets the cloud adopter's economic resource, namely Economic Denial of Sustainability attack (EDoS). In this paper, we advocate a novel solution, named EDoS-Shield, to mitigate the Economic Denial of Sustainability (EDoS) attack in the cloud computing systems. We design a discrete simulation experiment to evaluate its performance and the results show that it is a promising solution to mitigate the EDoS.

Abstract: Power consumption is one of the most critical problems in data centers. One effective way to reduce power consumption is to consolidate the hosting workloads and shut down physical machines which become idle after consolidation. Server consolidation is a NP-hard problem. In this paper, we propose two new algorithms, Dynamic Round-Robin (DRR) and Hybrid, which combines DRR and First-Fit, for energy aware virtual machine scheduling and consolidation. We also propose an accurate power model to estimate the power consumption resulted from each algorithm. Strategies we proposed are compared with GREEDY, ROUNDROBIN and POWERSAVE scheduling strategies implemented in the Eucalyptus Cloud system. Our experiment results show that our DRR and Hybrid algorithms reduce power consumption by 56.4% and 55.9% respectively, compared with the ROUNDROBIN scheduling strategy in Eucalyptus. DDR and Hybrid also result in 3% less power consumption on average, compared with the POWERSAVE scheduling strategy in Eucalyptus.

Abstract: Low latency in IT applications is an important aspect of improving the quality of the user's experience. Frequently, applications are run in a virtual machine in the cloud. Because cloud providers are datacentre facilities that are often distant from users, unacceptably high latencies are experienced in some applications. Fog computing can be seen as a cloud computing extension, namely cloudlets, located in access points at the edge of the network and hence able to provide lower latencies than the cloud. However, as mobile devices and applications become more popular, users' computing and data capacities should be maintained close to the user to keep latencies as low as possible. This paper discusses resource allocation in fog computing in the face of users' mobility and introduces MyiFogSim, an extension of iFogSim to support mobility through migration of virtual machines between cloudlets. Moreover, a migration policy is proposed, and MyiFogSim is used to analyze the policy impact on application quality of service. Results suggest that the policy can promote lower latencies when compared to a scenario without the migration policy.