Storage Capacity Of Nodes Research Articles

Kubernetes-Oriented Microservice Placement With Dynamic Resource Allocation

Microservices and Kubernetes are widely used in the development and operations of cloud-native applications. By providing automated placement and scaling, Kubernetes has become the main tool for managing microservices. However, existing work and Kubernetes fail to consider the dynamic competition and availability of microservices as well as the problem of shared dependency libraries among multiple microservice instances. To this end, this paper proposes an integer nonlinear microservice placement model for Kubernetes with the goal of cost minimization. Specifically, we calculate the number of instances based on microservice availability and construct a model in which the total resource demand of multiple microservice instances exceeds the appropriate proportion of node resources when dynamic resource competition exists and the size of the shared dependency library is less than the node storage capacity. Finally, this paper solves the microservice placement model using an improved genetic algorithm. The experimental results demonstrate that higher throughput is obtained with the same costs and that the same throughput is obtained with lower costs.

DLBN: Group Storage Mechanism Based on Double-Layer Blockchain Network

Blockchain, which stores data in an appending form, cannot achieve the purpose of expanding the storage capacity by increasing the number of nodes. As the system runs, nodes will face problems of insufficient storage space. In the existing peer-to-peer (P2P) blockchain network model, all network nodes participate in data storage, and the generated blocks need to be verified among the network-wide nodes. This approach suffers from low system transaction throughput and data storage redundancy. In order to solve the above existing problems, this article proposes a block data storage model based on the double-layer blockchain network (DLBN), which improves the internal data composition structure of the blockchain. The DLBN contains two types of blockchain nodes, which form the storage and consensus layers of the system, respectively. The consensus layer is responsible for tasks, such as transaction sequencing, validation, and block packing, thus increasing the system transaction throughput. The nodes in the storage layer are divided into multiple storage units (SUs), and all nodes in the SU jointly maintain a copy of the complete blockchain, thereby reducing the storage pressure on the nodes. Based on the DLBN model, we design a reputation-based consensus mechanism, block storage allocation algorithm, and transaction query optimization algorithm, respectively. Through experimental verification and analysis, the storage model based on the DLBN can effectively improve the system transaction throughput and reduce the node storage capacity while ensuring system security.

An adaptive read/write optimized algorithm for Ceph heterogeneous systems via performance prediction and multi-attribute decision making

The advent of the Big Data era has brought considerable challenges to storing and managing massive data. Moreover, distributed storage systems are critical to the pressure and storage capacity costs. The Ceph cloud storage system only selects data storage nodes based on node storage capacity. This node selection method results in load imbalance and limited storage scenarios in heterogeneous storage systems. Therefore, we add node heterogeneity, network state, and node load as performance weights to the CRUSH algorithm and optimize the performance of the Ceph system by improving load balancing. We designed a cloud storage system model based on Software Defined Network (SDN) technology. This system model can avoid the tedious configuration and significant measurement overhead required to obtain network status in traditional network architecture. Then we propose adaptive read and write optimization algorithms based on SDN technology. The Object Storage Device (OSD) is initially classified based on the Node Heterogeneous Resource Classification Strategy. Then the SDN technology is used to obtain network and load conditions in real-time and an OSD performance prediction model is built to obtain weights for performance impact factors. Finally, a mathematical model is proposed for multi-attribute decision making in conjunction with the OSD state and its prediction model. Furthermore, this model is addressed to optimize read and write performance adaptively. Compared with the original Ceph system, TOPSIS_PA improves the performance of reading operations by 36%; TOPSIS_CW and TOPSIS_PACW algorithms improve the elastic read performance by 23 to 60% and 36 to 85%, and the elastic write performance by 180 to 468% and 188 to 611%, respectively.

Performance improvement in wireless sensor networks by removing the packet drop from the node buffer

In Wireless Sensor Networks (WSNs) typed applications, the node buffer plays a vital role to store the processed data before transmitting it further to the base node through neighbor nodule. Nowadays, in Multi-Application Wireless Sensor Networks (MAWSNs) scenario, to improve network performance of the node buffer has to be maintained or efficiently balanced. Because, for implementing various applications, at the same time in MAWSNs, the processing, transmitting capability of the node should be enhanced and the node storage capacity (Buffer) should be maintained. Since, if the node buffer exhausted then automatically it drops the data packet and the data will be lost and hence performance degradation will occur in the network. It will also affect the network lifetime. Hence, this paper implements a mechanism to enhance the node buffer for improving network performance. The work is implemented on Network Simulator i.e., NS2 version 2.34/2.35. Our work outperforms in comparison with the existing work and improves network performance.

A new replica placement mechanism for mobile media streaming in edge computing

SummaryWith the advent of the Internet of things era, cloud computing platforms will face the challenges of massive equipment requirements for access, massive data, insufficient bandwidth, and high power consumption. Edge computing, as a new technology, makes it possible to stream media over the edge network. However, many problems related to file sharing among edge nodes and to the files provided by cloud servers exist. We propose a novel replica placement strategy for mobile media streaming in edge computing (RPME) to address the aforementioned problem. First, we introduce a multilevel replica placement model in the RPME. In the RPME, we acquire the user information and the user‐item rating matrix when the user requests the media data. At the server, we cluster the users according the user information, update the user‐item rating matrix, and then generate a replica recommendation sequence. For when the server submits the replica recommendation sequence to the edge node considering the constraints of the edge node storage capacity and the limitations on the service capacity of the requested replica, we propose an effective replica placement mechanism in the RPME. To show the benefits of the RPME, we also present several experiments to prove its validity.

Capacity of Clustered Distributed Storage

A new system model reflecting the clustered structure of distributed storage is suggested to investigate interplay between storage overhead and repair bandwidth as storage node failures occur. Large data centers with multiple racks/disks or local networks of storage devices (e.g., sensor network) are good applications of the suggested clustered model. In realistic scenarios involving clustered storage structures, repairing storage nodes using intact nodes residing in other clusters are more bandwidth consuming than restoring nodes based on information from intra-cluster nodes. Therefore, it is important to differentiate between intra-cluster repair bandwidth and cross-cluster repair bandwidth in modeling distributed storage. Capacity of the suggested model is obtained as a function of fundamental resources of distributed storage systems, namely, node storage capacity, intra-cluster repair bandwidth, and cross-cluster repair bandwidth. The capacity is shown to be asymptotically equivalent to a monotonic decreasing function of number of clusters, as the number of storage nodes increases without bound. Based on the capacity expression, feasible sets of required resources which enable reliable storage are obtained in a closed-form solution. Specifically, it is shown that the cross-cluster traffic can be minimized to zero (i.e., intra-cluster local repair becomes possible) by allowing extra resources on storage capacity and intra-cluster repair bandwidth, according to the law specified in the closed form. The network coding schemes with zero cross-cluster traffic are defined as intra-cluster repairable codes , which are shown to be a class of the previously developed locally repairable codes .

An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks.

In energy harvesting wireless sensor networks (EHWSNs), the energy tension of the network can be relieved by obtaining the energy from the surrounding environment, but the cost on hardware cannot be ignored. Therefore, how to minimize the cost of energy harvesting hardware to reduce the network deployment cost, and further optimize the network performance, is still a challenging issue in EHWSNs. In this paper, an energy conserving and transmission radius adaptive (ECTRA) scheme is proposed to reduce the cost and optimize the performance of solar-based EHWSNs. There are two main innovations of the ECTRA scheme. Firstly, an energy conserving approach is proposed to conserve energy and avoid outage for the nodes in hotspots, which are the bottleneck of the whole network. The novelty of this scheme is adaptively rotating the transmission radius. In this way, the nodes with maximum energy consumption are rotated, balancing energy consumption between nodes and reducing the maximum energy consumption in the network. Therefore, the battery storage capacity of nodes and the cost on hardware. Secondly, the ECTRA scheme selects a larger transmission radius for rotation when the node can absorb enough energy from the surroundings. The advantages of using this method are: (a) reducing the energy consumption of nodes in near-sink areas, thereby reducing the maximum energy consumption and allowing the node of the hotspot area to conserve energy, in order to prevent the node from outage. Hence, the network deployment costs can be further reduced; (b) reducing the network delay. When a larger transmission radius is used to transmit data in the network, fewer hops are needed by data packet to the sink. After the theoretical analyses, the results show the following advantages compared with traditional method. Firstly, the ECTRA scheme can effectively reduce deployment costs by 29.58% without effecting the network performance as shown in experiment analysis; Secondly, the ECTRA scheme can effectively reduce network data transmission delay by 44–71%; Thirdly, the ECTRA scheme shows a better balance in energy consumption and the maximum energy consumption is reduced by 27.89%; And lastly, the energy utilization rate is effectively improved by 30.09–55.48%.

A Combinational Buffer Management Scheme in Mobile Opportunistic Network

Nodes in Mobile Opportunistic Network (MON) have to cache packets to deal with the intermittent connection. The buffer management strategy obviously impacts the performance of MON, and it attracts more attention recently. Due to the limited storage capacity of nodes, traditional buffer management strategies just drop messages based on the property of message, and they neglect the collaboration between neighbors, resulting in an ineffective performance improvement. Therefore, effective buffer management strategies are necessary to ensure that each node has enough buffer space to store the message when the node buffer is close to congestion. In this paper, we propose a buffer management strategy by integrating the characteristics of messages and nodes, and migrate the redundant messages to the neighbor to optimize the total utility, instead of deleting them. The simulation experiment results show that it can obviously improve the delivery ratio, the overhead ratio and the average delays, and reduce the amount of hops compared with the traditional ones.

Capacity of Content-Centric Hybrid Wireless Networks

In this paper, we investigate the capacity scaling laws of content-centric hybrid wireless networks, where users aim to retrieve content stored in the network rather than to maintain source-destination communication. A total of $n$ nodes that have limited storage capacities are assumed to be independently and uniformly distributed in the square area. The storage at in each node is used to cache contents according to proposed caching schemes. A total of $m$ -base stations are regularly placed and act as relays during the content retrieving process. Two different content access schemes are considered: 1) homogeneous content access scheme, where each content is requested and cached with equal probability; and 2) heterogenous content access scheme, where the cached probability of each content is different and the requested contents follow a Zipf content popularity distribution. We present the closed form capacity formulae for the two different content access schemes in order sense, respectively. We also discuss the impact of the number of nodes $n$ , the number of base stations $m$ , the storage capacities of nodes $C$ , cached probability of content $q_{j}$ , and content popularity distribution $p_{j}$ on the capacity scaling. The capacity can be improved by increasing the storage capacities of nodes and adding base stations in the networks under homogeneous content access scheme. Under the heterogeneous content access scheme, increasing the cached probability of content and adding the base stations in the networks can effectively improve the capacity. We also present the capacity under different exponent of content popularity distribution. Finally, we compare our results with previous work, which appear as special cases of our work.

An Adaptive Query Search and Data Retrieval Techniques for Content Distribution in Distributed Computing Network

Object-On-Demand in Distributed computing has been an active area of research for the past few years. Replication techniques have greatly improved the availability of popular objects, enhancing the performance of query latency, fault tolerant, reliability and load balancing. However, caching, replication and searching objects on a large-scale distributed system is very challenging tasks due to nodes dynamicity that can compromise data availability according to the popularity of object. The main problem is to determine where to replicate? When to replicate? And how many copies to create replica? In order to solve this problem, An Adaptive query search and data retrieval algorithm for content distribution in distributed computing network is proposed. The solution proposed is done in two phases based on the cost model which could be taken account for several factors such as processor speed of nodes, access latency, and the flow of bandwidth and storage capacity of nodes. Routing is performed hierarchically by broadcasting the query to the strong and medium clusters respectively. From wide simulations using NS2 simulator, the proposed algorithm achieves less bandwidth consumption, reduced latency, increased system performance, high throughput, and strong connectivity and query coverage than the existing method.

General Fractional Repetition Codes for Distributed Storage Systems

In order to provide fault-tolerance and guarantee reliability, data redundancy should be introduced in distributed storage systems. The emerging coding techniques for storage such as fractional repetition codes, provide the required redundancy more efficiently than the conventional replication scheme. In this letter, we extend the construction of fractional repetition codes and present a new coding scheme, termed general fractional repetition codes. The proposed codes can be applied to storage systems in which the storage capacities of nodes may be different. Based on a combinatorial structure known as group divisible design, the new code construction is available for a large set of parameters. The performance of the proposed codes is evaluated by a metric called node repair alternativity, which measures the number of different subsets of nodes that enable the repair of a specific failed node.

PERFORMANCE EVALUATION OF MOFO BUFFER MANAGEMENT TECHNIQUE WITH DIFFERENT ROUTING PROTOCOLS IN DTN UNDER VARIABLE MESSAGE BUFFER SIZE

Delay Tolerant Networks (DTNs) uses the store-carry-forward scheme for the delivery of the messages, with this way data transmission can be successfully done despite of the absence of continuous end-to-end paths. The opportunities of message forwarding in such types of networks usually are limited due to the absence of contemporaneous paths. In such networks, the “store-carryforward” methodology is used for the transmission of the messages to be delivered to their intend destinations in a hop by hop manner. It arises many problems like how to schedule the messages, how to drop the messages in the buffer due to the impulsive nature of the nodes. It also arises many challengeable situations like short contact durations between the two nodes, limited storage capacity of nodes and so on. This paper evaluates the performance of MOFO buffer management technique with three routing protocols i.e. Epidemic, Prophet and MaxProp under variable message buffer sizes (5MB to 40MB). Such evaluation can improve the performance of the opportunistic networks by reducing the overhead ratio, enhancing the delivery rate, minimizing latency average and hop count average in a certain degree. So four performance evaluation metrics namely delivery probability, latency average, overhead ratio and hop count average are used in this study. This study uses ONE (Opportunistic Network Environment) simulator for the performance evaluation of the MOFO buffer management technique and routing protocols. The evaluation results shows that the performance of different protocols can benefit to optimizing the performance of delay tolerant networks in terms of delivery probability, Latency average, Overhead ratio and Hop count average of messages with the increase in message buffer size. Finally, this study suggested that which routing protocol is most suitable with MOFO buffer management technique.

Cooperative caching for homogeneous wireless sensor networks

In many scenarios, data generated at sensor nodes in wireless sensor networks must be stored temporarily in a network for subsequent use. An individual sensor node's storage capacity is too small to hold it and may soon overflow. Therefore, we propose an effective caching strategy based on cooperation among various sensor nodes to form a larger cumulative cache. As part of the complete caching scheme, a novel token-based cache admission control scheme is devised, which ensures proximity of cached data closer to sink(s). A cache discovery mechanism to efficiently fetch a copy of data items is developed. A single-copy cache coherency scheme is developed to avoid sink from fetching stale data and also avoids undesired data replication. Further, a least utility based item-replacement policy is developed to evict data items with the least utility in case of desired replacement. Simulation results show significant improvements in network energy savings and data availability when cooperative caching is used.

Storage routing for DTN congestion control

AbstractThe Delay Tolerant Networking (DTN) architecture approaches the problem of reliable message delivery in intermittent networks using a store‐and‐forward approach where messages may remain stored for relatively long periods of time in persistent storage at DTN routers. Forwarded messages are removed from persistent storage only when transfer acknowledgment to another router or final recipient is received. Congestion in such networks takes the form of persistent storage exhaustion. Several solutions exist including slowing sources, using alternative routes, discarding traffic, or migrating messages to alternative storage locations. We propose storage routing (SR), a congestion management solution of the last form. SR employs nearby nodes with available storage to store data that would otherwise be lost given uncontrollable data sources (such as sensors). SR determines a collection of messages and neighbors to migrate them to using a set of locally scoped distributed algorithms, possibly incorporating loops that are known to be optimal for some DTN routing scenarios and decouples storage management from global DTN route selection. Simulations show up to a 500 per cent performance improvement using SR as compared with a comparable scenario lacking SR. Furthermore, we show a desirable parameter insensitivity to node storage capacity, neighborhood search radius, and message lifetime. Copyright © 2007 John Wiley & Sons, Ltd.