Modelling and developing co-scheduling strategies on multicore processors

On-chip cache is often shared between processes that run concurrently on different cores of the same processor. Resource contention of this type causes performance degradation to the co-running processes. Contention-aware co-scheduling refers to the class of scheduling techniques to reduce the performance degradation. Most existing contention-aware co-schedulers only consider serial jobs. However, there often exist both parallel and serial jobs in computing systems. In this paper, the problem of co-scheduling a mix of serial and parallel jobs is modelled as an Integer Programming (IP) problem. Then the existing IP solver can be used to find the optimal co-scheduling solution that minimizes the performance degradation. However, we find that the IP-based method incurs high time overhead and can only be used to solve small-scale problems. Therefore, a graph-based method is also proposed in this paper to tackle this problem. We construct a co-scheduling graph to represent the co-scheduling problem and model the problem of finding the optimal co-scheduling solution as the problem of finding the shortest valid path in the co-scheduling graph. A heuristic A*-search algorithm (HA*) is then developed to find the near-optimal solutions efficiently. The extensive experiments have been conducted to verify the effectiveness and efficiency of the proposed methods. The experimental results show that compared with the IP-based method, HA* is able to find the near-optimal solutions with much less time

Modelling and developing co-scheduling strategies on multicore processors

On-chip cache is often shared between processes that run concurrently on different cores of the same processor. Resource contention of this type causes performance degradation to the co-running processes. Contention-aware co-scheduling refers to the class of scheduling techniques to reduce the performance degradation. Most existing contention-aware co-schedulers only consider serial jobs. However, there often exist both parallel and serial jobs in computing systems. In this paper, the problem of co-scheduling a mix of serial and parallel jobs is modelled as an Integer Programming (IP) problem. Then the existing IP solver can be used to find the optimal co-scheduling solution that minimizes the performance degradation. However, we find that the IP-based method incurs high time overhead and can only be used to solve small-scale problems. Therefore, a graph-based method is also proposed in this paper to tackle this problem. We construct a co-scheduling graph to represent the co-scheduling problem and model the problem of finding the optimal co-scheduling solution as the problem of finding the shortest valid path in the co-scheduling graph. A heuristic A*-search algorithm (HA*) is then developed to find the near-optimal solutions efficiently. The extensive experiments have been conducted to verify the effectiveness and efficiency of the proposed methods. The experimental results show that compared with the IP-based method, HA* is able to find the near-optimal solutions with much less time

Developing energy-aware workload offloading frameworks in mobile cloud computing

Mobile cloud computing is an emerging field of research that aims to provide a platform on which intelligent and feature-rich applications are delivered to the user at any time and at anywhere. Computation offload between mobile and cloud plays a key role in this vision and ensures that the integration between mobile and cloud is both seamless and energy-efficient. In this thesis, we develop a suite of energy-aware workload offloading frameworks to accommodate the efficient execution of mobile workflows on a mobile cloud platform. We start by looking at two energy objectives of a mobile cloud platform. While the first objective aims at minimising the overall energy cost of the platform, the second objective aims at the longevity of the platform taking into account the residual battery power of each device. We construct optimisation models for both objectives and develop two efficient algorithms to approximate the optimal solution. According to simulation results, our greedy autonomous offload (GAO) algorithm is able to efficiently produce allocation schemes that are close to optimal. Next, we look at the task allocation problem from a workflow's perspective and develop energy-aware offloading strategies for time-constrained mobile workflows. We demonstrate the effect of software and hardware characteristics have over the offload-efficiency of mobile workflows with a workflow-oriented greedy autonomous offload (WGAO) algorithm, an extension to the GAO algorithm. Thirdly, we propose a novel network I-O model to describe the bandwidth dependencies and allocation problem in mobile networks. This model lays the foundation for further objective developments such as the cost-based and adaptive bandwidth allocation schemes which we also present in this thesis. Lastly, we apply a game theoretical approach to model the non-cooperative behaviour of mobile cloud applications that reside on the same device. Mixed-strategy Nash equilibrium is derived for the offload game which further quantifies the price of anarchy of the system