Multi‐tier MEC offloading strategy based on dynamic channel characteristics

NoC (Network-on-Chip) in recent times has been broadly used in majority of present multi-core (MC) processing environment (i.e., processor) (especially in cloud computing servers). Then, SoCs (System-on-Chips), as a scalable and flexible strategy to the widespread integration of higher number of element in the chips. Nonetheless, a main issue is reliable processing, performance efficiency and energy efficacy perquisite of BigData processing framework. This work assume self-aware MC architectures that autonomously adjust or optimize their performance to accommodate users quality of service (QoS) performance requirement, job execution performance, energy efficiency, and resource accessibility. Numerous researchers are concerned about the lack of energy conservation in cloud computing environment and hence numerous power efficient technologies are introduced by different researchers to protect environment by enormous amount of power dissipation and enhance the performance of the environment. However, these power efficient technologies requires enormous amount of interaction cost between inter-processors. Moreover, these technologies provides insufficient results and energy consumption of cloud computing devices cannot be reduced largely due to enormous amount of memory utilization, routing etc. For overcoming research challenges, this work present a novel performance and energy balanced scheduling (PEBS) approach in cloud environment based on Dynamic voltage and Frequency Scaling (DVFS) technique is proposed. Experiment outcome shows PEBS_DVFS model attains good trade-off between system performance and energy consumption in multicore cloud computing (CC) architectures when compared with existing model.

It is widely accepted that the asynchronous parallel methods are more suitable than the synchronous ones on a grid architecture. Indeed, they outperform the synchronous methods, because they overlap the communications of the synchronous methods with computations. However, they also usually execute more iterations than the synchronous ones and thus consume more energy. To reduce the energy consumption of the CPUs executing such methods, the Dynamic voltage and frequency scaling technique can be used. It lowers the frequency of a CPU to reduce its energy consumption, but it also decreases its computing power. Therefore, the frequency that gives the best trade-off between energy consumption and performance must be selected. This paper presents a new online frequency selecting algorithm for parallel iterative asynchronous methods running over grids. It selects a vector of frequencies that gives the best trade-off between energy consumption and performance. New energy and performance models were used in this algorithm to predict the execution time and the energy consumption of synchronous, asynchronous, or hybrid iterative applications running over grids. The proposed algorithm was evaluated on the SimGrid simulator. The experiments showed that synchronously applying the proposed algorithm to the asynchronous version of the application reduces on average its energy consumption by 22% and speeds it up by 5.72%. Finally, the proposed algorithm was also compared to a method that uses the well-known energy and delay product and the comparison results showed that it outperforms this method in terms of energy consumption and performance.

The use of power management techniques is mandatory in embedded devices, which must provide high performance with low energy consumption. Due to the high variability present in the applications workload executed by these devices, this management should be executed dynamically. The use of traditional dynamic voltage and frequency scaling (DVFS) techniques proved to be useful in several scenarios to save energy. Nonetheless, due to technology scaling that limits the voltage variation and slow response of the DVFS schemes, the use of such technique may become inadequate. As alternative, the use of dynamic frequency scaling (DFS) may provide a good trade-off between power savings and power overhead. This paper proposes a distributed DFS scheme for NoC-based MPSoCs. Both NoC and PEs have an individual controlling scheme. The DFS scheme for PEs takes into account its computation and communication load to dynamically change the operating frequency. In the NoC, the DFS controller uses packet information to decide the router operating frequency. Real and synthetic applications were used to evaluate the proposed scheme. Results show that the number of executed instructions is reduced up to 41%, with an execution time overhead up to 18%. The power dissipation is reduced in PEs up to 26% and in the NoC up to 76%.

Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.

By offloading computation-intensive and latency-sensitive tasks from mobile devices to the mobile edge server, mobile edge computing (MEC) has been considered as a promising technology in 5 G and beyond to reduce task delay and energy consumption. This paper considers an optimization framework of computation offloading in a MEC system with multiple devices. We aim to minimize the energy consumption of all devices with their task delay constraints by jointly optimize the communication and computation resource allocation at both the devices and the mobile edge server. Specifically, dynamic voltage scaling (DVS) technology is considered in each device to adjust the operating frequency according to its delay constraint. The formulated problem is a non-convex problem due to a couple among multiple variables. To tackle the non-convex problem, we first decompose the original problem by optimizing the offloading ratio and transmission power iteratively. Then, we proposed a joint communication and computation optimization algorithm based on the difference of convex function algorithms (DCA) to solve the optimization problem. Finally, simulation results show that the proposed joint communication and computation scheme significantly improves the energy efficiency of the devices comparing with the local computing scheme and server computing scheme.

Utilizing the data caching technology to reduce data transmission is a promising technique for improving the performance of mobile edge computing (MEC), because the delay and energy consumption produced by data transmission constitute the dominant cost of task execution in MEC. Besides, computation tasks generally consist of input parameters, executive codes, and computation results. The executive codes are fixed and can output difference computation results under different input parameters. Motivated by this, we consider to proactively cache executive codes of tasks at the MEC server to reduce the weighted sum of task execution delay and users’ energy consumption. Aiming at establishing optimal system design, we formulate the problem as a non-linear programming problem which involves jointly optimizing the executive code caching strategy, computation offloading decision, wireless resource allocation, and computing resource allocation. We propose to find the optimal solution by employing an alternating optimization framework. The optimal wireless resource and computing resource allocation problem are firstly addressed by utilizing convex optimization technology. Then, a dynamic programming-based algorithm has been developed to achieve the optimal executive code caching and computation offloading strategies. Extensive simulation results show that the proposed scheme operates well and can substantially reduce the system cost over other benchmark schemes.

We present a dynamic voltage and frequency scaling technique within SoCs for per-core power management: the architecture allows for individual, self triggered performance-level scaling of the processing elements (PEs) within less than 100ns. This technique enables each core to adjust its local supply voltage and frequency depending on its current computational load. A test chip has been implemented in 28nm CMOS technology, as prototype of the SpiNNaker2 neuromorphic many core system, containing 4 PEs which are operational within the range of 1.1V down to 0.7V at frequencies from 666MHz down to 100MHz; the effectiveness of the power management technique is demonstrated using a standard benchmark from the application domain. The particular domain area of this application specific processor is real-time neuromorphics. Using a standard benchmark - the synfire chain - we show that the total power consumption can be reduced by 45%, with 85% baseline power reduction and a 30% reduction of energy per neuron and synapse computation, all while maintaining biological real-time operation.

With an exploding use of Mobiles, smartphones and IoT device, mobile edge computing (MEC) emerged as technological boon in computing paradigm. The device offloads the computationally intensive task as well as task relevant to storage to the MEC cloud server to meet the requirement of service delay, extend the battery lifespan of mobile, and resolve the problem of limited mobile device resources. With this reference we propose novel framework architecture with security to perform offloading of high storage and computationally intensive task to MEC server with minimum energy consumption and delay. For dynamic resource allocation, we employ two scheduling policy one at mobile side i.e. SJFP and other at MEC server side i.e. eSFFDRR. Before to offload task, first AES encryption technique is employed to secure input parameter, and then compressed this encrypted data to secure task data and utilize more bandwidth. Our experimental result depicts that for high storage and computationally intensive task our proposed framework can save 85–87% processing time, 70% of memory utilization with minimum energy consumption. The experimental result also proved that our proposed work improves the performance of computationally intensive mobile application with reduced consumption of mobile device resources like computation time, memory utilization, CPU usage and energy consumption.

Smart computational offloading for mobile edge computing in next-generation Internet of Things networks

Deep neural networks (DNNs) have been widely applied in diverse applications, but the problems of high latency and energy overhead are inevitable on resource-constrained devices. To address this challenge, most researchers focus on the dynamic voltage and frequency scaling (DVFS) technique to balance the latency and energy consumption by changing the computing frequency of processors. However, the adjustment of memory frequency is usually ignored and not fully utilized to achieve efficient DNN inference, which also plays a significant role in the inference time and energy consumption. In this paper, we first investigate the impact of joint memory frequency and computing frequency scaling on the inference time and energy consumption with a model-based and data-driven method. Then by combining with the fitting parameters of different DNN models, we give a preliminary analysis for the proposed model to see the effects of adjusting memory frequency and computing frequency simultaneously. Finally, simulation results in local inference and cooperative inference cases further validate the effectiveness of jointly scaling the memory frequency and computing frequency to reduce the energy consumption of devices.

As high quality real-time service demands on smart device applications are rapidly increasing, offloading the application task to the edge server to reduce total response time is emerging as a key issue. Meanwhile, smart devices are very sensitive to the energy consumption (to maximize the battery lifetime), where various energy consumption schemes have been attempted to minimize the power profile, especially for computationally heavy tasks. In this paper, an energy consumption minimization scheme that adjusts both the offloading ratio and CPU operating frequency of the device based on a Dynamic Voltage and Frequency Scaling (DVFS) technique under delay constraint is proposed. Simulation results show that the proposed scheme minimizes the energy consumption of the device while satisfying the delay requirement.

This paper presents a system level dynamic voltage and frequency scaling (DVFS) technique targeted toward non realtime applications running on a portable embedded system with multiple frequency-adjustable components. The key idea is to balance the frequency of processor core and memory. Some applications may be I/O bound, some may be CPU bound, and the others may be Memory bound. The proposed DVFS technique relies on pre-collected statistical information of different applications to calculate the expected workload for each coming time slice, and then, it adjusts the frequency of processor core, memory, and system bus to meet the timing constraints(with acceptable penalty) and achieves the maximum energy saving. The proposed technique has been tested on the PXA27x (XScale based) [8] embedded platform and the result was exciting, the processor energy saving can achieve up to 70% in memory bound applications, and the timing performance degradation was less than 5%. In CPU bound applications, the energy saving is relatively small, about 20~40%.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

In this paper, the joint communication and computation resource allocation problem for a single cell massive MIMO based mobile edge computing (MEC) network is discussed. In particular, we study the joint optimization of the pilot transmission power, data transmission power and the computing resource at the MEC server with the target of minimizing the maximal offloading delay over multiple users. Due to the non-convexity of the formulated problem and the defined optimal solution conditions, two suboptimal algorithms, i.e., the external point iteration and gradient descent based penalty function algorithm (EPF-GDA) and the external penalty function iteration based fruit fly optimization algorithm (EPF-FOA), are proposed to obtain the suboptimal solutions. The performance of the proposed algorithms, such as the users’ offloading delay, is verified by numerical simulations. And our results confirm the great performance gain of the algorithm EPF-FOA over EPF-GDA under different circumstances.

Energy consumption has become a key metric for evaluating how good an embedded system is, alongside more performance metrics like respecting operation deadlines and speed of execution. Schedulability improvement is no longer the only metric by which optimality is judged. In fact, energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system’s lifetime. In this work, we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption. Specifically, we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling (DVFS) technique. We address a variant of earliest deadline first (EDF) scheduling algorithm called energy saving-dynamic voltage and frequency scaling (ES-DVFS) algorithm that is suited to unpredictable future energy production and irregular job arrivals. We prove that ES-DVFS cannot attain a total value greater than C/000000, where 000000 is the minimum speed of any job and C is the available energy capacity. We also investigate the implications of having in advance, information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS. We show that such advance knowledge makes possible the design of semi-on-line algorithm, ES-DVFS**, that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive factor. The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.