Parallel Genetic Algorithms with Application to Load Balancing for Parallel Computing

A new coarse grain parallel genetic algorithm (PGA) and a new implementation of a data-parallel GA are presented in this paper. They are based on models of natural evolution in which the population is formed of discontinuous or continuous subpopulations. In addition to simulating natural evolution, the intrinsic parallelism in the two PGA\u27s minimizes the possibility of premature convergence that the implementation of classic GA\u27s often encounters. Intrinsic parallelism also allows the evolution of fit genotypes in a smaller number of generations in the PGA\u27s than in sequential GA\u27s, leading to superlinear speed-ups. The PGA\u27s have been implemented on a hypercube and a Connection Machine, and their operation is demonstrated by applying them to the load balancing problem in parallel computing. The PGA\u27s have found near-optimal solutions which are comparable to the solutions of a simulated annealing algorithm and are better than those produced by a sequential GA and by other load balancing methods. On one hand, The PGA\u27s accentuate the advantage of parallel computers for simulating natural evolution. On the other hand, they represent new techniques for load balancing parallel computations

Dynamic load balancing strategies in heterogeneous distributed system

Distributed heterogeneous computing is being widely applied to a variety of large size computational problems. This computational environments are consists of multiple het- erogeneous computing modules, these modules interact with each other to solve the prob-lem. Dynamic load balancing in distributed computing system is desirable because it is an important key to establish dependability in a Heterogeneous Distributed Computing Systems (HDCS). Load balancing problem is an optimization problem with exponential solution space. The complexity of dynamic load balancing increases with the size of a HDCS and becomes difficult to solve effectively. The solution to this intractable problem is discussed under different algorithm paradigm.The load submitted to the a HDCS is assumed to be in the form of tasks. Dynamic allocation of n independent tasks to m computing nodes in heterogeneous distributed computing system can be possible through centralized or decentralized control. In central-ized approach,we have formulated load balancing problem considering task and machine heterogeneity as a linear programming problem to minimize the time by which all task completes the execution in makespan.The load balancing problem in HDCS aims to maintain a balanced allocation of tasks while using the computational resources. The system state changes with time on arrival of tasks from the users. Therefore,heterogeneous distributed system is modeled as an M/M/m queue. The task model is represented either as a consistent or an inconsistent expected time to compute (ETC) matrix. A batch mode heuristic has been used to de-sign dynamic load balancing algorithms for heterogeneous distributed computing systems with four different type of machine heterogeneity. A number of experiments have been conducted to study the performance of load balancing algorithms with three different ar-rival rate for the task. A better performance of the algorithms is observed with increasing of heterogeneity in the HDCS.A new codification scheme suitable to simulated annealing and genetic algorithm has been introduced to design dynamic load balancing algorithms for HDCS. These stochastic iterative load balancing algorithms uses sliding window techniques to select a batch of tasks, and allocate them to the computing nodes in the HDCS. The proposed dynamic genetic algorithm based load balancer has been found to be effective, especially in the case of a large number of tasks

QoS routing in ad-hoc networks using GA and multi-objective optimization

Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications