Processing surges are fast and unexpected changes in the processing demand that commonly occur in cloud computing. The cloud elasticity enables to handle processing surges, increasing and decreasing resources as required. However, a surge can be very fast, so that the overhead to provide more resource is greater than the processing benefit. On the other hand, if the surge is slow and continuous, and the required resources are not provided, the application performance may be impaired or interrupted. This paper presents a machine learning-based approach to detect and classify processing surges, in order to improve the cloud resource management, minimizing losses for the application and cloud provider. We use a real cloud dataset to select features, to construct the classifier and to test our approach, which successfully detected and classified 99% of the processing surges.

Internet of Things (IoT) brings significant challenges to authentication schemes in a scenario with several appliances for a smart house that should be accessed by a technician for maintenance tasks, for instance. An Identity Management (IdM) can be applied to easily authenticate a technician that intend to access the appliances from the Internet. However, Internet context is significantly different from IoT, demanding context adaptation to work. Thus, integrate these contexts to allow the authentication on the Internet and provide Single Sign-On (SSO) in IoT is a challenge. The goal is to allow a technician to access an appliance that is not reachable from the Internet, using IdM and without creating a single compromising point in the gateway that links the two contexts. The proposal interacts two key-based scheme, one for Internet and another for IoT, to reach integration between both contexts. A proof-of-concept implementation shows the proposal is feasible and presents no significant overhead for messages with up to 4096 bytes and 50 appliances.

Testing distributed systems is challenging. Peer-to-peer (P2P) systems are composed of a high number of concurrent nodes distributed across the network. The nodes are also highly volatile (i.e., free to join and leave the system at any time). In this kind of system, a great deal of control should be carried out by the test harness, including: volatility of nodes, test case deployment and coordination. In this demonstration we present the PeerUnit framework for testing P2P systems. The original characteristic of this framework is the individual control of nodes, allowing test cases to precisely control their volatility during execution. We validated this framework through implementation and experimentation on two popular open-source P2P systems.