With the wide deployment of cloud computing in many business enterprises as well as science and engineering domains, high quality security services are increasingly critical for processing workflow applications with sensitive intermediate data. Unfortunately, most existing worklfow scheduling approaches disregard the security requirements of the intermediate data produced by workflows, and overlook the performance impact of encryption time of intermediate data on the start of subsequent workflow tasks. Furthermore, the idle time slots on resources, resulting from data dependencies among workflow tasks, have not been adequately exploited to mitigate the impact of data encryption time on workflows' makespans and monetary cost. To address these issues, this paper presents a novel task-scheduling framework for security sensitive workflows with three novel features. First, we provide comprehensive theoretical analyses on how selectively duplicating a task's predecessor tasks is helpful for preventing both the data transmission time and encryption time from delaying task's start time. Then, we define workflow tasks' latest finish time, and prove that tasks can be completed before tasks' latest finish time by using cheapest resources to reduce monetary cost without delaying tasks' successors' start time and workflows' makespans. Based on these analyses, we devise a novel scheduling approach with selective tasks duplication, named SOLID, incorporating two important phases: 1) task scheduling with selectively duplicating predecessor tasks to idle time slots on resources; and 2) intermediate data encrypting by effectively exploiting tasks' laxity time. We evaluate our solution approach through rigorous performance evaluation study using both randomly generated workflows and some real-world workflow traces. Our results show that the proposed SOLID approach prevails over existing algorithms in terms of makespan, monetary costs and resource efficiency.

Scheduling real-time workflows running in the Cloud often need to deal with uncertain task execution time and minimize uncertainty propagation during the workflow runtime. Efficient scheduling approaches can minimize the operational cost of Cloud providers and provide higher guarantee of the quality of services (QoS) for Cloud consumers. However, most of the existing workflow scheduling approaches are designed for the individual workflow runtime environments that are deterministic. Such static workflow schedulers are inadequate for multiple and dynamic workflows, each with possibly uncertain task execution time. In this paper, we address the problem of minimizing uncertainty propagation in real-time workflow scheduling. We first introduce an uncertainty-aware scheduling architecture to mitigate the impact of uncertainty factors on the quality of workflow schedules. Then we present a dynamic workflow scheduling algorithm (PRS) that can dynamically exploit proactive and reactive scheduling methods. Finally, we conduct extensive experiments using real-world workflow traces and our experimental results show that PRS outperforms two representative scheduling algorithms in terms of costs (up to 60%), resource utilization (up to 40%) and deviation (up to 70%).

Scheduling plays a significant role in improving observation effectiveness of distributed imaging satellites. Although extensive satellite scheduling algorithms have been proposed, none of them focuses on dynamic scheduling for emergency tasks. In this paper, a novel multi-objective dynamic scheduling model for emergency tasks on distributed imaging satellites is established for the first time. To improve user's satisfaction ratio and resource utilization, we propose the task merging strategy: establishing a task merging graph (TMG) model and proposing a task merging algorithm-CP-TM based on clique partition. In addition, a rehabilitation technique is suggested to overcome the disadvantage that task merging will make tasks have less imaging opportunities. To further enhance the schedulability, the task backward shift in the waiting sequence is considered in our study. Furthermore, a novel dynamic scheduling algorithm called TMBSR-DES is presented, which comprehensively considers task merging, backward shift, and rehabilitation. To demonstrate the superiority of our TMBSR-DES, we conduct extensive experiments by simulations to compare TMBSR-DES with three existing algorithm-RBHA, RTSSA, and LSA, as well as three baseline algorithms-BS-DES, TMR-DES, and TMBS-DES. The experimental results indicate that TMBSR-DES outperforms the others and is suitable for emergency task scheduling.

Nowadays, providing quality of service (QoS) guarantees for some applications such as signal data processing has become a critical issue. In this paper, we propose a novel self-adaptive QoS-aware scheduling algorithm called SAQA that sufficiently considers the adaptability for real-time tasks with QoS demands on heterogeneous clusters. When the system is in heavy load, the SAQA algorithm can degrade the QoS levels of new tasks or tasks waiting in local queues of nodes to guarantee high schedulability. The minimum QoS level is acceptable for each task. In contrast, when the system is in light load, SAQA can use slack time to adequately improve the QoS of new tasks. We compare SAQA with SAEDF algorithm by simulations. The experimental results indicate that SAQA has admirable adaptability while providing timing and QoS guarantees.

Electro-magnetic Detection Satellite (EDS) is an important branch of Earth Observation Satellites (EOSs). It has been widely applied in industry and military areas. The detecting duration of EDS is different from imagery satellites, for it is an imprecise parameter because of the uncertain electromagnetic environment within space surrounding targets. This factor makes the task scheduling for EDS becoming a complex combinatorial optimization problem. With consideration of this special property, we used fuzzy set and possibility theory to model imprecise parameters, other physical constraints, like on-board energy and transition time between different working patterns and sensor's rebooting, were also taken into account in the model. We presented an improved genetic algorithm to solve this problem by introducing new method of elitist and parents selection. The model and the algorithm have been tested by five experiments derived from STK's satellite database.