دانلود رایگان ترجمه مقاله بررسی استراتژی آفلود محاسباتی برای بهبود عملکرد برنامه های در حال اجرای موبایل – الزویر ۲۰۱۵
دانلود رایگان مقاله انگلیسی + خرید ترجمه فارسی | |
عنوان فارسی مقاله: |
بررسی راهبردهای آفلود رایانشی برای بهبود عملکرد برنامه هایی که روی دستگاه های موبایل اجرا می شوند |
عنوان انگلیسی مقاله: |
A survey of computation offloading strategies for performance improvement of applications running on mobile devices |
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مشخصات مقاله انگلیسی (PDF) | |
سال انتشار | ۲۰۱۵ |
تعداد صفحات مقاله انگلیسی | ۱۳ صفحه با فرمت pdf |
رشته های مرتبط با این مقاله | مهندسی فناوری اطلاعات و کامپیوتر |
گرایش های مرتبط با این مقاله | مهندسی نرم افزار، برنامه نویسی کامپیوتر، رایانش ابری و طراحی و تولید نرم افزار |
چاپ شده در مجله (ژورنال) | مجله شبکه و کاربردهای کامپیوتری – Journal of Network and Computer Applications |
کلمات کلیدی | آفلودینگ محاسباتی، رایانش موبایل، بهبود عملکرد، رایانش ابری موبایل، پیرسازی سایبری |
ارائه شده از دانشگاه | دانشگاه بهاالدین ذکریا، مولتان، پاکستان |
رفرنس | دارد ✓ |
کد محصول | F975 |
نشریه | الزویر – Elsevier |
مشخصات و وضعیت ترجمه فارسی این مقاله (Word) | |
وضعیت ترجمه | انجام شده و آماده دانلود |
تعداد صفحات ترجمه تایپ شده با فرمت ورد با قابلیت ویرایش | ۲۹ صفحه با فونت ۱۴ B Nazanin |
ترجمه عناوین تصاویر و جداول | ترجمه شده است ✓ |
ترجمه متون داخل تصاویر | ترجمه شده است ✓ |
ترجمه متون داخل جداول | ترجمه شده است ✓ |
درج تصاویر در فایل ترجمه | درج شده است ✓ |
درج جداول در فایل ترجمه | درج شده است ✓ |
درج فرمولها و محاسبات در فایل ترجمه | به صورت عکس درج شده است ✓ |
منابع داخل متن | به صورت انگلیسی درج شده است ✓ |
کیفیت ترجمه | کیفیت ترجمه این مقاله متوسط میباشد |
توضیحات | ترجمه این مقاله به صورت خلاصه انجام شده است. |
فهرست مطالب |
چکیده
۱- مقدمه
۲- رده بندی افلودینگ: معماری ها و اثر بخشی
۲-۱ معماری های رویکرد افلودینگ محاسباتی
۲-۲ ایجاد تعادل برای تصمیمات افلودینگ
۳- تغییرات فناوری بی سیم و افلودینگ
۴-رویکرد ها و معماری های افلودینگ
۴-۱ افلودینگ استاتیک
۴-۲ افلودینگ پویا
۵- پارتیشن بندی برنامه برای افلودینگ محاسبه
۶-مقایسه چارچوب های افلودینگ
۷- دامین های برنامه که از افلودینگ ذی نفع می شوند
۸- چالش های فعلی و افلودینگ رایانشی موثر
۸-۱ پارتیشن بندی
۸-۲ شفافیت و قابل حمل بودن خودکار
۸-۳ امنیت
۸-۴ نیاز های برنامه
۹-نتیجه گیری
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بخشی از ترجمه |
چکیده : ۱- مقدمه
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بخشی از مقاله انگلیسی |
Abstract Handheld mobile devices have evolved from simple voice communication devices to general purpose devices capable of executing complex applications. Despite this evolution, the applications executing on the mobile devices suffer due to their constrained resources. The constraints such as limited battery lifetime, limited storage and processing capabilities produce an adverse impact on the performance of applications executing on the mobile devices. Computation offloading addresses the issue of limited resources by transferring the computation workload to other systems having better resources. It may be oriented towards extending battery lifetime, enhancing storage capacity or improving the performance of an application. In this paper, we perform a survey of the computation offloading strategies correlated with performance improvement for an application. We categorize these approaches in terms of their workload distribution and offloading decisions. We also describe the evolution of the computation offloading based environment as well as a categorization of application partitioning mechanisms adopted in various contributions. Furthermore, we present a parameter-wise comparison of automated frameworks, the application domains that benefit from computation offloading and the future challenges impeding the evolution of computation offloading. ۱٫ Introduction With the advent of smartphone technologies, the mobile devices have become ubiquitous. These devices are no longer constrained to providing only communication services. Instead, these devices are capable of executing applications with diverse requirements. The processing required by these applications may range from simple mathematical computations performed by a calculator to a very complex voice recognition system. The execution of complex applications requires the mobile devices to possess powerful resources. The scarcity of these resources has adverse effects on the ever-growing usage of the mobile devices. For instance, the statistics according to StatCounter show that about 30.66% of the platforms used for web browsing are the mobile systems (smartphones/tablets) (StatCounter, 2014). Consequently, the mobile market plays a significant role in ecommerce and sales growth. This role is however diminished by the fact that the mobile systems have limited energy and power resources. Although there have been efforts to incorporate high performance multiple core processors in smartphones, the gap between the existing and the required resources continues to grow. In this context, the computation offloading is a mechanism that enables us to bridge the gap by making intensive computations execute on large systems having sufficient resources as required by the application. This not only makes a resource constrained mobile system seem like a high-end powerful machine, but also enables to perfectly utilize the existing resources. The computation offloading is not a novel idea as it has evolved from various paradigms incorporating distributed computing (Dinh et al., 2013; Kumar and Lu, 2010; Sanaei et al., 2014; Fontana et al., 2013). The performance improvement of an application is achieved by partitioning it into several subprograms each of which may be assigned to a different processor for execution. Each processor makes use of its own memory and/or shares the memory with other processors to perform computations in parallel. Subsequently, the results are returned to the processor controlling the overall execution. A cloud computing platform is also based on the intuition of distributed computing and offers the compute services through a Service Level Agreement (SLA) on a large network usually the Internet. It differs from other computing paradigms since an assurance regarding availability of services is provided to the users. The Mobile Cloud Computing (MCC) therefore refers to provision of services through a cloud to mobile devices that are characterized with limited resources (Dinh et al., 2013; Kumar and Lu, 2010; Sanaei et al., 2014; Fontana et al., 2013; Juntunen et al., 2012; Khan et al., 2014b; Berl et al., 2010). The computation of a mobile application may be offloaded to another resource-rich system termed as surrogate. Such kind of computation offloading not only mitigates the issue of limited resources of mobile devices but also enables to harness the processing power of high-end machines that will otherwise be idle (Barbera et al., 2013; Ou et al., 2007; Cui et al., 2013; Sanaei et al., 2012; Miettinen and Hirvisalo, 2009; Kumar et al., 2013; Satyanarayanan et al., 2009). In this paper, we perform a comprehensive survey of the computation offloading strategies impacting the performance of the applications executing on mobile devices. Although the computation offloading has also been aimed at saving energy required for executing an application (Lu et al., 2013; Hong et al., 2009; Wen et al., 2012; Rudenko et al., 1998; Nurminen, 2010; Nimmagadda et al., 2009; Miettinen and Nurminen, 2010; Mayo and Ranganathan, 2004; Sinha and Kulkarni, 2011; Ge et al., 2012), but in this paper, we mainly consider the contributions which impact the execution performance (computation speed) of applications running on mobile devices. The survey encompasses the research work for computation offloading arranged in terms of multiple aspects including the taxonomy, strategies, evolution pattern and relevant application domains. We also present a categorization of partitioning approaches adopted in different contributions and a parameter-wise comparison of main offloading frameworks. We also discuss main issues related to computation offloading and suggest possible approaches to address these issues effectively. The rest of the paper is organized as follows. Section 2 describes the offloading taxonomy in terms of architectures and criteria for its effectiveness. The evolution of offloading and wireless technologies is described in Section 3. The offloading approaches and contributions aimed at performance improvement are surveyed in Section 4. A categorization of partitioning approaches used in computation offloading is given in Section 5. A parameter-wise comparison of the automated computation offloading frameworks is described in Section 6, whereas the applications benefiting from computation offloading are discussed in Section 7. The main issues related to an effective implementation of computation offloading are discussed in Section 8 together with their solutions before concluding at Section 9. |