Today's data centres need efficient traffic management to improve resource utilisation in their networks. It helps to solve the load imbalance problem of each processor. This Letter presents a 'dynamic hierarchical load balancing' approach for higher traffic scalability. The method selects the most favourable host satisfying the multi-dimensional resource constraints in terms of computing capability and its performance. The proposed method achieves significant traffic scalability improvement upto 66% over sequential and random virtual machine placement heuristics.

Wireless sensor networks are energy constrained networks. Energy consumption in these networks can be reduced by processing the raw data at individual nodes through the application of suitable aggregation technique so that there is minimum amount of data that need to be transmitted towards the sink. The data aggregation functions that are applied should adhere to correctness, and should be computationally less complex considering the capabilities of the sensor nodes. In this paper, a brief survey on the present aggregation protocols and their impact, and some of the techniques that are applied at individual sensor nodes to reduce sensed data are presented.

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The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

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