Shahin Vakilinia

Energy Efficient Resource Allocation in Cloud Computing Environments

Power consumption is one of the major concerns for the cloud providers. The issue of disorganized power consumption can be categorized into two main groups: one caused by server operations and one occurred during the network communications. In this paper, a platform for virtual machine (VM) placement/migration is proposed to minimize the total power consumption of cloud data centers (DCs). The main idea behind this paper is that with the collaboration of optimization scheduling and estimation techniques, the power consumption of DC can be optimally lessened. In the platform, an estimation module has been embedded to predict the future loads of the system, and then, two schedulers are considered to schedule the expected and unpredicted loads, respectively. The proposed scheduler applies the column generation technique to handle the integer linear/quadratic programming optimization problem. Also, the cut-and-solve-based algorithm and the call back method are proposed to reduce the complexity and computation time. Finally, numerical and experimental results are presented to validate our findings. Adaptation and scalability of the proposed platform result in a notable performance in VM placement and migration processes. We believe that our work advances the state of the art in workload estimation and dynamic power management of cloud DCs, and the results will be helpful to cloud service providers in achieving energy saving.

Volume-based pricing in Stackelberg duopoly wireless markets

Volume based data pricing is getting to be the most popular pricing scheme in cellular wireless data networks. While the flat-rate and unlimited data plans are being replaced with this new criterion worldwide, anticipating the provider profit is highly vital in a competitive market. The user behavior acts like a foundation for any analytical structure and it is linked to many network and market parameters. In this paper, we define an analytical framework in a Stackelberg duopoly market in which each provider offers one data plan. Similar to general Stackelberg markets, one provider has better service quality in terms of coverage and offered data rate that puts it in the place to make the first move in service definition. Users are concerned about the quality of service while they have a limitation on their budget as well. We adopt the widely accepted utility theory to build our analysis. In this paper, the budget limits along with negative service quality due to existence of other users and offered data volume size are applied in the user utility and consequently provider profit to build our optimization framework.

Dynamic resource allocation of smart home workloads in the cloud

Cloud computing offers provision for elastic and scalable infrastructure resource allocation across the network that allows deployment of services for controlling home devices and appliances. Data generated from heterogeneous smart home devices are processed in different application services deployed in the cloud data center. The primary challenge of smart home service providers is to optimize the cloud resource allocation while satisfying the Quality of Service(QoS) constraints of the application services. Service execution time is one of the most vital QoS parameters. In this paper, a queuing theoretic approach is proposed to model the smart home workload. First, M/M/c queue model is applied to find the response time of smart home tasks with light variation over the arrival rate. Then, Markovian Modulated Poisson Process (MMPP) is used to extend the model to a more advanced type of smart home processing workloads. Next, the optimal number of Virtual Machines(VMs) required deploying the application servers that can satisfy the execution time constraint of incoming workloads is calculated. Finally, total service time of a smart home application is calculated considering into account the possible level of concurrency and dependency among tasks of an application service. In the end, some numerical and simulation examples are provided to validate our findings.

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.

Let's adapt to network change: Towards energy saving with rate adaptation in SDN

The exponential growth of network users and their communication demands have led to a tangible increment of energy consumption in network infrastructures. A new networking paradigm called Software-Defined Networking (SDN) recently emerged which simplifies network management by offering programmability of network devices. SDN assists to lower link data rates via rate-adaptation technique which reduces power consumption of the network. The main idea behind this paper is to find a distribution of traffic flows over pre-calculated paths which allow adapting the transmission rate of maximum links into lower states. We first formulate the problem as a Mixed Integer Linear Program (MILP) problem. We then present four different computationally efficient algorithms namely greedy first fit, greedy best fit, greedy worst fit and a meta-heuristic genetic algorithm to solve the problem for a realistic network topology. Simulation results show that the genetic algorithm consistently outperforms the three greedy algorithms.