DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

Abstract

Summary Dew computing is an emerging computing paradigm, which aims at minimizing the dependency over existing internetwork back‐haul, ie, being dependent on processing resources offered by remote servers. Smartphones and tablets ubiquity and powerful computing hardware motivated researchers to investigate the way of providing Dew computing services by exploiting the aggregated capabilities of devices in a vicinity, a smart device cluster. Consequently, research on resource management is necessary to learn how to scavenge resources from such a cluster, deal with devices heterogeneity, limitations, and dynamic resource availability. Simulation is commonly practiced for studying resource management in other distributed computing research fields, specially due to the complexity involved in the set up of experiments. However, a free‐to‐use purpose specific toolkit for studying smart device clusters do not exist or have been documented. Current simulation efforts do not allow researchers to faithfully represent key singularities of such environment, which are energy depletion and nondedicated nature of computing resources. We propose a trace‐based toolkit built on modular software artifacts to speed up research in resource management techniques in Dew environments. A trace‐driven methodology is adopted to assure practical value of simulated scenarios. The toolkit comprises a device profiler application for Android to capture generic battery and CPU traces from real devices, a profile mixer to create user interaction baseline traces through generic ones, and an extensible engine to simulate the execution of workloads configurable via text files. Verification and validation tests were run to show correctness and reliability of our simulation approach.

New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments

“…Smartphones are also modeled as first-class computing resource providers in DewSim [3]. In DewSim, a tracedriven approach is used to model battery behavior.…”

“…These days, we can find cheaper devices twice as powerful in regard to their computation, memory, and storage than devices present two years ago, capable of executing and managing complex tasks [2]. Due to these advances in mobile computing, researchers [3] found the need to treat these mobile devices as first-class resources in distributed computing environments, using these devices for complex tasks like executing Artificial Intelligence (AI) algorithms (for example, object detection, object tracking, and image recognition). Thus, it has become possible to integrate various mobile devices, such as smartphones, robots, sensors, and single-board computers (SBCs) in Cloud-Fog-Dew environments [4].…”

“…Those new algorithms were designed for exploiting clusters of Edge and Dew nodes with different computing capabilities provided by the battery-and non-batterydependent devices. Examples of battery dependent devices are smartphones, tablets, smartwatches, and portable IoT devices; for non-battery-dependent devices, we consider SBCs (single-board computers) or other IoT devices directly connected to a power grid (ii) Create an enhanced version of DewSim [3] to support hybrid networks to test our proposed algorithms…”

Cost-Efficient Dependent Task Offloading for Multiusers

“…In this section, our proposed algorithm (MRO) is evaluated by extensive simulations. We use the simulator proposed in [34] to model tasks. Compared with ITAGS algorithm [24], MAUI+ECS algorithm [29] and MAUI+Random algorithm [11], our algorithm can reduce the system cost efficiently.…”

Resource allocation scheme for Dew Computing paradigm using mobile grid

“…Significant improvement is achieved in terms of savings on bandwidth usage, ceaseless availability of data and providing a secured environment which are contentious issues with Cloud computing. With the ubiquitous availability of smartphones and a variety of mobile devices, Dew Computing paradigm (DCp) [1,2] or Mobile Grid(MG) computing [3] came into existence. DCp can form an adhoc network for computing and does not require any fixed or wired networked infrastructure.…”