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Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. In this paper, we propose a novel load balancing algorithm in cloud environments that performs resource allocation and task scheduling efficiently. The proposed load balancer reduces the execution response time in big data applications performed on clouds. Scheduling, in general, is an NP-hard problem. In our proposed algorithm, we provide solutions to reduce the search area that leads to reduced complexity of the load balancing. We recommend two mathematical optimization models to perform dynamic resource allocation to virtual machines and task scheduling. The provided solution is based on the hill-climbing algorithm to minimize response time. We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, and request distribution with some of the existing algorithms that show significant improvements, Comment: My supervisor has requested that i withdraw the article

Cassandra as a type of NoSQL databases has been put forward so as to surmount the hurdles of traditional relational databases in the scope of big data as well as real-time applications whose paramount traits are high-speed data production (volume) and miscellaneous data formats (variety). The dynamic nature of distributed data, distributed systems, and their concomitant applications results in skewed data access patterns, thereby causing imbalanced data issues, and in turn consecutive performance deterioration after all. In this study, we have proposed a dynamic data dissemination (D3) strategy well conforming to the dynamic behavior of distributed environment, including diversified as well as temporal popularity of data requests, and heterogeneous node capacity. The assessment results have shed light on performance improvement.

With the ever-expanding digital communications and the need for advanced interoperability and collaboration, organizations and entities need to share their digital assets. Cloud computing is now widely used for managing and storing resources. Access control is a critical issue, facing many challenges in distributed environments, including clouds. In this paper, we present a model of the cloud access control system. Our distributed model utilizes a role-based access control to enable the management of resources and the parties’ access securely. We provide interoperability between multiple organizations to access shared resources using Ethereum Blockchain smart contracts and access levels for available resources. Roles define access permissions; however, unlike the traditional role-based access control model, the roles are determined according to the organizations involved' collaborative project, sometimes may not exist in any organization. They can only be created in their interactions. Finally, for evaluating its cost and time parameters. We use Ethereum smart contracts and deploy them in the Ethereum test network called Rinkby

The efficiency of data-intensive applications in distributed environments such as Cloud, Fog, and Grid is directly related to data access delay. Delays caused by queue workload and delays caused by failure can decrease data access efficiency. Data replication is a critical technique in reducing access latency. In this paper, a fuzzy-based replication algorithm is proposed, which avoids the mentioned imposed delays by considering a comprehensive set of significant parameters to improve performance. The proposed algorithm selects the appropriate replica using a hierarchical method, taking into account the transmission cost, queue delay, and failure probability. The algorithm determines the best place for replication using a fuzzy inference system considering the queue workload, number of accesses in the future, last access time, and communication capacity. It uses the Simple Exponential Smoothing method to predict future file popularity. The OptorSim simulator evaluates the proposed algorithm in different access patterns. The results show that the algorithm improves performance in terms of the number of replications, the percentage of storage filled, and the mean job execution time. The proposed algorithm has the highest efficiency in random access patterns, especially random Zipf access patterns. It also has good performance when the number of jobs and file size are increased.

The emergence of Big Data applications has paved the way for enterprises to use Big Data as a value-creation strategy for their business; however, the majority of enterprises fail to know how to generate value from their massive volumes of data. Big Data Analytics results can help the enterprises in better decision-making and provide them with additional profits. Studying different researches dedicated to value creation through Big Data Analytics. This paper (a) highlights the current state of the art proposed for creating value from Big Data Analytics, (b) identifies the essential factors and discusses their effects upon value creation, and (c) provides a classification of the cutting-edge technologies in this field.

According to exascale computing roadmap, the dynamic nature of new generation scientific problems needs an undergoing review in the static management of computing resources. Therefore, it is necessary to present a dynamic load balancing model to manage the load of the system, efficiently. Currently, the distributed exascale systems are the promising solution to support the scientific programs with dynamic requests to resources. In this work, we propose a dynamic load balancing mechanism for distributed controlling of the load in the computing nodes. The presented method overcomes the challenges of dynamic behavior in the next generation problems. The proposed model considers many practical parameters including the load transition and communication delay. We also propose a compensating factor to minimize the idle time of computing nodes. We propose an optimized method to calculate this compensating factor. We estimate the status of nodes and also calculate the exact portion of the load that should be transferred to perform the optimized load balancing. The evaluation results show significant improvements regarding the performance by proposed load balancing in compared with some earlier distributed load balancing mechanisms.

Today’s ever-growing information world, in which we witness the juggernaut of information explosion stemming from social networks, medical records, diverse medias, IoT, and so forth, has called for a solution—encompassing boundless resources for this voluminous information’s storing as well as processing in a distributed manner. To do so, although cloud computing has come up with an applicable remedy, it has overwhelmingly required a well-defined load-balancing mechanism, lifeblood of any given distributed system; a load-balancing algorithm has consistently strove to pinpoint overloaded nodes so as to disseminate and shift the burden of extra workload towards the under-loaded ones—by which the overall system performance in terms of resource utilization, throughput, cost, and response time will be guaranteed after all. In the interests of placing a high premium on load-balancing issue in distributed systems, in this study, we have provided a review concerning load-balancing algorithms in cloud environment for Big Data environment.

Considering the dynamic nature of new generation scientific problems, load balancing is a necessity to manage the load in an efficient manner. Load balancing systems are use to optimize the resource consumption, maximize the throughput, minimize response time, and to prevent overload in resources. In current research, we consider operational distributed systems with dynamic variables caused by different nature of the applications and heterogeneity of the various levels in the system. Conducted studies indicate that many different factors should be considered to select the load balancing algorithm, including the processing power, load transfer and communication delay of nodes. In this work, We aim to design a dashboard that is capable to merge the load balancing algorithms in different environments. We design an adaptive system infrastructure with the ability to adjust various factors in the run time of a load balancing algorithm. We propose a task and a resource allocation mechanism and further introduce a mathematical model of load balancing process in the system. We calculate a normalized hardware score that determines the maturity of system according to the environmental conditions of the load balancing process. Evaluation results confirm that the proposed method performs well and reduces the probability of system failure.

In this article, the authors investigate the development of Multiplexed Information and Computing Service (Multics) and Plan 9, and illustrate how these approaches have influenced today's computing systems. "Computing service as an electricity service"' is the core mission of the distributed computing systems introduced by the Multics project in 1965. In developing the new generation of distributed computing systems, researchers have faced numerous obstacles, though many have already been addressed and dispelled by pioneer systems. Security, scalability, access transparency, resource-sharing, and dynamic reconfiguration are examples originally introduced and considered by both Multics and Plan 9. Moreover, many novel approaches have employed the basic ideas of these systems. However, there are further innovations that could be helpful in designing prospective distributed computing systems. As a result, studying previous systems' objectives and current statuses can facilitate new solutions and help point to possible failures.

In this paper, we propose an efficient resource discovery framework allowing pure unstructured peer-to-peer systems to respond to requests at run time with a high success rate while preserving the local autonomy of member machines. There are five units in the proposed framework that respectively gather information about the status of resources, make decisions, detect the states of member machines, discover resources to respond to requests in normal and dynamic conditions, and balance the load of local machines. Efficient resource discovery is achieved by the deployment of a newly introduced mechanism that is placed on every machine allowing it to figure out its states before and after accepting other machines' requests for its resources using a state model and deciding whether to accept or reject those requests. This state model accurately estimates the machine's state based on the resources and processes of the machine before and after accepting the request. We have experimentally compared the proposed mechanism with random, learning-based, and state-based search mechanisms with regard to the number of missed requests, network bandwidth due to transferred messages, number of associated machines in a discovery operation, time required to process information in discovery operation, processing time in machines, and the number of faults per request. The results show significant improvement of some of these parameters, specially network bandwidth and the number of missed requests in a dynamic condition, under our framework.

This paper presents a model for resolving two main issues of time in e-commerce. The first issue is the time value of e-commerce that represents the value of each moment of the commerce time from the perspective of buyers and sellers. Buyers and sellers can use this model to calculate the time value at each moment of time and accordingly decide whether it is profitable to buy or sell at that moment. The second issue is to allow buyers or sellers to increase their savings or decrease their costs by changing each of the factors governing the time value model of the concerned e-commerce. We present relevant model specifically for Amazon e-commerce to present a proof of concept of our proposed models.

SUMMARY Peer-to-peer (P2P) systems have been developed with the goal of providing support for transparent and efficient sharing of scalable distributed resources wherein size scalability is limited by the costs of all types of transparencies, especially data access transparency, which are due to the need for frequent data exchanges between peers and other related communication overheads. We present a model that formulates the relationship between scalability and data access transparency in P2P distributed systems to figure out how large these systems can be scaled up, given the overheads of establishing data access transparency. To validate our model and show how our model can be deployed in real life, we consider a real P2P distributed system as a case study and evaluate how CPU utilization, bandwidth, and data request frequency parameters of our model relate to the amount of effort required by the system management to establish data access transparency. We then calculate the strength of the coefficient of correlation of scalability and data access transparency in the system. The degree of strength of this coefficient allows the system designer to decide at design time whether to allow the use of the model in the management of system at runtime or not. Copyright © 2012 John Wiley & Sons, Ltd.

Linked data describes a method of publishing structured data which it can be interlinked and become more effective through semantic queries. This enables data from different sources to be connected and queried. It builds upon standard web technologies such as HTTP, RDF and URIs. This method helps human readers to share information in a way that can be read automatically by computers. Regarding the importance of Linked data, the main aim of this article is visualizing scientific mapping of linked data to show its progress through one decade. The scientometric study employs hierarchical cluster analysis, strategic diagrams and network analysis to map and visualize the linked data landscape of the "Scopus" publications through the use of co-word analysis. The study quantifies and describes the thematic evolution of the field based on a total of 717 Scopus articles and their associated 19977 keywords published between 1970s and 2014. According to the results the thematic visualization and the clusters show most concepts concentrated around computer related terms, such as big data; cloud computing semantic data; semantic technologies; semantic web; artificial intelligence; computer programming; semantic search, etc. In addition, we found that in recent years after librarians and information scientists doing researches in linked data on the behalf of computer scientist the “user” studies became important.

Interprocess communication (IPC) is a well-known technique commonly used by programs running on homogeneous distributed systems. However, it cannot be used readily and efficiently by programs running on heterogeneous distributed systems. This is because it must be given a uniform interface either by a set of middleware or more efficiently properly ported to the kernel of all varieties of open source and closed source proprietary operating systems running on heterogeneous nodes of distributed systems. This is particularly problematic to achieve when the kernel code of closed source operating systems are inaccessible to third parties. We propose an alternative nonproprietary approach to enable the use of IPC in heterogeneous distributed systems by wrapping IPC calls from the kernel of closed source operating systems, and converting them into equivalent IPC calls that are efficiently implemented inside the kernel code of open source operating systems. To show the superiority of our approach, we developed a wrapper for converting MS-Windows IPC calls into equivalent Linux IPC calls and benched our approach on a hybrid computer cluster running both types of operating systems.

Traditional Peer-to-Peer (P2P) systems were restricted to sharing of files on the Internet. Although some of the more recent P2P distributed systems have tried to support transparent sharing of other types of resources, like computer processing power, but none allow and support sharing of all types of resources available on the Internet. This is mainly because the resource management part of P2P systems are custom designed in support of specific features of only one type of resource, making simultaneous access to all types of resources impractical. Another shortcoming of existing P2P systems is that they follow a client/server model of resource sharing that makes them structurally constrained and dependent on dedicated servers (resource managers). Clients must get permission from a limited number of servers to share or access resources, and resource management mechanisms run on these servers. Because resource management by servers is not dynamically reconfigurable, such P2P systems are not scalable to the ever growing extent of Internet. We present an integrated framework for sharing of all types of resources in P2P systems by using a dynamic structure for managing four basic types of resources, namely process, file, memory, and I/O, in the same way they are routinely managed by operating systems. The proposed framework allows P2P systems to use dynamically reconfigurable resource management mechanisms where each machine in the P2P system can at the same time serve both as a server and as a client. The pattern of requests for shared resources at a given time identifies which machines are currently servers and which ones are currently clients. The client server pattern changes with changes in the pattern of requests for distributed resources. Scalable P2P systems with dynamically reconfigurable structures can thus be built using our proposed resource management mechanisms. This dynamic structure also allows for the interoperability of different P2P systems.

High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be useful for exascale computing. Processes running these applications may well demand unpredictable requirements and changes to system configuration and capabilities at runtime, thereby requiring fast system response without sacrificing the transparency and integrity of the reconfigured empowered system that is running on a heterogeneous platform. While a challenge in and of itself, platform heterogeneity is both useful and instrumental in the handling of unpredictable requests. The realization of such a dynamically reconfigurable and heterogeneous HPC cluster system for exascale computing requires a model to guide running processes to determine if they need empowerment of the current cluster, and if yes, by how much. To show the feasibility of empowerment of traditional HPC clusters for exascale computing, we have selected Beowulf as a noble candidate cluster and present a mathematical model for the empowerment of Beowulf clusters for exascale computing (EBEC). We have developed the model in line with Beowulf's cluster approach and by using vector space algebra. In contrast to traditional hardware-oriented approaches to improvise the performance of clusters, we use a software approach to the development of the proposed model by emphasizing processes, which act as the creators of the cluster and thus should decide on system (re)configuration, as the principal building blocks of the system. We have also adopted a new approach to heterogeneity by considering heterogeneity at different levels including hardware, system software, application software, and system functionality. In addition to support for heterogeneity and dynamic reconfiguration, the proposed model includes support for scalability that is crucial to exascale computing too.

One of the important aspects of decision making and management in distributed systems is collecting accurate information about the available resources of the peers. The previously proposed approaches for collecting such information completely depend on the system's architecture. In the server-oriented architecture, servers assume the main role of collecting comprehensive information from the peers and the system. Next, based on the information about the features of the basic activities and the system, an exact description of the peers' status is produced. Accurate decisions are then made using this description. However, the amount of information gathered in this architecture is too large, and it requires massive processing. On the other hand, updating the information takes time, causing delays and undermining the validity of the information. In addition, due to the limitations imposed by the servers, such architecture is not scalable and dynamic enough. The peer-to-peer architecture was introduced to address these concerns. However, due to a lack of complete knowledge of the peers and the system, the decisions are made without a precise description of the peers' status and are only based on the hardware data collected from the peers. Such an abstract and general image of the peers is not adequate for the purpose of decision making. In this paper, a 4-dimensional model is presented for the purpose of information collection and the exact description of the peer's status, including the features of the peer, the basic activity, the time, and the specifications of the system. The proposed model is for a server-oriented architecture, but it also adapts to the peer-to-peer serverless architecture. Based on this model, a new approach is introduced for information collection and an exact description of the peers' status in a peer-to-peer system based on the Latin square concept. We evaluate the model in the server-oriented and serverless situations. The workload is considered as the basic activity in our evaluation. Our evaluation demonstrates that in a server-oriented situation, increasing the size of the system has a direct relation with time. However, a serverless situation does not follow this behavior.

One of the important aspects of decision making and management in distributed systems is collecting accurate information about the available resources of the peers. The previously proposed approaches for collecting such information completely depend on the system’s architecture. In the server-oriented architecture, servers assume the main role of collecting comprehensive information from the peers and the system. Next, based on the information about the features of the basic activities and the system, an exact description of the peers’ status is produced. Accurate decisions are then made using this description. However, the amount of information gathered in this architecture is too large, and it requires massive processing. On the other hand, updating the information takes time, causing delays and undermining the validity of the information. In addition, due to the limitations imposed by the servers, such architecture is not scalable and dynamic enough. The peer-to-peer architecture was introduced to address these concerns. However, due to a lack of complete knowledge of the peers and the system, the decisions are made without a precise description of the peers’ status and are only based on the hardware data collected from the peers. Such an abstract and general image of the peers is not adequate for the purpose of decision making. In this paper, a 4-dimensional model is presented for the purpose of information collection and the exact description of the peer’s status, including the features of the peer, the basic activity, the time, and the specifications of the system. The proposed model is for a server-oriented architecture, but it also adapts to the peer-to-peer serverless architecture. Based on this model, a new approach is introduced for information collection and an exact description of the peers’ status in a peer-to-peer system based on the Latin square concept. We evaluate the model in the server-oriented and serverless situations. The workload is considered as the basic activity in our evaluation. Our evaluation demonstrates that in a server-oriented situation, increasing the size of the system has a direct relation with time. However, a serverless situation does not follow this behavior.

One of the main challenges of unstructured peer-to-peer (P2P) systems that greatly affects performance is resource searching. The early proposed mechanisms use blind searching, but they have a lot of shortcomings. Informed search strategies have better performance in comparison with blind ones, but they still suffer from low success-rates and long response times due to their inadaptability to dynamic P2P environments where nodes can frequently join and leave the system. To address this problem we propose a new search strategy called Dynamic Multilevel Feedback-based Search strategy (DMFS) in this paper which is greatly adaptable to dynamic P2P systems. DMFS is a new dynamic strategy that pays attentions to nodes heterogeneities and system variable conditions. It deploys important factors to guide resource selection process. These factors include changeable temporal popularity, temporal number of hits, temporal penalty, dynamic ranking, resource heterogeneity, and resource responsiveness status. In addition, DMFS improves system fault-tolerance in case of resources unavailablity. Extensive simulations of DMFS show considerable growth in the resource searching performance by decreasing response time and bandwidth consumption and increasing success-rate.