Abstract-Big data technologies like Hadoop, NoSQL, Messaging Queues etc. helps in BigData analytics, drive business growth and to take right decisions in time. These Big Data environments are very dynamic and complex; they require performance validation, root cause analysis, and tuning to ensure success. In this paper we talk about how we can analyse and test the performance of these systems. We present the important factors in a big data that are primary candidates for performance testing like data ingestion capacity and throughput, data processing capacity, simulation of expected usage, map reduce jobs and so on and suggest measures to improve performance of bigdata Index Terms-NOSQL, YCSB, HDFS.
Wireless sensor network has revolutionized the way computing and software services are delivered to the clients on demand. Wireless sensor network is very important to the mankind. It consist of number of sensor called nodes and a base station. Nodes collect data and send to the base station. There are number of nodes which send data at a time. So, number of problems are occurred. So, far this nodes are divided into cluster then a cluster head will be formed. WSN is a battery powered system. When the battery is died no data send or received. So when all nodes participate for sending and receiving data then system is died earlier. Our research work proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has improved performance to that of the other clustering approaches. The network area is divided into same sized small–small regions. Sensor nodes are randomly deployed in each predefined sub-area. Each region will have its region head (RH) and multiple member nodes. The member nodes in a specific region will send the data to the RH. RH within the region will be elected by distributed mechanism and will be based on fuzzy variables. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, transmission tuning algorithm for cluster-based WSNs has been proposed to balance the load among cluster heads that fall in different regions. This algorithm is applied prior to a cluster algorithm to improve the performance of the clustering algorithm without affecting the performance of individual sensor nodes.
A Wireless Sensor Network or WSN is supposed to be made up of a large number of sensors and at least one base station. The sensors are autonomous small devices with several constraints like the battery power, computation capacity, communication range and memory. They also are supplied with transceivers to gather information from its environment and pass it on up to a certain base station, where the measured parameters can be stored and available for the end user. In most cases, the sensors forming these networks are deployed randomly and left unattended to and are expected to perform their mission properly and efficiently. As a result of this random deployment, the WSN has usually varying degrees of node density along its area. Sensor networks are also energy constrained since the individual sensors, which the network is formed with, are extremely energy-constrained as well. Wireless sensor networks have become increasingly popular due to their wide range of application. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. Minimizing the energy consumption of a wireless sensor network application is crucial for effective realization of the intended application in terms of cost, lifetime, and functionality. However, the minimizing task is hardly possible as no overall energy cost function is available for optimization.
The usability of Mobile devices and portable computers has been increased very rapidly. The main focus of modern research is to handle the big data in Mobile and pervasive computing. This paper gives an overview of Mobile Data Challenge which was a smart phone based research done by Nokia through Lausanne Data Collection Campaign. The Mobile Data Challenge was introduced when the amount of mobile data was seen to be increased very much in a short period of time. The rise of big data demands that this data can be accessed from everywhere and anytime. For handling such a huge amount of data e.g. SMS, images, videos etc, this data must be compressed for easy transmission over the network. It also helps in reducing the storage requirements. There are various techniques for the compression of data that are discussed in this paper. SMAZ and ShortBWT techniques are used to compress SMS. JPEG and Anamorphic Stretch Transform are used to compress the images.
Wireless sensor network has revolutionized the way computing and software services are delivered to the clients on demand. It consists of number of sensors called nodes and a base station. Nodes collect data and send to the base station. The nodes are connected with each other without a wired connection. There are number of nodes which send data at a time and because of that numbers of problems occur. For this, nodes are divided into cluster and a cluster head will be formed.WSN is a battery powered system and when the battery dies no data send or received. So when all nodes participate for sending and receiving data then system dies early. When the energy of the cluster head is less, then next cluster head will be formed and at one time, one cluster head is formed. Cluster head collect data from nodes and then send to the base station. WSN is also used in many applications like, constant monitoring & detection of specific events, military and battlefield surveillance, forest fire and flood detection, habitat exploration of animals, patient and home appliances. This research work proposed a new method for cluster head selection having less computational complexity. It was also found that the modified approach has improved performance to that of the other clustering approaches. In this work, the network area is divided into same sized small regions. Sensor nodes are randomly deployed in each predefined sub-area where each region will have its region head (RH) and multiple member nodes. The member nodes in a specific region will send the data to the RH. RH within the region will be elected by distributed mechanism and will be based on fuzzy variables. The Region heads are divided into 2 different classes boundary RH and non-boundary RH. Only boundary RH will send the data to the BS only when it will come within its region and Non-boundary region heads will evaluate the lower bound limit and upper bound limit of both x-axis and y-axis to evaluate the next relay node. It will choose the relay node on the path where BS is closely located which will reduce the transmission delay and will also reduce the data packet size. It was found that the proposed algorithm gives a much improved network lifetime as compared to existing work. Based on our model, transmission tuning algorithm for cluster-based WSNs has been proposed to balance the load among cluster heads that fall in different regions. This algorithm is applied prior to a cluster algorithm to improve the performance of the clustering algorithm without affecting the performance of individual sensor nodes. The proposed work is validated using throughput, average energy consumption, average remaining energy and network delay metrics.
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