An Adaptive Load Balancing Algorithm for Computational Grid

Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.

It is our great pleasure to welcome you to BADS 2011, the third Workshop on Bio-Inspired and Self-* Algorithms for Distributed Systems, co-located with ICAC 2011, the 8th IEEE/ACM International Conferenceon Autonomic Computing, Karlsruhe, Germany, June 14-18, 2011. BADS 2011 aims to provide a forum for exploring bio-inspired and self-* algorithms to build autonomic distributed systems; i.e., self-managing distributed systems that can organize, configure, optimize, scale, protect and/or heal themselves with minimal human intervention. Computing inspired by biology has a long history since 1950s. Its common objectives are to formulate theoretical models that are faithfully designed after biological principles, phenomena and processes and to exploit the models for developing computational tools (e.g., algorithms) and systems. Well-known bio-inspired algorithms include artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, reaction-diffusion algorithms, epidemic algorithms, cellular automata algorithms, gene expression/regulatory algorithms and Lindenmayer system. They have been successfully used as alternative and/or superior solutions to the problems that traditional algorithms cannot solve satisfactorily. In recognition of their achievements and potential, bio-inspired algorithms were named one of Scientific American magazine's 10 "World Changing Ideas 2010." The BADS workshop series focuses on distributed computing and examines the opportunities, current state-of-the-art, challenges and future outlook to create autonomic distributed systems with bio-inspired algorithms. We cover a broad range of aspects in distributed computing, including communication protocols, resource clustering, allocation and management, topology management, parallelism and concurrency, overlay construction and maintenance, decentralized information search and delivery, load distribution, synchronization, service selection, composition and deployment, labor division and task allocation, anomaly and misbehavior detection, malware propagation and detection. Following the success of its former editions in 2009 and 2010, BADS 2011 continues to focus on bio-inspired, autonomic distributed systems. In addition, it expands its themes to cover a general class of algorithms that exhibit self-* properties (e.g., self-configuring, self-organizing, self-managing, self-healing, self-scaling and selfadaptive properties) in the context of distributed computing. BADS 2011 has seven research papers selected through a rigorous review process. Each paper was reviewed and judged by at least three PC members on merits including correctness, originality, technical strength, quality of presentation and relevance to the workshop themes. We would like to sincerely thank the Program Committee members who have greatly contributed to the success of BADS 2011. BADS 2011 is organized in two sessions and one keynote presentation. The first session includes papers that present bio-inspired approaches for distributed computing. The second session focuses on solving optimization problems with distributed bio-inspired and self-* algorithms. The keynote presentation, entitled "Methods for Self-organizing Distributed Software", is given by Elisabetta Di Nitto, and focuses on the applications of self-organization in distributed systems. In nature, self-organization occurs such that a global behavior emerges from simple and local decisions made autonomously by each element of the system. This keynote overviews the benefits and challenges to apply the notion of self-organization for the development of distributed systems. It also presents several example applications such as energy efficiency and configuration optimization in cloud computing. The first session, "Bio-inspired Approaches for Distributed Computing", comprises four papers. The first paper, "Memetic Algorithm for Web Service Selection" by Ludwig, studies the service selection process, whose aim is to locate services of interest in service-oriented environments. The author proposes two approaches based on a genetic algorithm and a memetic algorithm to match consumers with services based on Quality of Service attributes. Both approaches are compared with an optimal assignment algorithm, called the Munkres algorithm, as well as with random approaches. The next paper, "OZMOS: Bio-Inspired Load Balancing in a Chord-based P2P Grid" by Brocco, proposes a load balancing mechanism, called Ozmos, that follows the principle of osmosis to relocate tasks between nodes in a P2P-based Grid. Bio-inspired agents are deployed to share information about the status of the Grid as well as to reschedule tasks between nodes. The efficacy of the proposed algorithm is illustrated in achieving system-wide load balance in Grids of different scales, with both homogenous and heterogeneous resources. The third paper, "Model-Driven Performance Engineering for Wireless Sensor Networks with Feature Modeling and Event Calculus" by Boonma and Suzuki, proposes an application development framework for wireless sensor networks. The proposed framework allows developers to graphically configure their applications and generate application code. With event calculus, the framework also estimates each application's performance such as per-node power consumption and network lifetime without running it on simulators and real networks. The proposed framework currently targets a shortest-path routing; however, it is a promising next step to estimate the performance of bio-inspired routing protocols such as pheromone-based chemotaxis routing. The last paper of the first session, "Description and Composition of Bio-Inspired Design Patterns: the Gradient Case" by Fernandez-Marquez, Arcos, Di Marzo Serugendo, Viroli and Montagna, investigates a group of bioinspired mechanisms in terms of design patterns. The authors show the relations between the different patterns and identify the boundaries of each mechanism. The mechanisms are organized into different levels. In basic level, Spreading, Aggregation and Evaporation are studied. Gradient is investigated in mid-level, and top-level mechanisms are Chemotaxis, Morphogenesis, and Quorum sensing. The second session, "Solving Optimization Problems with Distributed Evolutionary Algorithms", opens with a paper entitled "Protein Structure Prediction Using Particle Swarm Optimization and a Distributed Parallel Approach" by Kondov and Berlich. It demonstrates the efficiency of the standard Particle Swarm Optimization (PSO) algorithm to find the folded state of two proteins of different sizes starting from completely extended conformations. They illustrate that the predicted structure of the larger protein is in good agreement with the structure from the Protein Data Bank within the experimental resolution. The parallelization of PSO is shown to speed up the simulation linearly with the number of workers and it reduces the prediction time dramatically without loss of accuracy. The next paper, "Discrete Optimization Problem Solving with three Variants of Hybrid Binary Particle Swarm Optimization" by Singh V., Singh D., Tiwari and Shukla, presents three variants of the binary PSO algorithm in order to solve discrete optimization problems. This is achieved by introducing an additional step of crossover that varies in all the three variants. These hybrid algorithms show competitive results compared to other state-of-theart metaheuristics. The third paper, "Self-organized Invasive Parallel Optimization" by Mostaghim, Pfeiffer and Schmeck, proposes a new parallelization framework for optimization algorithms. In this approach, the self-organized resources are represented as a unified resource to the user who only specifies the optimization problem and his/her preferences to the system. The invasive approach starts with one resource and automatically divides the optimization task stepwise into smaller tasks, which are assigned to more resources. The job assignment pattern is decided on demand, i.e., the number of required resources is estimated during the optimization process. The authors examine their generic framework on multi-objective problems.

Artificial life techniques for load balancing in computational grids

ABSTRACTThe rapid combination of the electric vehicles into the recent transportation prefers very efficient charging systems involved in grid conditions. This increasing adoption of electric vehicles demands a dynamic and intelligent framework to control charging, confirming optimal grid performance, load balancing, and cost efficiency. In this work, we developed an optimized deep learning framework using the combined structure of Whale‐Optimized Neuro‐Fuzzy Classification for controlling electric vehicle charging within the grid. The increasing involvement of electric vehicle's produces new difficulties, involving overload of grid, and energy management, in real‐time and optimized decision making process. The Whale‐Optimized Neuro‐Fuzzy Classification method uses the hybrid abilities of neuro‐fuzzy systems, integrates the capability of the neural networks and also optimize using a Whale Optimization Algorithm for improved accuracy and efficiency. This proposed method maintains the charging and discharging process of electric vehicles, which have several factors like grid load, priorities of the vehicle and preferences of the user. The neuro‐fuzzy system combines deep convolutional neural network and fuzzy logic to predict charging patterns, considering user preferences, grid demand, renewable energy availability, and so on. This method contribute to the enhancement of energy systems, denotes the future requirement of future smart cities. Evaluation metrics included power usage, energy loss, cost, and processing speed. Analysis of experimental results revealed the accuracy of 99% for the proposed method compared to existing techniques.

Load balancing is a key concern when developing parallel and distributed computing applications. The emergence of computational grids extends this problem, where issues of cross-domain and large-scale scheduling must also be considered. In this paper an agent-based grid management infrastructure is coupled with a performance-driven task scheduler that has been developed for local grid load balancing. Each grid scheduler utilises predictive application performance data and an iterative heuristic algorithm to engineer local load balancing across multiple processing nodes. At a higher level, a hierarchy of homogeneous agents are used to represent multiple grid resources. Agents cooperate with each other to balance workload in the global grid environment using service advertisement and discovery mechanisms. A case study is included with corresponding experimental results to demonstrate that both local schedulers and agents contribute to overall grid load balancing, which significantly improves grid application execution performance and resource utilisation.

Dynamic load balancing techniques prevents computer nodes from overloading unevenly while leaving other idle. It is considered as one of the most challenging topics in parallel computing. Moreover, it is essential for increasing the efficiency of highly parallel systems especially in solving multitask problems with unpredictable load estimates. Particularly, over each processor in the parallel systems and interconnection networks. This paper focuses on developing an efficient algorithm for load balancing on Hyper Hexa Cell (HHC) interconnection network, namely, HHCLB algorithm. Basically, the Dimension Exchange Method (DEM) approach is used in this paper to construct a new load balancing approach on the network of HHC interconnections. Thus, an algorithm was introduced and simulated using java threads, where the performance of the algorithm is evaluated both analytically and experimentally. The evaluation was in terms of various performance metrics, including, execution time, load balancing accuracy, communication cost. By implementing the proposed load balancing algorithm to the HHC network, a high degree of accuracy and minimal execution time was achieved. It is important to highlight that the algorithm recorded small gap between the execution time for small number of processors and large number of processors. For instance, the algorithm achieved 0.14 seconds for balancing the load of 6 processors while 0.59 seconds for balancing the load of 3072 processors. This proves how effective the algorithm is in balancing the load for different network sizes from small to large number of processors, with a slight difference in execution time.

DSMLB: Dynamic switch-migration based load balancing for software-defined IoT network

ABSTRACTThe performance of 3GPP long term evolution (LTE) network can be significantly influenced by unbalanced user distribution. In heavily loaded cells, users cannot obtain satisfied services because of shortage of resources, whereas in lightly loaded cells, there are plenty of unoccupied resources. Load balancing (LB) is proposed to deal with this problem, through which the load distribution among nearby cells can be greatly balanced. However, because of the usage of full frequency reuse in LTE, the cell‐edge throughput is severely deteriorated by strong intercell interference. Meanwhile, LTE network usually serves users with heterogenous quality‐of‐service (QoS) requirements, which requires LB to be aware of it. Furthermore, call admission control (CAC) is indispensable in the process of LB, and it depends heavily on the long‐term average performance of the scheduling scheme. Thus, design of an efficient LB algorithm with the consideration of intercell interference, QoS, CAC and scheduling together becomes an important issue. To deal with the previous problem, a multi‐objective optimisation problem is formulated in this paper. Its complexity is analysed, and a practical algorithm, which includes hybrid scheduling, QoS‐guaranteed CAC, QoS‐aware intracell and intercell LB, is proposed. Extensive simulations are conducted. Results show that the proposed algorithm can approximate the optimal solution efficiently, thus leading to significantly better performance, that is, a lower new call blocking rate for users with QoS requirement, a larger total utility and a higher cell‐edge throughput for users without QoS requirement, although the cost is a bit degradation of the total throughput. Copyright © 2013 John Wiley & Sons, Ltd.

3G wireless communication is the next version of mobile networks. Load balancing in networks causes efficient using of network resources. In this paper, we propose an efficient algorithm for load balancing of radio network controllers (RNCs) in radio access networks (RANs). As the characteristics of connections between RNCs and BTSs in a RAN are similar to those of graphs, we make use of graphs to solve our problem. The algorithm is based on the finding a path by which the RNCs could exchange their overloads. By using these paths the algorithm load balances the RNCs in an efficient and fast way. By testing our algorithm on networks with different topology and comparing the results, the efficiency and ability of our algorithm will be verified.

To achieve an internet with high availability and reliability, needs two or more data paths so the process for sending data can be faster. Load balancing is often plays a significant role for this technique to properly utilized every gateway in the network. This research, implemented load balancing in software defined network architecture using floodlight controller. Evaluation is done by measuring QoS (delay, bit rate, packet rate, packet success rate) while sending various traffics through the network such as UDP Flow, VoIP, and DNS. Performance of load balancer is work well, because the results after load balancing is better than before. Which is the value of delay after load balancing is decreased about 30-55% compared to before load balancing, also the values of bit rate, packet rate dan packet success rate after load balancing is increased about 10-30% compared to before load balancing.

The emerging computational grid infrastructure consists of heterogeneous resource in widely distributed autonomous domains, which makes resource scheduling even more challenging. In this paper, we propose an adaptive resource scheduling algorithm for computational grid called BLBD (based on load balancing and demand). According to the computational grid job's personal resource requirements, the system load of computational grid nodes and the load balancing of computational grid system, the algorithm choose an appropriate node self adaptively for computational grid job from the candidates. It has been successfully tested in NPU campus computational grid environment. The experimental result shows that the new task scheduling algorithm can lead to significant performance gain for a variety of applications

In the area of cloud computing load balancing, the Particle Swarm Optimization (PSO) algorithm is neoteric and now praised highly, but recently a more neoteric algorithm which deploys the classifier into load balancing is presented. Besides, an algorithm called red-black tree which is aiming at improving the efficiency of resource dispatching is also praised. But the 3 algorithms all have different disadvantages which cannot be ignored. For example, the dispatch efficiency of PSO algorithm is not satisfying; although classifier and red-black tree algorithm improve the efficiency of dispatching tasks, the performance in load balancing is not that good, as a result the improved PSO algorithm is presented. Some researches are designed to get the advantages of new algorithm. First of all, the time complexity and performance for each algorithm in theory are computed; and then actual data which are generated in experiments are given to demonstrate the performance. And from the experiment result, it can be found that for the speed of algorithm itself PSO is the lowest, and the improved PSO solve this problem in some degree; improved PSO algorithm has the best performance in task solving and PSO is the second one, the red-black and Naive Bayes algorithm are much slower; PSO and improved PSO algorithm perform well in load balancing, while the other two algorithms do not do well.

Internet Download Manager (IDM) is one of the popular applications used by users to download various types of files from the internet. Load balancing is a technique used to increase the efficiency of using network resources by distributing data traffic evenly across several connection lines. This research aims to determine the extent to which IDM influences the effectiveness of load balancing as well as the influence of using Internet Download Manager (IDM) in the context of load balancing using the Policy-based Load Balancing (PCC), Equal Cost Multi-Path (ECMP), and Next-Hop Target methods. (NTH) on the MikroTik RB751 device. In this research, we used the PPDiOO method for systematic data collection and Quality of Services (QoS) analysis as a parameter for network data collection techniques. The research subject is the system or user who uses the device, while the research object is the impact of using IDM on load balancing performance. The main independent variable is the use of IDM, while the dependent variable is load balancing performance with different methods. Our findings show that using IDM consistently provides significant improvements in network performance compared to not using IDM.

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

It has been seen that the number of users in (web server) are increasing every now and then. This results in bottleneck which may lead to crashing of web servers or reduces the quality of service. So, there is a need of efficient and robust algorithm for load balancing. We have proposed an algorithm that gives better result and is capable of balancing the load on data centre subjected to the key feature. As Load balancing is the critical task in the technical era and the traffic in the web is increasing exponentially, we propose a solution which deals with core load balancing to resolve this trending issue. Here we have designed an intelligent sensing algorithm that will help the server to distribute the load according to the availability of server, location and then redirect the request to comparatively most available server. Distribution of load is done according to the availability of the system instead of round robin fashion. Availability of a server is computed according to current trifle load, current critical load and a deviation value. Depending upon the computed load: the most available, available and least available server are found. Finally, the request is dispatched to the nearest server which is highly available.