The base motivation of Mobile Cloud Computing was empowering mobile devices by application offloading onto powerful cloud resources. However, this goal can't entirely be reached because of the high offloading cost imposed by the long physical distance between the mobile device and the cloud. To address this issue, we propose an application offloading onto a nearby mobile cloud composed of the mobile devices in the vicinity-a Spontaneous Proximity Cloud. We introduce our proposed dynamic, ant-inspired, bi-objective offloading middleware-ACOMMA, and explain its extension to perform a close mobile application offloading. With the learning-based offloading decision-making process of ACOMMA, combined to the collaborative resource sharing, the mobile devices can cooperate for decision cache sharing. We evaluate the performance of ACOMMA in collaborative mode with real benchmarks Face Recognition and Monte-Carlo algorithms-and achieve 50 execution time gain.
Few studies focused on the use of adjacent mobile devices as offloading surrogates. Transient cloud @cite_6 uses the collective capabilities of nearby devices in an ad-hoc network to meet the needs of the mobile device. A modified Hungarian method is applied as an assignment algorithm to assign tasks to devices that are to be run according to their abilities. The execution of each task by any device imposes some cost, and the assignment algorithm aims to find the minimum total cost assignment. To that end, @cite_6 has proposed a dynamic cost adjustment to balance the tasks based on costs between devices. @cite_1 proposed an architecture named mCloud that runs resource-intensive applications on collections of cooperating mobile devices and discuss its advantages. @cite_7 have gone one step further and formulated a decision algorithm for global adaptive offloading. They implemented the program components on mobile devices set to optimise Time to Failure (TTF) while taking into account the limitations of the effectiveness of the program. Having highlighted the benefits of collaboration for mobile task offloading, also implemented computational offloading schemes to maximise the longevity of mobile devices @cite_2 @cite_8 .
@cite_7 @cite_8 @cite_1 @cite_6 @cite_2
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Scavenging the idling computation resources at the enormous number of mobile devices, ranging from small IoT devices to powerful laptop computers, can provide a powerful platform for local mobile cloud computing. The vision can be realized by peer-to-peer cooperative computing between edge devices, referred to as co-computing . This paper exploits the non-causal helper’s CPU-state information to design energy-efficient co-computing policies for scavenging time-varying spare computation resources at peer mobiles. Specifically, we consider a co-computing system where a user offloads computation of input data to a helper. The helper controls the offloading process for the objective of minimizing the user’s energy consumption based on a predicted helper’s CPU-idling profile that specifies the amount of available computation resource for co-computing. Consider the scenario that the user has one-shot input-data arrival and the helper buffers offloaded bits. The problem for energy-efficient co-computing is formulated as two sub-problems: the slave problem corresponding to adaptive offloading and the master one to data partitioning. Given a fixed offloaded data size, the adaptive offloading aims at minimizing the energy consumption for offloading by controlling the offloading rate under the deadline and buffer constraints. By deriving the necessary and sufficient conditions for the optimal solution, we characterize the structure of the optimal policies and propose algorithms for computing the policies. Furthermore, we show that the problem of optimal data partitioning for offloading and local computing at the user is convex, admitting a simple solution using the sub-gradient method. Finally, the developed design approach for co-computing is extended to the scenario of bursty data arrivals at the user accounting for data causality constraints. Simulation results verify the effectiveness of the proposed algorithms. Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms. Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing. We consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a joint optimization of the radio resources??the transmit precoding matrices of the MUs??and the computational resources??the CPU cycles second assigned by the cloud to each MU??in order to minimize the overall users' energy consumption while meeting the latency constraints imposed by the applications running on the MUs. The resulting optimization problem is nonconvex (in the objective function and the constraints), and there are constraints coupling all the optimization variables. To cope with the nonconvexity, we hinge on successive convex approximation techniques and propose an iterative algorithm converging to a local optimal solution of the original nonconvex problem. The algorithm is also suitable for a parallel implementation across the access point, with limited coordination signaling with the cloud. Numerical results show that the proposed joint optimization yields significant energy savings with respect to more traditional schemes performing a separate optimization of the radio and computational resources.