Power efficiency is one of the main issues that will drive the design of data centers, especially of those devoted to provide Cloud computing services. In virtualized data centers, consolidation of Virtual Machines (VMs) on the minimum number of physical servers has been recognized as a very efficient approach, as this allows unloaded servers to be switched off or used to accommodate more load, which is clearly a cheaper alternative to buy more resources. The consolidation problem must be solved on multiple dimensions, since in modern data centers CPU is not the only critical resource: depending on the characteristics of the workload other resources, for example, RAM and bandwidth, can become the bottleneck. The problem is so complex that centralized and deterministic solutions are practically useless in large data centers with hundreds or thousands of servers. This paper presents ecoCloud, a self-organizing and adaptive approach for the consolidation of VMs on two resources, namely CPU and RAM. Decisions on the assignment and migration of VMs are driven by probabilistic processes and are based exclusively on local information, which makes the approach very simple to implement. Both a fluid-like mathematical model and experiments on a real data center show that the approach rapidly consolidates the workload, and CPU-bound and RAM-bound VMs are balanced, so that both resources are exploited efficiently.

Geographically distributed data centers (DCs) offer promising business opportunities to both big companies that own several sites and multi-owner inter-cloud infrastructures. In these scenarios, workload management is a particularly challenging task, since the autonomy of single DCs should be preserved while global objectives, such as cost reduction and load balance, should be achieved. In this paper, a hierarchical approach for workload management in geographically distributed DCs is presented. The proposed solution is composed of two algorithms devoted to workload assignment and migration. Both algorithms are based on the computation of a simple function that represents the cost of running some workload in the different sites of the distributed DC. The framework requires a very limited exchange of state information among the sites and preserves the autonomy of single DCs and, at the same time, allows for an integrated management of heterogeneous platforms. Performance is analyzed for a specific infrastructure composed of four DCs, with two goals: 1) load balance and 2) energy cost reduction. Results show that the proposed approach smoothly adapts the workload distribution to variations of energy cost and load, while achieving the desired combination of management objectives.

Traditional information management approaches in distributed systems are most often unsuitable for modern Internet of Things (IoT) environments due to the huge amount and the extreme dynamism of the entities involved. Smart objects (enhanced devices or IoT services belonging to a smart system) interact and maintain relations which need of effective and efficient handling mechanisms better to meet users and services requirements. This paper proposes an agent-based algorithm for achieving a distributed resources organization in an IoT environment. Software agents, representing each a single smart object, execute a set of local computations simultaneously, enabling an organized structure to emerge at global level. A natural language processing approach, able to capture the semantic context and represent objects with high-dimensional vectors, was exploited to map the IoT objects, so enabling advanced agents’ operations. The emerging organized virtual structure, i.e. a similarity based overlay network of agents, allows building informed resource selection/discovery services, therefore more efficient, in a highly dynamic and, basically, unstructured environment. Preliminary results confirm the validity of the approach.

The ICT market is experiencing an important shift from the request/provisioning of products toward a service-oriented view where everything (computing, storage, applications) is provided as a network-enabled service. It often happens that a solution to a problem cannot be offered by a single service, but by composing multiple basic services in a workflow. Service composition is indeed an important research topic that involves issues such as the design and execution of a workflow and the discovery of the component services on the network. This paper deals with the latter issue and presents an ant-inspired framework that facilitates collective discovery requests, issued to search a network for all the basic services that will compose a specific workflow. The idea is to reorganize the services so that the descriptors of services that are often used together are placed in neighbor peers. This helps a single query to find multiple basic services, which decreases the number of necessary queries and, consequently, lowers the search time and the network load.