2 research outputs found
A Self-adaptive Agent-based System for Cloud Platforms
Cloud computing is a model for enabling on-demand network access to a shared
pool of computing resources, that can be dynamically allocated and released
with minimal effort. However, this task can be complex in highly dynamic
environments with various resources to allocate for an increasing number of
different users requirements. In this work, we propose a Cloud architecture
based on a multi-agent system exhibiting a self-adaptive behavior to address
the dynamic resource allocation. This self-adaptive system follows a MAPE-K
approach to reason and act, according to QoS, Cloud service information, and
propagated run-time information, to detect QoS degradation and make better
resource allocation decisions. We validate our proposed Cloud architecture by
simulation. Results show that it can properly allocate resources to reduce
energy consumption, while satisfying the users demanded QoS
PREDICTIONS & MODELING ENERGY CONSUMPTION FOR IT DATA CENTER INFRASTRUCTURE
International audienceRecent statistics of energy consumption by Cloud datacenter show the DCs consumes more and more energy each year .that crated big challenge in Cloud research. IT industry is keenly aware of the need for Green Cloud solutions that save energy consumption in Cloud DCs. A great deal of attention has been paid to minimize energy consumption in cloud datacenter. However, to understand the relationships between running tasks and energy consumed by hardware we need to propose mathematical models of energy consumption. The models of energy consumption can be help as to saving energy. Both researchers aim to proposed mechanism for energy consumption. In this paper, we analyzed the relationships between Cloud system manager and energy consumption. This paper aims at proposing and designing energy consumption models with mechanism of prediction energy