Replication management in peer-to-peer cloud storage systems

Data availability represents one of the primary functionalities of any cloud storage system since it ensures uninterrupted access to data. A common solution used by service providers that increase data availability and improve cloud performance is data replication. In this paper, we present a dynamic data replication strategy that is based on a hybrid peer-to-peer cloud architecture. Our proposed strategy selects the most popular data for replication. To determine the proper nodes for storing popular data, we employ not only the feature specifications of storage nodes, but also the relevant structural positions in the cloud network. Our simulation results show the impact of using features such as data popularity, and structural characteristics in improving network performance and balancing the storage nodes, and reducing user response time.

     

Design a cloud storage platform for pervasive computing environments

An increasing number of personal electronic handheld devices (e.g., SmartPhone, netbook, MID and etc.), which make up the personal pervasive computing environments, are playing an important role in our daily lives. Data storage and sharing is difficult for these devices due to the data inflation and the natural limitations of mobile devices, such as the limited storage space and the limited computing capability. Since the emerging cloud storage solutions can provide reliable and unlimited storage, they satisfy to the requirement of pervasive computing very well. Thus we designed a new cloud storage platform which includes a series of shadow storage services to address these new data management challenges in pervasive computing environments, which called as “SmartBox”. In SmartBox, each device is associated its shadow storage with a unique account, and the shadow storage acts as backup center as well as personal repository when the device is connected. To facilitate file navigation, all datasets in shadow storage are organized based on file attributes which support the users to seek files by semantic queries. We implemented a prototype of SmartBox focusing on pervasive environments being made up of Internet accessible devices. Experimental results with the deployments confirm the efficacy of shadow storage services in SmartBox.     

HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems

In heterogeneous distributed computing systems like cloud computing, the problem of mapping tasks to resources is a major issue which can have much impact on system performance. For some reasons such as heterogeneous and dynamic features and the dependencies among requests, task scheduling is known to be a NP-complete problem. In this paper, we proposed a hybrid heuristic method (HSGA) to find a suitable scheduling for workflow graph, based on genetic algorithm in order to obtain the response quickly moreover optimizes makespan, load balancing on resources and speedup ratio. At first, the HSGA algorithm makes tasks prioritization in complex graph considering their impact on others, based on graph topology. This technique is efficient to reduction of completion time of application. Then, it merges Best-Fit and Round Robin methods to make an optimal initial population to obtain a good solution quickly, and apply some suitable operations such as mutation to control and lead the algorithm to optimized solution. This algorithm evaluates the solutions by considering efficient parameters in cloud environment. Finally, the proposed algorithm presents the better results with increasing number of tasks in application graph in contrast with other studied algorithms.     

Adaptive resource utilization and remote access capabilities in high‐performance distributed systems: The Open HPC++ approach

Development of high-performance distributed applications, called metaapplications, is extremely challenging because of their complex runtime environment coupled with their requirements of high-performance and Quality of Service (QoS). Such applications typically run on a set of heterogeneous machines with dynamically varying loads, connected by heterogeneous networks possibly supporting a wide variety of communication protocols. In spite of the size and complexity of such applications, they must provide the high-performance and QoS mandated by their users. In order to achieve the goal of high-performance, they need to adaptively utilize their computational and communication resources. Apart from the requirements of adaptive resource utilization, such applications have a third kind of requirement related to remote access QoS. Different clients, although accessing a single server resource, may have differing QoS requirements from their remote connections. A single server resource may also need to provide different QoS for different clients, depending on various issues such as the amount of trust between the server and a given client. These QoS requirements can be encapsulated under the abstraction of remote access capabilities. Metaapplications need to address all the above three requirements in order to achieve the goal of high-performance and satisfy user expectations of QoS. This paper presents Open HPC++, a programming environment for high-performance applications running in a complex and heterogeneous run-time environment. Open HPC++ provides application level tools and mechanisms to satisfy application requirements of adaptive resource utilization and remote access capabilities. Open HPC++ is designed on the lines of CORBA and uses an Object Request Broker (ORB) to support seamless communication between distributed application components. In order to provide adaptive utilization of communication resources, it uses the principle of open implementation to open up the communication mechanisms of its ORB. By virtue of its open architecture, the ORB supports multiple, possibly custom, communication protocols, along with automatic and user controlled protocol selection at run-time. An extension of the same mechanism is used to support the concept of remote access capabilities. In order to support adaptive utilization of computational resources, Open HPC++ also provides a flexible yet powerful set of load-balancing mechanisms that can be used to implement custom load-balancing strategies. The paper also presents performance evaluations of Open HPC++ adaptivity and load-balancing mechanisms. This revised version was published online in July 2006 with corrections to the Cover Date.     

Adaptive management in variable environments

Adaptive management (AM) encourages land managers to modify plans for the future based on carefully evaluating what happened in the past. Extreme abiotic variation overwhelms effects of management, making AM extremely difficult to implement. We demonstrate how passive AM, involving monitoring over a few years and using data to inform future decisions, was used to determine seeding rates of native plants in a restoration experiment conducted in the Irvine Ranch National Landmark in Southern California. We found that seedling density depended more on the year in which seeding was conducted than on seeding rates. Analysis of longer term data, using weather as independent variables in analyses, can help managers tease apart the relative importance of management actions and of precipitation. Seeding in multiple years provided a bet hedging strategy for increasing success of the restoration project, given inter-annual variation in precipitation. Limitations of our seeding rate example could be resolved through active AM, in the form of controlled, manipulative experiments testing different seeding rates and replicated over many years. Effective AM requires a willingness to repeat management actions that failed due to abiotic conditions in a single year. What failed last year may work well in the future, and evaluating the role of environmental variation requires repetition across multiple years.

     

Efficient chameleon hashing-based privacy-preserving auditing in cloud storage

Cloud storage is an important application service in cloud computing, it allows data users to store and access their files anytime, from anywhere and with any device. To ensure the security of the outsourced data, data user needs to periodically check data integrity. In some cases, the identity privacy of data user must be protected. However, in the existing preserving identity privacy protocols, data tag generation is mainly based on complex ring signature or group signature. It brings a heavy burden to data user. To ensure identity privacy of data user, in this paper we propose a novel identity privacy-preserving public auditing protocol by utilizing chameleon hash function. It can achieve the following properties: (1) the identity privacy of data user is preserved for cloud server; (2) the validity of the outsourced data is verified; (3) data privacy can be preserved for the auditor in auditing process; (4) computation cost to produce data tag is very low. Finally, we also show that our scheme is provably secure in the random oracle model, the security of the proposed scheme is related to the computational Diffie–Hellman problem and hash function problem.     

Janus: a framework to boost HPC applications in the cloud based on SDN path provisioning

Data centers, clusters, and grids have historically supported High-Performance Computing (HPC) applications. Due to the high capital and operational expenditures associated with such infrastructures, we have witnessed consistent efforts to run HPC applications in the cloud in the recent past. The potential advantages of this shift include higher scalability and lower costs. If, on the one hand, app instantiation—through customized Virtual Machines (VMs)—is a well-solved issue, on the other, the network still represents a significant bottleneck. When switching HPC applications to be executed on the cloud, we lose control of where VMs will be positioned and of the paths that will be traversed for processes to communicate with one another. To bridge this gap, we present Janus, a framework for dynamic, just-in-time path provisioning in cloud infrastructures. By leveraging emerging software-defined networking principles, the framework allows for an HPC application, once deployed, to have interprocess communication paths configured upon usage based on least-used network links (instead of resorting to shortest, pre-computed paths). Janus is fully configurable to cope with different operating parameters and communication strategies, providing a rich ecosystem for application execution speed up. Through an extensive experimental evaluation, we provide evidence that the proposed framework can lead to significant gains regarding runtime. Moreover, we show what one can expect in terms of system overheads, providing essential insights on how better benefiting from Janus.

     

A dynamic task scheduler tolerant to multiple hibernations in cloud environments

Cluster Computing - Cloud platforms usually offer several types of Virtual Machines (VMs) with different guarantees in terms of availability and volatility, provisioning the same resource through...     

Adaptive evolution by optimizing expression levels in different environments

Organisms adapt to environmental changes through the fixation of mutations that enhance reproductive success. A recent study by Dekel and Alon demonstrated that Escherichia coli adapts to different growth conditions by fine-tuning protein levels, as predicted by a simple cost-benefit model. A study by Fong et al. showed that independent evolutionary trajectories lead to similar adaptive endpoints. Initial mutations on the path to adaptation altered the mRNA levels of numerous genes. Subsequent optimization through compensatory mutations restored the expression of most genes to baseline levels, except for a small set that retained differential levels of expression. These studies clarify how adaptation could occur by the alteration of gene expression.     

Adaptive evolution and inherent tolerance to extreme thermal environments

Background  

When introduced to novel environments, the ability for a species to survive and rapidly proliferate corresponds with its adaptive potential. Of the many factors that can yield an environment inhospitable to foreign species, phenotypic response to variation in the thermal climate has been observed within a wide variety of species. Experimental evolution studies using bacteriophage model systems have been able to elucidate mutations, which may correspond with the ability of phage to survive modest increases/decreases in the temperature of their environment.     

Resource scheduling methods in cloud and fog computing environments: a systematic literature review

In recent years, cloud computing can be considered an emerging technology that can share resources with users. Because cloud computing is on-demand, efficient use of resources such as memory, processors, bandwidth, etc., is a big challenge. Despite the advantages of cloud computing, sometimes it is not a proper choice due to its delay in responding appropriately to existing requests, which led to the need for another technology called fog computing. Fog computing reduces traffic and time lags by expanding cloud services to the network and closer to users. It can schedule resources with higher efficiency and utilize them to impact the user's experience dramatically. This paper aims to survey some studies that have been done in the field of scheduling in fog/cloud computing environments. The focus of this survey is on published studies between 2015 and 2021 in journals or conferences. We selected 71 studies in a systematic literature review (SLR) from four major scientific databases based on their relation to our paper. We classified these studies into five categories based on their traced parameters and their focus area. This classification comprises 1—performance 2—energy efficiency, 3—resource utilization, 4—performance and energy efficiency, and 5—performance and resource utilization simultaneously. 42.3% of the studies focused on performance, 9.9% on energy efficiency, 7.0% on resource utilization, 21.1% on both performance and energy efficiency, and 19.7% on both performance and resource utilization. Finally, we present challenges and open issues in the resource scheduling methods in fog/cloud computing environments.

     

Enabling secure auditing and deduplicating data without owner-relationship exposure in cloud storage

The public cloud storage auditing with deduplication has been studied to assure the data integrity and improve the storage efficiency for cloud storage in recent years. The cloud, however, has to store the link between the file and its data owners to support the valid data downloading in previous schemes. From this file-owner link, the cloud server can identify which users own the same file. It might expose the sensitive relationship among data owners of this multi-owners file, which seriously harms the data owners’ privacy. To address this problem, we propose an identity-protected secure auditing and deduplicating data scheme in this paper. In the proposed scheme, the cloud cannot learn any useful information on the relationship of data owners. Different from existing schemes, the cloud does not need to store the file-owner link for supporting valid data downloading. Instead, when the user downloads the file, he only needs to anonymously submit a credential to the cloud, and can download the file only if this credential is valid. Except this main contribution, our scheme has the following advantages over existing schemes. First, the proposed scheme achieves the constant storage, that is, the storage space is fully independent of the number of the data owners possessing the same file. Second, the proposed scheme achieves the constant computation. Only the first uploader needs to generate the authenticator for each file block, while subsequent owners do not need to generate it any longer. As a result, our scheme greatly reduces the storage overhead of the cloud and the computation overhead of data owners. The security analysis and experimental results show that our scheme is secure and efficient.     

The adaptive value of energy storage and capital breeding in seasonal environments

Timing of reproduction in a seasonal cycle is a life history trait with important fitness consequences. Capital breeders produce offspring from stored resources and, by decoupling feeding and reproduction, may bend the constraints caused by seasonality in food or predation. Income breeders, on the other hand, produce offspring from concurrent food intake, with the disadvantage of less flexibility, but with high efficiency and no inventory costs of carrying stores. Here, we assess relative profitability of capital and income breeding in herbivorous zooplankton inhabiting seasonal, high-latitude environments. We apply a state-dependent life history model where reproductive values are used to optimise energy allocation and diapause strategies over the year. Three environmental scenarios were modelled: an early, an intermediate, and a late feeding season. We found that capital breeding was most important in the early season. Capital breeding ranged from 7–9% of the eggs produced but, because of the high reproductive value of early eggs, capital breeding ranged from 9–30% when measured in terms of reproductive value. The main benefit of capital breeding was reproduction prior to the feeding season – when the reproductive value of an egg peaked. In addition, capital breeding was also used to increase egg production rates at times of income breeding. For individuals born late in the season the model predicted a two-year cycle instead of the typical annual life cycle. These individuals could then reap the benefits of early reproduction and capital breeding in their second year instead of income breeding late in the first year. We emphasize the importance of evaluating reproductive strategies such as capital and income breeding from a complete life cycle perspective. In particular, knowing the seasonality in offspring fitness is essential to appreciate evolutionary and population-level consequences of capital breeding.     

Physiological responses to variable environments: storage and respiration in starving rotifers

1. Zooplankton exist in environments where food availability varies greatly over time, and success depends in part on the ability to store resources when food is abundant and to conserve them when food is scarce. This paper reports on interspecific differences in the size of stored reserves, and in respiration rate during food deprivation, of four species of planktonic rotifers.
2. The size of reserves varied from 42 to 71% of initial (well-fed) body mass. Interspecific differences in reserve size explained some of the previously observed differences in starvation time.
3. The initial response of respiration rate to food deprivation was quite variable between species. Brachionus calyciflorus was the only species to conserve energy by decreasing respiration rate in response to food deprivation. In contrast, the respiration rate of starved Asplanchna priodonta increased, while that of A. silvestrii and Synchaeta pectinata did not change, during food deprivation.
4. Theory predicts that temporal variation in resource level may facilitate the coexistence of competing species. This theory depends upon trade-offs between traits that confer competitive success in different environments. Although rotifers show a trade-off between competitive ability and maximum population growth rate, we found no evidence for trade-offs between either of those two traits and the size of reserves.     

Multi-prediction based scheduling for hybrid workloads in the cloud data center

Cloud computing can leverage over-provisioned resources that are wasted in traditional data centers hosting production applications by consolidating tasks with lower QoS and SLA requirements. However, the dramatic fluctuation of workloads with lower QoS and SLA requirements may impact the performance of production applications. Frequent task eviction, killing and rescheduling operations also waste CPU cycles and create overhead. This paper aims to schedule hybrid workloads in the cloud data center to reduce task failures and increase resource utilization. The multi-prediction model, including the ARMA model and the feedback based online AR model, is used to predict the current and the future resource availability. Decision to accept or reject a new task is based on the available resources and task properties. Evaluations show that the scheduler can reduce the host overload and failed tasks by nearly 70%, and increase effective resource utilization by more than 65%. The task delay performance degradation is also acceptable.     

Adaptive patterns of phenotypic plasticity in laboratory and field environments in Drosophila melanogaster

Identifying mechanisms of adaptation to variable environments is essential in developing a comprehensive understanding of evolutionary dynamics in natural populations. Phenotypic plasticity allows for phenotypic change in response to changes in the environment, and as such may play a major role in adaptation to environmental heterogeneity. Here, the plasticity of stress response in Drosophila melanogaster originating from two distinct geographic regions and ecological habitats was examined. Adults were given a short‐term, 5‐day exposure to combinations of temperature and photoperiod to elicit a plastic response for three fundamental aspects of stress tolerance that vary adaptively with geography. This was replicated both in the laboratory and in outdoor enclosures in the field. In the laboratory, geographic origin was the primary determinant of the stress response. Temperature and the interaction between temperature and photoperiod also significantly affected stress resistance. In the outdoor enclosures, plasticity was distinct among traits and between geographic regions. These results demonstrate that short‐term exposure of adults to ecologically relevant environmental cues results in predictable effects on multiple aspects of fitness. These patterns of plasticity vary among traits and are highly distinct between the two examined geographic regions, consistent with patterns of local adaptation to climate and associated environmental parameters.     

MHB*T based dynamic data integrity auditing in cloud storage

Cluster Computing - Integrity audit technology is proposed to protect data in remote cloud servers from being tampered with. However, the challenge is that the computational complexity is too large...     

RHAS: robust hybrid auto-scaling for web applications in cloud computing

Cluster Computing - The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives...