Cloud Offloading Research Articles

Secure and Energy Efficient Model with Modified Offloading Algorithm in Mobile Cloud Computing

Mobile cloud computing is an evolving technology that is still unclear to many security problems and ensuring the security of data storage in cloud servers is one of the most challenging task. This paper proposes an algorithm which ensure to solve data protection and user authentication problems. In the proposed model an encryption and key exchanging mechanism has been described based on hybrid Dieffie-Hellman algorithm. Moreover, this paper focus on to resolve the waste energy in the network and for this purpose an offloading algorithm with dynamic energy aware model is proposed here. As a result it is analyzed that the higher the repetition rate of tasks the more energy is saved by Dynamic Energy Aware (DES) than Cloud Offload.

Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks

Given the emergence of mobile cloud computing (MCC), its associated energy implications are witnessed at larger scale. With offloading computationally intensive tasks to the cloud datacentres being the basic concept behind MCC, most of the mobile terminal resources participating in the MCC collaborative execution are wasted as they remain idle until the mobile terminals receive responses from the datacentres. This is an additional wastage of resources alongside the cloud resources are already being addressed as massive energy consumers. Though the energy consumed of the idle mobile resources is insignificant in comparison with the cloud counterpart, such consumptions have drastic impacts on the mobile devices causing unnecessary battery drains. To this end, this paper proposes Mobilouds which encompass a multi-tier processing architecture with various levels of process cluster capacities and a software application to manage energy efficient utilization of such process clusters. Our proposed Mobilouds framework encourages the mobile device participation in the MCC collaborative execution, thereby reduces the presence of idle mobile resources and utilizes such idle resources in the actual task execution. Our performance evaluation results demonstrate that the Mobilouds framework offers the most energy-time balancing process clusters for task execution by effectively utilizing the available resources, in comparison with an entire cloud offloading strategy using 5G/4G networks.

DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems

To cope with increasing energy consumption in mobile devices, the mobile cloud offloading has received considerable attention from its ability to offload processing tasks of mobile devices to cloud servers, and previous studies have focused on single type tasks in fixed network environments. However, real network environments are spatio-temporally varying, and typical mobile devices have not only various types of tasks, e.g., network traffic, cloud offloadable/nonoffloadable workloads but also capabilities of CPU frequency scaling and network interface selection between WiFi and cellular. In this paper, we first jointly consider the following three dynamic problems in real mobile environments: 1) cloud offloading policy, i.e., determining to use local CPU resources or cloud resources; 2) allocation of tasks to transmit through networks and to process in local CPU; and 3) CPU clock speed and network interface controls. We propose a DREAM algorithm by invoking the Lyapunov optimization and mathematically prove that it minimizes CPU and network energy for given delay constraints. Trace-driven simulation based on real measurements demonstrates that DREAM can save over 35% of total energy than existing algorithms with the same delay. We also design DREAM architecture and demonstrate the applicability of DREAM in practice.

Transform recipes for efficient cloud photo enhancement

Cloud image processing is often proposed as a solution to the limited computing power and battery life of mobile devices: it allows complex algorithms to run on powerful servers with virtually unlimited energy supply. Unfortunately, this overlooks the time and energy cost of uploading the input and downloading the output images. When transfer overhead is accounted for, processing images on a remote server becomes less attractive and many applications do not benefit from cloud offloading. We aim to change this in the case of image enhancements that preserve the overall content of an image. Our key insight is that, in this case, the server can compute and transmit a description of thetransformationfrom input to output, which we call atransform recipe.At equivalent quality, our recipes are much more compact than JPEG images: this reduces the client's download. Furthermore, recipes can be computed from highly compressed inputs which significantly reduces the data uploaded to the server. The client reconstructs a high-fidelity approximation of the output by applying the recipe to its local high-quality input. We demonstrate our results on 168 images and 10 image processing applications, showing that our recipes form a compact representation for a diverse set of image filters. With an equivalent transmission budget, they provide higher-quality results than JPEG-compressed input/output images, with a gain of the order of 10 dB in many cases. We demonstrate the utility of recipes on a mobile phone by profiling the energy consumption and latency for both local and cloud computation: a transform recipe-based pipeline runs 2--4x faster and uses 2--7x less energy than local or naive cloud computation.

Analysis of the Energy-Response Time Tradeoff for Delayed Mobile Cloud Offloading

We develop a delayed offloading model to leverage the complementary strength of WiFi and cellular networks when choosing heterogeneous wireless interfaces for offloading. Optimality analysis of the energy-delay tradeoff is carried out by using a queueing model with impatient jobs and service interruptions, which captures both energy and performance metrics and also intermittently available access links.

Adaptive Cloud Offloading of Augmented Reality Applications on Smart Devices for Minimum Energy Consumption

The accuracy of an augmented reality (AR) application is highly dependent on the resolution of the object’s image and the device’s computational processing capability. Naturally, a mobile smart device equipped with a high-resolution camera becomes the best platform for portable AR services. AR applications require significant energy consumption and very fast response time, which are big burdens to the smart device. However, there are very few ways to overcome these burdens. Computation offloading via mobile cloud computing has the potential to provide energy savings and enhance the performance of applications executed on smart devices. Therefore, in this paper, adaptive mobile computation offloading of mobile AR applications is considered in order to determine optimal offloading points that satisfy the required quality of experience (QoE) while consuming minimum energy of the smart device. AR feature extraction based on SURF algorithm is partitioned into sub-stages in order to determine the optimal AR cloud computational offloading point based on conditions of the smart device, wireless and wired networks, and AR service cloud servers. Tradeoffs in energy savings and processing time are explored also taking network congestion and server load conditions into account.

CIMS: A Context-Based Intelligent Multimedia System for Ubiquitous Cloud Computing

Mobile users spend a tremendous amount of time surfing multimedia contents over the Internet to pursue their interests. A resource-constrained smart device demands more intensive computing tasks and lessens the battery life. To address the resource limitations (i.e., memory, lower maintenance cost, easier access, computing tasks) in mobile devices, mobile cloud computing is needed. Several approaches have been proposed to confront the challenges of mobile cloud computing, but difficulties still remain. However, in the coming years, context collecting, processing, and interchanging the results on a heavy network will cause vast computations and reduce the battery life in mobiles. In this paper, we propose a “context-based intelligent multimedia system” (CIMS) for ubiquitous cloud computing. The main goal of this research is to lessen the computing percentage, storage complexities, and battery life for mobile users by using pervasive cloud computing. Moreover, to reduce the computing and storage concerns in mobiles, the cloud server collects several groups of user profiles with similarities by executing K-means clustering on users’ data (context and multimedia contents). The distribution process conveys real-time notifications to smartphone users, according to what is stated in his/her profile. We considered a mobile cloud offloading system, which decides the offloading actions to/from cloud servers. Context-aware decision-making (CAD) customizes the mobile device performance with different specifications such as short response time and lesser energy consumption. The analysis says that our CIMS takes advantage of cost-effective features to produce high-quality information for mobile (or smart device) users in real time. Moreover, our CIMS lessens the computation and storage complexities for mobile users, as well as cloud servers. Simulation analysis suggests that our approach is more efficient than existing domains.

A Tradeoff Analysis of a Cloud-Based Robot Navigation Assistant Using Stereo Image Processing

The use of Cloud Computing for computation offloading in the robotics area has become a field of interest today. The aim of this work is to demonstrate the viability of cloud offloading in a low level and intensive computing task: a vision-based navigation assistance of a service mobile robot. In order to do so, a prototype, running over a ROS-based mobile robot (Erratic by Videre Design LLC) is presented. The information extracted from on-board stereo cameras will be used by a private cloud platform consisting of five bare-metal nodes with AMD Phenom 965 $\,\times\,$ 4 CPU, with the cloud middleware Openstack Havana. The actual task is the shared control of the robot teleoperation, that is, the smooth filtering of the teleoperated commands with the detected obstacles to prevent collisions. All the possible offloading models for this case are presented and analyzed. Several performance results using different communication technologies and offloading models are explained as well. In addition to this, a real navigation case in a domestic circuit was done. The tests demonstrate that offloading computation to the Cloud improves the performance and navigation results with respect to the case where all processing is done by the robot.

Energy Delay Tradeoff in Cloud Offloading for Multi-Core Mobile Devices

Cloud offloading is considered a promising approach for both energy conservation and storage/computation enhancement for resource-limited mobile devices. In this paper, we present a Lyapunov optimization-based scheme for cloud offloading scheduling, as well as download scheduling for cloud execution output, for multiple applications running in a mobile device with a multi-core CPU. We derive an online algorithm and prove performance bounds for the proposed algorithm with respect to average power consumption and average queue length, which is indicative of delay, and reveal the fundamental tradeoff between the two optimization goals. The performance of the proposed online scheduling scheme is validated with trace-driven simulations.

IEEE Access Special Section Editorial: Emerging Cloud-Based Wireless Communications and Networks

Cloud-based processing is expected to play a major role in the fifth generation (5G) wireless systems, which allows for a high-quality experience for mobile cellular access. Based on the idea of combining the cloud computing technology and cellular communications, the cloud base station architecture revolutionizes the design of cellular systems. In this Special Section, five papers are included, which investigate the cloud-based wireless communications and networks from various aspects, including the joint consideration of cloud radio access networks with, respectively, heterogeneous networks and fog networks, the issues of cloud offloading, and an Internet of Things (IoT) application taking advantage of the cloud-based wireless networks.

Shared resource network-aware impact determination algorithms for service workflow deployment with partial cloud offloading

Some service providers offer services for which physical devices, terminals and servers are installed at the customer׳s site, e.g. in a hospital setting where physical terminals are installed in patient rooms and used for communication. These devices work together to provide the service, forming service workflows. Due to a recent trend where parts of the management system for such services is offloaded to cloud environments, such services can no longer be isolated in a private subnet, that is specifically dimensioned for its purpose. Part of the network flows must now pass over a larger section of the customer network, and the network load can increase as new services are added, or when services are upgraded. Because of this, it is important to determine whether there is sufficient network and server capacity in the customer network to add new service workflows before they are deployed, or before an existing service is upgraded, ensuring a sufficient level of quality can be guaranteed.In this article we focus on how the impact of service workflows can be determined, ensuring service workflows do not negatively impact each other׳s execution. In particular, we determine an impact analysis strategy to evaluate the degree to which a given set of service workflows can be guaranteed in a given network topology. As not all flows are continuously active, the approach is designed to support the sharing of network and server resources. We then evaluate the quality of the resulting solutions using a simulation based on a hospital setting, and the execution speed of the designed algorithms. In our evaluations, we find that the developed hierarchical algorithm requires ±42% less resources than an approach without resource sharing, without any workflow failures occurring during the simulation.

Optimal multi-dimensional dynamic resource allocation in mobile cloud computing

In this paper, we propose a model for mobile application profiles, wireless interfaces, and cloud resources. First, an algorithm to allocate wireless interfaces and cloud resources has been introduced. The proposed model is based on the wireless network cloud (WNC) concept. Then, considering power consumption, application quality of service (QoS) profiles, and corresponding cost functions, a multi-objective optimization approach using an event-based finite state model and dynamic constraint programming method has been used to determine the appropriate transmission power, process power, cloud offloading and optimum QoS profiles. Numerical results show that the proposed algorithm saves the mobile battery life and guarantees both QoS and cost simultaneously. Moreover, it determines the best available cloud server resources and wireless interfaces for applications at the same time.

A hybrid mobile environmental and population density management system for smart poultry farms

Due to advances in technology and size reduction of portable mechanical and electronic devices, sensors have been involved in many sectors, including in agriculture, where sensors and their unique functionalities are extremely useful for both productivity gains and reducing operating costs. These benefits are attained by utilizing their real-time sensing capability over environmental stages affecting the animal breed to enable on-time decision support but with some limitations, i.e., mobility, coverage, ubiquitous access, and energy. Thus, this research focuses on the integration of wireless sensor and mobile system networks with a well-known sensor integration platform toward cloud offloading scalability services via a hybrid architecture used to collect sensing data, such as temperature, humidity, light intensity, and population density, for data analytics and then issuing on-time decisions to adjust the environmental behavior accordingly. Based on a smart poultry farm concept for evaporative cooling environments, the instrument and components of the system design are discussed in detail with the experienced selection criteria, including a discussion of practical topology and deployments, enhanced transmission logics, external environmental tuning control logics integrating mobile user management interfaces, and image processing units. Aside from the proposed prototype of the integration of mobile phones, sensors, and controllers, an experimental investigation was also performed on data sensing and transmission procedures regarding power consumption characteristics, especially on high-cost image data transmissions, including an illustration of the outstanding performance, i.e., 80% in accuracy with low computational complexity, of the image filter over other well-known image classification techniques.