OFFLOADING DECISION SELECTION METHOD FOR ENERGY EFFICIENCY AND LOW LATENCY IN HETEROGENE SIMUATION ENVIRONMENTS

Mobile Cloud Computing (MCC) is a technology that can overcome the problems of high computing and limited resources owned by mobile devices. However, in practice, MCC has a very long transmission distance from the mobile device, resulting in a large latency. Mobile Edge Computing (MEC) is a technology that exists to overcome this problem. However, new problems arise from the presence of this MEC.One of the problems that arise is the selection of offloading decisions from mobile devices. Several studies consider energy efficiency / large latency or both in determining offloading decisions. However, there are not many studies that consider the movement of mobile devices in determining offloading decisions. Even though the movement of mobile devices is also very influential on latency because tasks need to be migrated to another edge server when a mobile device has moved. Several studies that have addressed this issue apply the solution to smaller, less heterogeneous simulation environments.This study used a new method of offloading decision-making that pays attention to the movement of mobile devices in a heterogeneous environment. This proposed method uses Black Widow Optimization in solving the problem of decision selection when offloading. From the simulation results, the performance of the proposed method is better than the comparison method in terms of the amount of energy consumption and delay latency.

INSTRUMENTATION-BASED MONITORING TECHNIQUES SURVEY ON HOST, PLATFORM, AND SERVICE LEVEL IN MICROSERVICE ARCHITECTURE

Microservice is an application architecture that separates one big application into smaller ones. The architecture simplifies development, deployment, and management process. However, the architecture is quite complex thus the monitoring process becomes much more challenging. Classifications for the instrumentations that are used in the monitoring process is needed to achieve better practicality for the administrators. We surveyed the monitoring technique classification method in microservice architecture. The method is divided into three levels. They are host level, platform level, and service level. In this paper, we present the latest instruments that are being used in the monitoring process in each level. Correlation between the goals, needs, and stakeholder is also presented

Mobility-Aware Offloading Decision Method For Energy Efficient And Low Latency Delay On Heterogent Simulation Environment

Mobile Cloud Computing (MCC) adalah salah satu teknologi yang dapat mengatasi permasalahan komputasi tinggi dan keterbatasan sumber daya yang dimiliki oleh mobile device. Tetapi pada praktiknya MCC memiliki jarak transmisi yang sangat jauh dengan mobile device sehingga timbul latency yang besar pula. Mobile Edge Computing (MEC) merupakan teknologi yang hadir untuk mengatasi permasalahan ini. Namun demikian timbul permasalahan baru dari kehadiran MEC ini. Salah satu permasalahan yang timbul adalah pemilihan keputusan offloading dari mobile device. Beberapa penelitian mempertimbangkan efisiensi energi / besar latency ataupun keduanya dalam menentukan keputusan offloading. Namun demikian belum banyak penelitian yang mempertimbangkan pergerakan dari mobile device dalam menentukan keputusan offloading. Padahal pergerakan mobile device ini juga sangat berpengaruh besar terhadap latency karena task perlu dimigrasi ke edge server lain saat suatu mobile device telah bergerak. Beberapa penelitian yang telah mengatasi hal ini menerapkan solusinya kepada lingkungan simulasi yang kecil dan kurang heterogen. Pada penelitian ini digunakan sebuah metode baru dalam pengambilan keputusan offloading yang memperhatikan pergerakan dari mobile device pada lingkungan yang heterogen. Metode yang diusulkan ini menggunakan Black Widow Optimization dalam menyelesaikan permasalahan pemilihan keputusan saat oflloading. Dari hasil simulasi yang dilakukan performa dari metode yang diusulkan lebih baik daripada metode pembandingnya dari segi besarnya konsumsi energy dan delay latency. ================================================================================== problems of high computing and limited re-sources owned by mobile devices. However, in practice, MCC has a very long transmission distance from the mobile de-vice, resulting in a large latency. Mobile Edge Computing (MEC) is a technology that exists to overcome this problem. However, new problems arise from the presence of this MEC. One of the problems that arise is the selection of offloading decisions from mobile devices. Several studies consider en-ergy efficiency / large latency or both in determining offloading decisions. However, there are not many studies that con-sider the movement of mobile devices in determining offloading decisions. Even though the movement of mobile devices is also very influential on latency because tasks need to be migrated to another edge server when a mobile device has moved. Several studies that have addressed this issue apply the solution to smaller, less heterogeneous simulation environments. This study used a new method of offloading decision-making that pays attention to the movement of mobile devices in a heterogeneous environment. This proposed method uses Black Widow Optimization in solving the problem of decision selection when offloading. From the simulation results, the performance of the proposed method is better than the comparison method in terms of the amount of energy consumption and delay latency