Environment, Services and Network Management for Green Clouds

Green cloud computing aims at a processing infrastructure that combines flexibility, quality of services, and reduced energy utilisation. In order to achieve this objective, the management solution must regulate the internal settings to address the pressing issue of data centre over-provisioning related to the need to match the peak demand. In this context, we propose an integrated solution for environment, services and network management based on organisation model of autonomous agent components. This work introduces the system management model, analyses the system's behaviour, describes the operation principles, and presents a case study scenario and some results. We extended CloudSim to simulate the organisation model approach and implemented the migration and reallocation policies using this improved version to validate our management solution.


Introduction
• We extended CloudSim to simulate the organization model approach and implemented the migration and reallocation policies using this improved version to validate our management solution.
• Organization: 2 introduces a motivating scenario. 3 outlines the system design. 4 presents case studies. 5 presents some results.
• Our research was motivated by a practical scenario at our university's data center. • Organization theory model for integrated management of the green clouds focusing management of the green clouds focusing on: • (i) optimizing resource allocation through predictive models; • (ii) coordinating control over the multiple elements, reducing the infrastructure utilization; • (iii) promoting the "balance" between local • (iii) promoting the "balance" between local and remote resources; and • (iv) aggregating energy management of network devices.
Cloud computing • This structure describes the most common implementation of cloud; and • It is based on server virtualization • It is based on server virtualization functionalities, where there is a layer that abstracts the physical resources of the servers and presents them as a set of resources to be shared by VMs. Green cloud • The green cloud is not very different from cloud computing, but it infers a concern over the structure and the social over the structure and the social responsibility of energy consumption; and • Hence aiming to ensure the infrastructure sustainability without breaking contracts. Analysis • Table I  • To understand the problem scenario, we introduce the elements, interactions, and operation principles in green clouds. • The target in green clouds is: how to keep • The target in green clouds is: how to keep resources turned off as long as possible? • The interactions and operation principles of the scenario are:

The NIST Cloud Definition Framework
• (i) there are multiple applications generating different load requirements over the day; • (ii) a load "balance" system distributes the load to active servers in the processing pool; load to active servers in the processing pool; • (iii) the resources are grouped in clusters that include servers and local environmental control units; and • (iv) the management system can turn on/off machines overtime, but the question is when to activate resources on-demand? • In other words, taking too much delay to • In other words, taking too much delay to activate resources in response to a surge of demand (too reactive) may result in the shortage of processing power for a while.

Case Studies
• We modeled the system using Norms (NM), Beliefs (BL) and Plan Rules (PR), inferring that we would need (NM) to reduce energy consumption. consumption. • Based on inferences from NM, BL and PR agents would monitor the system and determine actions dynamically. We achieved the following results in the test environment: -Dynamic physical orchestration and service orchestration led to 87,18% energy savings, orchestration led to 87,18% energy savings, when compared to static approaches; and -Improvement in load "balancing" and high availability schemas provide up to 8,03% SLA error decrease.

Future Works
• As future work we intend to simulate other strategies to get a more accurate feedback of the model, using other simulation environment and testing different approaches of beliefs and plan rules. rules. • Furthermore, we would like to exploit the integration of other approaches from the field of artificial intelligence, viz. bayesian networks, advanced strategies of intention reconsideration, and improved coordination in multi-agent systems.