A cluster-based decentralized job dispatching for the large-scale cloud.

The remarkable development of cloud computing in the past few years, and its proven ability to handle web hosting workloads, is prompting researchers to investigate whether clouds are suitable to run large-scale computations. Cloud load balancing is one of the solution to provide reliable and scalable cloud services. Especially, load balancing for the multimedia streaming requires dynamic and real-time load balancing strategies. With this context, this paper aims to propose an Inter Cloud Manager (ICM) job dispatching algorithm for the large-scale cloud environment. ICM mainly performs two tasks: clustering (neighboring) and decision-making. For clustering, ICM uses Hello packets that observe and collect data from its neighbor nodes, and decision-making is based on both the measured execution time and network delay in forwarding the jobs and receiving the result of the execution. We then run experiments on a large-scale laboratory test-bed to evaluate the performance of ICM, and compare it with well-known decentralized algorithms such as Ant Colony, Workload and Client Aware Policy (WCAP), and the Honey-Bee Foraging Algorithm (HFA). Measurements focus in particular on the observed total average response time including network delay in congested environments. The experimental results show that for most cases, ICM is better at avoiding system saturation under the heavy load.N/

Cloud computing for energy management in smart grid - an application survey

The smart grid is the emerging energy system wherein the application of information technology, tools and techniques that make the grid run more efficiently. It possesses demand response capacity to help balance electrical consumption with supply. The challenges and opportunities of emerging and future smart grids can be addressed by cloud computing. To focus on these requirements, we provide an in-depth survey on different cloud computing applications for energy management in the smart grid architecture. In this survey, we present an outline of the current state of research on smart grid development. We also propose a model of cloud based economic power dispatch for smart grid

Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

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