Guided Ant Colony Optimization Based Variable Neighborhood Search For Optimistic Load Balancing

This algorithm is based on the nature of honey bees of finding and reaping food. The forager bees is a class of bees which forage for food sources and on finding one, they come back to the beehive to tell about this using waggle dance. This dance gives an idea about the quality or quantity of food and also its distance from the beehive. Scout bees then follow the forager bees to the location and then began to reap the food. Then they return to the beehive and do a waggle dance, which gives an idea of how much food is left resulting in further exploitation of the food sources. In load balancing ,as the demand for webservers changes(increases or decreases), dynamically the services are assigned to regulate the changing demands .

Furthermore, grid computing needs a sophisticated infrastructure: small servers, quick connections between the servers and at last, so as to maximize the potential of that infrastructure, it needs the utilization of quality tools, software system and skilled technicians to manage the grid. In different words, once of these elements are place along, it's obvious that this technology is

Straightforward access to large-scale distributed computational assets is provided by computational Grids. With the help of their size and geographic dissemination they help to create large computing centers. Several simulation studies are performed for evaluating the mechanisms to allocate resources in a Grid. There are three mechanisms to allocate resources in a Grid. The first method is the non-cooperative sealed-bid method where the tasks are auctioned off to the highest bidder. Second, the semi-cooperative n-round bid method in which each site delegates the task its work to others if it cannot perform the work itself. Last, the cooperative method in which all the sites cooperatively perform all the tasks as efficiently as possible. The simulation model has various leveled Grid structure in which machines are built around larger computing centers called “federations” [1].

provisioning, pay-as-you-go model making it reliable to run applications on the cloud. On the cloud environment, the running scientific applications are modeled as a workflow graph whereon completing one task, beginning of another task takes place which results in the formation of a Directed Acyclic Graph (DAG). One of the key

The Important of Cloud Computing is increasing and it is receiving a growing attention in Scientific & Industrial Communities. Cloud computing is one of the top 10 most important technologies and has a greater possibility in successive years by companies and organizations. Cloud computing implement everywhere, favourable, on demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications and servers) that can be immediately supply and released with minimal management effort.

This paper gives an overview about Grid Computing and the various key issues related to that.Today we are in the Internet world and everyone prefers to get fast access to the Internet. The growth of the internet with various resources and high speed networks changes the society enormously. We can improve the speed of the computer using Grid. This paper presents clearly the vision of the grid computing and the architecture of grid computing.The advantages and the disadvantages of the grid computing has also been discussed.

Various virtualized platform that helps user to accomplish their jobs with minimum response time and minimum completion time that are provided by cloud computing environment. There are three types of services in the cloud, they are Infrastructure as a Service (Iaas), Platform as a Service (Paas), Software as a service (SaaS). The characteristics of cloud computing are resources polling, elasticity, on demand services, broad network access and middleware capabilities from provider of delivering Iaas and Paas in the form of datacenter, above services provide by deployment models such as Public cloud, Private cloud, community cloud and hybrid cloud that are used by the cloud provider. Job Scheduling has the main goal to achieve a high performances computing and best system throughput. The important issues in scheduling are Priority of job because some jobs should be serviced earlier than other those jobs cannot wait for a long time in the system. Priority of jobs must be considered by a suitable job scheduling algorithm. To address this problem some of the priority of jobs scheduling algorithm must be considered. The role of Virtual Machine’s (VMs) is an important issue because, through virtualization technology, it makes the infrastructure of cloud computing to be scalable. Therefore the important issue is to develop on optimal scheduling of virtual machine.

and low power consumption is of great importance for both service/network providers and equipment manufacturers. An emerging technology called cloud computing can increase the utilization and efficiency of hardware equipment. It can potentially reduce the global CO2 emission. In this paper, we propose a virtual network architecture for cloud computing. In this paper Modified best fit decreasing algorithm (MBFD) is discuss for reduce the energy consumption . This paper presents a methodology for power and energy reduction in general purpose microprocessors, which is known as dynamic voltage frequency scaling (DVFS). The DVFS technique can be considered as an effective mechanism for reducing processor power and energy. In the last decade a lot of works have been done during the hardware and software implementation. In this paper a proposed control loop of DVFS technique has been introduced. The objective is to build a "green" virtual network in cloud computing.

A popular way of addressing this problem is server consolidation. It is an optimization approach that leverages technique called operating

algorithm with balances spiral method. The multiple objectives are maximizing the resources utilization and minimize the resource gap of idle resources. Job scheduling algorithms are developed to achieve several goals like high throughput, low makespan, expected outcome, QOS, maintaining efficiency. There are three types of services in cloud, they are Infrastructure as a Services (Iaas),

Abstract Cloud Computing is now extremely popular because of its wide variety of Applications through internet. The particular service based approaches which are aware from the server selection through the cloud can easily progress toward the cost and performance of cloud computing. In This paper hybrid ACO and Cuckoo is studied. The particular limitation of ACO is that it Converges at low rate in the preliminary phases and needs more energy and time to converge. This takes place due to wrong choice of initial probable parameter. The cost also increases because of merging and pruning of the internet service decomposition. In order to eliminate this problem a new energy efficient scheduling, a new hybrid Cuckoo Artificial bee colony optimization algorithm for cloud computing environment is proposed in this paper.The comparison between deterministic scheduling techniques and meta heu-ristic scheduling techniques is also done on the basis of various parameters i.e. mean Flow Time ,Makespan and mean waiting time.

Distributed computing is a technique where computing tasks are processed by a collection of networked computers and therefore can be seen a cluster of computers Grid computing creates a fast virtual computer from a network of computers by using their idle cycles.Even though grid computing is an important successor to distributed computing, the computing environments are essentially different. For distributed computing, resources are homogeneous and are reserved, leading to guaranteed processing capacity. On the other hand, grid environments are

A type of systems administration. Not at all like routine systems that attention on correspondence among gadgets, framework figuring tackles unused preparing cycles of all PCs in a system for taking care of issues excessively escalated for any stand-alone machine.

There are number of such example scenario that differs in many respects such as type or number of pervasive devices, their computation power and scale of interaction with user and environment and resources being demanded for the execution of job from Grid computational infrastructure. From this motivation, integration of ubiquitous computing into grid information infrastructure is necessary. The main aim is to provide seamless access to grid from wirelessly networked walk stations such as laptops, palmtops, PDAs and controller with certain quality of services (QoS) assurance. Computational and information

EDSs are mainly designed meshed but operated radially for some technical and financial concerns. Distribution networks can be represented with a graph in ordered pairs consisting of a set of vertices, i.e. buses and a set of edges, i.e. branches; in terms of mathematics this equivalents to a sparse matrix which its non-zero elements signifies the existence of an edge in the system. On this basis a typical distribution network is radial if it forms a tree where each load bus is exactly supplied from one source node, i.e. substation bus [11]. This suggests MOEDNRC problem as identifying the set of non-dominated trees of the given graph. In this section we’ve devised a heuristic technique based on this idea as well as the rules defined in [30] to retain the connectivity and radial properties of individuals during the optimization process. It’s worth mentioning that these properties are broadly disturbed by EAs due to the stochastic nature of these algorithms unless a heuristic plan is devised to preserve the mentioned properties. As a result generation of infeasible agents in sheer numbers by EAs is quite a normal observation. The proposed technique is able to prevail over this shortcoming and would increase the performance of EAs as well. Before proceeding with the designed technique, some terminologies are first introduced to set the stage for the plan.