For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the user’s resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the user’s budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this method’s performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.

A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing

Load balancing techniques in cloud computing environment: A review

We propose a task-oriented multi-objective scheduling method based on ant colony optimization (MOSACO) to optimize the finite pool of public and private computing resources in a hybrid cloud computing environment according to deadline and cost constraints. MOSACO is employed to minimize task completion times and costs using time-first and cost-first single-objective optimization strategies, respectively, and to maximize user quality of service and the profit of resource providers using an entropy optimization model. The effectiveness of the MOSACO algorithm based on multiple considerations of task completion time, cost, number of deadline violations, and degree of private resource utilization is verified using simulation and three application examples. Comparisons with similar scheduling methods demonstrate that MOSACO provides the highest optimality, and that the time-first and cost-first strategies provide definite advantages for minimizing completion time and cost, respectively.

A Multi-Objective Hybrid Cloud Resource Scheduling Method Based on Deadline and Cost Constraints

In cloud computing, resources are dynamic, and the demands placed on the resources allocated to a particular task are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called interlacing peak is proposed. First, the resource load information, such as CPU, I/O, and memory usage, is periodically collected and updated, and the task information regarding CPU, I/O, and memory is collected. Second, resources are sorted into three queues according to the loads of the CPU, I/O, and memory: CPU intensive, I/O intensive, and memory intensive, according to their demands for resources. Finally, once the tasks have been scheduled, they need to interlace the resource load peak. Some types of tasks need to be matched with the resources whose loads correspond to a lighter types of tasks. In other words, CPU-intensive tasks should be matched with resources with low CPU utilization; I/O-intensive tasks should be matched with resources with shorter I/O wait times; and memory-intensive tasks should be matched with resources that have low memory usage. The effectiveness of this method is proved from the theoretical point of view. It has also been proven to be less complex in regard to time and place. Four experiments were designed to verify the performance of this method. Experiments leverage four metrics: 1) average response time; 2) load balancing; 3) deadline violation rates; and 4) resource utilization. The experimental results show that this method can balance loads and improve the effects of resource allocation and utilization effectively. This is especially true when resources are limited. In this way, many tasks will compete for the same resources. However, this method shows advantage over other similar standard algorithms.

/pdf/a-multiqueue-interlacing-peak-scheduling-method-based-on-u0k1lv9sut.pdf

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

This paper presents an optimal power flow (OPF) solution based on maximizing distributed generator (DG) real power output with a restraint on network losses for radial and meshed distribution networks. The multi-objective nonlinear OPF problem with weighting terms for DG real power output and loss is linearized and solved by pure primal-dual interior point method. Besides explaining the basic algorithm, the paper concentrates on the determination of initial points and effective adjustment of barrier parameter to maintain accuracy and speed of convergence. The performance of the method is evaluated on two representative U.K distribution networks: a 12-bus rural upland meshed network where wind is prevalent and an 18-bus semi-urban radial feeder with two fossil fuel DGs. The results confirm that the developed OPF algorithm has the potential for network operation and implementation in an autonomous active network management system due to its ability to optimize different types of distribution network.

Optimal power flow for autonomous regional active network management system

This study proposed an algorithm to stabilize jittery videos directly without the need to estimate camera motion. A stable output video will be attained without the effect of jittery that caused by shaking the handheld camera during video recording. Firstly, salient points from each frame from the input video is identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization and less unallied area after the process of stabilization. The output of using such method showed good result in terms of stabilization and discarded distortion from the output videos recorded in different circumstances. Initial results showed that the proposed technique is suitable to be used and provide great deal of stabilization.

Video stabilization based on point feature matching technique

The demand for Cloud computing usage is increasing day by day due to the advantages and facilities it offers. The fast growing rate of the usage of large-scale computing machines on cloud platform has resulted in increased consumption of energy and emission of carbon. Such negative effects should be curbed for a more environmental-friendly computing platform, e.g. a green cloud computing platform. Scheduling is one of the aspects that can be looked into in regards to the improvement of the whole cloud architecture, specifically in the scope of resource management. One of the important features that need to be optimized in regards to scheduling is the load balancing process that emphasizes on optimal resource utilization, maximum throughput, maximum response time and prevention of overload. One way to optimize the load balancing process is by implementing the migration of virtual machines across multiple hosts, in which utilization of CPU resources can be optimized. The appropriate use of two different classes of resources e.g. low-powered and high-powered machines based on their Million Instructions Per Second (MIPS) metrics, will lead to minimal process execution time if both types of resources are being efficiently used and effectively mapped to suitable types of processing. According to different machine performance, the migration of virtual machines will be computed based on the current CPU utilization following three different conditions, i.e. when the CPU usage reaches its 90%, 10% and 0% marker. Based on this idea, a general algorithm in activating the virtual machine migration is proposed, in which the overall load balancing process could be improved.

Virtual machine migration implementation in load balancing for Cloud computing

Internet Protocol version 6 (IPv6) or IP next generation is the protocol that has been designed to replace the existing Internet Protocol version 4 (IPv4). These two protocols are expected to coexist for a number of years during the transition period. A number of IPv4-to-IPv6 transition tools are available to address the various needs of different networks. The two most basic transition tools available are the hybrid stack mechanism and tunneling. A hybrid or dual stack host, implements both IPv4 and IPv6, usually in a single stack in which most of the code is shared by the two protocols. Tunneling is the encapsulation of IPv6 traffic within IPv4 packets so they can be sent over an IPv4 infrastructure, allowing IPv6 hosts and routers to communicate without the need to upgrade the IPv4 infrastructure that exists between them. This paper looks at how tunneling can be performed over existing internetwork infrastructure.

http://ieeexplore.ieee.org/iel5/8033/22185/01033069.pdf

Deploying Internet Protocol version 6 (IPv6) over Internet Protocol version 4 (IPv4) tunnel

Digital System Fundamentals is an elementary course offered by the Faculty of Electrical Engineering in Universiti Teknologi MARA (UiTM) for semester 1 undergraduate students of the Electrical Engineering programs specializing in computer, electronics, communication and instrumentation. As this course is focusing more on designing circuit rather than only knowing the theoretical part of the circuit, students are introduced with a tool which is widely used in the industry known as Very High Speed Integrated Circuits Hardware Description Language or better known as VHDL. This is also to ensure that these students are already exposed and equipped with updated technology and skills that would meet the industry requirement as well as for accreditation purposes. This paper outlines how the VHDL is incorporated in teaching combinational logic circuit of this course.

Incorporating VHDL in teaching combinational logic circuit

Multicast is a well-known protocol that has been tested to be a highly effective method to deliver real time applications over the network. In this paper, two Virtual Private Network (VPN) based technologies are considered in delivering real-time based applications namely Multiprotocol Label Switching (MPLS) and Asynchronous Transfer Mode (ATM). The performance comparison of video multicast of the two technologies is described. Both platforms were modeled and simulations executed with multicast protocol enabled. For real time traffic, selected parameters were carefully defined and configured such as delay, throughput and packet loss. The objective of this study is to compare performance of MPLS and ATM based on these selected parameters, particularly in the case of delivering video traffic in the presence of congestion. The study showed that with VPN enabled on both technologies, MPLS had performed better than ATM in terms of throughput and packet drop.

Mustaffa Samad

Papers

Video stabilization based on point feature matching technique

Virtual machine migration implementation in load balancing for Cloud computing

Deploying Internet Protocol version 6 (IPv6) over Internet Protocol version 4 (IPv4) tunnel

Incorporating VHDL in teaching combinational logic circuit

Performance comparison of video multicasting over Asynchronous Transfer Mode (ATM) & Multiprotocol Label Switching (MPLS) networks