An Eco-Friendly Efficient Cloud-Searching Technique With Delay

An Eco-Friendly Efficient Cloud-Searching Technique With Delay

Saswati Sarkar, Anirban Kundu
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJGC.2018010102
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The authors propose a cloud-based disk-searching technique with delay in this article. Cloud computing is responsible for eco-friendly use of computers and other related resources. The proposed technique exerts less energy to search particular data. The searching technique finds a particular element through parallel channels. The energy efficiency is directly proportional to the number of channels for a specific set of data. The parallel searching technique is implemented to reduce time complexity and complexity of delay. The article exhibits a complexity of delay in a real-time scenario. The delay is depending upon the number of elements and if the number of elements is increased, then the overall delay is also increased. A time graph represents the relations between the number of elements and the number of channels. An energy-efficiency graph is also represented with respect to the number of channels. The delay is calculated with respect to the number of elements, cloud network delay, and waiting time for previous data execution. The authors have established relations between the delay and the number of elements, waiting time, and the cloud network delay.
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Cloud (Amazon Cloud Search, 2015) is a concept, based on network-based technique. Cloud disk searching technique (Cloud Computing, 2009) is mainly used to search data from memory or disk. Different types of searching techniques used in research works are linear search (Lipschutz, 2006), binary search (Lipschutz, 2006), hashing (Hashtag Definition, 2015). Delay means total time taken to search an element or item from the disk. There are two types of delay, such as cloud network delay and execution delay. Complexity is of two types, such as time complexity and space complexity. Time complexity is execution time taken to finish a program as suggested by researchers (Cormen et al., 2001).

Researchers have developed data searching techniques to find the location of a given item or data within a collection of items. There are different types of searching techniques such as linear search or sequential search (Lipschutz, 2006), hashing, and so on. Time complexity of linear search is O (n). In several papers, hashing techniques are utilized with division method (Tanenbau et al., 1983), mid-square method (Zhu, 2001), and folding method (Berenbrink et al., 2006). The utility of divide and conquered method is also a useful technique as suggested by the researchers (Falcou et al., 2006). In divide approach, problem is divided into specific sub-problems. Sub-problems are divided again into sub-sub-problems, provided that each sub-problem is represented the part of original problem. In conquer approach, sub-problems are solved individually and merge the solution of sub-problems and get the solution of original problem in this fashion. Examples of divide and conquered method are binary search, merge sort, quick sort.

Green cloud computing (Gleeson, 2009) is defined as study of designing the computing devices and manufacturing of computing devices which reduce the environmental impact. Green computing (Buyya et al., 2008) is also known as green information technology (Baliga et al., 2010). A preliminary discussion on disk searching has been published in (Sarkar & Kundu, 2015). Energy efficiency (Cavallo, 2001) helps to visualize the control of energy through a system framework. In an energy efficient system, the amount of energy required should be reduced in a controlled manner to achieve products and its corresponding services (Marti et al., 2000).


  • Definition 1: Cloud network Delay (Nd) – Data transmission time over the cloud network is known as cloud network delay.

  • Definition 2: Execution Delay (Ed) – Total time taken to finish execution of previously received data is known as execution delay.


Our aim is to calculate delay to search a data or item or element from the cloud disk in parallel fashion to optimize energy efficiency.


The scope of this paper is knowledge discovery in database that is KDD within cloud, which consists of delay calculation, and execution time representation.


We are trying to establish a searching technique with cloud delay to search data in less time having increase in storage performance.


Rest of the paper is organized as follows: next section describes about literature review; proposed works section exhibits the architecture of parallel channels for cloud disk search and workflow for green cloud-based searching; experimental discussions section deals with various observations & energy efficiency; and finally, we conclude our paper along with achievements.

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