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

Adaptive Dispatching of Tasks in the Cloud

The increasingly wide application of Cloud Computing enables the consolidation of tens of thousands of applications in shared infrastructures. Thus, meeting the quality of service requirements of so many diverse applications in such shared resource environments has become a real challenge, especially since the characteristics and workload of applications differ widely and may change over time. This paper presents an experimental system that can exploit a variety of online quality of service aware adaptive task allocation schemes, and three such schemes are designed and compared. These are a measurement driven algorithm that uses reinforcement learning, secondly a "sensible" allocation algorithm that assigns jobs to sub-systems that are observed to provide a lower response time, and then an algorithm that splits the job arrival stream into sub-streams at rates computed from the hosts' processing capabilities. All of these schemes are compared via measurements among themselves and with a simple round-robin scheduler, on two experimental test-beds with homogeneous and heterogeneous hosts having different processing capacities.Comment: 10 pages, 9 figure

Device-Aware Routing and Scheduling in Multi-Hop Device-to-Device Networks

The dramatic increase in data and connectivity demand, in addition to heterogeneous device capabilities, poses a challenge for future wireless networks. One of the promising solutions is Device-to-Device (D2D) networking. D2D networking, advocating the idea of connecting two or more devices directly without traversing the core network, is promising to address the increasing data and connectivity demand. In this paper, we consider D2D networks, where devices with heterogeneous capabilities including computing power, energy limitations, and incentives participate in D2D activities heterogeneously. We develop (i) a device-aware routing and scheduling algorithm (DARS) by taking into account device capabilities, and (ii) a multi-hop D2D testbed using Android-based smartphones and tablets by exploiting Wi-Fi Direct and legacy Wi-Fi connections. We show that DARS significantly improves throughput in our testbed as compared to state-of-the-art