This paper proposes and evaluates MALMOS, a novel framework for mobile offloading scheduling based on online machine learning techniques. In contrast to previous works, which rely on application-dependent parameters or predefined static scheduling policies, MALMOS provides an online training mechanism for the machine learning-based runtime scheduler such that it supports a flexible policy that dynamically adapts scheduling decisions based on the observation of previous offloading decisions and their correctness. To demonstrate its practical applicability, we integrated MALMOS with an existing Java-based, offloading-capable code refactoring framework, DPartner. Using this integration, we performed quantitative experiments to evaluate the performance and cost for three machine learning algorithms: instance-based learning, perceptron, and naïve Bayes, with respect to classifier training time, classification time, and scheduling accuracy. Particularly, we examined the adaptability of MALMOS to various network conditions and computing capabilities of remote resources by comparing the scheduling accuracy with two static scheduling cases: threshold-based and linear equation-based scheduling policies. Our evaluation uses an Android-based prototype for experiments, and considers benchmarks with different computation/communication characteristics, and different computing capabilities of remote resources. The evaluation shows that MALMOS achieves 10.9%∼40.5% higher scheduling accuracy than two static scheduling policies.
Virtual private networking (VPN) has become an increasingly important component of a collaboration environment because it ensures private, authenticated communication among participants, using existing collaboration tools, where users are distributed across multiple institutions and can be mobile. The majority of current VPN solutions are based on a centralized VPN model, where all IP traffic is tunneled through a VPN gateway. Nonetheless, there are several use case scenarios that require a model where end-to-end VPN links are tunneled upon existing Internet infrastructure in a peer-to-peer (P2P) fashion, removing the bottleneck of a centralized VPN gateway. We propose a novel virtual network -TinCan -based on peerto-peer private network tunnels. It reuses existing standards and implementations of services for discovery notification (XMPP), reflection (STUN) and relaying (TURN), facilitating configuration. In this approach, trust relationships maintained by centralized (or federated) services are automatically mapped to TinCan links. In one use scenario, TinCan allows unstructured P2P overlays connecting trusted end-user devices -while only requiring VPN software on user devices and leveraging online social network (OSN) infrastructure already widely deployed. This paper describes the architecture and design of TinCan and presents an experimental evaluation of a prototype supporting Windows, Linux, and Android mobile devices. Results quantify the overhead introduced by the network virtualization layer, and the resource requirements imposed on services needed to bootstrap TinCan links.
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