182,063 research outputs found
Cost-efficient Datacentre Consolidation for Cloud Federations
Cloud Computing has become mature enough to enable the virtualized management of multiple datacentres. Datacentre consolidation is an important method for the efficient operation of such distributed infrastructures. Several approaches have been developed to improve the efficiency e.g. in terms of power consumption, but only a few attention has been turned to combining pricing methods with consolidation techniques. In this paper we discuss how we introduced cost models to the DISSECT-CF simulator to foster the development of cost efficient datacentre consolidation solutions. We also exemplify the usage of this extended simulator by performing cost-aware datacentre consolidation. We apply real world traces to simulate cloud load, and propose 7 strategies to address the problem
Decentralized load balancing in heterogeneous computational grids
With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research
Energy-aware Load Balancing Policies for the Cloud Ecosystem
The energy consumption of computer and communication systems does not scale
linearly with the workload. A system uses a significant amount of energy even
when idle or lightly loaded. A widely reported solution to resource management
in large data centers is to concentrate the load on a subset of servers and,
whenever possible, switch the rest of the servers to one of the possible sleep
states. We propose a reformulation of the traditional concept of load balancing
aiming to optimize the energy consumption of a large-scale system: {\it
distribute the workload evenly to the smallest set of servers operating at an
optimal energy level, while observing QoS constraints, such as the response
time.} Our model applies to clustered systems; the model also requires that the
demand for system resources to increase at a bounded rate in each reallocation
interval. In this paper we report the VM migration costs for application
scaling.Comment: 10 Page
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