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1. Dynamic spatial index for efficient query process on the cloud
Owners of large quantities of data can acquire outsource spatial databases by utilizing the
less expensive cloud computing model which has striking features like extraordinary computing
strength and scalability. Confidentiality is key in outsourced databases thus data owners should
be keen with untrusted third-parties who might access their data. This article approves use of
DISC (Dynamic Index for Spatial data on the Cloud). DISC is a safe retrieval system meant to
respond to various inquiries by Cloud Service Providers on encrypted database. Further, it should
be noted that dynamic spatial index has the ability to sustain active apprises on the outsourced
information. Besides, spatial transformation is employed on data and then spatial index encoded
with the aid of Order-Preserving coding. The following is what is achieved with cryptography
techniques: balance between data confidentiality and query execution, a safer scheme, an
equilibrium between securities provided and query outcomes. It is also significant to note the
various attacks managed by Disc.
The research employed qualitative research methods. This was motivated by the recent
popularity of cloud computing which manages computing materials at the server and gives data
to trusted clients. Is should be clear that storing data on weak servers has security risks. The
article here tries to create stability on confidentiality and effective response to queries by service
providers. The article approves DISC approves methodology with a vigorous encoded index at
CSP for spatial figures. OPE technique helps in index encrypting. Besides, DISC enables
dynamic updates at CSP. Also, the study proposes a modern and secure version of DISC. Finally,
there were no red flags identified as the experiment revealed that the DISC retrieval improves the
performance of queries and is better that cryptographic approaches in the market.
2. A novel coordinated resource provisioning approach for cooperative cloud market
Cloud computing is the best innovation for transforming large scale computation and storage
requirements from private clients to high-demand and styled alternative that serves many
purposes. Nevertheless, the following need to be addressed wholesomely: data management
costs, keeping data at check, provision of cheap computation options wholesomely. Cloud
federations is the newest area of cloud research. Influenced by several advancements in
technology, this paper provides a comprehensive analysis of collaborative cloud market.
Methodologies of cloud markets with elaborative samples are provided. A two-phase
collaborated resources provisioning and reservation approaches that assign resources to users to
reduce costs are meet also. In this work, the cheapest algorithm resources. The effectiveness of
the algorithms has been assessed with simulation and synthetic data show the superiority of the
applied methodology over other non-coordinated approaches.
Propagation of open standards and research measures to federated clouds have provided
ways for apprehending global markets that have massive influence in delivering economic
services, environmental effects, and client satisfaction. The article endorses the pooled cloud
market giving roles and operation procedures. A novel, two-phase, capital strategies propelled by
cost-saving services for consumers. Another thing to note is that virtual outcomes with market
prices show the worth of the approved work over, non-collaborative options. Nevertheless, the
model in this article neglects some basic issues like customer subscriptions for several contracts,
risks on demand and selling prices mentioned by service providers.
3. Optimal and suboptimal resource sharing methods in cloud computing data centers
Improved performance is the basic drive for Cloud service providers. Service provider
companies give a diversified resources allocation alternatives, and foster application portability.
To realize performance and cost reduction targets, providers have always sought for a detailed
resource distribution mechanism that manages both network and computational network
resources. An original methodology is employed to handle the challenge of sufficient data
allocation at data centers to customer virtual machines, connection, and reservation requests. It
should be done according to client needs.
The issues of resources allocation in cloud computing data firms are tackled and amicable
solved. Besides, heuristic solutions are considered and applied as virtual machine connection and
reservation planning policies. Also presented is a relaxed semi-optimal solution founded on
breaking down the initial issues. The experimentation outcomes for various networks reveal that
relaxed explanations bring acceptable levels of connection queries average delays. The
recommended solution is capable of attaining good performances than heuristic options without
the problem of long running time. With that, is approved as a possible candidate for finding
solutions for problems of high traffic and broad range of data compared to best solution.
4. Autonomic prediction suite for cloud resource provisioning
Effective resource management is the biggest issue in cloud computing due to its autoscaling feature. Prediction mechanisms have been recommended for better cloud computing to
achieve improved cloud resources administration. In this article, an autonomic forecasting suite
to advance prediction precision of the auto-scaling mechanism in the cloud computing setting.
Up to this end, the article suggests that the forecast precision of the auto-scaling structure will
rise if an effective time-series prediction procedure founded on the workload pattern is chosen.
To evaluate the plan, a detailed theoretical research is provided on diverse risk reduction
principles and their effects on the correctness of the time-series estimate methods in the cloud
environmental context. Also, experiments are carried out to practically approve the theoretical
evaluation of the hypothesis. Deducing from the experimental outcomes, the article opts for selfadaptive prediction suite. The best suite can inevitably select the best prediction procedure from
the incoming workload standpoint.
5. Privacy preserving model: a new arrangement for auditing cloud stakeholders
The Paradigm of cloud computing gives several attractive products to customers. They
include: risk transfer, ubiquitous network access, consumption-based pricing, on-demand selfservice, etc. nevertheless, the safety of cloud computing, particularly the confidentiality of data is
a difficult task. To address the data confidentiality, several measures have been put in suggested
that use third party auditors (TPA) to safeguard the integrity of outsourced data for cloud
customer’s satisfaction. Nonetheless, it should be observed that the duty of the TPA may be the
source of potential insecurity itself and may lead to new risk exposures for client’s data. Besides,
cloud users and cloud service providers could al...