A Tree-based protocol for enforcing quotas in clouds

Services are increasingly being hosted on cloud nodes to enhance their performance and increase their availability. The virtually unlimited availability of cloud resources enables service owners to consume resources without quantitative restrictions, paying only for what they use. To avoid cost overruns, resource consumption must be controlled and capped when necessary. We present a distributed tree-based protocol for managing quotas in clouds that minimizes communication overheads and reduces the time required to determine whether a quota has been exhausted. Experimental evaluation shows that our protocol reduces communication costs by 42% relative to a distributed baseline solution and is up to 15 times faster

Energy Aware Resource Allocation for Clouds Using Two Level Ant Colony Optimization

In cloud environment resources are dynamically allocated, adjusted, and deallocated. When to allocate and how many resources to allocate is a challenging task. Resources allocated optimally and at the right time not only improve the utilization of resources but also increase energy efficiency, provider's profit and customers' satisfaction. This paper presents ant colony optimization (ACO) based energy aware solution for resource allocation problem. The proposed energy aware resource allocation (EARA) methodology strives to optimize allocation of resources in order to improve energy efficiency of the cloud infrastructure while satisfying quality of service (QoS) requirements of the end users. Resources are allocated to jobs according to their QoS requirements. For energy efficient and QoS aware allocation of resources, EARA uses ACO at two levels. First level ACO allocates Virtual Machines (VMs) resources to jobs whereas second level ACO allocates Physical Machines (PMs) resources to VMs. Server consolidation and dynamic performance scaling of PMs are employed to conserve energy. The proposed methodology is implemented in CloudSim and the results are compared with existing popular resource allocation methods. Simulation results demonstrate that EARA achieves desired QoS and superior energy gains through better utilization of resources. EARA outperforms major existing resource allocation methods and achieves up to 10.56 % saving in energy consumption

The Contemporary Review of Notable Cloud Resource Scheduling Strategies

Cloud computing has become a revolutionary development that has changed the dynamics of business for the organizations and in IT infrastructure management. While in one dimension, it has improved the scope of access, reliability, performance and operational efficiency, in the other dimension, it has created a paradigm shift in the way IT systems are managed in an organizational environment. However, with the increasing demand for cloud based solutions, there is significant need for improving the operational efficiency of the systems and cloud based services that are offered to the customers. As cloud based solutions offer finite pool of virtualized on-demand resources, there is imperative need for the service providers to focus on effective and optimal resource scheduling systems that could support them in offering reliable and timely service, workload balancing, optimal power efficiency and performance excellence. There are numerous models of resource scheduling algorithms that has been proposed in the earlier studies, and in this study the focus is upon reviewing varied range of resource scheduling algorithms that could support in improving the process efficiency. In this manuscript, the focus is upon evaluating various methods that could be adapted in terms of improving the resource scheduling solutions

Dynamic power management for QoS-aware applications

Reducing the power requirement of large IT infrastructures is becoming a major concern. Energy savings can be achieved with hardware and/or software solutions; in particular, modern CPUs can operate at different power levels that can be selected by software: low power modes reduce energy consumption at the cost of lowering also the CPU processing rate. In this paper we address the problem of reducing energy consumption of a large-scale distributed application subject to Service Level Agreements requiring a maximum allowed response time. Specifically, we propose Energy Aware reconfiguration of software SYstems (EASY), an on-line algorithm for dynamically adjusting the processing speed of individual devices such that the average system response time is kept below a predefined threshold, and the total power consumption is minimized. EASY uses a queueing networks performance model to proactively drive the reconfiguration process, so that the number of individual reconfiguration actions is reduced. We formulate the energy conservation problem as a Mixed Integer Programming problem, for which we propose a heuristic solution technique. Numerical experiments show that the heuristic produces almost optimal results at a substantially lower computational cost. Therefore, EASY can be effectively applied on-line to make a large system energy-proportional