Mobile Cloud Computing (MCC) augments capabilities of mobile devices by offloading applications to the cloud. Task Allocation is one of the most challenging issues in MCC considering neighboring mobile devices as the service providers. Given an application offloading request, the objective of the Task Allocation is to select service providers minimizing the completion time of the application offloading as well as consumed energy of all participating mobile devices while satisfying some QoS constraints. This paper models the Task Allocation problem in MCC as a constrained multi-objective optimization and proposes a two stages approach to solve the problem. In the first stage, a Multi-objective Resource Allocation based on Branch and Bound algorithm (MRABB) is designed to obtain Pareto solution set. In the subsequent stage, using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and given user’s preferences, the best compromise solution is determined. Furthermore, a context-aware software for Offloading in Mobile Cloud (OMC) is designed and implemented to collect contextual used in Task Allocation, and manage the offloading process. The results show the ability of the proposed multi-objective Task Allocation method to manage the trade-off between time and energy comparing to traditional algorithms.

Unlike conventional wireless sensor networks which are designed for a specific application, Software-Defined Wireless Sensor Networks (SDSN) can embed multiple sensors on each node, defining multiple Tasks simultaneously. Each sensor node has a virtualization program which serves as a common communication infrastructure for several different applications. Different sensor applications in the network can have different target functions and decision parameters. Due to the resource constraints of sensor network nodes, the multiplicity and variety of Tasks in each application, requirements for different levels of quality of service, and the different target functions for different applications, the problem of allocating resources to the Tasks on the sensors is complicated. In this paper, we formulate the problem of allocating resources to the sensors in the SDSN with different objective functions as a multi-objective optimization problem and provide an effective solution to solve it.

Proper Task Allocation among agents enhances a system’ s performance and reduces the probability of disorder in resolving a wide range of issues. Appropriate Allocations are critical for the efficient implementation of Tasks undertaken in natural hazard environments. Task Allocation plays an important role in coordinating a multi-agent system (MAS) among a set of agents. Multi-agent systems consist of several automatic and autonomous agents that coordinate their activities to achieve a goal. Agents fail to reach their ultimate goal without the proper assignment of Tasks. A proper approach to Task Allocations plays an important role in decision-making، particularly in urban search and rescue (USAR) operations in crisis-ridden areas. In the last decade، several studies were conducted regarding Task Allocation and different approaches have been presented to consider assigning Tasks in MASs. This paper intends to provide an approach to Task Allocation in disaster environments through the consideration of appropriate spatial strategies to deal with disturbances. The challenge of this study is to provide the possibility of Task reAllocation in order to deal with uncertainties and events during the implementation. The main innovation of the study is that it presents an approach to improve conditions during reAllocations، or future Allocations، when initial Allocations face problems due either to available uncertainties، or the addition of a new Task. In other words، based on the nature of the implementation environment (natural disaster environments)، current Allocation is not only considered but it is performed with regard to future Allocations. The selected spatial strategies for a change the order of Tasks (preparation for reAllocation) are different in accordance with the conditions and the studied phenomenon. In general، strategies are selected in such a way that the final cost of the system will not increase abnormally if initial Allocations face a problem. For example، building destruction level are uniformly distributed after an earthquake. Therefore، the convergence of rescue groups should be prevented as much as possible and the initial Allocation should be done in such way to decrease agent movement in future Allocations. The proposed method is presented in five phases: ordering existing Tasks، finding coordinating agent، holding an auction، applying Allocation strategies and implementation and observation of environmental uncertainties. The scalability of the proposed method was evaluated with the contract net protocol (CNP) method. In comparison with CNP، the standard time of rescue operations in the proposed approach includes at least 12% of improvement and the maximum improvement of 30% and the average percentage of recovery was 19%. Then obtained from the simulation of the proposed approach indicated that the time of rescue operations in the proposed scenarios was always less than the time required in the CNP method. Further، the evaluations based on deceased people and incorrect Allocations indicated the feasibility of the proposed approach. The comparison of the proposed strategies at different levels of uncertainty showed that an increase in uncertainty leads to an increased rescue time for CNP. An effective assigning approach should consider strategies for replanning in order to waste the least time during system disruptions. This optimizes planning to achieve better implementation time and provides conditions for fault tolerance. Also، considering strategies in the Task Allocation process، especially spatial strategies، resulted in the optimization and increased flexibility of the Allocation as well as conditions for fault tolerance and agent-based cooperation stability in emergency management.

Currently, cloud computing provides the necessary infrastructure and software services to provide the requested services needed by users on the Internet. Due to the spectacular growth of cloud computing, the number of users and the number of demands are increasing rapidly, which creates a high workload on servers and computing resources. In this situation, for the optimal use of resources, the need for an efficient and effective management approach is fully felt. For this purpose, game theory has been used. The game structure is designed in such a way that the leader (leader) owns a large number of resources and plans the Allocation of resources based on the request received from mobile users. The goal from the leader's point of view is to minimize the cost of using the resources located in the fog nodes, on the other hand, the goal considered from the mobile users' point of view is to minimize the cost of responding and transmitting the message to the desired fog node. For this purpose, the entire region is divided into regions and a fog node is considered for each region. The main goal of this research is to reduce the average delay in the provision of services related to Internet of Things applications in cloud computing platforms. For this purpose, an attempt is made to provide a new method for allocating multiple Tasks in mobile collective monitoring based on fog computing in the Internet of Things using the inverse Stackelberg game theory with the help of fuzzy logic and deep reinforcement learning algorithm.

time optimization could be one of the great challenges in business process management. Although there is much research on this subject, Task similarities have been paid little attention. In this paper, a new approach is proposed to optimize cycle time by minimizing entropy of work lists in resource Allocation while keeping workloads balanced. The idea of the entropy of work lists comes from the fact that the time it takes for a resource to do similar Tasks in a rather consecutive order is less than the time it takes to do the same Tasks separately. To this end, an entropy measurement is defined, which represents Task similarities on some given work lists. Furthermore, workload balancing is also regarded as an objective because not only is cycle time optimization important, but also workload fairness should also be met. Experimental results on a real-life event log of BPI challenge 2012 showed that the proposed method leads to 32% reduction in cycle time, compared with a reinforcement learning resource Allocation without involving the entropy.

Efficient distribution of service requests between fog and cloud nodes considering user mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for Task placement considering the mobility of users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile user leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.