On Optimal and Fair Service Allocation in Mobile Cloud Computing

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models

Towards delay-aware container-based Service Function Chaining in Fog Computing

Recently, the fifth-generation mobile network (5G) is getting significant attention. Empowered by Network Function Virtualization (NFV), 5G networks aim to support diverse services coming from different business verticals (e.g. Smart Cities, Automotive, etc). To fully leverage on NFV, services must be connected in a specific order forming a Service Function Chain (SFC). SFCs allow mobile operators to benefit from the high flexibility and low operational costs introduced by network softwarization. Additionally, Cloud computing is evolving towards a distributed paradigm called Fog Computing, which aims to provide a distributed cloud infrastructure by placing computational resources close to end-users. However, most SFC research only focuses on Multi-access Edge Computing (MEC) use cases where mobile operators aim to deploy services close to end-users. Bi-directional communication between Edges and Cloud are not considered in MEC, which in contrast is highly important in a Fog environment as in distributed anomaly detection services. Therefore, in this paper, we propose an SFC controller to optimize the placement of service chains in Fog environments, specifically tailored for Smart City use cases. Our approach has been validated on the Kubernetes platform, an open-source orchestrator for the automatic deployment of micro-services. Our SFC controller has been implemented as an extension to the scheduling features available in Kubernetes, enabling the efficient provisioning of container-based SFCs while optimizing resource allocation and reducing the end-to-end (E2E) latency. Results show that the proposed approach can lower the network latency up to 18% for the studied use case while conserving bandwidth when compared to the default scheduling mechanism