Case of ARM emulation optimization for offloading mechanisms in Mobile Cloud Computing

Shuja, Junaid; Gani, Abdullah; Anjum, Naveed; Ahmed, Ejaz; Hsu, Ching‐Hsien

doi:10.1016/j.future.2016.05.037

Cited by 20 publications

(20 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 The effect of increasing the number of users on the delay To solve our model, we use the GNU Linear Programming Kit (GLPK) tool. 2 . We run our experiments on a Dell Inspiron laptop equipped with a 6th Generation Intel Core i7-6700HQ Processor (with 6MB Cache) and 16GB RAM (in addition to 16GB swap space).…”

Section: Experimentation and Resultsmentioning

confidence: 99%

“…On the other hand, the increasing acceptance of Cloud Computing (CC) systems provides an opportunity for resource limited mobile devices to perform compute intensive applications on the cloud giving rise to Mobile CC (MCC) systems. The basic MCC concepts depend on a network-based resource sharing to increase the availability of the resources and to reduce the costs of operation and management [1,2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Delay-aware power optimization model for mobile edge computing systems

Jararweh

Al‐Ayyoub

Al-Quraan

et al. 2017

Pers Ubiquit Comput

View full text Add to dashboard Cite

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.

show abstract

Section: Experimentation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delay-aware power optimization model for mobile edge computing systems

Jararweh

Al‐Ayyoub

Al-Quraan

et al. 2017

Pers Ubiquit Comput

View full text Add to dashboard Cite

show abstract

“…This work forwards our earlier efforts of surveying the MCC‐offloading frameworks and analyzing the problem of SIMD instruction translation in existing MCC offload‐enabling techniques . Existing MCC‐offloading frameworks rely on either network‐overhead centric system virtualization or computation‐overhead centric application virtualization techniques to address the heterogeneity of compute ISAs.…”

Section: Related Workmentioning

confidence: 90%

“…Secondly, we target source code–based precompiled application in the SIMDOM framework as compared with ARM‐compiled applications targeted by the Houdini layer. The ARM to Intel translation overhead of Houdini has been explored in our earlier work . Moreover, the SIMDOM framework and Houdini translations are both based on earlier efforts of SIMD instruction translations incorporated in Zhislina et al…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Shuja

Gani

et al. 2017

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Edge computing: A systematic mapping study

Sakhdari

Izadpanah

Zolfaghari

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryEdge computing is a new way of computing that uses resources at the edge of a network to solve the problem of communication delays in applications that require immediate responses. This field has received a lot of attention from the research community over the past few decades, leading to a significant increase in publications. To better understand the field, a systematic mapping study (SMS) was conducted using a three‐tier search method that involved defining quality criteria to extract relevant search spaces and studies. This resulted in the selection of 112 search spaces out of 805 and 1440 studies out of 8725. The SMS addressed 8 research questions to identify the main topics, architectures, techniques, and other important aspects of edge computing.

show abstract

Case of ARM emulation optimization for offloading mechanisms in Mobile Cloud Computing

Cited by 20 publications

References 29 publications

Delay-aware power optimization model for mobile edge computing systems

Delay-aware power optimization model for mobile edge computing systems

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

Edge computing: A systematic mapping study

Contact Info

Product

Resources

About