Cloud computing is one of the most popular distributed environments, in which, multiple
powerful and heterogeneous resources are used by different user applications. Task …

This paper proposes a multi-objective evolutionary algorithm (MOEA)-based task scheduling
approach for determining Pareto optimal solutions with simultaneous optimization of …

Task allocation is a key combinatorial optimization problem, crucial for modern applications
such as multi-robot cooperation and resource scheduling. Decision makers must allocate …

Task scheduling and resource allocation are the key challenges of cloud computing.
Compared with grid environment, data transfer is a big overhead for cloud workflows. So, the …

MinMin, MaxMin, and Sufferage are constructive heuristics that are widely and successfully
used in assigning independent tasks to processors in heterogeneous computing systems …

Mobile learning is an emerging trend that brings many advantages to distributed learners,
enabling them to achieve collaborative learning, in which the virtual teams are usually built …

In this paper, we address the problem of energy-aware heterogeneous data allocation and
task scheduling on heterogeneous multiprocessor systems for real-time applications. In a …

This paper proposes a novel distributed multi-agent dynamic task allocation method based
on the potential game. Consider that the workload of each task may vary in a dynamic …

Most of the scheduling algorithms proposed for real-time embedded systems, with energy
constraints, try to reduce power consumption. However, reducing the power consumption …

Unmanned aerial vehicle (UAV) clusters usually face problems such as complex
environments, heterogeneous combat subjects, and realistic interference factors in the …