Fig 2 - available from: Journal of Cloud Computing
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This paper describes the development and implementation of a lightweight software solution, called OSSperf, which can be used to investigate the performance of object-based public cloud storage services like Amazon S3, Google Storage and Microsoft Azure Blob Storage, as well as private cloud re-implementations. Furthermore, this paper presents an e...
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... effect, that the bandwidth gets better with a grow- ing file size, is caused by the protocol overhead 1 of the object-based storage services (see Fig. 2). This overhead exists for file transmissions of any size and its portion of the potential throughput shrinks with a growing file size. The overhead is caused among others by theses ...
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... Those characteristics show the hardware limitations of the system but yet it is capable of running different Linux distributions. The hardware resources are also sufficient to use them for running object-based cloud storage services [4][5] and for the investigation of the scalability of parallel computation tasks [3]. With a cluster of low-cost nodes like Raspberry Pi 3 computers, the crash of nodes during operation is a common event. ...
Low-cost clusters are not equipped with costly, sophisticated tools and cannot be controlled remotely. This work aims at addressing this issue and develops a lightweight network-controlled power strip, which enables administrators to monitor the cluster and perform operation via remote. The power strip is controlled via a web interface and a RESTful web service, which are implemented with the programming language Python and the web framework Flask. The solution is inexpensive and easy to implement and use. In this paper, we describe in detail the development and construction of the prototype of the solution and discuss its purchase cost and power consumption.
Cloud providers must find out how to properly arrange data in a limited count of servers while ensuring latency assurances to reduce total storage expenses. Timeout is also important to consider because it has a substantial impact on response latency. The core aim of this task is to implement a new cloud object storage system strategy that handles challenges like “latency‐sensitive data allocation, latency‐sensitive data re‐allocation, and latency‐sensitive workload consolidation.” The main contribution here is that distributing the latency of the cloud object storage system allows for better data allocation, data reallocation, and workload consolidation. The primary aim is to use the fewest number of servers feasible to fulfill all requests while maintaining their latency requirements, lowering the overall data transmission cost. As a consequence, Whale Butterfly Optimization Method (WBOA) is a novel hybrid meta‐heuristic algorithm that solves NP‐hard problems by combining baseline advanced algorithms. The simulation outcomes reveal that the offered paradigm consistently provides the greatest outcomes regarding throughput utilization, lower latency, higher storage, and number of used nodes when compared to competing techniques.
