A Distributed Approach for Disk Defragmentation

تجزئة الملفات هي واحدة من مشاكل الحاسبة الالكترونية  في الجزء المخصص لخزن البيانات داخل الحاسبة (القرص الصلب) التي تحدث عندما يتم استبدال وحذف الملفات بصورة مستمرة. في هذا البحث  تم تصميم نموذج لتجميع اجزاء الملف الواحد الى مجموعة واحدة  في مكان واحد ويتم تنفيذ المهمة من قبل مجموعة من النمل وذلك باستخدام احدى خوارزميات الذكاء الاصطناعي المعروفة بمستعمرات النمل (Ant Colony) .الدراسة البحثية بينت قابلية النموذج للتطبيق في بيئة موزعة كما في انظمة الحوسبة السحابية لحل هكذا مشكلة.  تم عمل محاكاة للنموذج باستخدام بيئة النمذجة البرمجية (NetLogo).Fragmentation is a computing problem that occurs when files of a computer system are replaced frequently. In this paper, the fragments of each file are collected and grouped, thanks to ant-colony optimization ACO, in one place as a mission for a group of ants. The study shows the ability of ants to work in a distributed environment such as cloud computing systems to solve such problem.  The model is simulated using NetLogo

A Simulated Annealing Method to Cover Dynamic Load Balancing in Grid Environment

High-performance scheduling is critical to the achievement of application performance on the computational grid. New scheduling algorithms are in demand for addressing new concerns arising in the grid environment. One of the main phases of scheduling on a grid is related to the load balancing problem therefore having a high-performance method to deal with the load balancing problem is essential to obtain a satisfactory high-performance scheduling. This paper presents SAGE, a new high-performance method to cover the dynamic load balancing problem by means of a simulated annealing algorithm. Even though this problem has been addressed with several different approaches only one of these methods is related with simulated annealing algorithm. Preliminary results show that SAGE not only makes it possible to find a good solution to the problem (effectiveness) but also in a reasonable amount of time (efficiency)

Scheduling Algorithm for Real-Time Embedded Control Systems using Arduino Board

The time taken for the scheduling task in a control system to reduce the traffic within the system is one of significant field of research in modern era. There are different control systems that require time scheduling such as elevator control system, traffic control system and train control system. Currently, there are unique control logic strategies adopting scheduling algorithm that are implemented in real time systems like earliest deadline first and ant colony optimization. At the same time, the disadvantages possessed by them are the exponential dip in the performance ratio due to over loading. Despite of all the available resources there are many issues faced such as congestion in traffic networks due to non-adaptive scheduling algorithms, etc., which led to several misfortunes and danger for human life. Hence an improved algorithm that increases the efficiency of the system is required to validate the processing time and the deadlines. Our research is focused on validating a proposed idea of using Arduino microcontroller to implement the different scheduling tasks and validate the efficiency of the algorithm to optimize the results of the system. This take cares of assigning the critical paths which priorities the tasks and focuses on reducing the scheduling time. This rapidly increases the processing speed and efficiency of the algorithm. We plan to use the Arduino board which has an inbuilt error detection algorithm that helps in checking whether the time scheduling is done effectively. In the initial phase of the project we develop and fabricate the hardware design using CAD design software packages like Solid Works. This is later employed with suitable environmental interfaces like, sensors and microcontrollers that can work in an adaptable environment as per requirements to validate the scheduling algorithm. The scheduling algorithm can also be used for controlling the current flow and power storage which will contribute a lot in the power consumption aspect. Graphical data interpretation of various algorithms from the past literature is observed and few selected ones are to be implemented in the experimental set up that is built as an initial proof of concept. By analyzing the results from the simulations carried out using the Altera FPGA board with VHDL and Arduino it is clear that we obtain better results using the Arduino board. Finally, to have an extensive study on different intelligent control logics that are used in the above mentioned control systems, we use the prototyped miniature model of an elevator system and a train control system to validate the different disk scheduling approaches like First Come-First Serve (FCFS), Elevator (SCAN) and ant colonization to solve the discrete combinational optimization of the scheduling logic. Initial validation of the system focuses on the effectiveness of using the ant colonization strategies to enhances the efficiency of the scheduling algorithm and optimize it for real time application

An Agent-based Grouping Strategy for Federated Grid Computing

Characterizing users based on their requirements and forming groups among providers accordingly to deliver them the stronger quality of service is a challenge for federated grid community Federated grid computing allows providers to behave cooperatively to ensure required utility by users Grouping grid providers under such an environment thus enhance the possibility of more jobs executed whereas a single provider or organization might not be able to do the same In this paper we propose an agent-based iterative Contract Net Protocol which supports in building federated grid via negotiating distributed providers The main focus of this paper is to minimize the number of iterations using a grouping mechanism Minimizing the number of iterations would produce less communication overhead which results in the minimum queue waiting time for users to publish their jobs Simulation results further ensure the feasibility of our approach in terms of profit and resource utilization compared to that of the traditional non-grouped marke

Virtual machine placement in cloud using artificial bee colony and imperialist competitive algorithm

Increasing resource efficiency and reducing energy consumption are significant challenges in cloud environments. Placing virtual machines is essential in improving cloud systems’ performance. This paper presents a hybrid method using the artificial bee colony and imperialist competitive algorithm to reduce provider costs and decrease client expenditure. Implementation of the proposed plan in the CloudSim simulation environment indicates the proposed method performs better than the Monarch butterfly optimization and salp swarm algorithms regarding energy consumption and resource usage. Moreover, average central processing unit (CPU) and random-access memory (RAM) usage and the number of host shutdowns show better results for the proposed model

Analysis of power consumption in heterogeneous virtual machine environments

Reduction of energy consumption in Cloud computing datacenters today is a hot a research topic, as these consume large amounts of energy. Furthermore, most of the energy is used inefficiently because of the improper usage of computational resources such as CPU, storage and network. A good balance between the computing resources and performed workload is mandatory. In the context of data-intensive applications, a significant portion of energy is consumed just to keep alive virtual machines or to move data around without performing useful computation. Moreover, heterogeneity of resources increases the difficulty degree, when trying to achieve energy efficiency. Power consumption optimization requires identification of those inefficiencies in the underlying system and applications. Based on the relation between server load and energy consumption, we study the efficiency of data-intensive applications, and the penalties, in terms of power consumption, that are introduced by different degrees of heterogeneity of the virtual machines characteristics in a cluster