Abstract

Major interest is currently given to the integration of clusters of virtualization servers, also referred to as ‘cloudlets’ or ‘edge clouds’, into the access network to allow higher performance and reliability in the access to mobile edge computing services. We tackle the edge cloud network design problem for mobile access networks. The model is such that the virtual machines (VMs) are associated with mobile users and are allocated to cloudlets. Designing an edge cloud network implies first determining where to install cloudlet facilities among the available sites, then assigning sets of access points, such as base stations to cloudlets, while supporting VM orchestration and considering partial user mobility information, as well as the satisfaction of service-level agreements. We present linkpath formulations supported by heuristics to compute solutions in reasonable time. Task clustering has proven to be an effective method to reduce execution overhead and to improve the computational granularity of scientific workflow tasks executing on distributed resources. However, a job composed of multiple tasks may have a higher risk of suffering from failures than a single task job. In this paper, we conduct a theoretical analysis of the impact of transient failures on the runtime performance of scientific workflow executions. We propose a general task failure analysis Trade off Planner modelling framework that uses a maximum likelihood estimation-based parameter estimation process to model workflow performance. We further propose three fault tolerant clustering strategies to improve the runtime performance of workflow executions in faulty execution environments. Experimental results show that failures can have significant impact on executions where task clustering policies are not fault-tolerant, and that our solutions yield make span improvements in such scenarios. In addition, we propose a dynamic task clustering strategy to optimize the workflow’s make span by dynamically adjusting the clustering granularity when failures arise. A trace-based simulation of five real workflows shows that our dynamic method is able to adapt to unexpected behaviours, and yields better make spans when compared to static methods.

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