Design Architecture-Based on Web Server and Application Cluster in Cloud Environment

Cloud has been a computational and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. A framework is required to distribute the work of searching and fetching from thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The major idea is to design a web server in the map phase using the jetty web server which will give a fast and efficient way of searching data in MapReduce paradigm. For real time processing on Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in web server with multi-level index keys. The web server uses to handle traffic throughput. By web clustering technology we can improve the application performance. To keep the work down, the load balancer should automatically be able to distribute load to the newly added nodes in the server

The Chameleon Architecture for Streaming DSP Applications

We focus on architectures for streaming DSP applications such as wireless baseband processing and image processing. We aim at a single generic architecture that is capable of dealing with different DSP applications. This architecture has to be energy efficient and fault tolerant. We introduce a heterogeneous tiled architecture and present the details of a domain-specific reconfigurable tile processor called Montium. This reconfigurable processor has a small footprint (1.8 mm$^2$ in a 130 nm process), is power efficient and exploits the locality of reference principle. Reconfiguring the device is very fast, for example, loading the coefficients for a 200 tap FIR filter is done within 80 clock cycles. The tiles on the tiled architecture are connected to a Network-on-Chip (NoC) via a network interface (NI). Two NoCs have been developed: a packet-switched and a circuit-switched version. Both provide two types of services: guaranteed throughput (GT) and best effort (BE). For both NoCs estimates of power consumption are presented. The NI synchronizes data transfers, configures and starts/stops the tile processor. For dynamically mapping applications onto the tiled architecture, we introduce a run-time mapping tool

Restricted Adaptivity in Stochastic Scheduling

We consider the stochastic scheduling problem of minimizing the expected makespan on m parallel identical machines. While the (adaptive) list scheduling policy achieves an approximation ratio of 2, any (non-adaptive) fixed assignment policy has performance guarantee ?((log m)/(log log m)). Although the performance of the latter class of policies are worse, there are applications in which non-adaptive policies are desired. In this work, we introduce the two classes of ?-delay and ?-shift policies whose degree of adaptivity can be controlled by a parameter. We present a policy - belonging to both classes - which is an ?(log log m)-approximation for reasonably bounded parameters. In other words, an exponential improvement on the performance of any fixed assignment policy can be achieved when allowing a small degree of adaptivity. Moreover, we provide a matching lower bound for any ?-delay and ?-shift policy when both parameters, respectively, are in the order of the expected makespan of an optimal non-anticipatory policy

Clustered Node Based Load Balancing In Distributed Environment

Cloud computing having tremendous growth on recent years but it is not segregation on shared clouds.  Distributed file systems are key building blocks for cloud computing applications based on the Map Reduce programming paradigm. In such file systems, nodes simultaneously serve computing and storage functions; a file is partitioned into a number of chunks allocated in distinct nodes so that Map Reduce tasks can be performed in parallel over the nodes. Data storage and communication which are to be done in huge amount, in such cases clouds are most provably used. "The cloud", also focuses on increasing the effectiveness of the public resources. Cloud resources are usually not only shared by multiple users but are also vigorously reallocated per demand. This can work for apportioning resources to users .But In the time of apportionment  these are indeed .So In this paper we are introducing novel mechanism. We investigate to implement security provided for cloud computing and Evaluate the Quality of Service-QOS (Ex. Response Time) of whole system. In cloud computing one server controls number of sub servers, files, it can add, delete, and append dynamically  Freight stabilization in the cloud computing surroundings has an imperative impact on the performance. Excellent freight stabilizing makes cloud computing more efficient and improves user satisfaction. In this paper we are presenting freight stabilizing techniques for cloud segregating

Task swapping networks in distributed systems

In this paper we propose task swapping networks for task reassignments by using task swappings in distributed systems. Some classes of task reassignments are achieved by using iterative local task swappings between software agents in distributed systems. We use group-theoretic methods to find a minimum-length sequence of adjacent task swappings needed from a source task assignment to a target task assignment in a task swapping network of several well-known topologies.Comment: This is a preprint of a paper whose final and definite form is published in: Int. J. Comput. Math. 90 (2013), 2221-2243 (DOI: 10.1080/00207160.2013.772985

On the Value of Job Migration in Online Makespan Minimization

Makespan minimization on identical parallel machines is a classical scheduling problem. We consider the online scenario where a sequence of $n$ jobs has to be scheduled non-preemptively on $m$ machines so as to minimize the maximum completion time of any job. The best competitive ratio that can be achieved by deterministic online algorithms is in the range $[1.88,1.9201]$. Currently no randomized online algorithm with a smaller competitiveness is known, for general $m$. In this paper we explore the power of job migration, i.e.\ an online scheduler is allowed to perform a limited number of job reassignments. Migration is a common technique used in theory and practice to balance load in parallel processing environments. As our main result we settle the performance that can be achieved by deterministic online algorithms. We develop an algorithm that is $\alpha_m$-competitive, for any $m\geq 2$, where $\alpha_m$ is the solution of a certain equation. For $m=2$, $\alpha_2 = 4/3$ and $\lim_{m\rightarrow \infty} \alpha_m = W_{-1}(-1/e^2)/(1+ W_{-1}(-1/e^2)) \approx 1.4659$. Here $W_{-1}$ is the lower branch of the Lambert $W$ function. For $m\geq 11$, the algorithm uses at most $7m$ migration operations. For smaller $m$, $8m$ to $10m$ operations may be performed. We complement this result by a matching lower bound: No online algorithm that uses $o(n)$ job migrations can achieve a competitive ratio smaller than $\alpha_m$. We finally trade performance for migrations. We give a family of algorithms that is $c$-competitive, for any $5/3\leq c \leq 2$. For $c= 5/3$, the strategy uses at most $4m$ job migrations. For $c=1.75$, at most $2.5m$ migrations are used.Comment: Revised versio