CloudAware: A Context-Adaptive Middleware for Mobile Edge and Cloud Computing Applications

Orsini, Gabriel; Bade, Dirk; Lamersdorf, Winfried

doi:10.1109/fas-w.2016.54

Cited by 39 publications

(27 citation statements)

References 22 publications

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“…Instead of considering heavily loaded users and lightly loaded users, we consider four types of user loads: very light, moderately light, moderately heavy, and very heavy, where the sizes are uniformly distributed over the intervals [1,25], (25,50] Figure 5 shows the results of the third experiment set. The figure shows a simple trend: a linear increase in the loads generated by the users corresponds to linear increases in the total cost and the average delay.…”

Section: Experimentation and Resultsmentioning

confidence: 99%

“…In [25], the authors proposed CloudAware, a context adaptive mobile middleware that is responsible for automatically the changing of the context configuration by linking the distribution features of mobile middleware with contextaware self-adaptation techniques. The authors showed their evaluation by using real usage data supporting from Nokia Mobile Data Challenge (MDC) dataset.…”

Section: Related Workmentioning

confidence: 99%

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Delay-aware power optimization model for mobile edge computing systems

Jararweh

Al‐Ayyoub

Al-Quraan

et al. 2017

Pers Ubiquit Comput

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Reducing the total power consumption and network delay are among the most interesting issues facing large-scale Mobile Cloud Computing (MCC) systems and their ability to satisfy the Service Level Agreement (SLA). Such systems utilize cloud computing infrastructure to support offloading some of user's computationally heavy tasks to the cloud's datacenters. However, the delay incurred by such offloading process lead the use of servers (called cloudlets) placed in the physical proximity of the users, creating what is known as Mobile Edge Computing (MEC). The cloudlet-based infrastructure has its challenges such as the limited capabilities of the cloudlet system (in terms of the ability to serve different request types from users in vast geographical regions). To cover the users demand for different types of services and in vast geographical regions, cloudlets cooperate among each other by passing user requests from one cloudlet to another. This cooperation affects both power consumption and delay. In this work, we present a mixed integer linear programming (MILP) optimization model for MEC systems with these two issues in mind. Specifically, we consider two types of cloudlets: local cloudlets and global cloudlets, which have higher capabilities. A user connects to a local cloudlet and sends all of its traffics to it. If the local cloudlet cannot serve the desired request, then the request is moved to another local cloudlet. If no local cloudlet can serve the request, then it is moved to a global cloudlet which can serve all service types. The process of routing requests through the hierarchical network of cloudlets increases power consumption and delay. Our model minimizes power consumption while incurring an acceptable amount of delay. We evaluate it under several realistic scenarios to show that it can indeed be used for power optimization of large-scale MEC systems without violating delay constraints.

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Section: Experimentation and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Delay-aware power optimization model for mobile edge computing systems

Jararweh

Al‐Ayyoub

Al-Quraan

et al. 2017

Pers Ubiquit Comput

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“…In particular, Mobile Cloud Computing promotes the splitting of mobile applications to offload compute and storage intensive tasks to available cloudlet, as long as the latency requirement is preserved. This allows users to achieve better performance and extend battery life of their personal devices …”

Section: Background and Application Scenariosmentioning

confidence: 99%

Providing ultra‐short latency to user‐centric 5G applications at the mobile network edge

Farris

Taleb

Flinck

2017

Trans Emerging Tel Tech

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Mobile Edge Computing (MEC) will play a key role in next-generation mobile networks to extend the range of supported delay-sensitive applications. Furthermore, an increasing attention is paid to provide user-centric services, to better address the strict requirements of novel immersive applications. In this scenario, MEC solutions need to efficiently cope with user mobility, which requires fast relocation of service instances to guarantee the desired Quality of Experience. However, service migration is still an open issue, especially for resource-constrained edge nodes interconnected by high-latency and low-bandwidth links. In this paper, by leveraging the potential of lightweight container-based virtualization techniques, we investigate a novel approach to support service provisioning in dynamic MEC environments. In particular, we present a framework where proactive service replication for stateless applications is exploited to drastically reduce the time of service migration between different cloudlets and to meet the latency requirements. The performance evaluation shows promising results of our approach with respect to classic reactive service migration. INTRODUCTIONSeveral classes of new applications are challenging the current network and cloud infrastructures. As illustrated in a recent report from International Telecommunication Union, 1 applications such as tactile internet, mobile gaming, and augmented reality are pushing new requirements, especially in terms of latency. To guarantee the desired Quality of Experience (QoE) for the end user, not only the network access delay, but also the cloud service processing should be performed within a few milliseconds. To achieve the 1 ms latency dream for the next-generation 5G cloud-enabled services, both network and cloud providers should jointly cooperate and investigate novel approaches. In this regard, academic and industrial research communities have focused on Mobile Edge Computing (MEC) solutions to exploit processing and storage capabilities at the edge of the network, as near as possible to the end user. 2,3 Indeed, the deployment of micro datacentres in the network access points, known as cloudlets, 4,5 can guarantee remarkable benefits of low latency interaction and scalability, by balancing the workload over the distributed edge infrastructure. Furthermore, bringing the cloudlet concept into the Internet of Things (IoT) scenario results in the definition of the so-called Fog Computing paradigm, which envisages a highly virtualized infrastructure composed of distributed edge nodes to monitor and analyse the most time-sensitive data generated by network connected devices. [6][7][8] In a MEC environment, a key challenge is represented by the user mobility, which could cause significant application degradation, even in small scenarios, as reported in a previous study.9,10 To guarantee service continuity and to meet the strict requirements of application latency, the MEC framework needs to properly cope with service migration between edge nodes. I...

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“…Compute‐intensive applications require offloading to cloud and edge servers to help mobile devices function longer without power charging. The augmentation technologies for resource‐scarce mobile devices and resource‐rich edge network and cloud servers are called fog and mobile edge computing (FMEC) and mobile cloud computing (MCC), respectively …”

Section: Introductionmentioning

confidence: 99%

SIMDOM: A framework for SIMD instruction translation and offloading in heterogeneous mobile architectures

Shuja

Gani

et al. 2017

Trans Emerging Tel Tech

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Fog and mobile edge computing is a paradigm that augments resource‐scarce mobile devices with resource‐rich network servers to enable ubiquitous computing. Smartphone applications rely on code offloading techniques to leverage high‐performance computing opportunities available on edge and cloud servers for compute‐intensive applications. Mobile (ARM) and edge/cloud (x86) architectures are heterogeneous and necessitate dynamic binary translation for compiled code migration that increases the application execution time. The application execution time and energy consumption should be lesser on the edge/cloud server as compared with the local mobile execution for optimal offload. Multimedia‐based applications contain a large set of single instruction multiple data (SIMD) instructions that are compute and resource intensive. However, dynamic binary translation techniques of SIMD instructions lose the parallelism and optimization because of inefficient vector‐to‐scalar translation. We present a framework for SIMD instruction translation and offloading for mobile devices (SIMDOM) in cloud and edge environments. The SIMDOM framework reduces the execution overhead of migrated vectorized multimedia application by using vector‐to‐vector instruction mappings. The framework maps and translates ARM SIMD intrinsic instructions to x86 SIMD intrinsic instructions such that an application programmed for the mobile platform can be executed on the cloud server without any modification. The offload decision is based on inputs from the device energy, network, and application profilers. Experiments show that SIMDOM framework provides 84.78%, 3.41%, and 79.93% energy, time, and performance efficiency, respectively, compared with local offload‐disabled execution. Compared with compiled code offloading, the SIMDOM framework provides 55.99%, 57.50%, and 96.23% energy, time, and performance efficiency, respectively.

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CloudAware: A Context-Adaptive Middleware for Mobile Edge and Cloud Computing Applications

Cited by 39 publications

References 22 publications

Delay-aware power optimization model for mobile edge computing systems

Delay-aware power optimization model for mobile edge computing systems

Providing ultra‐short latency to user‐centric 5G applications at the mobile network edge

SIMDOM: A framework for SIMD instruction translation and offloading in heterogeneous mobile architectures

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